Data & analytics

AI that connects business data to customer decisions.

Explore how EkamFlow helps data and analytics teams evaluate customer predictions, define useful inputs, and connect model outputs to business actions.

Book a demoSee how it works
Decision workspaceIllustrative demo
Business signals
Consistent customer and account identifiersTimestamped activity and known business outcomesData access, delivery, and evaluation requirements
EkamFlow decisioningPrivate model

Account A-1042

Retention priority

Engagement patternEngagement is declining
Recommended decisionReview the priority against known renewal outcomes
Continue in an existing workflowData and retention owners

Illustrative workflow and sample data.

Put the decision to work

How does EkamFlow help data and analytics teams?

EkamFlow gives data and analytics teams a way to evaluate customer predictions around a specific business decision. The team can define a churn outcome, a sales conversion target, or a customer value horizon, then review the data required and how a business owner will use the output. Each business has a private model, as described in EkamFlow's data-handling information. A useful evaluation combines prediction quality with business usefulness: a score matters when a team can act on it and record what happened.

Evaluation measure 01Prediction usefulness
Evaluation measure 02Business outcome against baseline
Evaluation measure 03Data and workflow coverage

Agree on a baseline before rollout. Measures shown are evaluation criteria, not promised results.

Department workflows

Practical workflows for Data & analytics.

Connect a business challenge to a decision, an action, and an outcome your team can evaluate.

Workflow 01

Define a retention prediction the business can use

Different teams may use different meanings of churn or combine incompatible customer records.

Explore churn prediction
Signals
Timestamped usage or purchases, subscription records, stable IDs, and known retention outcomes.
AI decision
Identify customer churn risk for an agreed population and prediction window.
Team action
Agree with retention owners on the review process, available intervention, and outcome feedback.
Measure
Prediction quality at the team's review capacity and retention against baseline.

Workflow 02

Connect lead predictions to recorded sales outcomes

Incomplete CRM stages or activity recorded after conversion can make a sales evaluation misleading.

Explore lead scoring
Signals
Account activity available before the decision, lead stages, and recorded conversion outcomes.
AI decision
Rank leads for a defined conversion objective.
Team action
Deliver the priority to the sales owner and review performance across lead sources.
Measure
Qualified opportunity conversion, outcome coverage, and follow-up capacity.

Workflow 03

Agree on a customer value definition

Revenue, margin, and different time horizons can produce different meanings of customer value.

Explore customer lifetime value
Signals
Customer revenue history, purchase frequency, tenure, and the agreed value horizon.
AI decision
Estimate customer lifetime value for the defined business objective.
Team action
Review estimates with marketing or retention owners before using them in investment decisions.
Measure
Estimated versus realized value and usefulness in the chosen decision.

Illustrative example

A churn pilot with one shared outcome definition

  1. Data and customer success teams agree on an account cohort, renewal window, and definition of non-renewal.
  2. They connect historical activity to outcomes, evaluate the prediction using data available before the decision, and define how an owner will follow up.
  3. The teams compare prediction quality and business results with the baseline, then review outcome coverage before extending the pilot.

Start with one decision

What does a useful pilot need?

Choose one business decision with a named owner. Define the entity, outcome, prediction window, baseline, and permitted data. Review access and integration requirements, then agree on how actions and results will be recorded.

Considerations for this department

Evaluate with data available at the time of the decision and keep later outcomes separate. Review missing records, changing populations, and limited-history customers. Confirm data handling and delivery requirements for the specific workflow before rollout.

Review EkamFlow data handling

Common questions

Data & analytics questions, answered.

Practical answers about use cases, data, and evaluating a pilot.

What does a data team need to evaluate EkamFlow?

Define a business decision, a known outcome, a prediction window, and the team that will act. Review timestamped historical data, consistent entity IDs, access requirements, output delivery, and an agreed baseline before the pilot.

Does a good prediction score establish business value?

Prediction quality is one part of evaluation. The team also needs an action it can take and a way to compare business outcomes, costs, and workload with a baseline. A prediction that is never used may have limited practical value.

How does EkamFlow handle business data?

Each business has a private model. Customer source records are not shared with other businesses or retained by EkamFlow, as described on the privacy page. Website enquiries are handled separately. Review the specific data and deployment requirements during scoping.

Can EkamFlow replace a data warehouse or BI platform?

EkamFlow supplies customer predictions and recommended actions. A warehouse or BI platform continues to support data records, reporting, and broader analysis. The proposed data connections and business workflow determine the integration scope.

Work across teams

Connect this decision to the wider business.

Agree on customer definitions, action ownership, and outcome feedback across the teams involved. Then adapt the workflow to your industry's data and priorities.

Connected departments

Industry applications

Explore further

Connected customer decisions

Evaluate EkamFlow for a specific business goal.

Discuss the use case, available data, and the next step with the team.

Book a demo