My specialty is Marketing Automation, but Analytics and Reporting come up in pretty much any project I do. This presentation shows the 'why' behind marketing analytics, which metrics to choose, how to collect the data and how to create your reports.
This talk was held at the 12th meeting on July 22 2014 by Karen Zhang.
Customers in business-to-consumer (B2C) and business-to-business (B2B) markets go through similar buying journey: need, search, evaluate, and finally order. Thus similar customer analytics approaches are applicable to both scenarios. However company’s go-to-market strategies are usually different in B2C vs. B2B. This study discusses unique characteristics of analytic methodologies applied in B2B vs. B2C. Two case studies will be presented to illustrate similarities and differences.
Unifying Marketing Data & Multi-Touch Attribution AnalysisPrinciple America
Example approaches Unifying Marketing Data & Multi-Touch Attribution Analysis
Many businesses on a free Google Analytics aren’t leveraging existing capabilities to its full potentials, and marketers feel it is not enough to support their MTA reporting needs.
And most of the MTA solutions/services are a huge jump in terms of costs and capabilities for many businesses.
The cost of enterprise-grade attribution tools are not reasonable for many businesses investing in a digital marketing campaign.
Most of the tools that support MTA rely heavily on the marketer’s experience and leadership.
The majority of the enterprise-grade MTA solutions require a large monetary commitment from setup to analyzing performance. Lacks the bridge between data and recommendations.
steps included in the analytics process
why marketing analysis.
advantages of marketing analytics
the framework of marketing analytics
future of marketing analytics,
how analytics helped amazon small case study.
My specialty is Marketing Automation, but Analytics and Reporting come up in pretty much any project I do. This presentation shows the 'why' behind marketing analytics, which metrics to choose, how to collect the data and how to create your reports.
This talk was held at the 12th meeting on July 22 2014 by Karen Zhang.
Customers in business-to-consumer (B2C) and business-to-business (B2B) markets go through similar buying journey: need, search, evaluate, and finally order. Thus similar customer analytics approaches are applicable to both scenarios. However company’s go-to-market strategies are usually different in B2C vs. B2B. This study discusses unique characteristics of analytic methodologies applied in B2B vs. B2C. Two case studies will be presented to illustrate similarities and differences.
Unifying Marketing Data & Multi-Touch Attribution AnalysisPrinciple America
Example approaches Unifying Marketing Data & Multi-Touch Attribution Analysis
Many businesses on a free Google Analytics aren’t leveraging existing capabilities to its full potentials, and marketers feel it is not enough to support their MTA reporting needs.
And most of the MTA solutions/services are a huge jump in terms of costs and capabilities for many businesses.
The cost of enterprise-grade attribution tools are not reasonable for many businesses investing in a digital marketing campaign.
Most of the tools that support MTA rely heavily on the marketer’s experience and leadership.
The majority of the enterprise-grade MTA solutions require a large monetary commitment from setup to analyzing performance. Lacks the bridge between data and recommendations.
steps included in the analytics process
why marketing analysis.
advantages of marketing analytics
the framework of marketing analytics
future of marketing analytics,
how analytics helped amazon small case study.
Operationalizing Customer Analytics with Azure and Power BICCG
Many organizations fail to realize the value of data science teams because they are not effectively translating the analytic findings produced by these teams into quantifiable business results. This webinar demonstrates how to visualize analytic models like churn and turn their output into action. Senior Business Solution Architect, Mike Druta, presents methods for operationalizing analytic models produced by data science teams into a repeatable process that can be automated and applied continuously using Azure.
Data Analytics with Managerial Applications InternshipJahanvi Khedwal
Data Analytics with Managerial Applications Internship under Prof. Sameer Mathur,IIM Lucknonw-Presentation on "Simplify Your Analytics Strategy" by Narendra Mulani(Presentation by Jahanvi Khedwal)
Conversion Rate Optimisation is becoming increasingly important to online marketeers. But why? And where do you start?
Presentation given at the Content Marketing Association's Digital Breakfast conference on 14 May 2014.
Website: www.cloudmoyo.com
Types of Analytics:
Descriptive
Predictive
Prescriptive
Semantics
Data Sources:
Store Data
Sales Data
Demographic Data
Competitor Anchor Data
Weather Data
Season Data
Analytics can answer following questions:
FOR STORE DATA:
Where should we plan next store?
What will revenue of a store be at a planned location by season, by product?
What is correlation of Anchor Store revenue with Microsoft Store revenue?
What is the impact of season change, demographics, climate at a location on my sales?
What type of store that is suited for a given location?
