This document summarizes the findings of a study on conversion paths and multi-touch attribution models across eight industry sectors. Some key findings include:
1) Most revenue (61%) and transactions (59%) came from multi-step conversion paths, rather than single-step paths. No single industry sector had over 50% of conversions from single-step paths.
2) Conversion paths varied significantly by industry sector, with the average number of interactions per conversion ranging from 2.4 for B2B to 7.3 for fashion.
3) Over 80% of conversion paths in a sample month were unique and not seen in prior months, suggesting attribution models need periodic testing to remain optimal over time.
discovering high value ppc ad sequences with multi-touch attributionDON RODRIGUEZ
This document analyzes data from Q3 and Q4 paid search advertising campaigns to identify high performing ad sequences and message themes. Some key findings:
- Paid search conversion funnels shortened from Q3 to Q4, with more single-touch conversions. Multi-touch funnels were more valuable, especially in Q3.
- Trademark ad sequences outperformed non-trademark to trademark sequences. Top sequences involved consecutive trademark ads.
- In Q4, trademark first ads grew while non-trademark first ads declined. Trademark headlines concentrated while non-trademark diversified.
- Action-oriented and emotional trademark headlines rose in Q4, while product USP non-trademark
Multi Channel Attribution - Driving Marketing Spend Planning In The Big Data AgeAbsolutdata Analytics
This presentation was given by Eli Kling, Director - Analytics, AbsolutData at The Business Analytics Conference, AmsterDam, October 2013.
AbsolutData is a global leader in applying analytics to drive sales and increase profits for its customers. AbsolutData has built strong expertise and traction with Fortune 1000 companies across 40 countries. We specialize in big data, high end business analytics, predictive modeling, research, reporting, social media analytics and data management services. AbsolutData delivers world class analytics solutions by combining their expertise in industry domains, analytical techniques and sophisticated tools
Attribution modeling allows marketers to analyze how different ads or touchpoints contribute to sales over time, rather than only crediting the last interaction. This provides a more accurate understanding of each channel's influence on consumer decisions. Setting up an effective attribution model is challenging, as marketers must determine the appropriate credit to assign to each touchpoint while accounting for various other factors. Successful models require extensive testing and recalibration over long periods to ensure ads receive accurate credit.
The document discusses setting up an effective analytics framework. It outlines eight key phases: 1) attributing traffic sources, 2) defining the sales funnel, 3) integrating data, 4) attributing conversions, 5) optimizing conversions, 6) segmenting prospects, 7) analyzing the post-purchase funnel, and 8) ongoing reporting. It emphasizes regularly analyzing data and taking action. The goal is to understand customer behavior and improve revenue. Setting up the right analytics framework requires auditing current data, developing a collection strategy, and ongoing reporting and analysis.
This document announces the biggest marketing week ever to take place in Boston from September 14-16, 2011. It highlights statistics from research on inbound marketing trends, such as the percentage of companies with blogs growing from 48% to 65% from 2009 to 2011, inbound marketing organizations experiencing costs per lead 62% lower than outbound marketing, and 85% of businesses rating their company blogs as useful to critical. The document encourages registration for the upcoming marketing events.
How to show impact with your content marketing:Supriya Thakral
The document discusses key metrics for measuring the impact of content marketing. It identifies three pillars - content optimization metrics like shares and comments, customer acquisition metrics like click-through rate and conversions, and subscriber audience metrics like subscribers and retention rate. It emphasizes the importance of showing impact to prove return on investment and secure business buy-in. It provides examples and best practices for tracking different metric types and calculating customer lifetime value and revenue attribution across marketing touchpoints.
HubSpot surveyed 167 professionals on where they spend their marketing dollars, unveiling that companies that spend more money and effort on inbound marketing experience a lower cost per lead.
Learn How a New Kind of Marketing Mix Modeling is Better for Media PlanningThinkVine
This presentation discusses the use of agent-based modeling and its proven advantages to media planners, including the abilities to create effective media plans based on consumer differences, accurately attribute results to media tactics, quantify long-term effects, and forecast sales and ROI results.
discovering high value ppc ad sequences with multi-touch attributionDON RODRIGUEZ
This document analyzes data from Q3 and Q4 paid search advertising campaigns to identify high performing ad sequences and message themes. Some key findings:
- Paid search conversion funnels shortened from Q3 to Q4, with more single-touch conversions. Multi-touch funnels were more valuable, especially in Q3.
- Trademark ad sequences outperformed non-trademark to trademark sequences. Top sequences involved consecutive trademark ads.
- In Q4, trademark first ads grew while non-trademark first ads declined. Trademark headlines concentrated while non-trademark diversified.
- Action-oriented and emotional trademark headlines rose in Q4, while product USP non-trademark
Multi Channel Attribution - Driving Marketing Spend Planning In The Big Data AgeAbsolutdata Analytics
This presentation was given by Eli Kling, Director - Analytics, AbsolutData at The Business Analytics Conference, AmsterDam, October 2013.
AbsolutData is a global leader in applying analytics to drive sales and increase profits for its customers. AbsolutData has built strong expertise and traction with Fortune 1000 companies across 40 countries. We specialize in big data, high end business analytics, predictive modeling, research, reporting, social media analytics and data management services. AbsolutData delivers world class analytics solutions by combining their expertise in industry domains, analytical techniques and sophisticated tools
Attribution modeling allows marketers to analyze how different ads or touchpoints contribute to sales over time, rather than only crediting the last interaction. This provides a more accurate understanding of each channel's influence on consumer decisions. Setting up an effective attribution model is challenging, as marketers must determine the appropriate credit to assign to each touchpoint while accounting for various other factors. Successful models require extensive testing and recalibration over long periods to ensure ads receive accurate credit.
The document discusses setting up an effective analytics framework. It outlines eight key phases: 1) attributing traffic sources, 2) defining the sales funnel, 3) integrating data, 4) attributing conversions, 5) optimizing conversions, 6) segmenting prospects, 7) analyzing the post-purchase funnel, and 8) ongoing reporting. It emphasizes regularly analyzing data and taking action. The goal is to understand customer behavior and improve revenue. Setting up the right analytics framework requires auditing current data, developing a collection strategy, and ongoing reporting and analysis.
This document announces the biggest marketing week ever to take place in Boston from September 14-16, 2011. It highlights statistics from research on inbound marketing trends, such as the percentage of companies with blogs growing from 48% to 65% from 2009 to 2011, inbound marketing organizations experiencing costs per lead 62% lower than outbound marketing, and 85% of businesses rating their company blogs as useful to critical. The document encourages registration for the upcoming marketing events.
How to show impact with your content marketing:Supriya Thakral
The document discusses key metrics for measuring the impact of content marketing. It identifies three pillars - content optimization metrics like shares and comments, customer acquisition metrics like click-through rate and conversions, and subscriber audience metrics like subscribers and retention rate. It emphasizes the importance of showing impact to prove return on investment and secure business buy-in. It provides examples and best practices for tracking different metric types and calculating customer lifetime value and revenue attribution across marketing touchpoints.
HubSpot surveyed 167 professionals on where they spend their marketing dollars, unveiling that companies that spend more money and effort on inbound marketing experience a lower cost per lead.
Learn How a New Kind of Marketing Mix Modeling is Better for Media PlanningThinkVine
This presentation discusses the use of agent-based modeling and its proven advantages to media planners, including the abilities to create effective media plans based on consumer differences, accurately attribute results to media tactics, quantify long-term effects, and forecast sales and ROI results.
The definitive guide to measuring lead nurturing 8-2013Marcia Kadanoff
This document provides a guide to measuring the success of lead nurturing programs. It begins by defining lead nurturing as a series of tactics to build relationships with prospects even when they are not actively looking to buy. The document then discusses the challenges of measuring lead nurturing returns. It proposes a framework for justifying lead nurturing investments using metrics like deal size, opportunity close rates, and revenue from new and repeat customers. The document outlines seven types of lead nurturing campaigns aligned to stages in the buyer lifecycle. It concludes by recommending tracking over a dozen metrics to effectively measure lead nurturing across the different stages from lead to customer.
Digital marketing ROI - An introduction to attribution modellingDifferent Spin
To help you get started in the potentially daunting realm of attribution modelling, we’ve crafted this whitepaper to explore what it is and how you can implement it for your business. We go through some of the most common attribution models and help define which of these is likely to be the best starting point for you.
- The document analyzes the impact of Google removing ads from the right side of the search engine results page (SERP) and showing four top ads instead.
- It describes six hypotheses about potential effects, such as cost-per-click (CPC) inflation, decreased auction participation, and changes in traffic sources.
- Data from 12 million search impressions across four retail verticals before and after the change was analyzed to test the hypotheses. Most were supported by the data, except CPC was not found to increase for text ads as expected.
The document summarizes the findings of a survey on email marketing effectiveness in B2B environments. Key findings include:
- While email remains the top marketing channel, only 28% felt email marketing was becoming more effective.
- 61% reported average open rates below 15%, lower than the industry average of 19.7%. Open rates varied significantly by industry.
- Many marketers were ignorant of the impact of invalid addresses, catch-all addresses, and high-risk emails in their databases on effectiveness.
- 62% did not know their email reputation score, and those who did had an inflated view of it.
The document discusses the importance of marketing metrics and analytics for building accountability and respect within an organization. It argues that marketing should measure metrics that matter to executives like revenue, profits, and growth. The document also emphasizes that marketing should plan for ROI from the start of a program by establishing goals and estimating ROI, designing measurable programs, and focusing on decisions that can improve marketing performance.
This document discusses TrueMail Marketing, a company that enhances direct mail campaigns through digital targeting. It provides case studies showing how TrueMail increased response rates and revenue for clients in various industries including auto dealerships, non-profits, and universities. TrueMail claims it can surgically target ads to individuals' home addresses through IP address mapping at a lower cost than traditional digital ads, with an average 75% lift in ROI for clients.
This detailed report on the state of demand generation for 2013 was a the first look at the depths of marketing automations impact on the B2B demand generation landscape. Detailed analysis by leading experts this report set a new standard for baselines among B2B marketers.
Programmatic advertising is a critical part of any multi-channel recruitment marketing operation.
As the newest kid on the block in recruitment marketing, programmatic sourcing uses ad technology to continually buy, manage, and optimize job ads across the Web, allowing recruiters to get the highest conversion rates.
In this free white paper, we walk you through programmatic to educate you for this inevitable industry shift and explain its key benefits, including:
1. Improve 'apply rate' conversions.
2. Attract quality applicants.
3. Eliminate wasted spend.
Download this white paper to learn how you can start using programmatic to optimize your recruiting funnel for quality candidates and save time and money along the way.
[Whitepaper] Recruitment Analytics in a Programmatic Sourcing EraAppcast
Whitepaper Preview:
"Only twenty-five years ago jobs were posted on physical bulletin boards at job centers and listed in newspapers. Today, real-time, programmatic bidding engines and algorithms negotiate behind the scenes to determine who will see which job ads, and where and when they will see them.
As a result, the metrics, benchmarks, and KPIs used to measure recruitment advertising effectiveness have shifted. This whitepaper explores those changes and recommends a mix of revised and new measures based on the changing landscape of recruitment advertising."
Download this paper to learn the NEW metrics and KPIs for an age of programmatic recruitment advertising.
Written by Allan Schweyer - Founder, TMLU
Seeing Between The Lines Of The Search And The ClickDung Tri
The document discusses how search marketers previously had little visibility into consumer search behavior and competitor performance on search engine results pages (SERPs). It then summarizes findings from an analysis of millions of SERPs by Compete that provide insights for search marketers. Specifically:
- Over half of SERPs contain at least one paid ad, showing the competitiveness of search.
- Organic listings make up 85% of total listings but receive 53% of clicks from the first organic result, emphasizing the importance of organic search optimization.
- While most paid listings appear on the right side of SERPs, 85% of paid clicks go to top listings, highlighting the value of the top paid placement.
The article discusses 5 marketing research trends for 2014 predicted by Craig Kolb:
1. Big data and sampling will become complementary, with big data enhancing traditional sampling methods.
2. Online survey panels will become more important and sophisticated.
3. Rapid online surveys pose risks like excluding populations without internet access.
4. Social media will grow in importance for gathering qualitative insights.
5. Automated marketing managers will proliferate but the human element remains vital.
Duplicate Leads: Unrealized Value or Waste of Time?Velocify
Duplicate leads convert at significantly higher rates than average leads. Leads flagged as duplicates convert 167% higher on average, while dispositioned duplicates convert 181% higher. The likelihood of conversion increases with the number of times a lead is flagged as a duplicate and peaks for duplicates flagged more than six months apart. Properly identifying and dispositioning duplicates can substantially improve conversion rates.
This presentation on Attribution and Media Measurement was made at the 2012 Digital World Expo. For an electronic copy please email info@encoremetrics.com
This presentation explores the main issues that help to optimize media campaigns. Case studies and conclusions.
- What frequency is the most efficient for a campaign, creative?
