Best practice use case guide

Customer Lifecycle Management

Leveraging data with AI for payment and credit card issuers

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AI – The paradigm shift in customer lifecycle management

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.

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Utilizing first party data with AI can overcome the challenges of the “cookieless future”

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.

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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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