Use of Artificial Intelligence in Marketing

Transcription

1 Use of Artificial Intelligence in Marketing

2 Are We Ready To Give Data-Driven Decisions? Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The problem is not analytical talent or technology anymore, it is datadriven managers! 2

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4 4 AI?

5 Put Analytics Into Action for your Customer Engagements Create a Trustable & High Quality Data Platform for your customers & KPIs Using Data Quality and Integration Text Analytics Clustering Generate Customer & Operational Insight using Machine Learning, Artificial Intelligence Identify Root Cause Analysis & Correlations for your KPIs Identify & Predict Persona & Journey Violations Identify Moments of Truths Operationalize your Insights Optimize which improvement area is critical on your KPIs Impact analysis using forecasting Copyright SAS Institute Inc. All rights reserved.

6 What are CCO objectives? Recent studies show 81 % 14% 65% of the respondents said strategic thinking is the most important aspect of their jobs, and 68 percent believe it is more necessary today than it was five years ago. 3 Customer experience leaders enjoyed compound average revenue growth that was 14% points higher than customer experience laggards from 2010 to of the respondents want to widen the reach of the analytics tools to more people in their organizations. 2 Sources: 1. Forrester, Customer Experience Drives Revenue Growth, 2016, Harley Manning s Blog, June 21, Stepping up to the challenge: CMO insights from the Global C-suite Study, March 2014.

7 Expectations from Chief Customer Officer Business Builder Growing brand value with confidence Experience architect Captivating through the strength of human connection Insight Activist Discovering the opportunity in all data 7Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.

8 AI can help you concentrate on your Engagement Strategy. Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.

9 Omni Channel Engagement How would your engagement change if A unified platform could orchestrate all your messaging across your traditional and digital touchpoints? Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.

10 Customer Decision Hub Marketing Campaigns Advisory Collection Programs Ad-hoc Actions CUSTOMER DECISION HUB Contact Rules Channel restrictions Budget Limits Contact Permissions Transactional data Analytical models Events Context 10

11 How would your engagement change if You could easily personalize your customers experiences with your digital and traditional touchpoints? 11

12 12 Website and Mobile App Personalization

13 Coupon Optimization pages 400 coupons 4 pages 30 coupons

14 How would your engagement change if You had a way to automatically optimize the customer journeys for each pattern of interactions? 14

15 Customer Journey Optimization Analytics & Digital Intel. Analyze and understand cross channel customer touch points Customer Path Testing Understand which marketerdefined customer journeys perform better Marketing Attribution Understand important touch points. Customer Journey Analysis Discover and analyze segments & paths Journey Optimization Automatically build paths for individual visitors

16 16 Journey Mapping & Optimization

17 How would your engagement change if A self-learning system could explore new segments among your visitors and recommend which offers should go them by continually honing your personalized marketing and results? 17

18 Data Insights & Self-Learning Conventional A/B test Within segment test Automatically discovered Segment (also supports marketers segments) 18

19 Data Insights & Self-Learning Understanding key segment characteristics of selected focus segment Automatically discovered, most distinguishing segment criteria 19

20 Self-Learning Targeting Reinforcement Learning Learn & Adapt to Changes Multi-Armed Bandit Testing 20

21 21 Insights into Actions

22 Some Examples 22

23 AI Agent Conducts Conversation with Customer An artificial intelligence system which supports remote customer service Collections Dept. Set up list of debtors 1 2 Load data to AI agent system Components include: AI lead prioritization of calls Automatic voice recognition Semantic analysis Speech synthesis (TTS) Natural language processing (NLP) Engagement optimization AI agent verifies customer identity using voice recognition (ASR) AI agent collects information about repayment from customer AI agent informs customer about debt Source: BAI, AI Coming Soon to a Bank Near You. March Note: AI includes machine learning. Copyright SAS Institute Inc. All rights reserved.

24 Contemporary Machine Learning Insurance Case Study Customer has an accident Takes a photo of damage s to insurance company Machine learning happens Evaluates the image Determines severity of damage Updates claim system Adjustor is able to expedite claim Customer gets better service Claims are classified and rates are lower Copyright SAS Institute Inc. All rights reserved.

25 Thank You! sas.com Copyright SAS Institute Inc. All rights reserved.