FOR Traffic (Footfall) & Conversion:
What are most preferred categories by state, city, store, over time?
What is the user sentiment about the newly launched category/(s) based on Twitter feed analysis?
What is the competing product sentiment by demographics that will increase footfalls?
Conversion/Footfall change Vs Marketing Spend (ROI)
FOR Marketing Spend Optimization:
What is an optimal allocation between online and offline?
Which marketing channels should I invest to maximize footfalls at a store?
Customer buying pattern analysis to decide on ad spend?
What should be my allocation strategy by state? by Weather condition? By Season? By Store demographics?
Which channel will get impacted if I change allocation in particular channel (e.g. TV)
(Social Media Analysis) Competitor popularity dip opportunity to increase ad spend to increase sales
What is the competing product sentiment by demographics that will increase footfalls?
How to evaluate the return on marketing spend?
How to identify the Marketing Spend threshold w.r.t Revenue Anticipation ?
Workforce Management – Crew Scheduling:
Allocation of right crew to right function
Shift management
Optimum utilization
Scheduling, rostering
CX2016: Transform Retail Customer Engagement Across Every ChannelMaria Humphrey
In today’s connected world, every customer interaction matters. Hear first hand how The Land of Nod is leveraging data science, targeted content and personalized journeys as a vehicle for enhancing customer engagement through both on-line and in-store. Learn how to make the most of every shopper touchpoint to create loyal brand advocates.
Cenacle Research is engaged in building Predictive Analytics Engines for Automotive, Healthcare, Retail, Energy and BFSI sector. This presentation details how our Big data Analytics platform can help retail businesses in a brief manner.
Big Data offers: Actionable Insights that let you make Informed Decisions, with the capability to:
+ Gain Insight
+ Take Proactive action
+ Reduce waste
+ Plan better strategy
To know more, write to us at: http://cenacle.co.in/
I have made a slide for a marketing analytics course that I have recently completed on Coursera. The slide covers topics such as:
different types of marketing analytics and their uses,
brand value & brand architecture,
customer lifetime value and its applications,
before-after and full factorial marketing experiments
and regression analysis.
Machine learning and remarketing are two very popular ways of enhancing marketing campaigns. Used in tandem, they can deliver much better business outcomes. This session reveals how to get started with machine learning-driven remarketing using R.
Presentation from NRF 2019 Retail's BIG Show
Alexandre Hubert, Sr. Dir., IT Strategy and Logistics, Browns Shoes Inc.
Stephanie Richelieu, VP, Global Marketing, Generix Group
Big Data, customer analytics and loyalty marketingKevin May
Want to improve the customer experience while optimizing customer service, marketing spend and wallet share?
In this FREE webinar from Tnooz and IBM, attendees learn the benefits of big data analytics including:
Developing persona-level customer segmentation.
Improving products/services launches.
Optimizing return on marketing spend.
Utilizing social media analytics.
Webinar presenters are:
Kurt Wedgwood – information agenda consultant for travel and transportation, IBM
Tzaras Christon – executive vice president for growth, Aginity
Kevin May - editor and moderator, Tnooz
Gene Quinn - CEO and producer, Tnooz
Presentation from NRF 2019 Retail's BIG Show and NRF Foundation Student Foundation
Sherry Egerton, Director of Customer Success - ACEO Retail - 1
Ian Holland, VP, R&D, Professional Services, ACCEO Retail - 1
Scott Pearson, CEO, Curator Retail Consultant
Operationalizing Customer Analytics with Azure and Power BICCG
Many organizations fail to realize the value of data science teams because they are not effectively translating the analytic findings produced by these teams into quantifiable business results. This webinar demonstrates how to visualize analytic models like churn and turn their output into action. Senior Business Solution Architect, Mike Druta, presents methods for operationalizing analytic models produced by data science teams into a repeatable process that can be automated and applied continuously using Azure.
Data Analytics with Managerial Applications InternshipJahanvi Khedwal
Data Analytics with Managerial Applications Internship under Prof. Sameer Mathur,IIM Lucknonw-Presentation on "Simplify Your Analytics Strategy" by Narendra Mulani(Presentation by Jahanvi Khedwal)
Conversion Rate Optimisation is becoming increasingly important to online marketeers. But why? And where do you start?
Presentation given at the Content Marketing Association's Digital Breakfast conference on 14 May 2014.
Website: www.cloudmoyo.com
Types of Analytics:
Descriptive
Predictive
Prescriptive
Semantics
Data Sources:
Store Data
Sales Data
Demographic Data
Competitor Anchor Data
Weather Data
Season Data
Analytics can answer following questions:
FOR STORE DATA:
Where should we plan next store?