- What is the duration of media effect?
- Which creative or site gives more results?
- How to choose effective sites?
The advertising materials analyzed in this presentation:
10 slide: http://newage.com.ua/rabota_video/rabota.html
http://newage.com.ua/rabota_b1/rabota_smart.html
http://newage.com.ua/rabota_b2/rabota_HRs.html
11 slide: http://newage.com.ua/MOYO/moyo.html
Predicting the Next News Trends: The Advent of Intelligent Media AnalysisVMS
This document discusses methods for measuring public relations efforts through integrated competitive analysis of news, advertising, and social media. It emphasizes the importance of measuring PR outputs and outcomes quantitatively and qualitatively against competitors using metrics like share of discussion, voice, and social media. Key aspects include developing scoring systems to assess tone, messages, and prominence of coverage; weighting media costs or impressions; and analyzing how PR impacts advertising effectiveness. Taking an integrated view by comparing messaging and competitive shares across all communication mediums can provide valuable business intelligence to improve forecasting and strategy.
dataVISIONS is built with novel machine learning algorithms in combination with deep data mining by fraud concepts in response to a simple but profound question,"What should be the Pricing strategy to stop eCommerce fraud, improve Cyber-security, decrease Anti Money Laundry, Call center behavior analysis etc?" What segmentation techniques can be applied towards those goals?
Marketing automation disrupting the status quo - e book - 8-14-13Marcia Kadanoff
The document discusses considerations for maximizing ROI on marketing automation. It identifies two categories of marketing automation solutions - current status quo (CSQ) solutions and new class disruptors (NCD). NCD solutions focus on mitigating challenges faced by traditional solutions. The top 10 considerations marketers should look for are discussed, with a focus on data quality/integration, ease of use, templated content creation, attribution modeling, and social media integration. Ease of use was identified as the top factor for maximizing ROI based on feedback from top performing organizations.
This document discusses how companies can use behavioral scoring models built by Infer to supplement existing predictive lead scoring. By analyzing activity data like website visits and email clicks, Infer's machine learning identifies previously disqualified prospects from nurture databases that are re-engaging and likely to convert. Companies can prioritize these high-scoring "old" leads and resurface opportunities for sales. One Infer customer closed nearly half a million dollars in deals this way. The document provides examples of how companies can operationalize both fit and behavior scoring across sales and marketing.
Medicare Marketing in Our Digital World- The definitive digital marketing han...Scott Levine
The document discusses how Medicare marketers are struggling to develop digital marketing strategies despite their audiences increasingly using digital channels. It notes that 65% of Medicare marketers felt unprepared for digital strategies. While older generations are adopting digital at rising rates, with 76% of 60-69 year olds using the internet daily, Medicare marketers have been slow to shift marketing online. The document argues that Medicare marketers must recognize their audiences are digital and develop comprehensive digital strategies that incorporate all channels, including mobile. It provides data on older Americans' digital usage and outlines 50 questions organizations should consider before developing a digital Medicare marketing strategy.
Digital Marketing Overview for Ginsberg's Food ShowDragonSearch
This document provides an overview and advice for SEO/SMM and PPC strategies for restauranteurs. It discusses optimizing websites for search engines, using images and keywords effectively on pages, engaging with customers on social media, responding to reviews, and writing compelling copy for paid search advertising. The presentation emphasizes building online communities, focusing content, using hashtags and conversations on social platforms, and measuring various digital marketing tactics.
The definitive guide to measuring lead nurturing 8-2013Marcia Kadanoff
This document provides a guide to measuring the success of lead nurturing programs. It begins by defining lead nurturing as a series of tactics to build relationships with prospects even when they are not actively looking to buy. The document then discusses the challenges of measuring lead nurturing returns. It proposes a framework for justifying lead nurturing investments using metrics like deal size, opportunity close rates, and revenue from new and repeat customers. The document outlines seven types of lead nurturing campaigns aligned to stages in the buyer lifecycle. It concludes by recommending tracking over a dozen metrics to effectively measure lead nurturing across the different stages from lead to customer.
Digital marketing ROI - An introduction to attribution modellingDifferent Spin
To help you get started in the potentially daunting realm of attribution modelling, we’ve crafted this whitepaper to explore what it is and how you can implement it for your business. We go through some of the most common attribution models and help define which of these is likely to be the best starting point for you.
- The document analyzes the impact of Google removing ads from the right side of the search engine results page (SERP) and showing four top ads instead.
- It describes six hypotheses about potential effects, such as cost-per-click (CPC) inflation, decreased auction participation, and changes in traffic sources.
- Data from 12 million search impressions across four retail verticals before and after the change was analyzed to test the hypotheses. Most were supported by the data, except CPC was not found to increase for text ads as expected.
The document summarizes the findings of a survey on email marketing effectiveness in B2B environments. Key findings include:
- While email remains the top marketing channel, only 28% felt email marketing was becoming more effective.
- 61% reported average open rates below 15%, lower than the industry average of 19.7%. Open rates varied significantly by industry.
- Many marketers were ignorant of the impact of invalid addresses, catch-all addresses, and high-risk emails in their databases on effectiveness.
- 62% did not know their email reputation score, and those who did had an inflated view of it.
The document discusses the importance of marketing metrics and analytics for building accountability and respect within an organization. It argues that marketing should measure metrics that matter to executives like revenue, profits, and growth. The document also emphasizes that marketing should plan for ROI from the start of a program by establishing goals and estimating ROI, designing measurable programs, and focusing on decisions that can improve marketing performance.
This document discusses TrueMail Marketing, a company that enhances direct mail campaigns through digital targeting. It provides case studies showing how TrueMail increased response rates and revenue for clients in various industries including auto dealerships, non-profits, and universities. TrueMail claims it can surgically target ads to individuals' home addresses through IP address mapping at a lower cost than traditional digital ads, with an average 75% lift in ROI for clients.
This detailed report on the state of demand generation for 2013 was a the first look at the depths of marketing automations impact on the B2B demand generation landscape. Detailed analysis by leading experts this report set a new standard for baselines among B2B marketers.
Programmatic advertising is a critical part of any multi-channel recruitment marketing operation.
As the newest kid on the block in recruitment marketing, programmatic sourcing uses ad technology to continually buy, manage, and optimize job ads across the Web, allowing recruiters to get the highest conversion rates.
In this free white paper, we walk you through programmatic to educate you for this inevitable industry shift and explain its key benefits, including:
1. Improve 'apply rate' conversions.
2. Attract quality applicants.
3. Eliminate wasted spend.
Download this white paper to learn how you can start using programmatic to optimize your recruiting funnel for quality candidates and save time and money along the way.
[Whitepaper] Recruitment Analytics in a Programmatic Sourcing EraAppcast
Whitepaper Preview:
"Only twenty-five years ago jobs were posted on physical bulletin boards at job centers and listed in newspapers. Today, real-time, programmatic bidding engines and algorithms negotiate behind the scenes to determine who will see which job ads, and where and when they will see them.
As a result, the metrics, benchmarks, and KPIs used to measure recruitment advertising effectiveness have shifted. This whitepaper explores those changes and recommends a mix of revised and new measures based on the changing landscape of recruitment advertising."
Download this paper to learn the NEW metrics and KPIs for an age of programmatic recruitment advertising.
Written by Allan Schweyer - Founder, TMLU
Seeing Between The Lines Of The Search And The ClickDung Tri
The document discusses how search marketers previously had little visibility into consumer search behavior and competitor performance on search engine results pages (SERPs). It then summarizes findings from an analysis of millions of SERPs by Compete that provide insights for search marketers. Specifically:
- Over half of SERPs contain at least one paid ad, showing the competitiveness of search.
- Organic listings make up 85% of total listings but receive 53% of clicks from the first organic result, emphasizing the importance of organic search optimization.
- While most paid listings appear on the right side of SERPs, 85% of paid clicks go to top listings, highlighting the value of the top paid placement.
The article discusses 5 marketing research trends for 2014 predicted by Craig Kolb:
1. Big data and sampling will become complementary, with big data enhancing traditional sampling methods.
2. Online survey panels will become more important and sophisticated.
3. Rapid online surveys pose risks like excluding populations without internet access.
4. Social media will grow in importance for gathering qualitative insights.
5. Automated marketing managers will proliferate but the human element remains vital.
Duplicate Leads: Unrealized Value or Waste of Time?Velocify
Duplicate leads convert at significantly higher rates than average leads. Leads flagged as duplicates convert 167% higher on average, while dispositioned duplicates convert 181% higher. The likelihood of conversion increases with the number of times a lead is flagged as a duplicate and peaks for duplicates flagged more than six months apart. Properly identifying and dispositioning duplicates can substantially improve conversion rates.
This presentation on Attribution and Media Measurement was made at the 2012 Digital World Expo. For an electronic copy please email info@encoremetrics.com
This presentation explores the main issues that help to optimize media campaigns. Case studies and conclusions.
- What frequency is the most efficient for a campaign, creative?
- What is the duration of media effect?
- Which creative or site gives more results?
- How to choose effective sites?
The advertising materials analyzed in this presentation:
10 slide: http://newage.com.ua/rabota_video/rabota.html
http://newage.com.ua/rabota_b1/rabota_smart.html
http://newage.com.ua/rabota_b2/rabota_HRs.html
11 slide: http://newage.com.ua/MOYO/moyo.html
Predicting the Next News Trends: The Advent of Intelligent Media AnalysisVMS
This document discusses methods for measuring public relations efforts through integrated competitive analysis of news, advertising, and social media. It emphasizes the importance of measuring PR outputs and outcomes quantitatively and qualitatively against competitors using metrics like share of discussion, voice, and social media. Key aspects include developing scoring systems to assess tone, messages, and prominence of coverage; weighting media costs or impressions; and analyzing how PR impacts advertising effectiveness. Taking an integrated view by comparing messaging and competitive shares across all communication mediums can provide valuable business intelligence to improve forecasting and strategy.
dataVISIONS is built with novel machine learning algorithms in combination with deep data mining by fraud concepts in response to a simple but profound question,"What should be the Pricing strategy to stop eCommerce fraud, improve Cyber-security, decrease Anti Money Laundry, Call center behavior analysis etc?" What segmentation techniques can be applied towards those goals?
Marketing automation disrupting the status quo - e book - 8-14-13Marcia Kadanoff
The document discusses considerations for maximizing ROI on marketing automation. It identifies two categories of marketing automation solutions - current status quo (CSQ) solutions and new class disruptors (NCD). NCD solutions focus on mitigating challenges faced by traditional solutions. The top 10 considerations marketers should look for are discussed, with a focus on data quality/integration, ease of use, templated content creation, attribution modeling, and social media integration. Ease of use was identified as the top factor for maximizing ROI based on feedback from top performing organizations.
This document discusses how companies can use behavioral scoring models built by Infer to supplement existing predictive lead scoring. By analyzing activity data like website visits and email clicks, Infer's machine learning identifies previously disqualified prospects from nurture databases that are re-engaging and likely to convert. Companies can prioritize these high-scoring "old" leads and resurface opportunities for sales. One Infer customer closed nearly half a million dollars in deals this way. The document provides examples of how companies can operationalize both fit and behavior scoring across sales and marketing.
Medicare Marketing in Our Digital World- The definitive digital marketing han...Scott Levine
The document discusses how Medicare marketers are struggling to develop digital marketing strategies despite their audiences increasingly using digital channels. It notes that 65% of Medicare marketers felt unprepared for digital strategies. While older generations are adopting digital at rising rates, with 76% of 60-69 year olds using the internet daily, Medicare marketers have been slow to shift marketing online. The document argues that Medicare marketers must recognize their audiences are digital and develop comprehensive digital strategies that incorporate all channels, including mobile. It provides data on older Americans' digital usage and outlines 50 questions organizations should consider before developing a digital Medicare marketing strategy.
Digital Marketing Overview for Ginsberg's Food ShowDragonSearch
This document provides an overview and advice for SEO/SMM and PPC strategies for restauranteurs. It discusses optimizing websites for search engines, using images and keywords effectively on pages, engaging with customers on social media, responding to reviews, and writing compelling copy for paid search advertising. The presentation emphasizes building online communities, focusing content, using hashtags and conversations on social platforms, and measuring various digital marketing tactics.
Digital Marketing refers to advertising delievered through different channels such as search engines, social media, email and many more. Here Adaptra gives you an overview about Digital Marketing and its process in the presentation which will help you to manage and plan you digital marketing insights.
This is support and reinforcement of the 3 Day Fundamentals Real Estate Workshop. It includes many tips and tricks for building a buyer and cash buyer list.
Real estate has proven itself to be an assured revenue model for the media, especially the newspaper industry in particular. It has helped the industry to tackle the financial crisis many at times. Many newspapers across the globe have incorporated their own real estate platform and stayed afloat in the unworthy business scenarios.
