Cross channel-attribution-modeling-in-action


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Cross channel-attribution-modeling-in-action

  1. 1. iCrossing Capabilities report:Cross-Channel attributionModeling in aCtionby Kaylan Malm, Manager, Advanced Analyticswith Alan Gee, Manager, Business IntelligenceAugust 2009eXeCutiVe suMMarYMany brands use a last-click attribution model for their marketing efforts online because they do notknow that they have other options. iCrossing has successfully integrated data from several sources,created a display visualization dashboard using the iCrossing Marketing Platform that allows clientsto see what their consumers are doing before they convert, and has created a user interface thatprovides KPIs in a manner that helps answer questions and allows for data to be downloaded forfurther analysis.table oF Contents2 Background: Brands are vastly underutilizing an ocean of cross -channel attribution data Accurate cross-channel attribution models allow marketers to create holistic online strategies Multi-channel attribution research is actionable3 Mining a Wealth of Information: Data aggregation and integration Dataset includes conversion channel, site visits prior to conversion (“assists”) and display impressions Study focused on one client3 Making Sense of it All: Data visualization Data timeframe includes conversions from September 2008 – December 2009 Consumers may find websites through search or display, but will return through a referring or typed in URL Report shows results from Conversion Funnel, Keyword Funnel, Conversion Mix, Channel Conversion Mix, Source Conversion Mix and Visit Conversion Mix10 Conclusion: Cross-channel attribution model dashboards successfully integrate data from several sources Clients can see what consumers are doing before conversion The display process methodology allows clients to test models that are most appropriate for their businesses
  2. 2. Cross-Channel attribution Modeling in aCtion AUGUST 2009 background: brands are Vastly underutilizing an ocean of Cross -Channel attribution data The importance of cross-channel tracking in the digital and interactive space is an exhausted topic; what marketers are now appropriately focusing on is how to implement and analyze cross-channel tracking to correctly attribute conversion credit. By creating attribution models that more clearly and accurately depict the role of each channel and visit to a site prior to a conversion, marketers are creating the framework for creating holistic online strategies. The challenge with these models is not motivating the need, but understanding how to collect and integrate data across channels in a meaningful way that can be visualized and analyzed. This paper demonstrates how iCrossing’s business intelligence analysts are making this goal a reality for our clients and the doors that get opened when this type of data set can be gathered. “To gain efficiency and deeper understanding of campaign effectiveness, marketers must implement attribution measurement via click-path tracking, data mining, and predictive modeling.” (“Search and Attribution” November 2008). Most marketers understand the importance of cross-channel tracking, but most don’t even know where to begin to start putting the cross- channel dataset together. Most companies still use a last-touch conversion model attributing all conversion credit to the site visit when the conversion takes place, while a small group relies on the first-touch conversion model of attributing all the credit to the first customer visit to the site regardless of the channel through with the conversion took place. Both of these methods are flawed and most marketers know it, they just don’t know how to fix it. “Search marketers that assign 100 percent conversion value to the so-called last click leading to a conversion often unfairly remove much of the brand value in their display ads and overemphasize the value of keywords that immediately precede a purchase or lead.” (“Search and Attribution” November 2008). While this is true for search, most of the research on this topic is flawed as well by not considering conversions from referring URLs and direct loads, but rather by focusing only on media channels. “The last-click model is such a problem that one-fifth of advertisers rely on gut feeling when evaluating the success of brand campaigns online.” (“Transitioning from the Last-Click Model” July 2008). This is corrected by using a first touch conversion model, but this method also has its challenges for the same reason of not capturing the entire picture. When data fails to answer the entire question, markets fall back onto the measurement tool of comfort – their gut. But in an age where we have access to so much data, we just need to learn to use data in a smarter way. According to Forrester’s recently released “A Framework for Multicampaign Attribution Measurement” (February 2009), “Of 275 Web site decision-makers surveyed in 2008, a full 52 percent agree that attribution would enable them to spend marketing dollars more effectively. Yet only 31 percent are actively using attribution today, even though this concept is not new for marketers, who have long since appropriated credit to marketing endeavors in dubious ways.” Forrester’s research points out that the 31 percent who say they are currently using attribution today likely have differing definitions of what multi-campaign attribution is and we suspect most