Predicting Renewal Propensities for (and Segmenting) RBI Magazine Subscribers

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1 Predicting Renewal Propensities for (and Segmenting) RBI Magazine Subscribers John McConnell Analytical People PAW in DC, 21st October 2009 analytical-people 2009

2 We re currently working on A Market Segmentation for a UK electronics retailer Survey-based Continuing to develop an on-line Geospatial modelling application for Liverpool City Council Analyses and simulates sustainability A model to locate the most vulnerable/hard-to-reach UK citizens for the Digital Switchover In collaboration with the BBC and the Department of Work and Pensions (DWP) The Scoring Model Marketing mix analysis for an on-line recruitment site The 4 th in a series of conjoint projects for a UK CPG company Simulating Market Share 2

3 Overview Background Project detail Latest results What do these projects say more generally about Predictive Analytics 3

4 Product Process People 4

5 Segmenting PA software General Purpose Statistical Tools Specific Tools Specific Apps Workbenches Spreadsheets Databases Have other primary use but offer PA SAS SPSS R BMDP Require more technical /statistical know-how to use ForecastX Salford Systems Answer Tree Tend to be intuitive Focussed on specific areas e.g. CRM, Credit Scoring, etc. TouchClarity Epiphany Fair Isaac SAS Marketing Optimization SPSS Predictive Claims More integrated into application workflow Often the easiest to use SPSS Clementine SAS Enterprise Miner KXEN Enable workflow Fit the process e.g. CRISP More from the perspective of building models We will discuss deployment approaches and tools later Source: Foviance/Analytical People Intro to Predictive Analytics

6 The Process e.g. CRISP-DM Business Understanding Deployment Data Understanding Evaluation Data Preparation Modelling

7 Role Types The Business The SMEs typically sponsor the project, lead the definition of business objectives and success criteria. Assess the efficacy of the outputs. Make the business decisions based on them. In larger projects manages the change, if change ensues. CRM/Marketing, VP/Director of something, Brand Owner, Business Analyst, etc. 7

8 Role Types The Data The Data Expert provides and often describes data and meta data for the project. In bigger projects will architect the data flows. Is often the one to perform the data preparation/management DBA, BI Consultant, IT, DP, Data Analyst etc. 8

9 Roles Types The Technology The Technologist gets deployed models running. May build apps themselves. May integrate. In bigger projects architects the solution. CTO, Solutions Architect, IT Consultant, Systems Integrator 9

10 Role Types The Analyst The Analyst links most of the process together builds and initially evaluates the model. Often the PM is from the analytical side. Needs to understand the business process and how analy Statistician, Business Analyst, Analytical Consultant, Data Miner, etc. 10

11 Reed Business Information Part of Reed Elsevier Global publisher and information provider 100 UK brands including New Scientist, Flight International, Farmers Weekly, Caterer & Hotelkeeper Totaljobs US brands include Variety, Supply Chain Management Review, Hotels Kellysearch in both UK and US B2B supplies

12 Background Ed Garcia is a marketing manager responsible for subscriptions and retention across the main UK magazine brands Renewal campaign process is pretty fixed Multi channel for timed contact pre- and post- renewal Ed explained what he wanted to achieve to Alex Holmes, Business Analyst To better segment his subscriber base for renewal communications Alex suggested a consultation about the potential to apply Predictive Analytics technology

13 Background Business Understanding We picked up on the discussions between Ed and Alex Ed s business objectives were To significantly improve overall renewal rates and concentrate our effort and resource on the segments which are going to provide the best return. To allow us to adapt communication to new subscriber types and continuously re-evaluate our approach as subscriber behaviour changes. To at least break even Our translation into analytical objectives Need to score every subscriber with their likelihood to renew A classification problem Need to profile the segments to help Ed with his comms approach Chose Caterer & Hotelkeeper as a pilot

14 Roles in the RBI Retention project Ed Garcia - RBI Paul Devine - Quadrant A joint effort John McConnell - AP 14

15 Modelled Magazines - Circulation Caterer & Hotelkeeper [B] ICIS Chemical Business [B] Flight International [B] New Scientist [C] Estates Gazette [B] Farmers Weekly [B] Retail Subscriptions Source: ABC 15

16 Variables considered Lifetime Lifetime value Annualised value Back issue claims Payment method Time taken to pay Amount paid last time Company/Individual Company size Business type Job function Age Association membership Gender Location Frequency of contact Acquisition Channel Renewal channel Subscription term Preferred response method 16