Online Marketing ROI - For The Travel & Tourism IndustryBryan Rasch
Are your Web marketing efforts converting virtual visitors into real visitors? \"Your Web site is the only place consumers can fully experience your destination without visiting...”
Bryan Rasch - VP Marketing
Hanson Dodge Creative
www.hansondodge.com
The document discusses 11 key points about changes in the media landscape: 1) Newspaper readership has been declining for decades; 2) Newspaper readership skews older while online readership skews younger; 3) New online media like Google and Yahoo have seen much faster revenue growth than traditional media companies. The document examines how these trends are impacting newspapers and the future of journalism.
Digital Marketing Trends for 2014.
Discover what you need to know from Lisa Harrison, Strategic Marketing Director and cofounder of POMO.
Lisa has sifted through the hype and the fads to deliver you expert advice gained from working directly with businesses to deliver effective results-driven, digital marketing strategies.
What you’ll learn:
Customer retention in a digital environment
More effective ways to drive website traffic than SEO
Latest updates on Web 3.0
What your business should consider to be an effective part of the conversation
Understanding digital ROI
Lisa will share what’s new in the realm of digital marketing, including the importance of engagement, content marketing, real-time marketing, web 3.0, reputation management, influencers as well as the ideas around return on investment.
Lisa is also the developer and trainer of Certificate IV Social Media Mastery, a new Nationally Recognised Qualification – Certificate IV Business (BSB40212).
Follow Lisa on Twitter or subscribe to her on Facebook. POMO – is a creative agency specializing in customer engagement based in Brisbane and the Sunshine Coast, Queensland Australia.
Dan Golden's Internet Summit 2015 Presentation - Practical AttributionBFO
The document discusses practical approaches to multi-channel attribution across digital marketing channels to understand how different touchpoints contribute to conversions. It provides an overview of various attribution models and platforms, and recommends defining business goals, testing different attribution models with your own data, and continuously learning and improving attribution methodology. The objective is to go beyond last-click attribution and gain a stronger understanding of a customer's full purchase path and how different channels assist in driving conversions.
This presentation was shared on PAS Digital Marketing Conference "Dig-It 2.0"
Presentation: Campaign Attribution Model
Speaker: Umair Mohsin Global Director MI Digital
One of the crucial trends nowadays will be the growth of attribution modeling. However, many savvy marketers are still missing the opportunity to reap the benefits of attribution modeling, hence this whitepaper is aimed at providing a comprehensive overview of what marketing attribution is all about.
Webinar: Improve Campaign Results with Multi-Channel Funnels and Acquisio Att...Acquisio
While tracking page hits has become a mandatory practice, determining the path someone takes to land on a specific page can be challenging at best. However, if you can uncover this information, you can determine which marketing channels are most effective and fully leverage them.
To facilitate this, Google has developed an Attribution Modeling tool. Part of Google Analytics Premium, the tool provides valuable insight and analytics by breaking down and comparing the effectiveness of your key marketing channels, such as paid and organic search, email, affiliate marketing, display ads, mobile placements, and more.
To show you how it works, we will be hosting a webinar on August 30. Presented by James Thompson, President of Clix Marketing, and Marc Poirier, CMO & Co-Founder of Acquisio, this webinar will cover the following:
- Google’s Attribution Modeling concept
- The benefits of using Attribution Modeling
- Case studies on clients using this product
- Best practices
The webinar will also explain the value of combining Google’s Attribution Modeling tool with a platform like Acquisio, to receive additional insight towards your media channels, as well as the true value of display and Facebook advertising.
If you are looking to enhance your digital marketing efforts, enable better budget allocation across channels and increase your marketing ROI, this webinar is not to be missed.
This document discusses attribution and how to properly attribute leads and conversions to specific marketing activities and touchpoints. It notes that attribution has become more difficult as the number and diversity of marketing channels has increased. Several common attribution methods are described, including last touch, first touch, equal weighting, and customer reported touchpoint. The document advocates for aggressive data collection, various web analytics techniques to track attribution over time, and careful analysis of attribution including dark testing and correlation analysis to understand how marketing activities relate to outcomes.
As far as digital marketing is concerned, attribution is unquestionably one of the major stakes in these last years. Nevertheless, the concept of attribution still seems to be a fuzzy subject. So what exactly is attribution and which model should you choose?
It is in order to answer these questions that a taskforce lead by Facebook dealt with the challenges of attribution.
This whitepaper discusses the challenges of accurate marketing attribution and analyzes different attribution models. It notes that while analytics spending is rising, many brands still rely on simplistic rules-based attribution models that likely distort the true value of marketing tactics. These models make broad assumptions rather than being evidence-based. The whitepaper argues effective attribution requires a custom, algorithmic approach based on analyzing clean, high-quality data to understand causality across marketing events and customer characteristics.
Beyond Omnichannel: Determining the Right Channel MixCognizant
Many companies believe that simply adding more customer channels or reducing the time it takes to handle customer queries will boost customer satisfaction and enhance the customer experience. Yet the proliferation of digital technologies and touchpoints have made it more difficult to track customer preferences and purchasing traits. By identifying customers’ preferred contact channels, companies can more effectively engage, serve, and retain them while driving profitable growth.
The customer journey has become very complex – there are so many touch-points that influence the ultimate buying decision.
Oftentimes, the customer journey begins with a high-intent search. Crafting and serving up strong, relevant ads will bring the lead directly to your website. However, keeping the potential customer on your website long enough to convert is challenging – this is where live chat comes in. It acts as a powerful conversion tool to facilitate a positive customer experience ending in a sale.
If you want to boost conversion rates in the buying cycle, our search and chat experts Armen Vartanyan of WordStream and Sebalis Davis of Pure Chat, are here to help. Join us for this live webinar to learn how to perfect the customer journey through key touch-points like paid search and live chat.
During the webinar, you'll learn:
-How the customer journey has evolved
-How to capture more conversions through paid search
-How to identify if you should implement a live chat function
Chris McLaren, Director, Strategy | Wunderman Minneapolis, February 2014.
McLaren presented insights into best practices in ROI tracking. To ascertain the relative effectiveness of social media versus other marketing channels, he advised mapping social metrics to marketing KPIs and assigning comparative values using “known” media costs specific to clients.
Case Study: Mutli-Touch Attribution Reveals the full effect of upper funnel m...Jessie De Luca
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Beyond The Numbers: Answers With AnalyticsPiano Media
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To see the complete webinar, click here:
http://blog.pianomedia.com/beyond-the-numbersanswers-with-analytics/
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2. 2
CONTENTS
EXECUTIVE SUMMARY 3
INTRODUCTION 4
ATTRIBUTION, ATTRIBUTION – NOT AN ‘IF’, BUT A ‘WHEN’ 5
THE ATTRIBUTION SPECTRUM 5
HYPOTHESES 6
METHODOLOGY 6
CUSTOM CHANNEL DEFINITIONS 6
FINDINGS 8
HYPOTHESIS: MOST REVENUE COMES FROM MULTI-STEP CONVERSION PATHS. 8
HYPOTHESIS: CONVERSION PATHS VARY BY SECTOR 10
HYPOTHESIS: CONVERSION PATH LENGTHS ARE POSITIVELY CORRELATED TO AOV. 23
HYPOTHESIS: ORGANIC SEARCH DRIVES TOP OF THE FUNNEL TRAFFIC WHILE PAID SEARCH DRIVES THE BOTTOM. 24
CONVERSION PATH POSITION CONTRIBUTION BY CHANNEL BY SECTOR 28
LAST CLICK INTERACTION BREAKDOWN BY CHANNEL 32
HYPOTHESIS: DISPLAY CAMPAIGNS APPEAR LATER DUE TO RETARGETING 33
HYPOTHESIS: SOCIAL MEDIA IS A TOP OF THE FUNNEL FACTOR 34
ATTRIBUTION IMPLICATIONS AND SUGGESTED ACTIONS 36
UNDERSTAND HOW YOUR BUSINESS BEHAVES FROM A CONVERSION PATH STANDPOINT. 36
DECIDE HOW YOU WILL ATTRIBUTE NON-ATTRIBUTABLE REVENUE, E.G. DIRECT AND MISCELLANEOUS REFERRALS 37
INCLUDE ALL MARKETING-RELATED EXPENSES IN YOUR ATTRIBUTION MODEL INCLUDING HEADCOUNT. 37
EXPERIMENT WITH DIFFERENT ATTRIBUTION MODELS 38
LIFETIME VALUE PLAYS A ROLE 40
3. 3
EXECUTIVE SUMMARY
Market forces will eventually force retailers who use a single interaction attribution model to migrate
to multi-touch attribution. It is not an ‘if’ question, it is a ‘when’ question. Here at NetElixir
University, we decided to study conversion paths and multi-touch attribution models to discern the
best approaches to take towards evolving your attribution process. We started with the following
hypotheses:
1. Most revenue comes from multi-step conversion paths.
2. Conversion paths vary by length by vertical.
3. Conversion path length is positively correlated with AOV.
4. Organic Search drives the top of conversion funnels. Paid Search, especially trademark
terms, drives the bottom.
5. Using display campaigns for retargeting causes it to appear later in the conversion path.
6. Social media is a top of the funnel phenomena.
Using a set of custom channel definitions, we built a dataset with over 89,000 different conversion
paths that ranged from single step paths to paths with over 300 steps. This dataset included over
500,000 transactions and $58MM across 8 industry sectors.
We found the first 3 hypotheses, half of #4, and #5 to be true.
Here are some of the other interesting findings from this study:
• None of our 8 industry sectors had more than 50% of their conversions from single step
conversion paths.
• The average number of site visits per conversion is 4.5. The range by sector goes from low of
2.4 for B2B sites to a high of 7.3 for fashion.
• Social media sites, both the big names and smaller more industry specific sites, participated in
the conversion paths for just a little more than 1% of the total revenue in this study.
• Direct traffic was 82% of the total sessions in this study and had its hand in over 60% of the
$58MM.
• Organic traffic is 4.35x more likely to be the top of funnel than the bottom.
• Paid Search is 1.67x more likely to be the top of the funnel vs anywhere else. If your business
mirrors this pattern, there are significant implications to consider for your SEO efforts.
• Branded Paid Search is 1.8x more likely to be the top of funnel vs anywhere else. A key
takeaway for retailers from this finding is to think twice about cutting back on your trademark
search campaigns.
• In the Apparel and Fashion sectors, Google Shopping campaigns are significantly better
avenues for bringing customers into the conversion funnel than non-branded terms and
branded terms on Bing.
• Display’s impact is small and has most of its impact in the middle of the funnel.
• CSEs, another small overall contributor and perhaps a dying channel, garnered most of that
contribution through first click interactions. These channels are a good way to fill the top of
your conversion funnel.
• Affiliates are 3.5x more likely to be last click vs first click, an example of the impact of coupon
sites. This channel outperformed non-trademark or non-brand campaigns combined in
Google and Bing.
When you are ready to start creating your optimal attribution model, here are a few key points to
consider.
• You need to understand the contribution of as many marketing channels as you can
measure.
• Attribution models are not something to “set it and forget it”. You will need to periodically test
your model to make sure it remains optimal.
4. 4
• Consider an attribution method that incorporates a weighted model based on the channel’s
strength (% of total contribution value) and position in the conversion path.
Here is an action plan outlined in this study you can use to take the first step along the Attribution
Spectrum to multi-touch attribution for your digital marketing efforts.
1. Understand how your business behaves from a conversion path standpoint.
2. Decide how you will attribute non-attributable revenue, e.g. Direct and miscellaneous
referrals.
3. Include all marketing-related expenses in your attribution model
4. Experiment with different attribution models including custom models.
5. Know your average customer LTV.
6. Revisit your attribution models periodically to ensure they remain optimal.
INTRODUCTION
Figure 1 Conversion Path View from Top Conversion Path Report
“LAST CLICK ATTRIBUTION IS STUPID”
– Justin Cutroni, Google Analytics Evangelist at the Wharton Customer Analytics Initiative April,
2015
5. 5
ATTRIBUTION, ATTRIBUTION – NOT AN ‘IF’, BUT A ‘WHEN’
Figure 1 is an actual conversion path we found in a Google Analytics Top Conversion Path report
after applying the custom channel definitions created for this study. Perhaps it is conversion paths
like this one that have retailers throwing their arms up in the air saying “I know last click attribution is
stupid, but what else am I supposed to do when I have a conversion path with over 500 steps in it?”
We decided to conduct this study to see if we could arm retailers like you with ammunition for your
request to get resources, either human or financial, to help you tackle the attribution question. Like
we said for mobile-friendly sites a year ago, it’s not an ‘if’ question, but a ‘when’ question. If you are
not thinking about your attribution methodology now, competition will eventually force you to do so.