What will revenue of a store be at a planned location by season, by product?
What is correlation of Anchor Store revenue with Microsoft Store revenue?
What is the impact of season change, demographics, climate at a location on my sales?
What type of store that is suited for a given location?
FOR Traffic (Footfall) & Conversion:
What are most preferred categories by state, city, store, over time?
What is the user sentiment about the newly launched category/(s) based on Twitter feed analysis?
What is the competing product sentiment by demographics that will increase footfalls?
Conversion/Footfall change Vs Marketing Spend (ROI)
FOR Marketing Spend Optimization:
What is an optimal allocation between online and offline?
Which marketing channels should I invest to maximize footfalls at a store?
Customer buying pattern analysis to decide on ad spend?
What should be my allocation strategy by state? by Weather condition? By Season? By Store demographics?
Which channel will get impacted if I change allocation in particular channel (e.g. TV)
(Social Media Analysis) Competitor popularity dip opportunity to increase ad spend to increase sales
What is the competing product sentiment by demographics that will increase footfalls?
How to evaluate the return on marketing spend?
How to identify the Marketing Spend threshold w.r.t Revenue Anticipation ?
Workforce Management – Crew Scheduling:
Allocation of right crew to right function
Shift management
Optimum utilization
Scheduling, rostering
CX2016: Transform Retail Customer Engagement Across Every ChannelMaria Humphrey
In today’s connected world, every customer interaction matters. Hear first hand how The Land of Nod is leveraging data science, targeted content and personalized journeys as a vehicle for enhancing customer engagement through both on-line and in-store. Learn how to make the most of every shopper touchpoint to create loyal brand advocates.
Cenacle Research is engaged in building Predictive Analytics Engines for Automotive, Healthcare, Retail, Energy and BFSI sector. This presentation details how our Big data Analytics platform can help retail businesses in a brief manner.
Big Data offers: Actionable Insights that let you make Informed Decisions, with the capability to:
+ Gain Insight
+ Take Proactive action
+ Reduce waste
+ Plan better strategy
To know more, write to us at: http://cenacle.co.in/
I have made a slide for a marketing analytics course that I have recently completed on Coursera. The slide covers topics such as:
different types of marketing analytics and their uses,
brand value & brand architecture,
customer lifetime value and its applications,
before-after and full factorial marketing experiments
and regression analysis.
Machine learning and remarketing are two very popular ways of enhancing marketing campaigns. Used in tandem, they can deliver much better business outcomes. This session reveals how to get started with machine learning-driven remarketing using R.
Presentation from NRF 2019 Retail's BIG Show
Alexandre Hubert, Sr. Dir., IT Strategy and Logistics, Browns Shoes Inc.
Stephanie Richelieu, VP, Global Marketing, Generix Group
Big Data, customer analytics and loyalty marketingKevin May
Want to improve the customer experience while optimizing customer service, marketing spend and wallet share?
In this FREE webinar from Tnooz and IBM, attendees learn the benefits of big data analytics including:
Developing persona-level customer segmentation.
Improving products/services launches.
Optimizing return on marketing spend.
Utilizing social media analytics.
Webinar presenters are:
Kurt Wedgwood – information agenda consultant for travel and transportation, IBM
Tzaras Christon – executive vice president for growth, Aginity
Kevin May - editor and moderator, Tnooz
Gene Quinn - CEO and producer, Tnooz
Presentation from NRF 2019 Retail's BIG Show and NRF Foundation Student Foundation
Sherry Egerton, Director of Customer Success - ACEO Retail - 1
Ian Holland, VP, R&D, Professional Services, ACCEO Retail - 1
Scott Pearson, CEO, Curator Retail Consultant
[Handout from webinar collaboration with Nimble] Marketers are increasingly being held responsible for growth and revenue. Today, data analytics provide the foundation for finding, keeping, and growing the value of customers - your growth engine. Join this webinar to learn about the skills and analysis needed to intelligently understand your customers, allowing you to reach them with the right content, at the right time, and in the right channel.
Real-time Single Customer View
Create a single customer view of your prospects and customers with data from your website, mobile apps, social and phone calls. Use the out of the box dashboards to generate advanced and actionable insights based on your customer data.
While many working in marketing, analytics and data science roles fully grasp the opportunities surrounding investment in front-end marketing technology and back-end database solutions, many struggle to effectively communicate these data management benefits to the executives in control of budgeting for these items. For this very reason we’ve put together a downloadable presentation outlining these benefits in plain terms to help make your case in EOY budget meetings.
