Understand customer intent, retention and demand—and turn the evidence into practical decisions. The engagement connects investigation to implementation, with a baseline, reviewed changes and clear acceptance criteria.
What’s included.
Three steps.
Understand
Agree the task, baseline, source systems and delivery boundary.
Build
Develop and review the code, configuration and integrations in scope.
Verify
Test representative cases, compare with the baseline and hand over.
Recognise the challenge?
Start with the problem you see. Explore what we can change, then open the implementation notes for the detail.
01AI demand-forecasting implementation
What you may be seeing
- We keep running out of popular products.
- We buy too much stock that sells slowly.
- Promotions or seasons make our forecasts unreliable.
- New or irregular products defeat our spreadsheets.
- The forecast looks precise but nobody knows its error.
We build and test demand forecasts that help teams plan, while showing the uncertainty behind each estimate.
Implementation & verification
We build demand models using relevant sales history and available business signals such as promotions, seasonality and stock availability. We compare simple seasonal baselines with more advanced models using rolling historical backtests that avoid future-data leakage. We code reproducible forecast pipelines with uncertainty ranges and separate treatment for sparse, new or stock-constrained products. We connect forecasts to planning reports and validate error, bias and usefulness for the actual replenishment decisions.
How we verify the work
- Model demand separately from observed sales when stockouts hide possible purchases.
- Backtest by time and product segment against the forecast horizon planners actually need.
- Choose complexity only when it improves out-of-sample results; retain explainable fallbacks.
- Measure forecast error/bias and relevant stockout or excess-stock consequences.
Tools considered for this work
Delivery: data/ML development + planning/reporting integration. Baselines/models: Nixtla StatsForecast; scikit-learn-based feature models where justified. Data: ERP (a system for managing core business operations)/order/stock records, promotion calendar and warehouse transformations. Operations: MLflow tracking plus the existing scheduler. Reports: existing BI with forecast ranges, overrides and realized outcomes.
02Customer-churn prediction setup
What you may be seeing
- We notice customers leaving only after they have gone.
- Staff cannot decide which accounts need attention.
- Retention campaigns contact many customers who were not at risk.
- Our model labels customers after the cancellation already happened.
- High-risk predictions do not lead to useful staff actions.
We identify patterns associated with customers leaving, so your team can decide where timely support may help.
Implementation & verification
We define churn and the prediction window from the business's actual account or repeat-purchase model. We build time-correct customer features and compare simple rules with validated risk models using later-period holdouts. We code a ranked staff queue with permitted evidence and a specific supported intervention, rather than only producing a score. We evaluate prediction calibration and operational capacity, then test interventions separately because predicted risk alone does not prove saved revenue.
How we verify the work
- Choose an actionable lead time and prevent future or cancellation information entering features.
- Use time-based evaluation and compare precision at the number of accounts staff can handle.
- Provide evidence, a review path and a relevant intervention for each risk group.
- Track calibration, outreach capacity and experimentally evaluated retention impact separately.
Tools considered for this work
Delivery: data/ML + CRM (a system for managing customer relationships)/backend integration. Model: scikit-learn classification or a simple interpretable scoring baseline. Data: CRM (a system for managing customer relationships), billing, order and supported usage/support history. Operations: MLflow and scheduled scoring into the CRM (a system for managing customer relationships). Intervention test: GrowthBook/custom account-level randomization where feasible.
03AI customer-feedback theme analysis
What you may be seeing
- Reviews and support messages contain the same problems, but we miss the patterns.
- Staff manually tag feedback inconsistently.
- We cannot tell which complaints affect valuable customers.
- AI summaries hide the original customer evidence.
- A rise in messages looks like a bigger issue even when customer volume also rose.
We organise customer feedback into useful themes and link summaries back to the original comments.
Implementation & verification
We unify approved feedback sources and build a topic taxonomy around actionable customer problems. We code extraction/classification that links each theme to representative source passages and allows human correction. We aggregate themes by journey, product and relevant customer segment while normalizing counts against the population or channel volume. We publish an evidence-backed issue queue and evaluate label quality on held-out messages before automating ongoing updates.
How we verify the work
- Separate issue, sentiment and requested action; remove duplicate copies of the same conversation.
- Use clustering to propose themes, then validate and maintain the business taxonomy.
- Keep excerpts and proportions visible; distinguish more feedback from a higher incidence of a problem.
- Measure label precision, evidence coverage and the backlog of actionable unresolved issues.
Tools considered for this work
Delivery: data/AI development + support/review connectors + reporting. Sources: existing Zendesk/Intercom exports/APIs (controlled connections between applications), surveys and approved review data. Analysis: Claude structured/tool-based classification; embeddings for candidate clustering. Storage/reporting: warehouse/SQL (the language used to query a database) and existing BI. QA: labelled feedback set, reviewer interface and taxonomy versioning.
