Innovation Lab

Transcript

1. KPMG INNOVATION LAB November 2015 Discussion Document
2. Context & Objectives Our View Proposed Process 2 1 3 kpmg
3. kpmg Context & Objectives 1 Our understanding of ASB’s objectives and challenges
4. STRATEGY AND OBJECTIVES • A step change in ASB’s approach in supporting and managing channels and a shift to next best proposition • Improve and harmonise the quality and efficiency (e.g. time to market) of products and services based on customer journey in relation to channels • Optimise the costs of the frontline utilisation in reducing administration and duplication of activities through technology, advanced analytics, stronger set of centers of excellence, shared and outsourced services kpmg
5. • Cultural shift from using standard ways to using advanced analytics for decision making at a business and group level • Identification of key analytics supporting business processes and relevant decision making • Introduction of data science to support evolving business model and corresponding market conditions kpmg KEY CHALLENGES / SHIFTS
6. • A key component of the advanced analytics business requirements and initial study are already in place • Hadoop, Tableau, R and other third generation tools chosen for advanced analytics • Approval on investment in Customer and Channel strategy and use of advanced data science analytics for fact based decision making activities at Group level to support individual business units in their strategic business and digital agenda kpmg ACHIEVEMENTS
7. • Identify and design a compelling proof of concept fit for purpose for formulating a business strategy supporting investments in channels, products, processes and services for ASB’s customers in retail, commercial and wealth • Organise a series of workshops to discuss proof of concepts and prioritise them • Deliver first proof of concept in Q1 of 2016 • Please note that the discussions so far have been customer/channel/product centric and this could also expand out to payment solutions, risk, fraud, regulatory, lending, service specific use cases etc. kpmg NEXT STEPS / KEY MILESTONES
8. kpmg Our View 2 Data Science Solutions For Various Use Cases
9. • Provide relevant content, services and products to customers on their preferred channel • Correlate multi channel behavior that leads to customer acquisition, retention and referrals • Predict channel performance and utilisation supporting customer journey e.g. factors impacting frontline utilisation • Influence customers to adopt low cost channels e.g. identify triggers, features and behaviours to influence channel switch • Apply complex algorithms such as decision tress, text analytics for linking behavior and other factors to optimise channel performance kpmg CHANNEL JOURNEY
10. • Profile customers and apply nearest neighbor algorithm to identify the affluent, loyal, value, service oriented customers • Identify factors influencing product usage on specific channel. Derive predictions for their product preference and develop suitable features • Detect customers who are about to leave based on complex modelling influenced by various factors including behavior on channels (e.g. stopping automatic payment), economic factors (e.g. increase in unemployment, migration), demographic influence, sentiment analysis, text analytics on feedback etc. kpmg CUSTOMER CENTRIC VIEW
11. • Predict the next best product that the customer wants by applying decision trees, clustering and item to item based recommendations to enhance loyalty, cross/up-sell • Recommend products that the customer is most likely to be interested during acquisition • Predict product offers or bundles that will promote cross/up-sell kpmg NEXT BEST OFFER
12. kpmg CUSTOMER SEGMENTATION • Target customers for products and services they care about by recommending suitable products, services and features • Develop loyalty by proving personalised cash back offers based on their usage, proximity, inferred relationships, economic factors utilizing clustering techniques and link analysis models • Predict pricing point to suit maximum price customers would pay for a product or service • Build relationship with profitable customers by accurately predictive their lifetime value, profitability and tipping point in their customer lifecycle journey
13. Past Details of static historic journey including transactions, behaviours, patterns, interactions, feedback, statistics etc. Present Dynamic view reflecting context of data measuring channels, sentiments, influence, proximity, relationships (direct and inferred) etc. Predictive/Prescriptive What is the possible direction including lifetime value, profitability, investment, risk, optimisation etc. When did it happen? What is the trend? What if? What is the best answer? What should we do? Clustering / Segmentation Machine Learning Recommen- dation Optimisation Behaviour Analysis Data Lake Data Science Concepts Supporting Initiative at ASB What is the profitability? What is the lifetime value? Who are the customers? What is the utilisation? What products should we sell? How should we sell? How should we target and optimise? How much should we invest? Channel Centric View
14. kpmg Workshops 3 Proposed Workshop Schedule
15. Description Audience Objectives Timeline Workshop I Setting the scene - Describing the opportunity Grant Frear Russel Jones i. Align expectations ii. Agree with stakeholders Workshop II Explaining the opportunity, process and next steps Level 2 and or 3 delegates Agree: i. Next steps ii. Workshop schedule Workshop III Diagnostic i. Generate ideas ii. Develop list of options iii. Analyse options iv. Rank options (Cost, Benefit, Risk, Challenge) Workshop IV Scoping Develop project plan and agree: i. Resourcing requirements ii. Key deliverable dates KPMG to then work on POC for a mid Feb delivery Four Workshops kpmg
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