THE ATTRIBUTION SPECTRUM
Like your other marketing processes, your marketing attribution methodology will evolve along The
Attribution Spectrum, depicted in Figure 2. As you move along the spectrum, your methods will
become more complex, incorporate more touchpoints and become increasingly more sophisticated
as you improve the ROI you generate from each marketing dollar you invest. The far right of the
spectrum represents the “Holy Grail” of Attribution where you are able to assign a cost and a dollar
value to every interaction touchpoint, online and offline, by individual person, aggregate and
segment that data by such values as existing customer vs prospect, and build predictive models to
help you identify which marketing/touchpoint initiatives to apply to those individuals.
Figure 2 Attribution Spectrum
Google Analytics uses a modified single interaction attribution model as the default condition which
puts it at the beginning of the Attribution Spectrum. That said, the use of its multi-channel funnel
reports, such as the Top Conversion Path report used extensively in this study, and its multi-step
attribution models are first steps you can take down the Attribution Spectrum. Additionally, Google
Analytics’ data import features allow you to integrate other data sources, such as offline marketing
program data, so that you can include more marketing intelligence in your analytics account and in
your attribution efforts.
Attribution Spectrum
1 touch interaction - e.g. last
click digital attribution
GA default model
All digital interactions considered
not just first or last click.
GA multi-channel funnel
reports
GA data imports
All digital interactions considered
categorized by known
individuals and unknown.
• UA's Unique User ID enables individual
tracking.
predictive analytics are used
to model marketing mixes for
known vs unknown.
• Only in Premium GA
Holy Grail of Attribution
Integrates all touchpoints that
cost $$ by individual including
offline AND online touches.
Applies math to figure revenue
and margin value of each
individual touchpoint, sums
those by touchpoint across all
customers and non-customers
and weighs each area
accordingly.
Predictive analytics are used at
the individual customer level.
6. 6
HYPOTHESES
Here at NetElixir University, we decided to study conversion paths to see what we can discern about
the best approaches to take towards attribution. To start our study, we first formulated a set of
hypotheses that were based on common perceptions among our clients, as well as anecdotal
evidence.
Here are those hypotheses.
1. Most revenue comes from multi-step conversion paths.
2. Conversion paths vary by length by vertical.
3. Conversion path length is positively correlated with AOV.
4. Organic Search drives the top of conversion funnels. Paid Search, especially trademark
terms, drives the bottom.
5. Using display campaigns for retargeting causes it to appear later in the conversion path.
6. Social media is a top of the funnel phenomena.
METHODOLOGY
CUSTOM CHANNEL DEFINITIONS
In order to start creating your optimal attribution model, you will need to understand the contribution
of as many marketing channels as you can you measure. That is why we took great pains to define
a set of over 20 custom channels in Google Analytics in undertaking this study. We attempted to
segment traffic and define segments in ways that would identify all traffic that was driven by some
kind of marketing investment or effort. For example, one of our clients is involved in providing
college scholarships. That program delivers tangible traffic to their site from their education
partners, which we categorized in the Other Advertising & Marketing channel.
Through an iterative process, we defined a set of custom channels and applied them to a substantial
sample of our clients’ accounts. We then studied conversions that occurred between January 1st
and April 30th
, 2015 with a 90 day “look back” period.
The definitions that we ended up with are in Table 1 below.
Table 1 Custom Channel Definitions
Channel Group Description
Direct Same as GA Default
Organic GA Default augmented with 3rd
tier search engines from referrals
Email System definition plus webmail
Google Brand CPC AdWords Campaigns featuring the site owner’s trademarked terms
Affiliates Affiliate Marketing – coupons, etc.
Bing Brand CPC Bing Ads Campaigns with owner’s trademark
Google NTM CPC Non-trademark terms AdWords Campaigns
Google PLA CPC Google Shopping
Bing Non Brand CPC Non-trademark terms in Bing Ads
Display Any type of display advertising
Social Traffic from known social media sites, blogs, forums, etc.
Self-Referrals Referrals from either subdomains of owner’s web site or from
company owned branded sites
Other Ads Any other known marketing that does not fit in one of the definitions
Referrals All remaining referrals
CSEs - Non-Google or Bing Comparison Shopping Engines
7. 7
Payment Related Referrals Referrals caused by issues with alternative payment methods, such
as PayPal.
Vendor/Suppliers Inbound links from suppliers, channel partners, etc..
Amazon referrals Referral traffic from Amazon whose marketing source is not
determinable
Media Sites Online media sites, online sites of print publications, such as Cosmo
Bing Product Ads / cse Bing Product Listing Ads
The dataset we ended up with had over 89,000 different conversion paths that range from having a
single step to conversion paths with over 300 steps. Figure 3 below shows the frequency
distribution of conversion paths by number of steps. As you can see, there are almost 30,000
conversion paths with up to 10 steps.
Figure 3 Frequency Distribution of Conversion Paths by Path Length
Figure 4, shown below, charts the conversion count by # of path steps or path length. This dataset
includes over 500,000 transactions across 8 industry sectors.
-
5,000
10,000
15,000
20,000
25,000
30,000
0 1 2 3 4 5 10 20 100 5000
# PATHS
# PATHS
-
50,000
100,000
150,000
200,000
250,000
1 2 3 4 5 10 20 100 5000
# CONVERSIONS BY PATH STEPS
8. 8
Figure 4 Conversion Path Count by Path Length
In Figure 5, we chart the total conversion value by path length. The original dataset we pulled had
over $60MM. After removing outlying data, we ended up with a working dataset for the study of
about $58MM.
Figure 5 Conversion Value by Path Length
FINDINGS
HYPOTHESIS: MOST REVENUE COMES FROM MULTI-STEP CONVERSION PATHS.
In our study, we found that 61% of the $58MM in conversions resulted from conversion paths with
more than a single step, as well as 59% of all transactions. This mix between single step path and
multiple paths is consistent with other published research on this topic.
We also found that not one of our 8 industry sectors had
more than 50% of their conversions come from single step
conversions. The implication here is that a single
interaction attribution model like GA’s default last non-direct
click is treating most of your sales unfairly and that
decisions made from that data are misguided. Figure 6
depicts the single step, i.e. path length = 1, conversions
share of total conversions (transactions) and total
conversion value (ecommerce revenue) for our entire
dataset and for the industry sectors included in that dataset.
$-
$5,000,000
$10,000,000
$15,000,000
$20,000,000
$25,000,000
1 2 3 4 5 10 20 100 5000
CONVERSION VALUE BY #
STEPS
The average conversion path
was 4.5 steps with a low of 2.4 in
B2B and a high of 7.3 in Fashion.
9. 9
Figure 6 Single Step Conversions Share by Sector
Figure 7 Average Path Length Per Conversion by Sector
As you can see in Figure 7, the average number of site visits per conversion after adjusting for
outliers was 4.5. That number is a little deceiving, as the range by sector goes from a low of 2.4 for
B2B sites to a high of 7.3 for fashion products. The low number of visits per conversion for B2B
sites is a bit misleading and shows one of the limitations of Google Analytics’ multi-path conversion
tracking. With a lookback period limit of just 90 days, it is possible that some touchpoints are being
missed for highly considered B2B purchases that have long sales cycles that exceed the 90 day
lookback period from the time of conversion. Purchase frequency is another possible factor. For
example, one of our B2B clients is in a business where many of their customers make only a single
purchase each year.
25%
30%
35%
40%
45%
50%
Single Step Conversions Share
% of conversions % of value
-
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
Avg Touches Per Conversion By Sector
10. 10
HYPOTHESIS: CONVERSION PATHS VARY BY SECTOR
The variation in average touches per conversion (Figure 7) and conversion share for single step
conversions by sector (Figure 6) clearly shows that conversion paths vary by sector, if not by
retailer. The implication here is that you need to build your own attribution system and experiment
to find the optimal model for your business.
Additionally, attribution models are not something to “set it and forget it”. You will need to
periodically test your model to make sure it remains optimal. To demonstrate this point, we took one
client’s May top conversion path data and compared it to the January through April data that is part
of our dataset. Table 2 summarizes the findings. 82% of May’s conversion paths were unique.
That is, the number and order of the channel touchpoints in those paths did not match any of the
more than 13k conversion paths we had in our dataset for this client. More importantly, look at the
share of revenue and orders those unique paths contributed in May. 40% of orders and 41% of
revenue in the month of May came from conversion path sequences we did not see in the
previous 4 months. In theory, an attribution model that was built and optimized on the January –
April dataset may not be the optimal model to apply to the May results.
Table 2 Sample Comparison of Conversion Path Overlap May to Jan - Apr
# Paths $ Orders
May paths overlap with Jan - Apr 667 $1,288,434 12,063
May unique paths 3,074 $901,246 8,083
May Total 3,741 $2,189,680 20,146
May unique % of Total 82% 41% 40%
CHANNEL GROUP CONVERSION PATH DATA SUMMARY
The below table shows the participation in conversion paths by the custom channels we defined.
Table 3 Channel Group Conversion Path Data Summary
Channel Group
# of
Conversions
(Transactions)
# of
Conv.
Paths
Conversion
Value
% of
Total
Conv.
Value AOV
Avg.
Path
Len
(# Steps)
Direct 283,668 74,426 $ 35,525,363 61% $ 125 6.8
Organic 155,286 40,117 $17,925,019 31% $ 115 5.6
Email 116,670 44,853 $11,355,356 20% $ 97 8.7
Google Brand CPC 94,178 27,797 $10,859,029 19% $ 115 6.1
Affiliates 53,995 17,401 $4,744,113 8% $ 88 6.6
Bing Brand CPC 22,367 6,387 $3,501,182 6% $ 157 3.9
Google NTM CPC 34,678 12,947 $3,384,983 6% $ 98 6.3
Google PLA CPC 26,721 9,451 $2,042,689 4% $ 76 6.2
Bing Non Brand CPC 17,342 6,011 $1,180,444 2% $ 68 5.9
Display 9,287 6,244 $ 800,993 1% $ 86 9.4
Social 8,202 3,245 $ 622,256 1% $ 76 5.4
Self-Referrals 5,950 2,762 $ 519,315 1% $ 87 11.5
Other Ads 14,989 3,304 $ 511,960 1% $ 34 5.4
11. 11
Referrals 2,610 1,618 $ 314,067 1% $ 87 14.6
CSEs - Non-Google/Bing 2,245 951 $ 201,711 0% $ 90 5.0
Payment Related Referrals 2,174 865 $ 196,296 0% $ 90 7.1
Vendor/Suppliers 882 464 $ 120,973 0% $ 137 5.5
Amazon referrals 1,017 552 $48,470 0% $ 48 8.3
Media Sites 744 403 $44,362 0% $ 60 9.8
Inbound Calls 51 38 $16,160 0% $ 317 4.0
Bing Product Ads / cse 28 25 $ 1,905 0% $ 68 6.2
The conversion value you see in Table 3 represents the total revenue for all of the conversion paths
within which the particular channel participated. Given today’s multi-tabbed browsing environment
and GA’s session timeout behavior, it is not surprising that “Direct” traffic had their hand in over 60%
of the conversion value. Yet, there were a few
surprises in this data:
1. The lack of social’s contribution is surprise,
contributing to little more than 1%.
2. Affiliates slightly outperforming NTM
combined in Google and Bing.
Other noteworthy findings:
1. Display channel’s low contribution. Like social, this might be a function of the dataset of
clients in this study which skews from small to midsized retailers. Because we use GA to
collect data, all of our clients using enterprise class analytics are excluded.
2. The death of CSEs as a channel. Google’s shifting from GoogleBase to a paid medium back
in 2012 sounded the death toll for CSEs and this evidence is clearly proof of that impact.
Speaking anecdotally, this data seems to indicate that Google Shopping has taken that share
from CSEs for when I ran an ecommerce channel for a mid-sized specialty retailer around the
time of Google’s shift, CSEs were contributing 5-6% of my last click revenue. At that time, my
CSE channel did not include GoogleBase.
ATTRIBUTION MODEL CONSIDERATIONS
Although we identified 20 different channels to categorize traffic sources based on marketing
initiatives, you can see in Table 3 that a few channels dominate the conversion paths in our dataset.
Another point to take note of is the range in average path lengths by channel which goes from less
than 4 to over 14. Given these findings, considering an attribution method that incorporates a
weighted model based on the channel’s strength (% of total contribution value) and position in the
conversion path makes sense.
TOP 5 CONVERSION PATHS BY VERTICAL
Table 4 shows the top 5 conversion paths for each of 9 verticals based on their share of total
conversion value. The 45 paths are comprised of just 7 unique paths. All verticals’ top 5 paths
included single step conversions in the Direct, Organic Search and Google Brand CPC channels,
with some rank order differences by vertical. 8 of the 9 verticals have the single step Direct
conversion as the top producer, with the single step Organic Search conversion path a close second
in 7 of those 8 sectors. Email appears in 6 of the 9 sectors. The Bing Brand CPC channel appears
in the Health & Beauty, Food and B2B sectors while Google Shopping campaigns cracked the top 5
for the Apparel and Fashion sectors.
Direct traffic was 82% of the total
sessions in this study and participated
in 60% of the total revenue.
