AutoEncoded Segments

Build user segments and keep their track.

What makes journeys similar and what makes them different? How can we cluster them? We define the shared characteristics and make use of common behavior pattern shown by users to define segments. However the linearly referenced data (also known as events) are not fixed and evolve over various dimension to predict existence of a unique segment.

We find each and every segment within your consumer base through different models.

Dynamic Targeting

How best should we model different users at different points on different types of journeys? Every user is walking through her digital life providing sequence of information about his future moves. We can pick the effects of marketing / digital products and user’s predisposition to the way sequences of events unfold. This can happen if we are adaptive and dynamic in two ways. Segmenting him, finding his inclinations and adapting the communication as per his journey.

Our Dynamic targeting is self encoded to predict users new affliction and adapt the message according to it.

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