Personal Marketing Microservice


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Get to know your customer base like never before with CLM Personal Marketing Microservices. Every customer is unique and the age of "one size fits all" mass marketing has come and gone. From Retail, Oil & Gas, Travel, to Finance and Telecommunications companies – your customers are smarter today than ever before. This cutting-edge microservice allows you to speak to your base on an intimate level with hyper-personalized offers and promotions for all CLM loyalty program members.

Key Features

Frequent Pattern Mining

Suggest relevant products to customers based on their intended purchases and boost recurring revenue. Frequent pattern mining allows AI-boosted shopping cart analysis which can detect which products are often purchased together in a single transaction. Based on historical data, individual product recommendations for new transactions are generated in real-time, ensuring that each customer’s experience is tailored to their particular preferences. With this FPM, you now have the power to implement smart features such as the popular “other customers also bought” section in member portals, mobile apps, and e-commerce websites.

Customer Lifetime Value (CLV) calculation & prediction

Large enterprises can have millions of active loyalty members engaging with their program. In this vast landscape, it can be difficult to determine which customers deserve the most of your attention. CLV is a fully customizable formula that analyzes the Recency, Frequency, and Monetary value of an individual member. This highly valuable data gives you the insights to your most valued customers – enabling smart, strategic decision making.

Purchase Patterns

Optimize your marketing efforts by understanding which products your customers purchase and exactly when they purchase them. Detection of cyclical recurring purchases of specific products made by selected customers unlock key insights into the timing of future purchases. This allows for personalized communication with customers at the exact right moment.

Behavior Patterns

Thanks to the continuous analysis of patterns in customer interactions, it is possible to identify members that are most likely to:

  • churn
  • be active only in selected time periods (seasonality)
  • react exceptionally well to particular promotions
  • become the most valuable members (“rising stars”)
This feature promotes a proactive approach, allowing you to react to any change in the customer journey of a member early enough to reward positive behaviors and deflect negative behaviors.


Higher conversion rates

Personalized marketing techniques are proven methods to improve your bottom line. A variety of studies have proved that applying these techniques results in increased conversion rates and boosted revenues by saving resources usually spent on mass offers with a low consumption ratio.

Increased customer engagement

By providing customers with the right product and services offers at the right time via a preferable channel, companies can motivate a highly engaged member base in a relatively short time. Such actions can also be crucial for raising brand awareness, driving organic member base growth, and creating buzz on social media.

More relevant communication

Today, there is a constant battle over the loyalty of your customers with competing brands. With so many channels and ways to communicate, generic messages lacking a personal touch have become obsolete and inefficient. Personalized offers based on customers’ profiles and historical interactions give you the competitive edge and strengthen your chance of achieving the goals of your loyalty program.

Customer churn prevention

This Personal Marketing Microservice allows you to retain your customers and prevent loss. Focusing on those who have left the program or who have been inactive for long periods of time, “comeback” campaigns are an important tool designed to bring your customer back before it’s too late.

The Science Behind the Solution

Deep Neural Networks (DNNs)

Multiple models in Personal Marketing Microservice use deep neural networks as part of their architecture. DNNs can process high volumes of data and adapt their calculations depending on the training datasets they receive. The same model can be re-used at a member level, as it can adapt to the input data without any additional manual corrections.

Product Embeddings

One of the more exciting Machine Learning techniques is a method initially developed for Natural Language Processing [NLP]. Its goal is to detect the latent meaning of a word by analyzing other words that are frequently used in close association or similar context. From the marketing perspective, this approach allows the program to recognize related products by examining what items are commonly purchased together. By and large, it relies on the assumption that if products A and B are purchased with products C, D and E, then it is highly probable that there is some relation between those products – whether it be that they belong to the same category or are purchased by similar customers.

Deep Collaborative Filtering

Deep Collaborative Filtering assumes that customers with a similar purchase history are likely to purchase similar products in the future. This straightforward idea powers a vast majority of commercially used recommender systems.

Dynamic Time Warping

DTW is a technique used to assess distance and similarity between two different temporal sequences or time-series, which may vary in speed or frequency. In PMM, it is one of the methods used for pattern identification and detection for both supervised and unsupervised learning approaches.

Association Rule Mining

Association rule mining is a category of Machine Learning techniques that is used to identify relations between variables in huge datasets. Frequent Pattern Mining within Personal Marketing Microservice utilizes the FP-Growth and Apriori association rule mining algorithms to detect sets of products frequently purchased together in a single transaction. FPM also follows the continuous learning approach, meaning that the model keeps evolving over time and adapts to new transactions coming through a wide range of supported data streams.

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