Adtelligence AI Framework


Move from insight to automated action and decision

Machine Learning is key when it comes to scaling your digital business. Starting with manual one time scores and manual triggers, most use cases need a lot of manual efforts, working hours and costs to be successful. This lack of scalability and automation leads to average results in the long term. To create a pillar for your digital business models, automation, self learning and optimization is key.

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Apply Machine Learning to automate processes and scale your revenue

Credit card issuers need to leverage all of their data with AI to learn and act on user behavior in real-time and personalize communication automatically.

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Triggering personalized actions in real-time

With any new data set our Machine Learning models update in on the fly to trigger next best action on the preferred channel.

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“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 GmbH

Creating Wow experiences for your customers

The model library provides a wide range of capabilities to increase revenue and drive revenue along the customer lifecycle. The Adtelligence AI framework enables connectivity via APIs. Input your data and let our models train you. We provide you with pre-built models, from affinity scores to clustering to optimization with neural networks or Baysean Bandits. You can use your scores and models as well, but also create new models.


Nurture leads



Lookalike AudiencesLead ScoreConversion ScoreAudience SegmentationActivity ScoreReferrer Score
FunctionalitySegmentierungPredictionPrediction and
DefinitionLookalike audiences are a group of potential customers who have similar data characteristic such as current accountsLead Score ranks prospects according to their potential value as high potential customersConversion Score calculates the potential for a prospect to become a customer within a certain time periodCreating customer segments is one of the most common ways to target campaigns more specific to groups based on specific data points which describe the segment such as age, gender, location or product categoriesActivity Scoring analyzes customer behavior and ranks it compared to other current accountsPotential of a customer to invite friends and family to your product
Typical use caseLookalike audiences are customer segments that are similar to current account segments and are used to target new customers across different channels such as Google or FacebookLead scoring is used in lead mgmt to optimize potential efforts to win a customerConversion scoring is used to predict and optimize website personalization and abandoned cart campaignsCreating customer segments manually can be a good start to avoid sending the same message to all customers. The amount of customer master data, behavioral and transactional data is increasing to a level that manual segmentation produces a high amount of workload and a trade-off for each campaign manager to which level it is feasibleActivity scoring is used by e.g., retailers, banks, gaming and payment card companies to analyze the health of a customer relationshipRefer-a-friend offers create a simple and often used way to win new customers at a statically calculable cost, in addition to traditional marketing channels
Challenges of traditional data science approachLookalike audience campaigns do not allow a 100% matching but a rough audience selection, resulting in average targeting and average conversion ratesStatic and periodic lead scoring does not allow to leverage current prospect behavior and leads to wrong or incorrect predictionsStatic website or campaigns are no longer competitive, as prospects can bounce any second due to irrelevant content and product offeringsThe ML based approach automates segmentation, clustering of key segments. It automates manual process steps and allows the marketeer to create audiences in real-time. Audience segments are updated automatically up to real-time with every new data pointTraditional activity scoring is periodic and analyzed by data science or tech savvy growth marketeers. Automated alerting functions are often not in place or based on a small number of data pointsStatic referrer scoring models overlook the huge potential of "refer-a-friend" campaigns, partner products and other viral invitation methods
Advantage of dynamic Machine Learning modelsAn automated clustering of your current account data improves the audience target segments continuously and automates optimization of lookalike audiences fast and iterativeAutomated optimization of lead scores allows to increase the amount of data and to score in real-time depending on a variety of data parameters from marketing channel to context and live behavior on a website or appTailoring personalized contents and productsThe ML based approach automates segmentation, clustering of key segments. It automates manual process steps and allows the marketeer to create audiences in real-time. Audience segments are updated automatically up to real-time with every new data pointA modern approach increases the level of sensitivity and takes all data points into account in real-time and creates affinity scores per customer segmentsHighly personalized offers for friend referrers optimize number of new customers for a low CPA
Machine Learning techniquesHDBScan, k-Means (vector quantization method)Regression, Neural Network, Decision TreeReinforcement Learning, Neural Network, Multi-Armed Bandits & Bayesian OptimizationHDBScan, k-Means (vector quantization method)Statistical correlation, Bayesian classification, Hidden Markov ModelsRegression, Neural Network, Decision Tree
Business potentialIncrease conversion rates in ad campaigns and reduce customer acquisition costsIncrease the number of high potential customers and reduce CLV campaign costsHigher customer acquisition rates, more new customers, higher revenuesHyper-personalisierte Kampagnen erhöhen den WarenkorbwertImproved alerts and targeted campaigns based on customer activity increase lifetime value and customer revenues per monthHigher customer acquisition rates with lower costs per new customer

