Absolute Data Tagging

It is important to build a data aware model where captured data is directly stored against the tag. For new data, it is important to build the relationship as it’s being stored. With this, each time data is collected, it can store against the pre-existing tag. If done for each customer/user, it would create a holistic view across all channels and sources.


Behavioural Scoring

Now that we have the customer journey of a single customer, successful and unsuccessful patterns can be identified. Assigning meaningful scores to each event can help in creating auto segmentation based on behavioral parameters which broadly comprises of customer interactions, responses, transactions and user sentiments.


Rule Engine

The behavioral data provides insights that help in delivering recommendations based on past purchases, wish lists or by patterns derived from users with similar profiles. We can use real-time recommendation models to arrive at a product list or an engagement campaign or any next best action based on the scores of the associated parameters.

Real-time pattern matching or state identifiers for trigger activation

Using a lifecycle grid analysis, you can create states across the customer lifecycle and use the behavior or recommendation scores to trigger communication. A rule library for each state or segment type will allow the machine to trigger communications if a particular rule is satisfied.

Response recording and the insight churn loop

After the communication is triggered, the system needs to loop back the responses of the customer with respect to the communication and store data against the pre-existing tag. This ensures that the new responses are added to the existing insights and the new derivative is rendered for next action. Storing against the tag will help maintain single customer data at all times.

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