12. 12
You’ll also notice that 7 of the 8 verticals have a step in the top 5 that is a 2 step conversion path. A
2 step Direct to Direct conversion path appears in 7 sectors’ top 5. The fact that this 2 step path of
Direct sessions is the only multi-step conversion path in any of these sectors’ top 5 and the fact that
the Direct channel was 82% of the total conversion path steps in the entire dataset makes us believe
that a significant portion of the Direct sessions in this study are the result of a previous session
timing out due to inactivity.
The top 5 paths’ share of total conversion value for the sector ranges from 29% in Home
Furnishings to almost ½ in the B2B sector.
Table 4 Top 5 Conversion Paths by Vertical
Vertical Path
Conv
$ % Path
Conv
$ %
All Sectors Direct 14% Apparel Direct 12%
Organic Search 9% Organic Search 10%
Google Brand CPC 6% Google Brand CPC 6%
Direct > Direct 5% Direct > Direct 3%
Email 4% Google PLA CPC 3%
Top 5 Share 37% Top 5 Share 33%
Health/Beauty Direct 12% Fashion Direct 12%
Email 10% Organic Search 9%
Organic Search 9% Google Brand CPC 5%
Google Brand CPC 6% Direct > Direct 4%
Bing Brand CPC 3% Google PLA CPC 3%
Top 5 Share 41% Top 5 Share 32%
Hobby Direct 11% Home Furn. Direct 9%
Organic Search 9% Organic Search 9%
Google Brand CPC 6% Direct > Direct 4%
Direct > Direct 4% Email 4%
Email 4% Google Brand CPC 4%
Top 5 Share 34% Top 5 Share 29%
Food Email 13% Gifts Direct 14%
Direct 13% Organic Search 9%
Organic Search 8% Google Brand CPC 6%
Google Brand CPC 7% Email 5%
Bing Brand CPC 4% Direct > Direct 4%
Top 5 Share 44% Top 5 Share 37%
B2B Direct 21% B2C Direct 11%
Organic Search 10% Organic Search 9%
Direct > Direct 7% Google Brand CPC 6%
Google Brand CPC 6% Email 4%
Bing Brand CPC 4% Direct > Direct 3%
Top 5 Share 47% Top 5 Share 33%
To give you a greater sense for how conversion paths vary by sector, Table 5 shows the top 5
sources of the first interactions, or steps, in the conversion paths in our dataset for a few of the
sectors. Although Direct is the top source of initiating the conversion path journey for these sectors,
we will see later that Direct plays a stronger role in the last interaction. 4 of the 5 have Direct
followed by Organic Search traffic, with Health & Beauty as the exception which has Email as its #2
source of initial interactions. The presence of non-trademark campaigns in Home Furnishings and
13. 13
Gifts seems logical as those categories include infrequent purchase patterns, along with the fact that
they tend to be more highly considered or planned purchases.
Table 5 Top 5 1st Conversion Path Steps by Vertical
Sector 1st step AOV % of
Total
Conv.
% of
Total
Conv.
Value
Avg.
Path
Length
All Direct $ 132 30% 35% 6.07
Organic Search $ 115 22% 23% 3.12
Google Brand CPC $ 116 13% 14% 3.20
Email $ 93 14% 12% 6.55
Bing Brand CPC $ 155 3% 4% 2.38
Top 5 Share 82% 88%
Apparel Direct $ 104 29% 36% 11.96
Organic Search $ 83 24% 24% 3.42
Google Brand CPC $ 92 14% 15% 3.75
Google PLA CPC $ 62 10% 7% 3.64
Email $ 90 7% 7% 4.06
Top 5 Share 83% 89%
B2B Direct $ 217 41% 43% 2.34
Organic Search $ 201 23% 23% 2.35
Google Brand CPC $ 197 14% 13% 2.30
Bing Brand CPC $ 218 7% 7% 2.14
Email $ 173 8% 6% 3.27
Top 5 Share 91% 93%
Health & Beauty Direct $ 114 30% 29% 3.49
Email $ 131 21% 23% 3.32
Organic Search $ 120 20% 20% 2.83
Google Brand CPC $ 124 13% 13% 2.65
Bing Brand CPC $ 132 6% 6% 2.34
Top 5 Share 90% 91%
Home Furn Direct $ 165 20% 29% 4.68
14. 14
Organic Search $ 121 27% 28% 2.79
Email $ 108 12% 12% 3.84
Google Brand CPC $ 150 8% 11% 2.98
Google NTM CPC $ 140 5% 6% 2.63
Top 5 Share 72% 86%
Gifts Direct $ 100 28% 32% 8.06
Organic Search $ 90 19% 20% 3.37
Email $ 79 20% 18% 7.71
Google Brand CPC $ 93 13% 14% 3.44
Google NTM CPC $ 74 5% 4% 3.64
Top 5 Share 84% 87%
Table 6 lists the top 5 single step conversion paths by sector. The conversion paths that make up
Table 6 are a subset of those that make up Table 5 since a single step conversion path will be
counted as both a first and last interaction. Therefore, as you saw in Table 5, 4 of the 5 have Direct
and Organic Search as the top 2 contributors. The contribution of those 2 channels by sector varies
from 20% to 33% as a single step conversion whereas in Table 5 the contribution as the first click
varies from 52% to 66%.
There are some differences when you compare Table 6 to Table 5. For example, in Home
Furnishings, non-trademark campaigns in Google is the 5th
ranked first interaction channel vs that
position in the Single Step Conversions being held by the Affiliate Channel. In Gifts, Email ranks 3rd
for first clicks while in Single Step Conversions the Google Brand CPC channel takes that position
slightly ahead of Email.
15. 15
Table 6 Top 5 Single Step Conversion Paths By Vertical
Sector Single Step Path AOV % of Total
Conv
% of Total
Conv Value
All Direct $ 125 13% 14%
Organic Search $ 103 10% 9%
Google Brand CPC $ 110 6% 5%
Email $ 93 4% 3%
Bing Brand CPC $ 145 2% 2%
Top 5 Share 34% 35%
Apparel Direct $ 104 10% 12%
Organic Search $ 84 10% 10%
Google Brand CPC $ 92 5% 6%
Google PLA CPC $ 61 3% 3%
Email $ 87 2% 2%
Top 5 Share 30% 32%
B2B Direct $ 192 22% 21%
Organic Search $ 176 11% 10%
Google Brand CPC $ 174 7% 6%
Bing Brand CPC $ 197 4% 4%
Email $ 148 3% 2%
Top 5 Share 46% 42%
Health & Beauty Direct $ 97 15% 12%
Email $ 128 9% 10%
Organic Search $ 112 10% 9%
Google Brand CPC $ 121 6% 6%
Bing Brand CPC $ 127 3% 3%
Top 5 Share 43% 41%
Home Furn Direct $ 127 8% 9%
Organic Search $ 81 13% 9%
Email $ 93 4% 4%
Google Brand CPC $ 130 3% 4%
Affiliates $ 31 9% 3%
Top 5 Share 38% 28%
Gifts Direct $ 98 12% 14%
Organic Search $ 86 9% 9%
Google Brand CPC $ 90 6% 6%
Email $ 82 5% 5%
Bing Brand CPC $ 115 2% 2%
Top 5 Share 33% 35%
16. 16
After seeing patterns emerge in the first click and single step data, we were curious to know if we
would find similar behavior when we increased the path length of our view of the data. We studied
conversion paths with just 2 interactions by sector and did indeed find points of commonality. Table
7 lists the 2 step conversion paths that appeared in every vertical’s top 10 conversion paths for that
length. Based on what we see in Table 4, there is no surprise that the 2 step Direct > Direct path
shows up at the top. 3 of the 6 appear to be the behavior of repeat visitors, already familiar with
your site, who use the same approach each time to get to your site.
Table 7 Top 2 Step Conversion Paths
Top 2 Step Conversion
Paths
Appearances In
Sector Top 10
Direct > Direct 9
Email > Direct 9
Google Brand CPC >
Direct 9
Google Brand CPC >
Google Brand CPC 9
Organic Search > Direct 9
Organic Search > Organic
Search 9
PAID SEARCH MOST COMMON PATHS BASED ON CONVERSION VALUE
Table 8 lists the most common conversion paths for Paid Search-related campaigns across our
entire dataset. Similar to Table 7, the Total column below indicates how many times that particular
path shows up in the top 5 Paid Search related conversion paths for the 9 sectors in our study.
Given that we are using conversion value to rank the conversion paths, it is not surprising that the
list is dominated by campaigns featuring the site owners’ trademark terms. Given the amount of
overlap seen in Tables 4 through 7, it is somewhat surprising to see just 2 paths, the single step
conversions from branded (trademark) campaigns, appear in all 9 sectors.
Table 8 Top Paid Search Conversion Paths
Top 5 CPC Conversion Paths Total
Bing Brand CPC 9
Google Brand CPC 9
Google Brand CPC > Google
Brand CPC
8
Google NTM CPC 7
Bing Non Brand CPC 4
Google Brand CPC > Direct 4
Google PLA CPC 2
Bing Brand CPC > Direct 1
17. 17
Table 9 lists the top 5 Paid Search related conversion paths by segment. As most would expect, position
#1 is held by single path trademark campaigns in Google across all sectors. That said, there are some
notable differences by sector in the other positions.
• In Home Furnishings & Hobby & Leisure, single step non-trademark AdWords campaigns are #2
• Single step Google Shopping campaign driven conversions were #2 in the Apparel and Fashion
sectors.
Shopping campaigns’ success in Apparel and Fashion makes sense, given the visual medium and
the nature of those products. In Home Furnishings, the success of non-trademark campaigns can be
attributed to 2 factors.
• In this study, campaigns containing keywords with the names of 3rd
party brands that the
retailer sells are included in the non-trademark segment. For example, queries looking for a
particular brand of window covering or area rug would be in the non-trademark segment.
• Home Furnishings is an industry where search queries tend to be more broad and product
category-related, such as ‘vertical blinds’ or ‘flatware sets’, versus the Fashion and Apparel
industry where queries tend to be more specific, such as ‘leather thigh high boots’ or ‘black
size 8 tunics’ and therefore more likely to trigger a Shopping campaign ad.
Table 9 Top Paid Search Conversion Paths by Vertical
All Sectors
Google Brand CPC
Bing Brand CPC
Google NTM CPC
Google Brand CPC > Direct
Google Brand CPC > Google Brand CPC
Apparel
Google Brand CPC
Google PLA CPC
Google Brand CPC > Google Brand CPC
Bing Brand CPC
Google Brand CPC > Direct
B2B
Google Brand CPC
Bing Brand CPC
Google Brand CPC > Direct
Google Brand CPC > Google Brand CPC
Bing Brand CPC > Direct
Health /Beauty
Google Brand CPC
Bing Brand CPC
Google NTM CPC
Google Brand CPC > Google Brand CPC
Bing Non Brand CPC
Home Furnishings
Google Brand CPC
Google NTM CPC
Bing Brand CPC
Google Brand CPC > Direct
Google NTM CPC > Direct
18. 18
Gifts
Google Brand CPC
Bing Brand CPC
Google NTM CPC
Bing Non Brand CPC
Google Brand CPC > Google Brand CPC
Food
Google Brand CPC
Bing Brand CPC
Google NTM CPC
Bing Non Brand CPC
Google Brand CPC > Google Brand CPC
Hobby and Leisure
Google Brand CPC
Google NTM CPC
Bing Non Brand CPC
Bing Brand CPC
Google Brand CPC > Google Brand CPC
Fashion
Google Brand CPC
Google PLA CPC
Google Brand CPC > Google Brand CPC
Google Brand CPC > Direct
Bing Brand CPC
B2B/B2C
Google Brand CPC
Bing Brand CPC
Google NTM CPC
Google Brand CPC > Direct
Google Brand CPC > Google Brand CPC
Pure B2C
Google Brand CPC
Bing Brand CPC
Google NTM CPC
Google PLA CPC
Google Brand CPC > Google Brand CPC
ORGANIC SEARCH MOST COMMON PATHS BASED ON CONVERSION VALUE
It is a commonly held belief among many retailers that organic search tends to be a top of the funnel
channel. Our research echoes this belief. Below are the top 5 conversion paths that include at least
one Organic Search step. All 5 start with an organic search.
19. 19
Table 10 Most Common Paths Including Organic Search
Top Organic Conversion Paths Conversion
Value
Conv. Value
Share
Organic Search $ 5,326,544 9%
Organic Search > Direct $ 1,442,353 2%
Organic Search > Direct > Direct $ 589,481 1%
Organic Search > Organic Search $ 526,823 1%
Organic Search > Direct > Direct >
Direct
$ 290,959 1%
Table 11 is the breakdown by sector. Demonstrating its dominant role as a top of the funnel driver,
Organic Search kicked off the conversion path in 49 of the 50 paths.
