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Making Better Decisions: Customer Churn and Data Labs at Hiscox

Learn how Hiscox, a global specialist insurer, used Data Labs to improve decision-making and tackle customer churn.

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Making Better Decisions: Customer Churn and Data Labs at Hiscox

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  1. Making better decisions Customer churn 1. Why and what 11 September 2019 Dr Richard Marshall Data Strategy Consultant

  2. Hiscox: global specialist insurer - $4.2bn revenuePicasso’s to satellites to hurricanes to SMEs

  3. Hiscox global presence UK Birmingham Colchester Glasgow London Maidenhead Manchester York Guernsey St Peter Port Europe Amsterdam Berlin Bordeaux Brussels Cologne Dublin Hamburg Lisbon Lyon Madrid Munich Paris Bermuda Hamilton USA Atlanta Chicago Dallas Las Vegas Los Angeles New York City San Francisco White Plains Latin American gateway Miami Asia Bangkok Singapore

  4. Take a step change in the way we use data and analytics at Hiscox

  5. Our solution was to run Data LabsHighly focused projects to deliver value from data Goal: Demonstrate value from using data We didn’t want to do a large data strategy project We needed to show where data can work (and where it can’t) We needed to do this quickly, with controlled investment Solution: Focus on key decisions the business makes Small, focused, pragmatic projects delivering value from data by improving the decision making process Hiscox Data Labs

  6. We had a clear message on how data would help4 areas to tackle for us to make progress Analytics Framework Data (Int / Ext) Cultural Roadmap Technology Roadmap Identify the decisions that have the biggest impact on our business..... ... ensure we are collecting and storing good quality data that underpin those decisions... ... that we have expertise to identify ways to improve these and run analytics on a regular basis... ... and the technology infrastructure required to support data and analytics.

  7. Focusing on decisions is at the heart of thisIt takes time to identify the key decisions Analytics Framework Data (Int / Ext) Cultural Roadmap Technology Roadmap Identify the decisions that have the biggest impact on our business..... ... ensure we are collecting and storing good quality data that underpin those decisions... ... that we have expertise to identify ways to improve these and run analytics on a regular basis... ... and the technology infrastructure required to support data and analytics. Focus of Data Labs

  8. Hiscox Data Lab – 4 stages to deliver valueDecisions are the key focus throughout Baseline Map the current state and identify all decisions 1 Opportunities Prioritise decisions to decide the key areas of focus 2 Ideas Develop proof of concepts and understand value 3 Deliver Move from POC to Minimal Viable Product – deliver value 4

  9. Case study: US Direct business insuranceUsing the Data Labs framework to drive value • SME and micro business insurance • US customers only • Purchased via a website (Hiscox or partners)

  10. Align to business strategy and focus on decisionsCustomer churn used as proxy for satisfaction Goal: Deliver meaningful, tailored products that customers value How can we provide an improved experience for our customers? • Who is most likely to benefit from our products and how do we market to them? Key Decisions: • Predictive customer churn model • Who is likely to cancel? • Why are they going to cancel? • When will they cancel? Idea:

  11. 3 components to the churn model to understand the full picture about customer cancellations Why Who When How likely is a customer to complete the policy year? When do cancellations take place over a policy year? For what reason will the customer cancel? • Simple analysis of cancellation dates • Machine learning approach led to worse results • Predictive machine learning model • Over 20 predictive factors used • Predictive machine learning model

  12. “Who” model – tiered approachHelps to understand the model and take action 3. Using predefined thresholds, segment into tiers Highest probability of cancelling Tier 5 (20% of policies) Tier 4 (20% of policies) 1. Input policy profiles 2 . Score policies in “Who” model Tier 3 (20% of policies) • Partner channel, Industry, products purchased, switcher, etc. • A probability of cancelling in this policy year • 0 – 100% Tier 2 (20% of policies) Lowest probability of cancelling Tier 1 (20% of policies) • Within each tier we can track: • New Binds • # of Cancellations • Retention rate... • All vs. an expected baseline based on model / history

  13. High probability of cancellationTiers used to focus actions to improve decisions Decision Action Tier 5 (20% of policies) How can we provide an improved experience for our customers? Targeted actions to improve customer satisfaction e.g. coverage consultation Tier 4 (20% of policies) Tier 3 (20% of policies) Tier 2 (20% of policies) Tier 1 (20% of policies)

  14. Low probability of cancellationTiers used to focus acquisition spend Tier 5 (20% of policies) Tier 4 (20% of policies) Tier 3 (20% of policies) Decision Action Tier 2 (20% of policies) • Who is most likely to benefit from our products and how do we market to them? Target acquisition spend on businesses similar to those in tiers 1 and 2 Tier 1 (20% of policies)

  15. “When” model key in testing frameworkReduces feedback time on impact of actions Without model Long time to feedback With model Short time to feedback • Action • Review results vs. high level metric (can take up to a year) Repeatable Cycle • Hard to iterate due to length of process

  16. Practicalities of making this workAutomate as much as possible Customer Data Policy data is stored in our underwriting systems

  17. Practicalities of making this workAutomate as much as possible SQL Database Customer Data Data stored into a SQL database overnight

  18. Practicalities of making this workAutomate as much as possible SQL Database Predictive models Customer Data Predictive models run in R from policy data in SQL database

  19. Practicalities of making this workAutomate as much as possible SQL Database Predictive models Customer Data Model results are joined onto policy information and stored back in SQL

  20. Practicalities of making this workAutomate as much as possible SQL Database Predictive models Actions Customer Data Customer tier and other characteristics used to take targeted actions

  21. Practicalities of making this workAutomate as much as possible SQL Database Predictive models Actions Testing Framework Customer Data Actions taken stored in testing framework and will affect customer behaviour

  22. Practicalities of making this workAutomate as much as possible SQL Database Predictive models Actions Testing Framework Customer Data Customer cancellations recorded in underwriting system and stored in SQL

  23. Practicalities of making this workAutomate as much as possible SQL Database Predictive models Actions Testing Framework Customer Data Cancellations are compared against expectations to analyse impact of actions

  24. Key takeaways:Focus on decisionsYou can be more confident in the business changingGet to an MVP quicklyDon’t aim for perfection in version 1 – show value first

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