Presentation Transcript

1. Cartesian, the Precision Practice Helping marketers bring precision to their initiatives
2. Precision Marketing Bringing back the left brain into Marketing = How do you target, recognize patterns, find clusters, optimize How do you explain what happened, and then use that insight to predict what will happen How do you identify, retain and build relationships with your best customers
3. Our work in marketing analytics Exploratory Targeted Root cause analysis Hypothesis testing Hypothesis driven surveys Predictive customer analytics Modeling/ forecasting Data exploration Data mining Dimensional analysis Data discovery Regression Descriptive Customer analysis Scoring Segmentation & profiling Pattern recognition Real Time Forward Looking Retrospective Performance projections Target setting Dashboards KPIs Balanced scorecards Exception reports Fraud detection Control
4. Our solution Consult Manage Implement Precision Marketing/ CRM/ Loyalty consultancy How to create a marketing strategy for precision/ CRM Campaign design and management How to design campaigns and implement them for ROI Marketing Insight How to analyze the data and get insights that are actionable Insight ready systems How to capture and enrich data, make it insight ready, processes to follow Precision Marketing Infrastructure How to set up a platform for precision marketing
5. Steps to Analysis Infrastructure Database Cleanup, standardisation, dedupe, etc Data enrichment

6. Step 4: Analysis

7. Three approaches to analytics Exploratory analysis, Data discovery Statistical Model build MIS and Reports Analysts and business users browse data in a discovery mode, create and test hypothesis on the fly. Who fits this target? How many such customer do I have? When is the last time… Business defines an objective that can be modeled. Statisticians select appropriate modeling technique, select variables, build and validate models, score databases. Who will respond? Forecast sales. Segment customers. Given some recurring needs of business, standard reports are created and automated. Standard reports can be batch-processed and output sent to Excel for easy use by business users Profiling, cross tabs, Venn diagrams Needs high speed environment that allows flexible browsing of data Sales reports, service reports, model-wise growth reports, loyalty points earn and burn reports, upgrade and down grade reports… Regression models, Logistic, segmentation models, forecasting models, market basket.
8. Nature of Analysis Predictive Models: Which of my customers is most likely to purchase Segmentation: What clusters exist that I can reach out to Hypothesis tests: What is the impact of a personalized mailer with a strong offer? Market basket: What is the next best product to cross sell/ up sell? Profiling: What profile of customers are likely to show this behavior

9. Step 5: Campaigns

10. Campaigns Creation of campaign calendars Set up campaigns Campaign cell creation Control groups and seeding Testing of media-message-offers Response tracking
11. 4 Major Processes Create Unique Customer Master Create customer one view Analysis and Insight Campaigns, ROI Profiling, segmentation, market basket, predictive models, response models, adoption analysis, store scoring, KPI setting… Campaign management, design, control groups, multi-stage, multi-channel, response tracking, ROI measures Pull in data from all available sources to create one view, aggregates, expressions, decodes to enrich view. ETL, Merge files, cleanup standardize and dedupe, improve capture on ongoing basis Analysts Business rules, scripting to automate expression creation. Alterian platform. IT resources SQL/ Oracle Db, Harmony Software, Automated ETL to Alterian Statisticians Modelers, KXEN software in Alterian, SPSS where needed. Consultants Liaise with all business groups, take briefs and design campaigns, support execution
12. Customer Segmentation Models Low value Average ticket size of Rs. 5037 Usually weekend shoppers Transact more at shop-in-shop format stores
13. Adoption Analysis Early Majority Early adopters Late Majority Innovators Laggards Where are the innovators coming from?
14. Store/ Branch scoring models
15. Management Dashboards
16. Media Effectiveness Models

17. Cleartrip examples

18. An example of segmentation

19. Premium airline one-off fliersX Customers. Rs. 5,500 Avg. Single airline Single sector 1-2 bookings V low value
20. High ticket size, return bookersY Customers. Rs. 23,000 Avg. High ticket size of Rs. 15,000 Mostly Return bookings – 75% avg. Long journeys, 7 days between first and last flight More than 1 segment High incidence of intl fliers (434)
21. Single budget airline, 1 segmentZ Customers. Rs. 10,400 Avg. Single budget airline flown on Single sector flown Low incidence of premium airlines Low incidence of return bookings
22. 10 Segment Summary