aren’t using it to its full ability. “Cross-channel management allows coordination of all marketing initiatives: messaging and creative development, media buying, and analytics that allow marketers to measure the influence of seemingly disparate campaigns on each other.” (“Search and Attribution” November 2008). The problem is that marketing analytics tools on the market are specialized based on the channel and purpose, often aggregating information in a way that makes it difficult to match records across systems. According to Juniper Research, a Forrester Company, “In reality, the technology and benchmarks to achieve accurate attribution are in the early stages.” (July 2008). Forrester outlines the following problems clients encounter when trying to gather this data: + Extended sales cycles mask the impact of first clicks + Independent tracking systems result in fuzzy math that doesn’t add up + Search looks heroic, but advertising really provides lift Advertising such as display media is not the only channel getting let down by last-click conversion tracking, but that conversions credited to referring URLs and direct load traffic will give up partial credit to both media and search channels. iCrossing’s multi-channel attribution research is not conceptual, it is actionable. Our solution is presented in detail in the following pages and was built using client data to meet specific objectives. We focused on the process because findings are unique to every client and therefore should not be generalized. The important task at hand is to identify how to arrive at that solution.© ICroSSInG. ALL rIGHTS rESErVED. 2
  3. 3. Cross-Channel attribution Modeling in aCtion AUGUST 2009 Mining a Wealth of information: data aggregation and integration The most daunting task of multi-channel campaign tracking is gathering the appropriate data set for visualization and analysis. Using Interest2Action (I2A), a site analytics tool proprietary to iCrossing to collect all of the customer visits data, we kept the data integration to a minimum. I2A uses a site-side pixel to collect cross-channel visits and stores data for the lifetime of the cookie, a characteristic that is helpful for clients with longer purchase cycles. The dataset provided by I2A includes: channel, referring URL, timestamp, keyword searched and engine for all search visits, ad campaign and size for all display media visits, type of conversion, and revenue. While I2A captures all site visits, for the purpose of this research we looked at the conversion channel and up to six site visits prior to the conversion which we refer to as “assists.” I2A also collects data on direct loads and referring URL conversions, two channels that are often ignored by the solutions proposed by media channel tools. The only data missing from the I2A dataset was display impressions, an important factor when testing the hypothesis that display media is often under credited when it comes to conversion tracking. Partnering with Atlas, we pulled cookie-level impression data after passing a unique identifier between I2A and Atlas during the display campaign. Matching this data back to the I2A conversion file, we added display impressions into the site visits and conversions data, creating a dataset that then told the entire conversion story. The concepts presented below and the data shown are for one particular client, but the method of data collection and analysis presented are true of all clients using I2A, and with some hard work and data integration these could be gathered through many other Web analytics tools. iCrossing’s control over the I2A tool has helped to streamline the process for clients using our proprietary tools. Making sense of it all: data Visualization After the cross-channel dataset is collected, we set out to aggregate and visualize the data. The trick to cross-channel reporting is to only aggregate the data after all the channels are incorporated; aggregating before each channel is added leaves the story incomplete. The amount of data for most clients is overwhelming, but iCrossing’s business intelligence team and the iCrossing Marketing Platform are the perfect team to take on the challenge and specialize in data integration and display. Using the iCrossing Marketing Platform, we created a standard user interface for multi-channel marketing attribution and focused the UI on helping us answer the following questions: + How do we attribute credit to assists? + What is the true marketing attribution across channels? How is it different from traditional last-click attribution? + Is display media “assisting” other media channels? + Is direct load and referring URL traffic taking conversion credit away from media and search channels? + Does the search keyword funnel show searchers going from general to more specific brand keywords when they are closer to converting? + Do customers that see display ads more frequently convert on branded keywords? The iCrossing solution is focused on data integration and visualization to analyze the customer journey and attribution models that will be unique to each client. We do agree with Forrester that an attribution model should address recency, frequency, and time on site (“A Framework for Multicampaign Attribution Measurement” February 2009). The dashboard consists of four data tabs including: Attribution, Conversion Funnel, Keyword Funnel and Conversion Mix.© ICroSSInG. ALL rIGHTS rESErVED. 3