Increase usage

Cross-sell / upsell


Prevent churn

CLV ScoreNext Best ActionNext Best ProductNext Best CategoryRisk ScoreChurn Score
FunctionalityPrediction and ClassificationAssociation and CorrelationAssociation and CorrelationAssociation and CorrelationPrediction and ClassificationPrediction and Classification
DefinitionCustomer Lifetime Value Score predicts the potential monetary value of a customer during his lifetimeNext Best Offer scores the affinity to a special offerThe affinity score predicts the products which fit best as next purchase for a specific customer data set or behaviorNext Best Category ranks a customer against a variety of product categories and its affinity for an up- or cross-sell in that categoryA risk score predicts the potential risk of a customer not paying according to terms or risk of behavior for potential loss caused by his behaviorChurn Scoring predicts potential customer attrition or reduction in spend
Typical use caseCustomer lifetime values are used to classify a specific customer type based on its past purchasing behavior compared to other customers and calculate a score to benchmark its valueCreating targeted marketing campaigns via email, app or onsiteCreating upsell or cross-sell opportunities for customers. Recommendation engines, "You may also like" are common on any shop to show further products in the same categoryTypically used for cross-sell campaigns, onsite, across mail or email campaigns as well as in-app offersAnalyzing the risk of fraud, payment defaults and other business risksChurn can occur in a variety of ways: revenue churn, usage reduction - predicts churn, customer churn
Challenges of traditional data science approachStatic periodic CLV scoring may trigger wrong campaigns, wrong timing and overlooking of high-potential customers during a certain periodFor creating affinities, purchase data is of much greater interest than purely demographic data. In addition, active behavior on shops and higher churn potential if neither transaction data nor behavioral changes or marketing channel preferences are consideredTraditional recommendation engines can not take real-time customer behavior into account and are based on past purchases of other customersNext best category campaigns are often created manually by category managers or marketeers for bundle campaigns and need high amounts of efforts to be preciseTraditional decision-tree or statistical risk scoring do not take real-time behavior into account and lead to incorrect scores and negative scoring of potential customers according to static rulesStatic churn scoring reacts episodically and potentially too late in a customer’s lifecycle leading in declining results and higher churn rates. Today, churn scores are often calculated based on CRM data without leveraging online touchpoints
Advantage of dynamic Machine Learning modelsDynamic CLV scoring allows highly targeted and personalized customer experiences in real-time which leads to higher conversion rates and purchase volumeHighly targeted offers triggered by AI based behavior and context analysis algorithms improve purchase rates and customer engagementDynamic recommendations and real-time customer behavior data improve product recommendations up to 1to1 personalized offers and allow long tail campaignsReal-time optimization of scores allow using customer behavior to increase cross-selling opportunities and incentive usage and purchasesDynamic approaches score on a multidimensional data signal from installed font types to historic purchase history and allow a much higher granularityMachine Learning powered RFM modelling - Scoring recency, frequency and monetary values in real-time including online customer behavior and card usage data. This allows churn prevention campaigns to be segmented and tailored on time on the right channel. Optimizing anti churn campaigns is key for a long-term revenue stream
Machine Learning techniquesStatistics, Recurrent Neural Networks (RNN) Statistical correlation, Bayesian classification, Reinforcement Learning, Q-Learning, Association Rule LearningStatistical correlation, Bayesian classification, Reinforcement Learning, Q-Learning, Association Rule LearningStatistical correlation, Bayesian classification, Reinforcement Learning, Q-Learning, Association Rule LearningRegression, Neural Network, Decision TreeRegression, Neural Network, Decision Tree
Business potentialHigher focus on profitable customer segments and higher retention ratesHigher revenues, higher value per purchase, lower manual effortsHigher revenues, higher value per purchase, lower manual effortsHigher revenues, higher value per purchase, lower manual effortsLower payment defaults, lower fraud and improved customer acquisition Reduced churn risks, improved customer retention and higher revenues


Data quality is key for success

Credit card issuers have excellent data sources to leverage through data-driven customer lifecycle management

Available data sources range from static customer relationship management (CRM) data – such as age and gender – to more sophisticated information, such as creditworthiness. Product information, loyalty data provide insights about the products to which a customer is currently subscribed, and opportunities to cross-sell and upsell other products that the customer is not yet using. Credit card transaction data provide live information about the customer’s spending behavior. These insights can be used to make marketing decisions in real-time.

Utilizing first party data with Machine Learning 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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Examples of the variety of data points

Adtelligence AI Framework


Move from insight to automated action and decision