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Module4 multichannel analytics Presentation Transcript

  • 1. MULTI- CHANNEL ANALYTICS Module 4
  • 2. Agenda  Section 1: Multi channels – Problem statement  Section 2: Solution – Attribution models  Section 3: Conversions analysis at Multi- channel environment
  • 3. Section 1 Multi channels – Problem statement
  • 4. Preface  Today digital user is consuming content via multiple screens i.e. desktops, laptops, smartphones, digital TVs, tablets etc.  These touch points are to provide similar/same experience to consumers across the brand taking location, time and other constraints into account  This is possible only when we have a central data source which connects, collects and analyses data
  • 5.  Multi channels analytics is the “Nirvana” of all analytics. This is the last piece in the analytics jigsaw puzzle  This cover both offline and online channels and provides a ultimate consumer business intelligence which leads to profits Goal for all marketing channels is to increase revenue Revenue Other Online Offline
  • 6. Problem statement  However for this application to work flawlessly, the problem is measurement  Using current technology, we cant identify the user across all channels. This is making user remain anonymous  No common data point between the offline and online scenarios to map them and get the required analysis
  • 7. Problem Illustration – Detail  Multi-device environment causing inaccurate unique visitors counting  Multiple channels are causing repeated unidentified visitors Fixed website Mobile Site Offline Unique visitor 1 Unique visitor 2 Cant identify that this is a visitor for Fixed website User 1 User 2 No primary key
  • 8. Solution  Finding a primary key that is common across all data sources is the ultimate solution  Attribution models can be considered as a second best solution – This is discussed in the Section2 of this module  Offline profiling and segmentation, targeting users via online channels is also considered as part of attribution  Other working models has been identified as a work around solutions – This is discussed in the section3 of this module
  • 9. Section 2 Solution – Attribution models
  • 10. Attribution analysis – Definition  Attribution is the process of assigning a rational value to the elements which contributes to a common goal  In simple words, taken a marketing campaign with an objective of sales which ran across 4 different channels, an attribution model can allocate the total sales to 25% of each channel  The percentage of attribution can be set using the historical trends or growth rates or budget spends or targeted segments or target audience profile
  • 11. Attribution analysis – Details  This is a business decision that has to be taken by the management  For building an attribution model, the variable you need are,  Number of channels  Final conversion details  % of attribution  Type of attribution  Types of segments of audience  Business logic behind the attribution rate
  • 12. Attribution model – Examples
  • 13. Attribution analysis – Example  Currently highly used model is – Last click attribution.  Ex: Consider a credit card company. While launching a marketing campaign for new type of credit card, it takes the segment of people from offline data who are likely to convert to this officer and uploads those profiles to database. Using Cookies, they targets the uses with the related messages. They can even figure out which channels these profile of people use highly. That way we have the highest
  • 14. Advantages of attribution analysis  Advantages are,  Highly accurate measurement of marketing contribution to revenue generation  Significantly improved campaign management as a result of deeper visibility into performance  Time cost savings due to elimination of need to do painful manual analysis  Structured data points to allocate the performance per channel which has a direct contribution to revenue
  • 15. Limitations of attribution analysis  This attribution model does not take the “duration” of the campaign into consideration. So effects of seasonality is not taken into consideration  The % of attribution is subjected to business justification. It would be difficult to assign these values if there are no other guidelines  Certain reach channels influence might be undermined when only taking final conversions into consideration  Can be costly to implement. Cookie data might not be available all the time
  • 16. Section 3 Conversions analysis for multi- channels
  • 17. As you can see in the diagram, there are many missing data points and constraints that are in the flow. So its always advisable to follow work around to get this data into system Data collection strategy for Multi-channels
  • 18. Data collections techniques  In the following slides, we will review mainly two types of data collection techniques  How to measure online impact on offline campaign  How to measure offline impact on online campaign  This will be possible only when you have a web analytics system – Campaign tracking is enabled and tagging of these links/information is completed  Once the data starts recording, reporting will be available in campaign reports in Site
  • 19. Online impact of offline campaign 1. Use Vanity URL’s. Ex: Dell.com/TV (the one which appears at the end of a TV suggesting to visit the website is vanity URL  Vanity URL tracks the demography and referral source like TV / Billboard / OOH /Radio  Use any tool to track the results of the Vanity URL – The data comes in campaign reports  If the conversions are good the campaign is a success on these channels as well
  • 20. Online impact of offline campaign 2. Use Unique Coupons / Offers in the offline campaign Ex : Magazine / cat log  Coupons code  Taxi entrainment network - usage of codes such as “Taxi” acts as a primary key to track the campaign  All these tracking are part of  Ecommerce Tracking  Event Tracking
  • 21. Online impact of offline campaign 3. Use online Surveys Ex: Segmentation questions  How did users came to the website ?  Use the survey results to measure effect of the channel 4. Correlate Traffic Patterns  Correlation of TV ads. Notify that the live TV ads been played now on the website.  Purchase on online happens while the commercial been aired on TV  Possible to track demography-wise  Visits / Conversion / Referrals are the metrics that are tracked
  • 22. Ex correlation of offline data to online success
  • 23. Offline impact of online campaign 1. Measure offline calls to action Ex: store locator which uses the zip code in the URL as a primary key  Having a goal and valuing that goal are key metrics – For ex how many searches have been for store locator and the conversion rate  Tracking of customer intent based on the key word used by him to locate a store. If the key word is more specific the intent is higher. Tracking of the same is important  Goal 1 – ca_storefinder_results.do  Goal 2 – ca_storefinder_directions.do (Make sure the pages are tagged – Make sure to make a goal and Make sure to add value to the goal )
  • 24. Ex of providing different Call centre numbers
  • 25. Offline impact of online campaign 2. Track Phone calls and Live Chats –  Track the calls at the website number (posted on the website 1800 series)  There would be customized numbers based on the search results (B2B impact)  Once the call is made by a customer and landed on the Call Management System of the company the data is collected and parsed by CallTrackID which then fires a call to a dedicated page on your site that contains GA Tag than data recorded can be analysed to measure ROI conversion etc  The same can be tracked separately as traffic from Phone as referral
  • 26. Offline impact of online campaign  Web-Live chat both chat and phone  Integrated data for live chat can be created in GA  Further the same can be measured and track the source of traffic as live chat
  • 27. Offline impact of online campaign 3. Use Unique Coupons and Offers  Web coupons  Websites allow the customer to custom print the coupons  Tracking happens by the way of bar code on the coupon which distinguishes that the same were printed online  Measuring the coupon by way redeems to arrive at the conversions  Tying the online data with the offline measure – like membership number / card being tracked while the purchase is done online
  • 28. Offline impact of online campaign
  • 29. Offline impact of online campaign 4. Online Survey and Exit Survey  Likelihood of making a retail purchase - Used to track the conversion rate of those who can visit the store who has visited the website 5. Conduct Controlled Experiments (Read the article about it in Wikipedia for further information):  To measure the value of the website in terms driving traffic to offline
  • 30. Summary  Implement Multi-channel analytics if possible by having a centralized data wearhouse  Set up of an attribution model  Measure offline calls to action  Track phone calls & live chat  Use unique coupons & offers  Use online surveys  Conduct controlled experiments
  • 31. For Q&A: Contact: gayatrichoda@gmail.com Thank you