  4. 4. Cross-Channel attribution Modeling in aCtion AUGUST 2009 ATTrIBUTIon (FIGUrE 1) Data Source: Interest2Action, Merchantize; Data Timeframe: Conversions that took place from September 2008-December 2009, adjustable through the provided drop down menus. The Attribution tab of the Cross-Channel Attribution Modeling dashboard (Figure 1) is designed to show the Conversions by Attribution, as well as the Conversions (+/-) Assists. The reports are described within the dashboard by hovering over the (?) icon. The Conversions by Attribution is the display of Conversions using the client’s attribution model rather than the commonly-used Last Click Attribution. In this case, we gave equal credit to any visit to the site prior to the conversion, and then added that credit up across each conversion to get the new attribution model. For most clients, the model would be much more complicated and involve a mixture of recency, frequency, time on site and channel weighting, but the important feature is that clients can redesign the attribution model and display their new attribution, not just last click attribution. The Conversions (+/-) Assists report shows the difference between the client’s selected attribution model and the traditional last click model. This shows net change from the last-click to full conversion attribution. In this example, when we give equal credit to all visits (not weighting based on recency, time on site or channel) and compare that to the model where only the last click receives full credit, then we see that natural search (2.9 percent), paid search (1.3 percent) and direct load (1 percent) receive more credit than they are currently receiving with the last click model, while Referring URL lost 5.2 percent of its attribution credit. This supports the generally accepted idea that consumers may originally find a site through search or display, but will come back to the site later by either typing the URL or visiting the site through a referring URL when they eventually convert. The Conversion (+/-) Assists report gives credits to all the other visits leading up to the action. By looking at conversions in this manner, we predict that most clients will see that their natural search, paid search, and display media channels deserve more credit for conversion than they are currently receiving using a last click model. On each dashboard tab you can also select the timeframe. In this case, the time controls the month of the conversion and will pull all subsequent visits to the site, even if they occurred before the beginning of the month. A filter can be added for clients who want to look at only at visits within a particular timeframe of the conversion. Also, below each graph the table of raw data is provided and can be exported to Excel for additional analysis if needed. You will also notice on all the dashboards that there is a Conclusions section that can be edited by business analysts to provide key findings and insights.© ICroSSInG. ALL rIGHTS rESErVED. 4
  5. 5. Cross-Channel attribution Modeling in aCtion AUGUST 2009 ConVErSIon FUnnEL (FIGUrE 2) Data Source: Interest2Action, Merchantize; Data Timeframe: Conversions that took place from September 2008-December 2009, adjustable through the provided drop down menus. The Conversion Funnel tab provides a more granular view of the journey that customers take before the conversion. The default page shows the top five most common conversion funnels where the conversion channel is ‘all channels,’ but from the Conversion Channel drop down, you can choose Natural Search, Paid Search, Direct Load, Display, Referring URL, and Social Media. Choosing another channel will show only conversion funnels that converted on the chosen channel and are useful to service line experts. In all cases, the visits’ paths start at the top showing the first visit to the site within the conversion timeframe, and the last visit that resulted in the conversion is shown at the bottom of the funnel. Using the ‘1st’ Channel as an example, this means that the most common conversion path for this client was visitors who first came through Direct Load, then later visited the site through the same channel, Direct Load. They represent 17.5 percent of total conversions during the time frame (775 total), and on average it took them 7 days to convert between their first visit and their conversion. More interesting are the 2nd and 3rd funnels that show that Natural Search or Paid Search is the channel visited first, but the conversions actually came through Referring URLs, together those represent a total of 23.6 percent of total conversions. At the bottom of the dashboard, you can also choose to see All Conversion Funnels if you want to see more than the top 5, and also switch the funnels to see the First Touch analysis for clients that use a First Touch attribution model.© ICroSSInG. ALL rIGHTS rESErVED. 5
  6. 6. Cross-Channel attribution Modeling in aCtion AUGUST 2009 KEyworD FUnnEL (FIGUrE 3) Data Source: Interest2Action, Merchantize; Data Timeframe: Conversions that took place from September 2008-December 2009, adjustable through the provided drop down menus. The Keyword Funnel tab (Figure 3) is similar to the Conversion Funnel tab except it focuses only on conversions that came from search. In the drop down menu you can also choose to look at conversions from only Brand or Non-Brand search. The keywords for both Natural Search and Paid Search were then labeled as brand and non-brand to show keyword cross-over. For this client there was little cross-over between branded and non-branded search or between search and other channels. The Percentage of Conversions, Total Conversions, and Average days in funnel metrics are also provided for the Keyword Funnel. ConVersion MiX (Figures 4,5,6) By selecting the Conversion Mix tab, you can see the most granular data provided in this dashboard. In the drop down you can select the Conversion Type as Channel, Source, or Visit to see three separate reports.© ICroSSInG. ALL rIGHTS rESErVED. 6
