Affinity Marketing: Turning Insight Into Profit Through Business Intelligence Visualization

Transcription

1 Affinity Marketing: Turning Insight Into Profit Through Business Intelligence Visualization Insert Title David Savournin Senior Marketing Manager Direct Marketing Canada Life Assurance Company

2 Background Canada Life Assurance Company (CLA), founded in 1847 now part of Great West Life Co. Provides insurance and wealth management products for Canadians One of the largest credit insurance providers in Canada CLA Direct Marketing promotes life, accident, critical illness and related products to the customers/credit cardholders of clients such as: University alumni groups Credit unions Professional associations Over one million Certificates of Insurance in force

3 New Client Challenges Acquired a new direct marketing client with large customer/credit card database No prior experience with this database No response data No access to profiles, models, segmentation strategies or targeting information No post-campaign analysis reports Limited control over the selection of records for campaigns

4 New Client Implications

5 New Client Implications RESULT TOTAL CONFUSION!

6 At this point, it s worth noting

7 Canada Life s Norm CLA s marketing activities have been driven by traditional predictive models Typical attributes that rank high: Postal Code Recency of Purchase/Service Usage Credit Score Age Gender Tenure Client s RFM Score

8 New Client CLA s Needs WE NEEDED TO QUICKLY Acquire knowledge and understanding of the New Client s database Identify potential target market segments Assess the potential of leveraging our existing Canada Life policyholder base to find out what learning could be gained, and how it could be applied to this new database Create a methodology that would allow us to quantify the true size of the prize Find a partner to do all this with

9 New Client Solution CLA had used in the past, targeting specific client campaigns CLA approached Environics Analytics, explained our challenges and asked them to come back with strategies and tactics they believed would help

10 New Client Solution Environics Analytics solution was straightforward: 1. Leverage existing CLA policyholder database i) Build an in-depth understanding of CLA s existing policyholders and compare them to the New Client database 2. Develop a cost-effective segmentation strategy ii) Provide profiles of our policyholders based on PRIZM C2 clusters iii) Establish and quantify key response drivers through indepth multi-dimensional analysis iv) Build predictive response models

11 Initial Milestones Q2 10 Q3 10 Q3 11 Q4 11 Development of CLA Segmentation Strategy using PRIZM C2 Target Promotion Using Segmentation Develop Predictive Response Model Implement Predictive Response Model Refine target audience based on data discovery and insights

12 Simply Put We needed to get to the lowhanging fruit quickly Environics Analytics would be the ladder to get us to the higher-hanging fruit!

13 What Was the Outcome?

14 Program Results and Driving Program Efficiency Enter A lot of refinement taking place in this period Product indexation Recency of instore purchasing Age Geography Offer Cluster profitability List challenges high internal demand for leads

15 How We Got There

16 Economical Segmentation Methodology Identify typical buying audience Segment buyers into PRIZM C2 clusters Create a Product Penetration Index for each cluster Score Client records with appropriate index for each CLA product

17 Segmentation Methodology The priority was to develop and leverage an economical segmentation strategy, and to identify the profitable target audience Client Database Contact History Age Province Etc. Profiles + Product Penetration Index Business Insights

18 Product Penetration Indexation The next step was to create Product Penetration Indexation Bands, and then test them to see what response we achieved in the live environment. Hurdle Gross Response Rate Prospects indexing 110 and higher all met or exceeded our Hurdle Gross Response Rate between May and August this year September and October has seen this stabilize, and November is looking even better

19 CLA Product Penetration Indices Score Summary

20 But It Was Not Just the Product Indexing At Play

21 Mining for Other Nuggets Once the positive impact of Product Penetration Indexation was firmly established, we worked with EA to uncover other nuggets Other attributes surfaced as having impact too: Province Age Recency of Card Usage Recency and Frequency of Contact Credit Score

22 Results Visualization of Key Variables

23 Data Visualization Aids in identifying relevant patterns and trends that might take hours or days with tabular reports Speed is a big factor Highly Effective At: Analyzing time-series data Comparing categories Detecting outliers Analyzing performance data Analyzing segments Analyzing dimensional data Analyzing variances Enables us to visually explore data to discover new insights speed of thought exploration

24 Provincial Distributions

25 Provincial Results

26 Age Distributions

27 Age Results

28 Recency of Usage Distributions

29 Recency of Usage Results

30 Contact Recency Distributions

31 Contact Recency Results

32 Contact Frequency Results

33 Credit Score Ranges

34 Credit Score Ranges

35 RFM Scores Results

36 Policyholders Distribution by PRIZM C2 by Channel

37 What We ve Learned So Far

38 Revised Timelines Q3 10 Q4 10 Q1&2 11 Q3& Development of CLA Segmentation Strategy using PRIZM C2 Target Promotion Using Segmentation Test & Learn Execute Develop Predictive Response Model Implement Predictive Response Model

39 Lessons Learned Using PRIZM C2 and existing customers data can provide an effective and efficient segmentation methodology Leveraging data mining and insights can maximize your investment in segmentation prior to predictive models Use of data visualization provides an easyto-use and intuitive means of communicating quantitative results, trends and areas of improvement that will deliver measurable gains

40 Questions and Answers

41 Questions? Catherine Pearson