AI business analyst for digital commerce

Turn commerce data into decisions that move revenue

The DataKnobs E-commerce Analysis Agent connects product, traffic, funnel, purchase, and post-purchase usage signals. It explains what changed, why it matters financially, and which action to take next.

Product intelligence
Journey intelligence
Next-best actions

Commerce intelligence

Business Insight Summary

Conversion opportunity

AI finding

High-intent visitors engage with the category, but checkout conversion falls after shipping cost appears. Buyers shown a bundle earlier have higher order value and stronger 30-day repeat behavior.

Top driver

Shipping

Best lever

Bundles

Next step

A/B test

Revenue by group

Journey alert

Cart → CheckoutFriction
Bundle → RepeatStrong

The problem

Dashboards show metrics. Teams still have to find the answer.

  • Traffic, catalog, transaction, and usage signals sit in separate systems.
  • Analysts assemble reports before they can investigate causes.
  • Teams see what changed but not the impact or next action.
  • Good hypotheses stall before becoming measurable tests.

The DataKnobs solution

One agent connects evidence, explanation, and action.

  • Unify the journey from acquisition through repeat usage.
  • Identify performance drivers, anomalies, friction, and high-value cohorts.
  • Translate behavior into revenue, margin, conversion, and lifetime-value impact.
  • Recommend prioritized actions and experiment-ready hypotheses.

How it works

From fragmented commerce signals to a prioritized growth decision

The agent follows the analytical path a strong cross-functional team would use—continuously and consistently.

01

Connect signals

Ingest traffic, product views, cart events, checkout, purchases, catalog attributes, campaigns, returns, and usage.

02

Find drivers

Compare products, cohorts, channels, journey stages, and time periods to locate meaningful patterns and friction.

03

Explain impact

Turn observations into grounded findings tied to conversion, revenue, margin, retention, and customer value.

04

Recommend action

Prioritize changes to pricing, bundles, merchandising, checkout, onboarding, and lifecycle journeys.

Capabilities

An AI analyst for product, growth, merchandising, and financial performance

Ask business questions in plain language and receive a structured answer with supporting evidence, impact, confidence, and recommended next steps.

01

Product performance

See which products drive attention, conversion, revenue, margin, repeat purchase, and engagement—and which underperform despite visibility.

02

Journey and funnel analysis

Trace movement from acquisition to product detail, cart, checkout, purchase, activation, and repeat behavior.

03

Cohort and usage intelligence

Compare first-time, repeat, bundled, discounted, subscribed, activated, and at-risk cohorts after purchase.

04

Financial analysis

Connect behavior to revenue, margin, average order value, acquisition efficiency, returns, repeat rate, and lifetime value.

05

AI recommendations

Generate prioritized ideas for pricing, bundles, placement, promotions, content, checkout, onboarding, and lifecycle programs.

06

Experiment handoff

Turn recommendations into hypotheses with target segments, success metrics, guardrails, and expected outcomes.

Example output

A decision brief—not another dashboard

Each response separates observation, evidence, business impact, recommended action, and the measurement plan.

Business insight payload

Checkout friction in a high-intent category

High priority
Observation
Product engagement is strong, but progression drops when shipping cost is revealed.
Financial impact
Improving this step can increase purchases and revenue per session in a high-volume category.
Cohort insight
Buyers who add a relevant accessory show stronger activation and repeat purchase.
Recommended action
Test earlier shipping transparency and move the bundle recommendation from cart to the product page.

Executive view

What leaders see

Top growth leverCheckout experience
Highest-value cohortBundled first-time buyers
Primary riskShipping-cost surprise
Next moveRun controlled test

Experiment handoff

Ready for measurement

Package the test audience, variants, primary metric, margin guardrail, duration, and decision rule for DataKnobs Knobs or your existing experimentation stack.

Where it creates value

One intelligence layer for every commerce team

The agent presents the same underlying commerce evidence through role-relevant questions and outcomes.

Growth

Improve funnel conversion

Diagnose channel, landing-page, product-page, cart, and checkout friction by segment.

Product

Increase adoption and usage

Connect what customers buy with activation, engagement, support, and repeat behavior.

Merchandising

Optimize catalog and bundles

Find hero products, attach-rate opportunities, weak placements, and promotion-sensitive items.

Finance

Connect behavior to value

Identify journeys, offers, products, and cohorts that improve profitable revenue and lifetime value.

Business outcomes

Move from reporting cycles to a learning loop

Faster

Answers without manual report assembly

Clearer

Drivers behind product and funnel outcomes

Smarter

Pricing, bundle, and placement decisions

Safer

Recommendations with evidence and guardrails

Continuous

Insight, action, test, and learning

Start with a focused pilot

See what your commerce data is trying to tell you

Use a priority product category or customer journey to surface high-impact findings, quantify value leakage, and produce an action plan your team can test.