  7. 7. Cross-Channel attribution Modeling in aCtion AUGUST 2009 CHAnnEL ConVErSIon MIX (FIGUrE 4) Data Source: Interest2Action, Merchantize; Data Timeframe: Conversions that took place from September 2008-December 2009, adjustable through the provided drop down menus. The Conversions by Channel (Figure 4) report shows the number of channels used before a conversion. For example, if a user comes to the site on their first visit from display, then from paid search, and finally converts through a referring URL, that is three total channels. On the other hand, if a visitor comes three times all through natural search, that is only one channel. In this example, the fact that one channel represents 85 percent of total conversions shows that consumers for this brand are unlikely to switch from one channel to another during their journey to an eventual conversion. This same metric is trended over time in the Conversions by channel timeline and the raw data is provided at the bottom of the KPI panel.© ICroSSInG. ALL rIGHTS rESErVED. 7
  8. 8. Cross-Channel attribution Modeling in aCtion AUGUST 2009 SoUrCE ConVErSIon MIX (FIGUrE 5) Data Source: Interest2Action, Merchantize; Data Timeframe: Conversions that took place from September 2008-December 2009, adjustable through the provided drop down menus. The Source Conversion Type (Figure 5) report in the Conversion Mix tab will load a report very similar to the Channel selection, but instead of showing the number of channels, it shows the channel that lead to the conversion. This is the traditional last touch attribution model and is provided for clients for comparison purposes.© ICroSSInG. ALL rIGHTS rESErVED. 8
  9. 9. Cross-Channel attribution Modeling in aCtion AUGUST 2009 VISIT ConVErSIon MIX (FIGUrE 6) Data Source: Interest2Action, Merchantize; Data Timeframe: Conversions that took place from September 2008-December 2009, adjustable through the provided drop down menus. The last report provided in the iCrossing Cross-Channel Attribution Modeling dashboard is the Visit Conversion Type (Figure 6). This report shows the number of visits prior to a conversion. For this example, more than 75 percent of conversions happened on the first visit, but there are one percent of customers that visit the site more than seven times before converting. Using the Conversion Funnel tab, users can explore these funnels more to determine the channels these frequently visiting consumers are using. © ICroSSInG. ALL rIGHTS rESErVED. 9
  10. 10. Cross-Channel attribution Modeling in aCtion AUGUST 2009 Conclusion By building Cross-Channel Attribution Modeling dashboards for our clients, iCrossing has successfully integrated data from several sources, created a display visualization dashboard using the iCrossing Marketing Platform, allowing clients to see what their consumers are doing before they convert, and has created a user interface that provides KPIs in a manner that helps answer questions and allows for data to be downloaded for further analysis. Our transparency in this process shows the industry that we are creating actionable solutions to client needs and providing those solutions. We aren’t just talking about the importance of cross-channel attribution; we are doing it because we agree with Forrester that “Agencies and service providers must provide increasingly approachable solutions for attribution to become the de facto measurement model.” (“Transitioning from the Last-Click Model” 2008). The data integration and display process methodology presented above allows clients to appropriately attribute credit to assists from other channels and even test several models to determine the one that is most appropriate for their business. Once the attribution is determined, the models can be compared to the traditional last click model. This can help to explain how display media is assisting other channels, and to determine how much credit media and search channels are giving up to referring URL and direct load traffic. Also, by looking at keyword break outs, clients can see how branded and non-branded search terms fit into the conversion funnel differently, and if seeing display ads causes users to search brand terms more frequently. All of these questions are addressed in industry research, but clients are now asking for, and deserve to see what their customers are doing before converting on their site. iCrossing’s approach now makes that possible. reFerenCes Andrews, Evan. “Search and Attribution: Maximizing ROI in a Tight Economy.” Jupiter Research, a Forrester Research Company. November 24, 2008. Lovett, John. “A Framework For Multicampaign Attribution Measurement.” Forrester Research. February 19, 2009. Riley, Emily. “Attribution: Transitioning from the Last-Click Model.” Jupiter Research, a Forrester Research Company. July 28, 2008. ContaCt Find out more at Call us toll-free at 866.620.3780 Follow us at Become a fan at© ICroSSInG. ALL rIGHTS rESErVED. 10