Table 11 Top Organic Search Conversion Paths By Vertical
Sector Top Organic Conversion Paths Conversion
Value
Conv. Value
Share
Apparel Organic Search $ 1,020,537 10%
Organic Search > Direct $ 227,518 2%
Organic Search > Affiliates $ 106,254 1%
Organic Search > Organic Search $89,474 1%
Organic Search > Direct > Direct $79,176 1%
B2B Organic Search $ 1,861,942 10%
Organic Search > Direct $ 556,415 3%
Organic Search > Direct > Direct $ 228,143 1%
Organic Search > Organic Search $ 188,589 1%
Organic Search > Direct > Direct >
Direct
$91,523 0%
Health & Beauty Organic Search $ 519,555 9%
Organic Search > Direct $ 101,355 2%
Organic Search > Organic Search $49,017 1%
Organic Search > Direct > Direct $29,764 1%
Organic Search > Email $22,591 0%
Home
Furnishings
Organic Search $ 655,265 9%
Organic Search > Direct $ 267,443 4%
Organic Search > Direct > Direct $ 133,046 2%
Organic Search > Direct > Direct >
Direct
$78,250 1%
Organic Search > Organic Search $69,863 1%
Fashion Organic Search $ 929,303 9%
Organic Search > Direct $ 217,379 2%
Organic Search > Affiliates $ 105,603 1%
Organic Search > Organic Search $99,788 1%
20. 20
Organic Search > Direct > Direct $74,511 1%
Food Organic Search $ 305,414 8%
Organic Search > Direct $47,822 1%
Organic Search > Organic Search $20,372 1%
Organic Search > Email $15,911 0%
Organic Search > Direct > Direct $14,319 0%
Hobby and
Leisure
Organic Search $ 1,667,746 9%
Organic Search > Direct $ 412,246 2%
Organic Search > Direct > Direct $ 172,997 1%
Organic Search > Organic Search $ 168,443 1%
Organic Search > Direct > Direct >
Direct
$92,583 1%
B2B/B2C Organic Search $ 1,565,474 10%
Organic Search > Direct $ 528,152 3%
Organic Search > Direct > Direct $ 223,839 1%
Organic Search > Organic Search $ 173,278 1%
Organic Search > Direct > Direct >
Direct
$ 110,110 1%
Gifts Organic Search $ 2,023,008 9%
Organic Search > Direct $ 438,248 2%
Organic Search > Organic Search $ 185,700 1%
Organic Search > Direct > Direct $ 169,103 1%
Google Brand CPC > Organic Search $90,534 0%
TOP CONVERSION PATHS WITH EMAIL INCLUDED (BASED ON CONVERSION
VALUE)
Table 12 shows the top 5 conversion paths containing an Email step and their breakdown by
vertical. All but one path involved either a combination of Email and Direct, or solely Email-driven
visits. The one exception was the 5th
ranked path involving Email for the Apparel sector, which
starts with an organic search followed by Email.
22. 22
One well-worn lesson in marketing is the idea of integrated communications and speaking with a
consistent voice across all of your marketing channels. Looking at these top paths for Email, the fact that
there is little interaction with other non-Direct channels could lead one to the assumption that email can
be treated separately.
GREAT EXAMPLE OF WHY SINGLE CLICK ATTRIBUTION IS FLAWED
This breakdown is a great example to point out one of the flaws in a pure last click interaction
attribution model, or even in GA’s last non-direct click model. The majority of the 40 paths listed
across the verticals have more than 1 step. In fact, there are 11 paths with more than 2 steps.
Across the 8 verticals, the 40 paths total 80 steps. Therefore, a single click interaction attribution
model would ignore ½ of those steps and gives 100% credit to whichever click you choose, usually
first or last, thereby giving them more credit than they deserve. Of the 80 steps, 55 are email-driven.
For the sake of this demonstration, let’s assume each Email-driven step represents a separate email
marketing campaign so there are 55 email campaigns. 29 of the 40 paths are terminated by Email
which means that 26 (55 minus 29) Email campaigns would receive no revenue credit in a last click
attribution model. As the numbers work out, those 29 conversion paths contain a total of 55 steps
so 26 steps, 15 Email Campaign steps and 11 non-Email steps get shortchanged in last click
attribution. Those 29 conversion paths drove $5.8MM in revenue and in last click attribution each
Email campaign in the last click position would have averaged $199k in attributed revenue.
In order to demonstrate the point about multi-touch attribution and to keep the math simple, we will
use a linear model where each step gets equal weighting or credit for the revenue that the path
generated. In that model, the 29 last click Email campaigns would have only received $166k each,
so in total those last steps would receive ~$1MM less in revenue credit. That $1MM gets credited to
the other 26 steps which included 15 email marketing campaigns. Those email campaigns go from
$0 in revenue credit using last click to about $40k each using a linear model.
Now let’s put some costs against those 55 email campaigns and we’ll assume each campaign costs
$10k to prepare and distribute. Table 13 shows how the math breaks down with respect to sales
credit and ROI. In the linear model, all campaigns show a positive return. In the last click model,
the 29 email campaigns in the last position generate a great 1900% ROI while the other 15
campaigns’ ROI is less than 0.
Table 13 COMPARING LAST CLICK TO LINEAR ATTRIBUTION EXAMPLE
# Campaigns Costs Last Click
Sales
Last Click
ROI
Linear Click
Sales
Linear Click
ROI
29 $290k $5,8MM 1900% $4.8MM 1555%
15 $150k $0 0% $1MM 667%
23. 23
Now let’s envision the people side of this example. Let’s assume in this organization different
managers put together each of the email campaigns. In a last click attribution, the people who
developed the 29 last click campaigns receive revenue credit that is essentially 20% greater than it
would be in a linear model. The other managers sit around trying to figure out why their campaign
didn’t work and update their resumes. Some people get promoted, some people get fired. Hopefully
you will agree with Justin Cutroni -- Last click is indeed stupid.
HYPOTHESIS: CONVERSION PATH LENGTHS ARE POSITIVELY CORRELATED TO
AOV.
The theory with this hypothesis is that higher ticket purchases are more considered purchases with
longer sale cycles and therefore would have longer conversion paths. Average conversion value
(AOV) is positively correlated to conversion path length when viewing the entire dataset of 89k
conversion paths, but it is a weak positive at 0.15. When you break it down by sector, you get
different results. Interestingly, there were 3 sectors (B2B, Health & Beauty and Hobby and Leisure)
where the correlation factor was actually negative, (that is, as AOV drops, conversion path
lengthens).
Table 14 Correlation of Path Length to AOV by Vertical
Sector
AOV
Correl to
Path
Length
All 0.15
Apparel 0.1
B2B (0.32)
Health & Beauty (0.1)
Home Furn 0.1
Hobby, Leisure (0.3)
Food (0.0)
Fashion 0.11
Gifts 0.15
B2B/B2C 0.02
Pure B2C 0.15
24. 24
HYPOTHESIS: ORGANIC SEARCH DRIVES TOP OF THE FUNNEL TRAFFIC WHILE
PAID SEARCH DRIVES THE BOTTOM.
Many advertisers believe that their paid search campaigns, especially the campaigns featuring
terms including their trademarks, drive the bottom of their conversion funnel. Table 15 breaks down
each channel’s position in conversion paths based on their conversions’ share of total conversion
value for that path position. In order to assess this hypothesis, we identified whether the channel
was:
• The driver of a single path conversion, i.e. the
user converted on their initial visit.
• The first touchpoint or a click in a multi-step
conversion path.
• The last interaction in a multi-step conversion
path.
• One of the intermediate or middle steps in the
conversion path.
• Both the first interaction point and the last
interaction point in a multi-step conversion path.
For example, the Direct channel was the first click in multi-step conversion paths that contributed
21% of the ~$58MM in total conversion value in our dataset. Similarly, single path conversions that
were attributed to the Direct channel were 14% of the $58MM. When Direct showed up somewhere
in the middle, those paths contributed 7% while those paths with Direct as the last click were 35%.
In the column labeled 1st
Interaction, we add the Single Path % to the First Click % to give you a better
perspective on each channel’s total contribution for attracting customers to the top of your conversion
funnel. Organic Search has a 23% first interaction contribution share vs 10% combined for the
middle and last click, so that part of our hypothesis appears to be true.
Table 15 Channel Breakdown by Funnel Position
Channel Single Path
% - All
Sectors
First Click
%
1st Interaction =
Single Path +
1st Click %
Middle
Steps
%
Last
Click
%
First
& Last
Direct 14% 21% 35% 7% 35% 16%
Organic 9% 14% 23% 5% 5% 2%
Google Brand CPC 6% 8% 14% 3% 5% 2%
Email 4% 8% 12% 4% 7% 3%
Bing Brand CPC 2% 2% 4% 1% 1% 0%
Google NTM CPC 1% 2% 4% 2% 1% 0%
Affiliates 1% 1% 2% 2% 4% 0%
Google PLA CPC 1% 2% 2% 1% 1% 0%
Bing Non Brand CPC 1% 1% 1% 0% 0% 0%
Display 0% 0% 0% 1% 0% 0%
Self-Referrals 0% 0% 0% 0% 0% 0%
CSEs - Non-Google or Bing 0% 0% 0% 0% 0% 0%
Referrals 0% 0% 0% 0% 0% 0%
Vendor/Suppliers 0% 0% 0% 0% 0% 0%
Other Ads 0% 0% 0% 1% 0% 0%
Payment Related Referrals 0% 0% 0% 0% 0% 0%
Amazon referrals 0% 0% 0% 0% 0% 0%
Media Sites 0% 0% 0% 0% 0% 0%
Social 0% 0% 0% 0% 0% 0%
Organic traffic is 4.35 more likely
to be top of funnel than bottom.
25. 25
When you look specifically at the subset of conversions that Organic Search is involved in, this
propensity for the beginning of the conversion funnel becomes very clear. Figure 8 below shows the
breakdown by funnel position for the Organic, Direct and Email channels for the subsets of
conversion paths that included those channels. You can see that Organic and Email tend to have
more impact as the first interaction (Single Path + First Click). Organic Search as a first interaction
participates in 74% of its total conversion value ($17.9MM, see Table 3) vs just 17% for last click.
Said differently, Organic Search is 4.35x more likely to be seen in the top of a conversion path
funnel than the bottom. Similarly, Email as first interaction participates in 61% of its total conversion
value vs 34% when it was the last click. That is 1.8x more likely to be top of funnel vs bottom.
WHERE ARE YOUR SEO EFFORTS FOCUSED?
If your business resembles this type of a bias towards the top of the funnel for organic search, there
are significant implications for your SEO efforts in these numbers.
• Are your site-side SEO efforts aimed at the top of the funnel terms?
• Have you done everything you can to optimize your broad category pages, the ones that are
likely to appear in early information gathering queries?
• Are you authoring authentic content that your customers (and more importantly your non-
customer prospects) will find useful? Product reviews are great for attracting users who are
2/3 of the way through their buying cycle. What do you have on your site or in your blog that
shoppers further up the funnel might find useful?
Figure 8 Conversion Path Position Breakdown for Direct, Email, Organic Search
0%
10%
20%
30%
40%
50%
60%
Single Path First Click Last Click Middle
Steps
first = last
Direct, Email, Organic Conversion Path
Position as % of The Channel's Total
Direct
Organic
Email
26. 26
LAST CLICK IS DIRECT CHANNEL’S DOMINANT FUNNEL POSITION
Earlier in this paper we mentioned that the Direct channel has a dominant role as a last click
interaction. You see this in Table 15 and Figure 8. In Table 15, you can see Direct as the last click
occurred for 35% of the $58MM vs 21% for the first click. When you filter the $58MM to look in
Figure 8 at the $35MM that the Direct channel participates in at any position in the conversion path,
Direct as the last click occurs in 57% of that $35MM. We posit that multi-tabbed browsing and
Google Analytics’ session timeout parameter are causing some of this dominance for Direct in the
last click position. This trend is undoubtedly what led Google to switch the default attribution in
Google Analytics to last non-Direct click a few years ago.
DON’T TOUCH THOSE TRADEMARK CAMPAIGNS
With respect to paid search, some readers might be
surprised to see in Table 15 and in Figure 10 that
trademark term campaigns actually contribute more at the
top of the funnel. In Table 15, we see that the 2 trademark
campaign channels (Google Brand CPC, Bing Brand CPC)
combined have a first interaction contribution of 18% vs
just 10% for the middle and last click positions. Therefore,
trademark campaigns are 1.8x more likely to start the
conversion path. This top of the funnel dominance is
consistent with results we have seen for multiple verticals in which our clients are members in
Google’s interactive customer journey tool. Figure 9 is a view of this tool for the Hobbies & Leisure
sector showing that Brand Paid Search occurs in the top of the funnel 31% of the time vs 24% at the
bottom. You can see this tool at https://www.thinkwithgoogle.com/tools/customer-journey-to-online-
purchase.html.
