Case studies

One Customer View. Multiple Revenue Workflows.

A United States membership business unified customer data across prize draws, ecommerce, and lifecycle marketing, then deployed governed AI workflows for conversion and retention.

  • Membership & giveaways
  • E-commerce
  • CRO & LTV
  • Customer data

Client identities are withheld under NDA. These examples reflect work delivered directly or through ASI's specialist network.

How it works

Scattered customer data resolves into one customer graph. The graph ships a decision the operator approves.

SCATTERED CUSTOMER DATA CUSTOMER GRAPH DECISION, SHIPPED SIGN-UPS PRIZE ENTRIES STORE ORDERS DECISION RECORD SHIPPED Show the offer that converts Store conversion · approved by ops DECISION CYCLE weeks days
Case study 01

Data Foundation First. Governed Workflows on Top.

The opportunity was clear. The missing piece was a trusted customer view the operation could use every day.

01 Membership & giveaways · United States

A US Membership Business, Powered by One Customer View.

Context

A United States membership brand runs prize draws alongside an online store. Years of growth left customer data spread across sign-ups, prize entries, store orders, email, and on-site behaviour, with no reliable view across the full customer relationship.

Decision bottleneck

The team could not reliably identify which visitor was likely to buy, which member was at risk of lapsing, or where retention effort would have the greatest value. Decisions depended on manual exports and incomplete customer histories.

System built

The data foundation came first. Sign-ups, prize entries, store orders, email, and site behaviour were resolved into one governed customer graph, creating a consistent identity across every source system.

Action shipped

Governed AI workflows were then deployed across store conversion and retention. They prepare personalised offers, recommendations, and win-back actions, with operators controlling what reaches the customer.

Measured result

The business moved from periodic, manual analysis to daily customer decisions across conversion, retention, and lifetime value. Each action now starts from the same governed customer view.

What became reusable

The customer graph now powers every play across the store and the membership. Conversion, retention, and lifetime value all read from one place, so the next use case starts from a foundation, not a blank page.

What the AI did

One Customer Graph, Multiple Workflows.

Conversion, retention, and lifetime-value workflows now operate from the same governed customer data.

01

Store conversion (CRO)

Personalised offers, product recommendations, and checkout nudges lift conversion on the store, with an operator approving what goes live.

02

Customer lifetime value

Every customer is scored for lifetime value, so spend and attention go to the ones actually worth keeping.

03

Retention and win-back

Churn is predicted before it happens, and the win-back offer is drafted while the member is still in reach.

04

Personalised draws

Every member sees the prize draw they are most likely to enter, ranked from their own history and similar members.

05

Lifecycle email

The right message goes at the right moment, triggered off the customer graph, not a fixed weekly blast.

06

On-site recommendations

The store recommends from the customer graph, not a generic best-seller list, so the offer fits the shopper.

More case studies

The Same Delivery Pattern Across Sectors.

Unify the required data, deploy the governed workflow, and keep consequential actions under operator approval.

Case study 02

Grocery chain: forecasting, replenishment, and waste.

  • Retail & grocery
  • Demand forecasting
  • Inventory

A multi-site grocery retailer running thousands of perishable lines wanted the order to write itself, store by store, without the overbuy that ends up in the bin.

  • Per-store demand forecast for perishables, read against season, weather, local events, and promotions.
  • Replenishment orders drafted automatically, with the buyer approving the exceptions, not every line.
  • Markdown and waste pulled down by acting on what would not sell before it spoiled.
Case study 03

Specialist retailer: one platform, ready to scale.

  • E-commerce
  • Platform
  • Data

A specialist retailer with a deep catalogue moved off legacy infrastructure onto one modern commerce platform, with the data layer rebuilt to carry growth.

  • Catalogue, pricing, and stock unified across regions on one platform.
  • Search and merchandising rebuilt so the right product surfaces first.
  • A foundation set for AI on top, not bolted onto a system that could not carry it.
Case study 04

Distributor: fragmented supplier data into one engine.

  • Wholesale
  • Data platform
  • Automation

A food and beverage distributor turned fragmented supplier and order data into one scalable commerce engine its buyers could actually run.

  • Supplier feeds normalised into one catalogue and one source of truth.
  • Ordering and reconciliation automated off the clean data layer.
  • The base every later AI workflow reads from.
Case study 05

Bank: one data foundation for AML, KYC, and offers.

  • Banking
  • AML
  • KYC
  • Data platform

A global commercial bank replaced siloed point systems with one governed data foundation, then put AI to work across compliance and growth. Investigations got faster, and cross-sell campaigns moved from a monthly cycle to a daily one.

  • Anti-money laundering: risk models read across internal and external activity to cut false positives, with investigations completed in a fraction of the time.
  • Onboarding and KYC: one interface with entity resolution, sanctions and negative-news checks, and risk scoring, so analysts spend their effort on the higher-risk customers.
  • Next best offer: propensity and segmentation models drive cross-sell campaigns to segments the bank could not reach before.
Case study 06

Life insurer: pricing, claims, and advisor engagement.

  • Life insurance
  • Underwriting
  • Claims

A life and annuity insurer unified policy, claims, and external data on one foundation, then layered AI across pricing, claims, and marketing. Actuaries moved off manual cycles, and the operator keeps sign-off on the consequential calls.

  • Pricing: automated experience studies and external data enrichment moved assumption refreshes from annual to quarterly, freeing senior actuaries for strategic work.
  • Claims: a unified claims view triages work into fast, routine, and complex, and auto-adjudicates the simpler claims to cut handling time.
  • Engagement: predictive loyalty models score advisors and customers, so outreach is personalised and tailored to each segment.
Case study 07

P&C insurer: underwriting, claims, and catastrophe exposure.

  • P&C insurance
  • Underwriting
  • Claims
  • Risk

A national property and casualty insurer fused messy internal and external data into one insurance data layer, then put AI on underwriting and claims. Submissions, risk signals, weather and geospatial data, and claims now sit in one place, so underwriters and adjusters decide faster.

  • Submission intake is automated into a richer risk picture, so underwriting turnaround drops from weeks to hours, with the underwriter approving the call.
  • Claims triage flags fraud, subrogation, and leakage potential, and routes each claim to the right next action.
  • Weather and geospatial data feed catastrophe exposure modelling, cutting the surprises across the portfolio.

Turn Existing Data into a Measurable Workflow.

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