Customer DNA is Notus' vector representation layer for customers. It converts profiles, behaviors, and context into high-dimensional embeddings that power segmentation, similarity search, prediction, and personalization at scale.
Works best with unified profiles from Identity Resolution (Notus or equivalent), but remains a distinct product with its own outputs and use cases.
Customer DNA is the representation layer, not the identity stitching layer. It encodes customer behavior and context into vector space so mathematical similarity can power downstream decisions.
Unified profiles (preferred), behavioral events, product interactions, and contextual signals such as weather or media activity.
Customer embeddings, similarity scores, cluster memberships, and predictive features for downstream models and activation.
A strong customer profile layer, including Notus Identity Resolution or an equivalent unified customer profile source.
Identity Resolution and Customer DNA are separate products with a strong handoff. Identity Resolution creates trusted profiles. Customer DNA encodes those profiles into a vector representation that downstream systems can use for segmentation and prediction.
A unified profile is the input, not the endpoint. Customer DNA encodes that profile into vector space so each dimension contributes to a shared representation of behavior, affinity, and propensity. This becomes the foundation for segmentation, personalization, and prediction.
Customer DNA helps you:
Customer DNA is a vector embedding composed of encoded dimensions that evolve as new behavior is observed. These dimensions work together as a shared representation, allowing multiple predictive tasks to learn from the same customer context.
DNA-encoded dimensions can include:
Customer DNA embeddings enable segmentation, lookalikes, propensity scoring, and personalization workflows that are difficult to build with rule-based systems alone.
How: Customer DNA clusters in vector space. Similar customers group together based on distance, revealing segments automatically.
Outcome: Discover high-value micro-segments and segment with precision beyond demographic rules.
How: Vector distance measures behavioral similarity across full customer context, not just a handful of attributes.
Outcome: Build precise lookalikes and identify high-potential customers who mirror top performers.
How: Recommendations, offers, and messages are informed by the customer's embedding instead of brittle rule engines.
Outcome: Deliver hyper-relevant experiences with less manual segmentation logic.
How: Vector space reveals non-obvious relationships and creates reusable features for downstream predictive models.
Outcome: Improve prediction quality for churn, CLV, next purchase, and cross-sell opportunities.
Customer DNA runs on the same embedded runtime that powers Notus Identity Resolution and other products. That means privacy-first deployment, reduced data movement, and a consistent platform for adding capabilities over time.
We can help you assess readiness, map inputs, and show how Customer DNA fits your segmentation, prediction, and personalization goals.