Figure 9 Google Customer Journey Tool Example
This top of the funnel dominant position doesn’t just apply to trademark campaigns. From Table 15,
if we aggregate the first interaction position for the 5 paid search channels it totals 1/4th
of the
$58MM vs a combined 15% for the middle and last click positions. In other words, Paid Search is
1.67x more likely to start a conversion path than to be anywhere else in the path.
Branded Paid Search 1.8x
more likely to be top of funnel
vs anywhere else
27. 27
The first interaction dominance also holds to each of the 5 channels when you look at them
individually, which we do in Figure 10. Similar to Figure 8, this chart breaks down the 5 CPC
channels by position contribution for the conversions those channels participated in.
Figure 10 Path Position Breakdown for Paid Search Conversion Paths
For example, Google and Bing trademark campaigns (the 2 leftmost columns in each category) are
significantly more likely to contribute to a conversion as a first click than a last click. Google Brand
CPC was the first click in 43% of its total conversion value ($10.9MM, see Table 3) vs just 24% for
last click. Bing Brand CPC was the first click in 37% of its conversion value ($3.5MM, see Table 3)
vs 21% for last click.
THINK TWICE BEFORE CUTTING TRADEMARK BUDGETS
One key takeaway for retailers from this finding is to think twice about cutting back on your
trademark search campaigns. Many of you may share the belief that your trademark campaigns are
bringing you users who are already familiar with your company and that this spend is essentially
“wasted”. We suggest:
• Take a look at your New User % for your Trademark campaigns. Is it greater than or less
than the % for your other channels?
• Look at your top conversion path report data like we did in this study to understand the role
these campaigns play by funnel position for your specific business.
• Look at the impact of cutting this spend on your total ad spend. In most cases, trademark
campaigns tend to be the lowest portion of your spend and therefore cost cutting here won’t
have a huge impact.
One exception to the rule about trademark spend significance is those of you whose trademark
contains a commonly used word or phrase that causes your CPCs to be higher. One tactic we have
deployed for some clients looking to squeeze their ad spend wherever possible is to create
Remarketing Search Ad lists for previous site visitors with a duration that covers your typical sale
cycle. We then exclude those audiences from your trademark campaigns. The theory is that if
someone has been to your site recently and is entering one of your trademark terms into Google,
this is their way of returning to your site and there is no need to spend on an ad when they will most
likely click through organically. With this strategy, you still get to promote your brand to users who
are entering your trademark into Google because they heard of you through word-of-mouth, saw
one of your YouTube videos or were exposed to your brand in some other way.
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Single Path First Click Last Click Middle Steps first = last
CPC Channels Conversion Path Position as % of
Total
Google Brand CPC
Bing Brand CPC
Google NTM CPC
Google PLA CPC
Bing Non Brand CPC
28. 28
COUPON SITE REALITY
In Table 15 you can see the impact of coupon sites on ecommerce conversion paths. Those sites
dominate the Affiliate channel whose contribution is heavily weighted towards the middle to bottom
of the conversion funnel with a 2% first interaction contribution and a 3x larger 6% contribution from
the middle and last click. The impact of coupon sites is very likely understated in today’s multi-tab
browsing environment. A consumer who decides to make a purchase online and opens up a 2nd
tab
to go to his or her favorite coupon site looking for a deal in many cases doesn’t have to click through
to the retailer’s site from the affiliate’s site to use a promo code they found there. These types of
coupon site visits don’t get captured in their conversion path and don’t change the attribution of their
visit to the retailer’s site in Google Analytics, so the site owner has no visibility to this behavior.
CONVERSION PATH POSITION CONTRIBUTION BY CHANNEL BY SECTOR
Given the hypotheses we had regarding the position of several channels in the purchase conversion
cycle, we decided to break down that cycle by channel and by sector.
SINGLE STEP CONVERSION PATHS SHARE OF TOTAL CONVERSIONS BY
SECTOR
Table 16 shows the single path conversion share by channel by sector. The first column is the
baseline for all sectors for each channel and matches the first column in Table 15. The green
shading in the individual sector columns indicates at least a 10% positive difference from the All
Sectors baseline while red indicates a similar negative difference. For example, the B2B sector had
21% of its total conversion value from single step
Direct visits. It is shaded green because it is 50%
above the baseline of 14%. One theory to explain
this large variance for B2B is that B2B companies
are more likely to have repeat customers who re-
purchase more frequently and therefore enter the
site URL directly.
For Apparel and Fashion, Google
Shopping campaigns are better avenues
for attracting customers than
non-branded terms
29. 29
Table 16 Single Path Conversion % By Channel By Sector
Direct, Organic and trademark campaigns in Google dominate the top 3 spots for all sectors except
Health & Beauty and Food where Email is the 2nd
highest driver of one touch conversions. This makes
sense when you consider that both sectors sell products that are consumables, and therefore will need to
be replaced frequently.
Apparel and Fashion perform above average for Google Shopping campaigns as single step
conversions, most likely due to the visual nature of that shopping experience. When you look at
these two similar sectors in Table 16 and Table 17, which contains a similar breakdown for the first
click position, you see that Google Shopping campaigns are significantly better avenues for bringing
customers into those sectors’ funnels than non-branded terms or even branded terms on Bing.
• Google Shopping single step conversions are 3% of Apparel’s conversion value vs 0% for
both the Google NTM CPC and Bing Non Brand CPC channels and just 1% for the Bing
Brand CPC Channel.
• When you look at the first click data in Table 17, the disparity is even larger with Google
Shopping campaigns at the first click position participating in 5% of Apparels’ total value vs
just 1% for Google NTM CPC and Bing Brand CPC and 0% for Bing Non Brand CPC.
FIRST CLICK INTERACTIONS BY CHANNEL
Table 17 shows the breakdown for First Click Interactions in conversion paths with more than 1 step.
The trends seen above in the single path conversions hold true here with a couple of notable
exceptions. In Hobby & Leisure and Gifts, Email jumps into the #3 position for both sectors in
driving initial clicks. Again, there is some logic to difference. Hobby & Leisure retailers sell products
to highly engaged users who are predisposed to pay attention to emails from brands they use in
their primary leisure pursuits. In addition, several of our clients in that sector sell to both businesses
and consumers so some of that email traffic is likely coming from established business relationships.
Channel
SINGLE
PATH %
- ALL
SECTORS APPAREL B2B
HEALTH
&
BEAUTY
HOME
FURN FASHION FOOD
HOBBY,
LEISURE
B2B/
B2C GIFT
Direct 14% 12% 21% 12% 9% 12% 13% 11% 13% 14%
Organic 9% 10% 10% 9% 9% 9% 8% 9% 10% 9%
Google
Brand CPC 6% 6% 6% 6% 4% 5% 7% 6% 5% 6%
Email 4% 2% 2% 10% 4% 2% 13% 4% 3% 5%
Bing Brand
CPC 2% 1% 4% 3% 1% 1% 4% 1% 3% 2%
Google
NTM CPC 1% 0% 1% 1% 2% 0% 2% 2% 1% 1%
Affiliates 1% 2% 2% 0% 3% 2% 1% 0% 0% 1%
Google
PLA CPC 1% 3% 0% 0% 0% 3% 0% 1% 0% 1%
Bing Non
Brand CPC 1% 0% 0% 1% 1% 0% 1% 1% 0% 1%
Display 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
30. 30
Table 17 First Click Conversion Contribution By Channel By Sector
Channel
FIRST
CLICK
% APPAREL B2B
HEALTH
&
BEAUTY
HOME
FURN FASHION FOOD
HOBBY,
LEISURE
B2B/
B2C GIFTS
Direct 21% 24% 23% 17% 20% 27% 12% 20% 20% 18%
Organic 14% 14% 13% 11% 19% 15% 7% 14% 18% 11%
Google
Brand CPC 8% 9% 8% 7% 7% 9% 7% 9% 8% 8%
Email 8% 4% 4% 13% 8% 4% 16% 13% 7% 13%
Bing Brand
CPC 2% 1% 4% 3% 2% 1% 2% 1% 3% 2%
Google NTM
CPC 2% 1% 2% 2% 4% 1% 2% 3% 3% 3%
Affiliates 1% 3% 0% 1% 3% 3% 1% 1% 0% 1%
Google PLA
CPC 2% 5% 1% 0% 1% 5% 0% 1% 1% 1%
Bing Non
Brand CPC 1% 0% 0% 1% 1% 0% 2% 2% 1% 1%
Display 0% 1% 0% 1% 0% 0% 1% 0% 0% 0%
As indicated above, the Google Shopping channel’s impact in Apparel and Fashion for First Click
conversion paths is almost double that of Single Step paths.
SURPRISING BELOW AVERAGE SHOPPING CAMPAIGN PERFORMANCE
The below average performance of Shopping campaigns in sectors like Food, Gifts and the Hobby
and Leisure market is surprising given that those industries sell items in which appearance can be a
key part of the selling equation. One potential reason for this shortfall is the challenge many small to
mid-size retailers have in optimizing the content of their merchant feeds. Often times the source of
data that gets put in those feeds is from internal systems that serve multiple masters and therefore
tweaking elements like product names or descriptions is not an insignificant undertaking. For those
of you who can easily modify your data feed content, one tactic we have deployed successfully with
clients is adding product category terms to their product names so that their shopping ads have an
opportunity to get triggered by broader minded search queries. We’ve seen huge jumps in
impressions after deploying this tactic.
MIDDLE STEPS BREAKDOWN BY CHANNEL
Table 18 breaks down the contribution share by channel by sector for the middle of the conversion
paths. These conversions represent instances when these channels were in the conversion path at
least once but were not the first or last interaction, so this is a true look at the middle of the funnel
and is commonly referred to as assisted conversions.
31. 31
Table 18 Middle Step Conversion Path Contribution by Channel By Sector
Channel
MIDDLE
STEP % -
ALL
SECTORS APPAREL B2B
HEALTH
&
BEAUTY
HOME
FURN FASHION FOOD
HOBBY,
LEISURE
B2B/
B2C GIFTS
Direct 7% 7% 5% 7% 7% 7% 8% 8% 6% 8%
Organic 5% 7% 3% 4% 5% 7% 3% 6% 4% 6%
Email 4% 5% 2% 4% 5% 5% 4% 5% 4% 6%
Google
Brand CPC 3% 5% 2% 3% 3% 5% 2% 3% 3% 3%
Affiliates 2% 5% 2% 1% 2% 5% 1% 1% 2% 2%
Google NTM
CPC 2% 1% 1% 0% 1% 1% 1% 4% 1% 3%
Google PLA
CPC 1% 1% 0% 0% 1% 1% 0% 1% 0% 1%
Display 1% 3% 0% 3% 0% 3% 4% 0% 0% 1%
Bing Brand
CPC 1% 1% 1% 1% 1% 1% 1% 1% 1% 1%
Bing Non
Brand CPC 0% 0% 0% 0% 0% 0% 1% 1% 0% 1%
The first thing you will notice is the above average performance in Apparel and Fashion for Organic,
Email, Google Brand CPC and Affiliates channels. One reason for the latter’s performance is a
plethora of web sites that combine affiliate marketing with the idea of social media like
fashiononlineadvisor.com,
Right next to the Apparel column, you can see the B2B column has below average performance
almost across the board. Compare that to industries where customers can be passionate about the
brands they use like Fashion, Hobby, Leisure and Gifts where the performance of Direct, Organic
and Email is generally above the average. For the B2B market, there are 2 questions that this data
raises.
1. Is the poor performance in the middle a function of the industry, i.e. it’s not worth investing
time or money or
2. Does this data indicate there is an opportunity to do a better job of engaging users in the
middle of a purchase cycle?
DISPLAY SHOWS UP
An opportunity to engage users in the middle of their purchase cycle that the data in Table 18
suggests is the Display channel. Essentially negligible in the other steps of the cycle, here we see
contributions from Display in Apparel, Health & Beauty, Fashion, and Food. It’s an interesting
curiosity that sectors with poor performance using the PLA channel such as Health & Beauty and
Food have success with another visual medium, Display ads.
32. 32
MIDDLE MIGHT MEAN “TRUE LAST CLICK”
Keep in mind when you look at middle path steps that you might be looking at the true last meaningful
interaction that brought the user back to your site to complete his/her purchase. If Direct is the last click
or touch, it’s possible that entering the URL directly was just the convenient way of returning to your site
to complete a purchase that had already been decided upon. Also, that Direct session could be a user
who left his browser open on his shopping cart page, went to lunch and clicked “checkout” upon his
return starting up a new Direct attributed session. The traffic source you care about in that scenario is
the one that drove the person to your site when he added the items to his/her cart. That source is now a
middle of the path step because the session after lunch is now the last click session in this user’s
conversion path. This behavior is one reason to consider weighting your middle steps in a multiple step
conversion flow almost as much as your last click source.
LAST CLICK INTERACTION BREAKDOWN BY CHANNEL
Last but not least is the Last Click breakdown shown in Table 19. This is one most readers will
probably find familiar since it is very close to the attribution you see by default in your GA account.
