Session 6 Building a Successful Analytics Team Ashim Avinash Sahu - PDF document

SOA Predictive Analytics Seminar – Malaysia 27 Aug. 2018 | Kuala Lumpur, Malaysia Session 6 Building a Successful Analytics Team Ashim Avinash Sahu

BUILDING A SUCCESSFUL Ashim Sahu ANALYTICS TEAM

WHAT’S THE AGENDA ? Describing the 8 Step What does an analytics 01 03 process team do ? 02 04 The 8 step process Some Do’s and Don'ts

WHAT’S THE “IDEA” IN INSURANCE? 04 01 Pricing Marketing • Am I predicting the right risk against my customer’s • How can I improve my leads quality while health ? keeping costs down ? • Are my assumptions of customer’s health still valid ? • Can I do targeted customer campaigns to improve my brand awareness ? 05 02 Product Development Customer Experience • Why are my customers dropping their policies ? • What are the features in my product do my • Where should I set up my branches ? customer like ? • Do customers have a preferred channel ? • Does the new dynamic world create new insurance needs for my customers ? 03 06 Sales Claims • Who and where should I recruit ? • Am I paying the right amount of claims for the • What can I do to maintain to retain my agents treatment / damage ? performance ? • Are my providers overcharging me ? Template downloaded from https://www.showeet.com/

HOW DOES AN ANALYTICS TEAM SOLVE THIS ? The job of any Analytics team is to answer business questions – by extracting the right solutions from the right data Data Sciences and Data Engineering Insights and Strategy Experimentation, Testing Analytical Solutioning and Implementation Icons downloaded from https://www.flaticon.com/packs/data-analytics

THE 8 STEP SETUP AND SCALE UP PROCESS Identify the Low Hanging Fruits Scale up • Take the next step by identifying the • Defining easy to implement business newer business problems and the problems which can generate 01 08 resources needed. significant business value. • Re-iterate the cycle. Get the right stakeholders Market it ! 02 07 • The Low Hanging fruits will define • Market your results. this. Generally Senior Stakeholders. • Start exploring newer business problems with the broader organization. 03 Solve it and Test it ! Hire the right people 06 • The first few hires are the most • The real work. Re-iterate till it critical since they will define the works. If possible test many times. medium term direction as well as 04 05 team perception. Integrate team with the business Invest in the right technology • The best solutions come together with • Should be designed to cater to the the business understanding. Inter needs of the first hires and have low team collaboration becomes critical. setup time.

FOCUS ON LOW HANGING FRUITS FIRST The first year defines the team • Data will not be freely available and questionable quality. • Low on resources. • The technology would be basic. • Only work on analytical problems which have are valuable and not difficult to implement. https://hatrabbits.com/

STAKEHOLDER MANAGEMENT AND INTEGRATION TO BUSINESS BECOMES THE NEXT KEY STEP Implement your solutions • Will find the resources to test your hypothesis and implement your proposals and be your biggest advocates. They define the problem Engage the right • Affected directly by the problem, so Support you in the boardroom they know what they want as an output. Senior Stakeholders • Will fight the battles for you in the boardroom to get you the initial investment and fend off detractors. Introduce you to the working level • Will get you integrated with the people who know how things are run, the knowledge of which is vital for the success of any analytical project.

LETS PUT THE CLASSICAL CUSTOMER LAPSE PROBLEM THROUGH THE ANALYTICAL PROCESS What’s the right solution ? • Build multiple supervised learning models and select the model with best prediction power Develop Solution Size of Price How to Test and Defining Business Problem Get Data operationalize this ? • Can I predict policies which have the Define highest risk of lapsing and improve • How should I design a pilot Test my lapse rates? campaign using the right sample size ? • How do I scale up and operationalize if the pilot is successful? What Data do I need ? How much value will this create? • Customer Policy and Attached Agent • If we try to target the top decile of historical buying/selling behavior customers at the highest risk, how will this • Call Centre and claims behavior improve my persistency ? • External data enrichment ?

LET’S TWEAK THE BUSINESS PROBLEM – NO MORE A LOW HANGING FRUIT What’s the right solution ? • Build a robust Regression model for customer micro-segments and understand which variables have the most impact after partialling out Develop Solution Size of Price How to Test and Defining Business Problem Get Data operationalize this ? • Can I understand why my policies Define are lapsing and how can I reduce it? • How should I design a pilot Test campaign using the right sample size ? • How do I scale up and operationalize if the pilot is successful? What Data do I need ? How do I interpret this? • Customer Policy and Attached Agent • Can I affect to reduce the impact of the historical buying/selling behavior predictors through business strategy ? • Call Centre and claims behavior • Can I leverage on the predictive model ? • External data enrichment ?

THE RECRUITMENT NEEDS TO BE CAREFULLY THOUGHT OF Working level under the experts Maturity Breadth of skills per hire • Junior level hires with some experience in one of the fields below to be trained and molded based on established processes Senior Experts hiring Scaling Up • Experienced professionals hired to fine tune and operationalize the first Time solutions created. Find newer problems to work on. • Experts in1 or 2 of the skills listed. Analytics Breadth and Business understanding Inception • First Hires. Help define the goal and strategy of the team. • Solve the first analytical problems. • Have at least 3 of the 4 skill sets listed below. Testing and Data Engineers Data Scientists Insights and Strategy Implementation • Management Consultants • Lean Innovation • Statisticians / Quants • ETL, SQL Experts practitioners • Visualization Experts • Machine Learning • Mostly Software • Campaign Managers Engineers oriented • Project Managers

TECHNOLOGY INVESTMENT NEEDS TO BE STRUCTURED AND FLEXIBLE Big Data / Real Maturity Time Analytics / GPUs Scale Up Enterprise Enterprise Server Versions Time Servers Inception Workstations Insights and Testing and Data Scientists Data Engineers Strategy Implementation • Management • ETL, SQL Experts • Statisticians / Quants • Lean Innovation Consultants practitioners • Mostly Software • Machine Learning • Visualization Experts oriented Engineers • Campaign Managers • Project Managers

THE PROCESS OF SOLVING THE PROBLEM … IS HIGHLY ITERATIVE ! Improve Solution Get More Data Refine Business Understanding Develop Solution Size of Price and Explore Get Data Define Value Test Creation Refine Business Understanding Improve Solution

SHARE, SHARE AND SHARE. SCALE UP ! Scale Up ! Upgrade and scale up people, technology and value creation targets. Prepare for your next 8 Step cycle. Plan to solve new business problems Starting devising plans for solving newer and Get the detractors on Step 03 tougher business problems. your side Step 02 Value Creation is the biggest winner in the board room. Good opportunity to start getting other senior stakeholders on board. Share Successes across multiple forums Step 01 Share your success across multiple business verticals. Both bottoms up and top down sharing are essential.

SOME OFTEN FORGOTTEN DO’S AND DON’TS Do’s Don'ts Hire both internal and Believe analytics is an Do Don’t external people immediate magic wand Hold Analytics accountable Forget to upskill your Do Don’t for promised value analytics teams Invest in Data Quality – Forget to Test before Do Don’t Garbage In = Garbage out deploying Keep it simple in the Give in to temptation to use Do Don’t beginning analytics for brownie points

Ashim Sahu AshimAvinash.Sahu@aia.com THANK YOU! Do you have any questions?