23. Vacation Mailer Campaign Analysis

24. Recap- Targeted Mailers Objectives To increase repeat business and reduce dependency on cash back and discounts To use past purchase data to send relevant, personalized communication Execution Last week of August
25. Data Overview The data considered for the campaign was air bookings for travel during Diwali period in the previous year On average customers who traveled in Diwali and booked early (August/September) saved around 20%
26. Campaign Segments Vacation mailer targeted towards customers in the following segments: Traveled during festive season last year but did not save Traveled during festive season last year and saved Did not travel during festive season last year Registered non-users
27. Messaging Strategy For the customers who traveled during festive season the previous year, the savings they made (or failed to make) were highlighted For everyone else, average savings made by people traveling to specific segments were highlighted
28. Creative: Late Booker
29. Creative: Early Booker
30. Creative: Generic
31. Response Of those who had traveled and saved: 6.5% conversion amongst opens OF those who had traveled but not saved last year: Over 11% conversion amongst opens Typical conversion amongst opens is about 1 to 1.5%

32. Case: Dominos CRM Mailers

33. The brief Get Dominos customers to order more pizza Use our knowledge of their consumption habits to strike a chord and push up response Final metric to be monitored: Coupon redemptions
34. Approach Who to target: Score entire customer database with probability of response How many to target: Estimate most profitable depth-of-file to reach out to Segment: Create clusters of targets based on our understanding of their Pizza ordering behavior and personalize the communication and offer
35. 3 different predictive models built and then blended to arrive at a list of best prospects to target Logistic regression used to build models Step 1: Model Build Model Build 2 Model Build 3 Model Build 1 Any coupon respondent Order propensity Past campaign base Coupon user profile Likely to order Likely to respond Score 1 Score 2 Score 3 Wtd. Score Target list
36. Step 3: Clusters
37. Variables/ Segments to consider
38. Creative routeBy Black Swan Life – the ideas generation agency A final list of 12 clusters were created Each cluster was assigned a “Pizza Sign” based on their Pizza behavior The message and offer were tailored to the cluster and put across in a highly engaging piece of communication
39. Nostalgios: someone who’s been missing for a while
40. Loyalos: they have a favourite Pizza
41. Nightos: they tend to order at night
42. Partios: Have placed “party sized” orders
43. Impact Over 30% coupon redemptions Targeted customers on average did over 50% more sales than non-targeted control group of similar customers

44. Examples of Analytics led Marketing

45. Case: Pantaloons EOSS

46. Case: Pantaloons EOSS

47. Situation Pantaloons, one of India’s largest apparel retailers has a bi-annual EOSS (End Of Season Sale) cycle Pantaloons also runs a loyalty program Pantaloons Green Card for its best customers Green Card members get invited to an exclusive EOSS “preview” The task was to select Green Card members to send a direct mailer to such that Those with best propensity to shop during the EOSS would respond Cost of mailers is high so there was a need to optimize budgets
48. Approach A predictive model was built using various behavioral variables. Over 50 variables were used, of which after iterations about 14 were deemed important Logistic regression, using Robust regression principles Outputs of: A list of who to target, how many to target, and information on why targeting them makes sense
49. Outputs and implementation Direct mailers sent to 85,000 members selected from the model Control group held out to test response Two versions of mailing ensued, the more expensive DM and the cheaper non-personalized “Inland letter”
50. Actual Vs Predicted In line with predictions
51. Highlights > 3 times the non targeted group
52. Campaign details DM Vs Inland Higher than Avg Tier Wise Higher than Avg

53. Case: Reporting for Levis

54. Filters applicable at SBU – State – City – Store level (also Month in other pages)
55. Key-Indicators Sheet (with lots of filters applied)
56. More details below + in an attachment
57. Selections for Action

58. Thank You!

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