Manufacturing, a slow-adopter of Analytics, is now catching up in leaps and bounds. Across all business domains, applying analytics is providing answers to the most critical questions of the business.With exponential expansion of data, data driven insights have become a strategic necessity.
This booklet explores a few use cases of Big Data for manufacturing and how it can be leveraged.
For more info visit: https://www.teamcomputers.com/businessanalytics/Manufacturing/Booklet-Manufacturing-Digital.pdf
1. This document contains information and data that AAUM considers confidential. Any disclosure of Confidential Information to, or use of
it by any other party (i.e., a party other than Aaum), will be damaging to AAUM. Ownership of all Confidential Information, no matter in
what media it resides, remains with AAUM.
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AAUM Research and Analytics Private Limited
01 N, 1st floor IIT Madras Research Park, Kanagam road, Chennai – 600113
Tel +91 44 66469877 | Fax +91 44 66469877 Email: info@aaumanalytics.com | Web www.aaumanalytics.com
Your affordable, customizable and scalable advanced analytics
solutions to help you to scale your eTail business
2. - 2 -
Your affordable, customizable and scalable advanced analytics
solutions to help you to scale your eTail business
And Many More…
3. - 3 -
Research
data
CRM
data
FInance
data
Dept n
data
Other
private
data
Client data sources
SQL NOSQL
geniSIGHTS enterprise data warehouse
R2
R1
A2
A1
Reportingengine
Analytical engine
Analytically enriched dashboards and applications
• SQL
• Hadoop
• Hbase
• MongoDB
• DynamoDb
…
Aggregator and integrators
4. - 4 -
Problem Statement:
• How can I analyze my web traffic data?
• On what days and time slots do I experience
maximum traffic on my website?
Data required
Web traffic data on sites and campaigns
6. - 6 -
Data required
Clickstream data
Cookie data on attributes if captured
Problem Statement:
• How can we identify which touch point is more
dearer to me in contributing to conversions?
• How can I calculate my channel contributions to
conversions to channelize my yield effectively?
8. - 8 -
Problem Statement:
• How do we decompose the sales to key drivers
like Price, Competition, Market channels such as
TV, Press, Internet, etc to understand their
combination to sales?
• How do we calculate the efficiency or ROI from
these market channels?
Data required:
Past sales data
Past market channel data
9. - 9 -
Sales = Base_sales x Incremental_Sales1 x
Incremental_Sales2 x…x Random_effect
Demo
10. - 10 -
Data required
Web traffic data on sites and campaigns
Problem Statement:
• Would marginal improvements/changes in my
marketing strategy or on campaigns/site
layouts, images, colors, text, etc bring in
significant improvements on my yield?
• How can I calculate my channel contributions to
conversions to channelize my yield effectively?
12. - 12 -
Problem Statement:
• Are my marketing campaigns targeted to the
right customers?
• Which of the customers would respond
positively to my market campaigns?
Data required:
Marketing channel transactions data
Demographic, Psychographic data
13. - 13 -
The Persuadable: customers who only respond only because they were targeted
The Sure Things: customers who would have responded whether they were
targeted or not
The Lost Causes: customers who will not respond irrespective of whether or not
they are targeted
The Sleeping Dogs: customers who are less likely to respond because they were
targeted
Demo
14. - 14 -
Problem Statement:
• A media company wants to allocate campaign ads
so that the clicks are maximized. The company has
different sites to which it can allocate impressions.
How much of impressions should be distributed
among these sites so that the clicks for the
campaign is maximized?
Data required
Past transaction data
16. - 16 -
Problem Statement:
• How well do my campaigns perform across sites
over time?
• Can I compare the performance of my campaign
with that of the industry performance?
• How do I evaluate if my campaigns appeal well to
my audience?
Data required
Past transaction data
18. - 18 -
1st floor IIT Madras Research Park, Kanagam road, Chennai – 600113
+91 44 66469877 +91 44 66469887 +91 44 66469877
info@aaumanalytics.com b.rajeshkumar
AaumAnalytics http://www.youtube.com/aaumanalytics
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http://www.linkedin.com/company/aaum-research-and-analytics-iit-madras
Aaum Research and Analytics founded by IIT Madras
alumnus brings in extensive global business experience
working with Fortune 100 companies in North America &
Asia Pacific. Established at IIT Madras Research Park with a
focus on researching and devising sophisticated analytical
techniques to solve pressing business needs of corporations
ranging from travel & logistics, finance, insurance, HR,
health care, entertainment, FMCGs, retail, telecom.