IBM BW Lead Scoring, Product Recommendation, Retention Solution

Transcription

1 IBM BW Lead Scoring, Product Recommendation, Retention Solution Watson Advance Analytics Cognitive & AI Offering David Trinh Managing Director Global Industry Solutions Lead, GBS ix Bluewolf Salesforce GTM

2 Salesforce customers can benefit from a variety of cognitive solutions to enhance sales & digital marketing strategy and optimize investment return Marketing Management Sales Management Customer Life Cycle Lead Scoring Propensity to Buy Lead Touch Optimization Sales Channel Optimization Propensity to Close Expansion - Next Best Call - New Products - Product recomendaton Retention - Next Best Call - Omni-Channel Cost Reduction - Sales Effectiveness - Sales Automation Discover Learn Progress Close Retain & Grow

3 Cognitive Lead Scoring Using advanced analytics approaches, high value leads can be identified and appropriate follow-up actions recommended to convert responses to leads Customer persona What characteristics does a typical lead generating customer have? Identification of high value leads using advanced analytics 1 Recommendations to convert and achieve high value from responses Prioritize responses Classification of historical responses Lead generation mechanisms How do potential customers respond to marketing campaigns? New Responses Products and solutions of interest What products and solutions are customers interested in? Potential Opp. value Lead conversion propensity 2 Recommend actions for follow-up Propose action by comparing with similar successfully converted responses Class X leads attributes Large account size (>$100M) Response to multiple channels Resp ID 2 attributes Machine learning applied to new responses Which responses are most likely to convert to leads and with what value? Interested in gas turbines Successful conversion with /phone calls Initiate /phone conversations

4 Product Recommendation Product Recommendation predicts which product(s) have the highest probability of being purchased by any given customer 1 Data Sources 2 Cognitive Engine 3 Actionable Insights Product & Promotion Product Categories Products Promotions Machine Learning / Cognitive Analytics Map customer purchase history against product catalog For new customers with no history present fastest moving products and/or products which are hot. Use customer attributes to estimate their likes. For existing customer with purchase history use a matrix factorization algorithm with gradient descent to find similar customers and predict probability to buy each product Recommended Products Prioritized Recommendations Prod A Prod C Customer & Sales Prod A Prod B Prod C Prod D Prod E Prod F Prod G Prod H Cust Prod G Customer Customer Attributes Customer Interactions & purchases Cust Cust Cust Cust Cust Others Matrix Factorization with gradient descent will infer scores for the empty cells in the matrix. High scores not yet purchased should be recommended first. Recommendations based on optimal algorithm

5 Cognitive Retention Cognitive Retention identifies drivers of customer attrition risk and enables proactive mitigation actions for optimal client experience Internal Client Data Illustative Customer Attrition Risk Identification & Mitigation Business Performance Data Call Center History 1. Customer Attrition Risk Identification: prioritize attrition by risk level categorization. Delivery Data External Data Pricing Data 2. Mitigation Action Recommendation: provide attrition driver insights for proactive mitigation actions to address unwanted customer loss CALL CENTER SENTIMENT COMPETITIOR ANALYSIS PRICE ANALYSIS LATE DELIVERIES Census Data Demographics Enable customer engagement for business outcome PRODUCT QUALITY REP TURNOVER Customer revenue and profit attainment and growth can be realized through better customer experience via proactive and surgical mitigation actions

6 Cognitive Differentiation IBM Watson differentiates the analytics engine with insights from unstructured data, harnessing natural language processing to create nuanced client relationship profiles. Concept & Sentiment Analysis Personality Insights External & Social Insights ounderstand key topics and associated sentiment concerning prospects and clients Establish personality profiles for clusters of customers and/or products Explore external and social insights to the sales process Connect personality profiles to buying behavior to improve product recommendation Twitter Mentions & Sentiment ounderstand the sentiment trending over time Sentiment of exchanges with client X peaked in March. Client Cluster Profile Recommended Portfolio #relevant tags

7 Proposed Architecture Solution Architecture Solution architecture Solution Highlights Architecture Diagram Results Push 1 Input is pulled via Salesforce API; results are pushed back to Salesforce interface. 2 External database stores data for modeling and transformation through IBM analytics engine. Data Connect Analytics Engine MySQL DB 3 System of periodic pulls, monitoring, and alerts enables continuous updates. Swift Object Storage Availability Monitoring Architecture notes: 1. Both Database, Analytics Engine, ETL Process and API Routes are stored as different apps on the same server. 2. SalesForce collects required data and pushes it into Object Storage. 3. Data Connect merges, cleans and transform files into a generic data model 4. ETL process acts as a pre-processing step, handling unstructured data and enriching the base generic model. 5. Support WT business hours. IBM Alert

8 Customer Benefits Customers can benefit from fully integrated Cognitive solutions covering important use cases where Salesforce doesn t have a compelling offering currently Top / bottom line growth, powered by cognitive Rapid & low risk deployment Base solution for expansion and customization E2E solution delivered & supported by Bluewolf

9 Key Assets Asset available from solution for reuse Reusable code (includes connection to Salesforce) Product Specification Training & Materials for Bluewolf s team Solution Demo