Surprises here include the below average performance of Direct in several sectors, especially
Health & Beauty and Food. In fact, if you go back and look at Tables 16 through 18, you can see
that Direct never performs above average for Health & Beauty and only exceeds the average for
Food in the middle (Table 18). Clicking through from an email seems to be a more preferred route
to visit those sites as it outperforms the average in all positions but one, where it had average
performance. Perhaps these retailers have “trained” their customers to wait for offers in their email
before they re-stock their supplies.
Table 19 Last Click Contribution by Channel by Sector
Channel
LAST
CLICK %
ALL
SECTORS APPAREL B2B
HEALTH
&
BEAUTY
HOME
FURN FASHION FOOD
HOBBY,
LEISURE
B2B/
B2C GIFTS
Direct 35% 34% 35% 28% 44% 38% 21% 36% 38% 32%
Email 7% 5% 4% 12% 5% 5% 15% 9% 5% 10%
Organic 5% 5% 5% 5% 5% 6% 4% 6% 6% 5%
Google
Brand CPC 5% 5% 4% 4% 4% 5% 4% 5% 4% 4%
Affiliates 4% 8% 4% 2% 5% 8% 2% 2% 5% 2%
Bing Brand
CPC 1% 0% 2% 2% 1% 1% 2% 1% 2% 1%
Google NTM
CPC 1% 0% 1% 1% 1% 0% 1% 2% 1% 2%
Google PLA
CPC 1% 2% 0% 0% 0% 2% 0% 0% 0% 1%
Bing Non
Brand CPC 0% 0% 0% 0% 0% 0% 0% 1% 0% 1%
Display 0% 1% 0% 1% 0% 1% 1% 0% 0% 0%
33. 33
HYPOTHESIS: DISPLAY CAMPAIGNS APPEAR LATER DUE TO RETARGETING
Figure 11 Other Marketing Channels Conversion % by Path Position
Figure 11, similar to Figures 8 and 10, breaks down the contribution by path position for the Display,
Affiliates, Other Ads and CSEs channels. You’ll note that Display and the Other Ads channel kick in
during the middle phases of the conversion path with both reaching 60% of their channel’s conversion
value. The huge difference between the middle steps and the other positions for Display where last click
is the 2nd
largest contributor at 17%, a little more than 1/4th
the level of the middle steps, appears to
validate this hypothesis that retargeting shifts display visits later in the conversion path. One reason for
this behavior is the fact that a number of our clients use the GDN strictly for retargeting purposes. By
definition, retargeting is aimed at the middle to bottom of your conversion funnel since the primary goal is
to entice a previous non-buying visitor back to your site to make a purchase.
Another interpretation of this data and the data in Table 18
for the Display channel is that these campaigns may be an
opportunity to nurture prospects in the middle of their sales
cycle. That said, it should be noted that the overall
contribution for Display remains small. It was only
tangible, in the low single digits, in the middle steps
breakdown (Table 18).
There are 2 other notable values in this chart.
1. Notice that CSEs, although like Display with a small overall contribution, garnered most of that
contribution through first click interactions indicating that these channels were a good way to fill the
top of your conversion funnel.
2. Affiliates are 3.5x more likely to be last click (49%) vs first click (14%), another example of the
impact of coupon sites.
0%
10%
20%
30%
40%
50%
60%
Single Path First Click Last Click Middle
Steps
first = last
Other Marketing Channels Conversion
Path Position as % of Total
Affiliates
Display
Other Ads
CSEs - Non-Google or Bing
Display, a small overall
contributor, is most impactful in
the middle of the funnel
34. 34
Social media sites, both the big names and
smaller more industry specific sites,
participated in the conversion paths for a little
more than 1% of the total revenue in this study.
HYPOTHESIS: SOCIAL MEDIA IS A TOP OF THE FUNNEL FACTOR
The thought process behind this hypothesis is that a user sees something on social media, clicks through
to the retailer’s site to check it out and eventually comes back to buy. There were a couple of surprises in
the data when we went to assess this hypothesis.
• First and foremost was the total lack of conversion contribution. Total conversions containing
social sources (which includes traffic from social advertising and social referrals) was only $622k
(Table 3), a little more than 1% of the $58MM in our dataset. This finding is in stark contrast to
published reports of social marketing spend growth and how important marketers say social is to
their business. Here are a couple of recent examples.
o For example, businessinsider.com estimated that 2014 spend in the US on social ads grew
39% YOY to $8.5B.
o Figure 12 shows 2 charts taken from Salesforce’s 2015 State of Marketing report which can
be viewed at https://www.salesforce.com/blog/2015/01/2015-state-of-marketing.html. Note
in the top chart that Social Media related spending comprises the top 3 areas for increased
spending in 2015. In the bottom chart, almost 2/3 of the surveyed marketers believe that
social media is a critical enabler to their business which was an increase of 39% from the
same question a year earlier. Almost 60% the respondents report social media marketing
as producing ROI either directly or indirectly.
35. 35
Figure 12 Taken From Salesforce.com 2015 State of Marketing Report
• The second surprise was that social contributed more as Last Click and Middle Steps than they do
as originally posited as initial interaction drivers. In Figure 13 below for the referral-related
channels in our study, the Social channel is the leftmost, lighter orange colored columns. Note that
the Last Click and Middle Steps contributions are significantly larger than Single Step or First Click
conversions. Those 2 positions are 68% of Social’s $622k in revenue.
36. 36
Figure 13 Referral Based Channels Conversion Path Breakdown by Position
ATTRIBUTION IMPLICATIONS AND SUGGESTED ACTIONS
Here is an action plan that you can pursue to take the first step along the Attribution Spectrum to multi-
touch attribution for your digital marketing efforts.
1. Understand how your business behaves from a conversion path standpoint.
2. Decide how you will attribute non-attributable revenue, e.g. Direct and miscellaneous referrals.
3. Include all marketing-related expenses in your attribution model
4. Experiment with different attribution models including custom models.
5. Know your average customer LTV.
6. Revisit your attribution models periodically to ensure they remain optimal.
We will look at each step in more detail.
UNDERSTAND HOW YOUR BUSINESS BEHAVES FROM A CONVERSION PATH
STANDPOINT.
Here are a couple of questions to guide your thinking when it comes to understanding your own
conversion path phenomena.
1. How is it similar or different from the data presented here?
2. What are the implications of those similarities or differences?
3. What does your data suggest might be an appropriate attribution model type to use?
Before you explore your top conversion path report, we strongly encourage you to follow the approach we
did for this study and define your own set of custom channels to segment your traffic based upon the way
you view your different marketing initiatives and would like to measure their performance. One goal to
0%
10%
20%
30%
40%
50%
60%
70%
Single Path First Click Last Click Middle Steps first = last
Referral Channels Conversion Path Position
as % of Total
Social
Self Referrals
Referrals
Payment Related
Referrals
Vendor/Suppliers
Amazon referrals
Media Sites
37. 37
keep in mind is to minimize your non-attributable revenue from referrals. For example, in many cases in
the dataset for this study we were able to reduce the non-attributable referral revenue to around 1% of
total revenue.
Once you have your custom channel groups defined, study your Multi-Channel Funnel Reports in the
Conversions section of Google Analytics to understand your conversion flow distribution and relative
weighting. You’ll want to look at the Top Conversion Paths, Path Length and Time Lag reports.
DECIDE HOW YOU WILL ATTRIBUTE NON-ATTRIBUTABLE REVENUE, E.G. DIRECT
AND MISCELLANEOUS REFERRALS
If you are successful with your custom channel definition process, you will have at most 2 segments of
traffic, the Direct channel and any remaining miscellaneous, un-categorized referrals which you couldn’t
say were related or a result of a specific marketing initiative. You then need to decide how you want to
deal with that bucket of revenue in your attribution modeling. Some options to consider:
1. Ignore this segment. Hopefully your custom channel groups will capture the majority of your
revenue.
2. Spread this revenue across the other channels evenly.
3. Spread this revenue across the other channels in some kind of intelligent, weighted fashion.
a. For example, if you assume that much of the Direct traffic is a result of the session timeout
factor, you can weigh the allocation for Direct’s revenue based on the last non-Direct
touchpoint that precedes the Direct step, much like Google Analytic’s default attribution
model.
b. Another rationale would be to assume that Direct traffic’s returning visitors revenue should
be allocated more heavily to channels that drive retention or repeat business such as your
email marketing. You could then allocate the new user portion of Direct in a manner like the
one suggested above.
INCLUDE ALL MARKETING-RELATED EXPENSES IN YOUR ATTRIBUTION MODEL
INCLUDING HEADCOUNT.
The more relevant data you incorporate in your model, the more sophisticated the model can be. You can
use Google Analytics custom data imports to bring in data from other sources for your attribution
modeling. You can also execute the experimentation in Excel, which is how we conducted one of our
experiments.
There are 2 non-online marketing costs we recommend you consider incorporating in your model.
1. Decide how to allocate any offline marketing expenditures. As an example, consider direct mail
campaigns. If you don’t use unique or vanity URLs to track traffic from your direct mail pieces,
such as catalog mailings, you can estimate the mailing’s impact on your traffic sources by
studying the trends before and after the mailing and then building a cost allocation schema
accordingly. For example, it is likely that a catalog mailing will end up in driving traffic to your
Direct, Email, Organic and Paid Brand channels since those are the most likely methods for
that person to respond online.
2. It makes sense to include your marketing team’s headcount expenses. It’s an easy to calculate
number that you can allocate accordingly based on job functions, etc…
38. 38
EXPERIMENT WITH DIFFERENT ATTRIBUTION MODELS
The goal is to find an attribution model that considers all of your channels’ participation and gives you the
highest return for marketing $ spent. We conducted an experiment with one account to show you the
sensitivity your data can have to the different attribution models available in Google Analytics including custom
models.
First, here is some background on how we chose which models to try. The 2 charts below show the share of
conversion value by path length and the share of transactions and conversion value based on the time lag
between initial visit and conversion event in days. The Path Length report suggested to us that using a time
decay model which puts most weight on the most recent visits would be one model to test. Similarly, the time
lag data with its distinctive U shape suggested that a position-based model would be appropriate to test.
In our experiment, we ended up evaluating 9 different models. We used all of the standard models
available in Google Analytics except the AdWords last click model as well as 3 custom models. The
results are listed in Table 20.
Table 20 Attribution Model Comparison Results
39. 39
Here is a brief recap of the steps we took.
1. We used the same custom channel groups from this study to pull the different attribution model
reports for a single month with the maximum 90 day lookback.
2. We assigned marketing spend to all channels that typically have an expenditure associated
with them. For example, Email was set to $8,000 per month, the SEO program was set to
$3,500 a month and Affiliates were estimated at $1,000 monthly service plus 7% average
commission.
3. We chose to ignore the non-attributable revenue for simplicity sake.
4. For custom models, we experimented with the 3 multi-touch models: Time Decay, U Shaped
and Linear.
a. The custom settings we used were:
i. Giving 0 credit to Direct steps and Bounced Visits.
ii. Derating Brand CPC steps by 50%.
iii. Increasing Non-Brand CPC steps by 50%.
iv. For both of the U Shaped or Position models we used the default 40% - 20%- 40%
settings.
1. For each model, we summed up the conversion value for all the custom channels that had a
marketing expense allocated to it which in this case amounted to 8 channels. Those figures
are shown in the column in Table 20 labeled MKTG PROG REV for marketing program
revenue.
2. We then calculated the revenue per marketing spend and the CPA for each model.
Here are a few points to notice about our experiment.
1. Look at the revenue difference between the default Last Non-Direct Model and the true Last
Interaction Model. The former is the most productive model while the latter is the least
productive model and there is a 49% difference in the marketing program revenue attributed in
those 2 models. This shows you the influence of Direct in that last click position as discussed
earlier.
2. Note the impact of the simple customizations we applied to the 3 baseline model types. All 3
went from having around $1.2MM attributed to them to just over $1.6MM. Most of that change
comes from the exclusion of Direct steps in the calculations.
3. Perhaps most importantly, notice how close the 3 custom models, highlighted in yellow come to
the default last non-Direct model. Less than 1% difference across all 4 models.
So in our case, we went through a fairly elaborate process to build and compare some custom models
that use the “smarter” multi-touch approach which doesn’t give any credit to Direct visits and visits where
the end user didn’t engage. We also valued non-brand terms more heavily assuming they bring in more
new prospects than the devalued brand terms, a logical adjustment for most retailers to consider. Still, at
the end of the day, those models don’t do a better job of allocating revenue than the default single
interaction model. It’s quite possible this could occur with your attempts to experiment. Options for
moving forward from here are to:
• Give up and continue using a “stupid” single interaction model, the default model.
• Pull data for a longer period and see if the results change.
• Choose one of the 3 “smarter” models, which are all within 1% of each other, to use for the time
being until we decide to check our model for optimization opportunities next month, next quarter,
etc.
• Keep experimenting with more customizations to see if we can create a “smarter” model that out-
produces the default model in ROI and CPA.