Best practice use case guide
CUSTOMER LIFECYCLE MANAGEMENT
Leveraging data with AI for payment and credit card issuers
- Know the latest industry trends
- Turn your transaction data into profit
- Get exclusive blueprints and use cases
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.
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.
Contents of our whitepaper
- Executive summary
- Page 2
- Preface and table of contents
- Page 3
- Introduction customer lifecycle management (CLM)
- Page 4
- AI – A paradigm shift
- Page 5
- Data in the customer lifecycle
- Page 6
- Introducing AI
- Page 7
- Campaign automation along the lifecycle
- Page 8
- Applying Machine Learning models
- Page 9
- Scaling personalized campaigns
- Page 10
- The holistic CLM model
- Page 11
- Phase 1: Attract and acquire
- Page 12
- Lookalike audience and email re-targeting
- Page 13
- Boost customer acquisition
- Page 14
- Personalization & checkout funnel optimization
- Page 15
- Dynamische und personalisierte Check-out-Funnel
- Page 15
- Phase 2: Activate and incentivize
- Page 16
- EMOB – customer activation
- Page 17
- Spend incentivation
- Page 18
- Upsell to platinum card
- Page 19
- Up-Sell zur Premiumkarte
- Page 19
- Cashback and loyalty
- Page 20
- Services – dunning/receivables management
- Page 21
- Phase 3: Cultivate and retain
- Page 22
- Retention and anti-churn
- Page 23
- Re-activation
- Page 23
- Status quo portfolio
- Page 24
- The art of the start with CLM
- Page 25
- About Acceleraid
- Page 26
„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 & Co-Founder, Acceleraid
CEO & Co-Founder, Acceleraid