Best practice use case guide
Leveraging data with AI for payment and credit card issuers
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Most data sources provide deterministic data – e.g., credit card transactions – which are used by traditional business intelligence to create insights such as purchase probability for a financial product.
Marketing departments then use probabilistic data to create campaigns that target certain segments of customers. This approach is leading to challenges such as top customers being targeted disproportionately often and campaigns not being timed optimally per individual customer. In contrast, AI solutions allow for optimizing the omnichannel customer experience by addressing the right person at the right moment on the right channel.
Most browsers no longer support tracking by third-party cookies. Google has announced that the Chrome browser will stop accepting third-party cookies in 2023. Marketers will have less information about online customer behavior, and brands will be less able to reach customers with targeted messages – unless they can make customers accept first-party cookies and use artificial intelligence to exploit available data as effectively as possible.
Page 2 Executive summary
Page 3 Preface and table of contents
Page 4 Introduction customer lifecycle management (CLM)
Page 5 AI – A paradigm shift
Page 6 Data in the customer lifecycle
Page 7 Introducing AI
Page 8 Campaign automation along the lifecycle
Page 9 Applying Machine Learning models
Page 10 Scaling personalized campaigns
Page 11 The holistic CLM model
Page 12 Phase 1: Attract and acquire
Page 13 ▪ Lookalike audience and email re-targeting
Page 14 ▪ Boost customer acquisition
Page 15 ▪ Personalization & checkout funnel optimization
Page 16 Phase 2: Activate and incentivize
Page 17 ▪ EMOB – customer activation
Page 18 ▪ Spend incentivation
Page 19 ▪ Upsell to platinum card
Page 20 ▪ Cashback and loyalty
Page 21 ▪ Services – dunning/receivables management
Page 22 Phase 3: Cultivate and retain
Page 23 ▪ Retention and anti-churn
▪ Re-activation
Page 24 Status quo portfolio
Page 25 The art of the start with CLM
Page 26 About Adtelligence
„Machine Learning is the automation of data science. Automated Machine Learning models boost productivity when it comes to personalized customer interactions along the lifecycle and increase scalability. When data is the new oil, payment providers sit on the biggest nearly untouched oil field. Machine Learning will become the keystone of future revenue models of payment providers and card issuers.“
Michael Altendorf, CEO, Adtelligence
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