04Search and product-interest analysis
What you may be seeing
- We cannot see what customers search for.
- Popular searches return no useful products.
- People browse and compare without buying, and we do not know why.
- We cannot connect search terms to purchases or enquiries.
- Catalogue gaps and poor search results look the same in our reports.
We connect searches and browsing patterns to product interest, helping you find missing information and gaps in your offer.
Implementation & verification
We instrument search results, result clicks, filters, comparisons and completed buying outcomes using stable product/query references. We code or repair the event linkage required to separate unmet demand from relevance or interface failures. We build reports for zero-results, repeated queries, low-result engagement and downstream completion by query family. We feed the evidence into Discovery & Decisions search improvements and verify whether the targeted query problems improve.
How we verify the work
- Normalize query variants without merging different intent; filter bots and internal traffic.
- Link clicked products and purchases to observed searches within the defined window.
- Classify no stock, missing catalogue, poor ranking and confusing filters separately.
- Measure useful-result engagement, zero-result incidence and completed actions per search user.
Tools considered for this work
Delivery: analytics configuration + frontend/SQL (the language used to query a database) development. Search evidence: Algolia analytics or existing Elasticsearch/query logs. Behavior/outcomes: GA4 (Google Analytics 4)/PostHog and authoritative order/enquiry data. Reporting: SQL (the language used to query a database) warehouse and existing BI. Code: query/result IDs, product events and safe query-text handling.
05Returning-customer and cohort analysis
What you may be seeing
- We do not know how many customers come back.
- Repeat visits look good, but repeat purchases are falling.
- We cannot compare retention across customer cohorts.
- Anonymous visitors and known accounts get mixed together.
- We cannot tell which customers have a sensible reorder or renewal opportunity.
We compare customer groups over time to understand repeat use, retention and differences between acquisition channels.
Implementation & verification
We build cohort and returning-customer reports from verified customer identities and meaningful return actions. We code the joins and observation windows needed to separate repeat browsing, repeat purchases and ongoing product use. We compare comparable cohorts and surface relevant saved-task, reorder or renewal opportunities without claiming that a correlation caused retention. We validate repeat-action counts against account/order records and coordinate supported journey improvements with Discovery & Decisions and Self-Service & Workflows.
How we verify the work
- Define meaningful return, the eligible customer population and the expected repeat interval.
- Use stable permitted identity and do not treat unknown cross-device activity as confirmed customer behavior.
- Compare cohorts with equivalent follow-up time and separate acquisition mix changes.
- Measure repeat purchase/use rates, time to return and relevant repeat revenue with known coverage limits.
Tools considered for this work
Delivery: analytics configuration + SQL (the language used to query a database)/data development; application changes for follow-on improvements. Sources: CRM (a system for managing customer relationships)/account, order/subscription and permitted product events. Analysis: GA4 (Google Analytics 4)/PostHog retention/cohorts or SQL (the language used to query a database) cohort models. Reporting: existing BI with cohort size and complete observation windows. Validation: customer/order identity reconciliation and reference cohort fixtures.
Turn patterns into useful decisions.
We build and evaluate forecasting, customer-risk and feedback-analysis models around a defined business decision. Automation brings a reviewed insight into the workflow where your team can act on it.
Explore the technical toolkit
Selected around your existing stack, licences, permissions and agreed scope.
Define what better means.
Trust
Reconciliation, event completeness and data freshness.
Decisions
Time to answer a defined operational or customer question.
Outcomes
Qualified conversions, retention and experiment uncertainty.
Targets follow the baseline. These are measurement areas, not promised results.
Engineering terms, explained
- Grounded AI / RAG
- An assistant retrieves relevant approved evidence before generating an answer. Access controls and evaluations still need to be engineered and tested.
- Idempotency
- Retrying the same operation does not create an extra order, payment or record.
- Controlled experiment
- A concurrent comparison of assigned variants, with a defined outcome, observation window and decision rule.
- Core Web Vitals
- Measures of main-content loading, responsiveness to interaction and visual stability.
Technical references: Web Vitals · Azure document access · Structured data guidelines.
Quick answers.
We already have analytics. What would you change?
We check whether it answers your actual business questions. That can mean repairing collection, reconciling reports or connecting activity to orders and qualified leads before adding another dashboard.
Can you run A/B tests on a low-traffic product?
Not every product has enough traffic for a useful result. We assess the sample and decision window first. Where a test is impractical, we use task research and monitored changes, with the limits of the comparison made clear.
Can staff ask questions in plain English?
Yes, within a defined set of data and permissions. We build the governed queries and evidence retrieval behind the answer, then check factual accuracy and access boundaries.