17 Data Preparation Data Preparation Thinking about all available behavioural, marketing and demographic data Ed and John specify and Paul creates the extract Broadly speaking there are two approaches Take an event-based data extract Take a summarised data extract 17

18 Data starting points for the analyst Data Preparation Detailed Longitudinal Offers the most flexibility An Analyst can construct different views and tables e.g. Time slicing Most risky A simple mistake can invalidate the whole model Summarised Cross-sectional Less flexible Often introduces iterative time lags with data provider Typically dealing with the data people who know the data Less Risky

19 Modelling Modelling Business (Ed) and Analysis (AP) agreed to use Decision Trees Based on discussions during the Understanding phase We use SPSS Classification Trees Multiple candidate trees developed which emphasise different drivers dominating A more campaign based tree A more value-based tree Success criteria At least 80% accurate overall Balance predictive accuracy for both groups Create meaningful trees 19

20 Modelling - Similarities & Differences Publication Caterer(Model 1) Caterer(Model 2) Estate s Gazette Flight International ICIS New Scientist Farmer s Weekly Best Predictor Value of Current Subscription Value of Current Subscription Value of Current Subscription Country of subscriber Country of subscriber Renewal promotion code Renewal promotion code 20

21 Evaluation Evaluation AP look for good levels of accuracy against test samples and we sense test the trees Ed dissects the trees to look for: Segments he can use from a messaging/creative perspective Anything that is odd Unexplained variables are usually dropped Reference Number Supplied was prominent in first C&H model. This was an alias for an on-line acquisition 21

22 A branch 22

23 Evaluation Overall Accuracy Evaluation Caterer & Hotelkeeper % Caterer & Hotelkeeper % ICIS Chemical Business 90% Flight International 95% New Scientist 89% Estates Gazette 86% Farmers Weekly 83% C&H Model 1 (July 2008) predicting at 69% on C&H Model 2 data (Sep 09) 23

24 Ed s Plan Subscriber type Country Channel Score decile 1 (91-100%) Donor Direct UK O/S Post TM 2 (81-90%) 3 (71-80%) Associations Students UK O/S Post Expiries 4 (61-70%) 5 (51-60%) Direct Donor UK TM 6 (41-50%) Students O/S Post 7 (31-40%) Direct TM 8 (21-30%) 9 (11-20%) Donor Generic Series UK Post 10 (0-10%) O/S TM Re-acquisition UK O/S Post Deployment Rules supplied in SQL/Spreadsheet format for deployment TM

25 Model performance C&H 1 Renewal Score % 52% 71% 52% 59% 62% 51% 42% 48% 83% Model Control 44% 59% 0% 20% 40% 60% 80% 100% 25

26 The bottom line C&H 1 16% point increase in renewal rate Subs renewing before expiry has increased by 8% Overall ROI has improved by 10% Cost per renewal has gone down by 6% The cost of set-up has been covered by the savings which have resulted from reducing costly print and telemarketing efforts

27 The benefits Maximised retention Improve cost per renewal Target areas for development/up-sell Target areas not worth trying to renew Test price points / communication timing Continuously re-evaluate Improve customer service

28 PPA Industry Awards for RBI Best use of data Best segmentation strategy 28

29 Where does the time go C&H Model 1 Subsequent models Business Understanding Data Understanding Data Preparation Modelling Evaluation Deployment

30 Where are we now Waiting to (re)deploy across the 5 titles Finishing Evaluation with a 2 nd UK publisher In the Understanding with a 3 rd about retention modelling across all publications 30 magazine titles Where do we go from here Look at the web channel Ed takes on more of the Analyst role Look for slicker deployment 30

31 Some take outs The most successful projects align the roles Communication is key Do we really mean the same thing? Analysts need to adapt to the domain Understand process, vernacular, priorities Get immersed Consider risk Aim to become more systematic, less ad hoc Important to include QA Deployment is really the point Though insight (incite) is still valuable The RBI project gets a lot of this right 31

32 More generally Today it is less about the technology we need some more in The Cloud Apps & Deployment becoming more important We need more people in the roles which drive PA Within and across the disciplines. Need more hybrids people and teams. e.g. Marks & Spencer CIU Need to spread the knowledge Need alignment of high level buy-in with execution Visionaries and practitioners To avoid the Trough of Disillusionment Gartner 32

33 A conjoint analogy Share of Voice Share of Preference Share of Distribution Share of Market 33

34 Thanks for listening analytical-people 2009

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