Industry Retail & consumer brands

The gap between what the stores know and what the site knows ends up paid for twice — in markdowns and in stockouts.

+17%Margin on end-of-season clearance
+134%Cross-team follow-through on flagged issues

Figures reflect relative change before and after implementation, presented in de-identified form under client confidentiality terms.

The online-order collection point in a store, parcels on shelving and a scanner on the counter
AI-generated illustration

The problem in this sector is rarely missing data. It is that the data sits in systems and departments that are out of step with each other. Stores and the website never see the same inventory truth, and the market signal support hears never reaches the people making product decisions.

Once that gap persists, organisations develop two compensating habits: clearing inventory with broad markdowns, and substituting survey scores for what customers actually feel. Both conceal the problem rather than solve it, and both are expensive.

Our sequence is fixed: give the decision a single, current source of truth first, then automate. In the omnichannel engagement, dynamic pricing only became meaningful once POS, ecommerce and ERP were reconciled. In the support engagement, cross-team action could only be triggered automatically once conversations had become structured data. Reverse the order and automation simply makes the wrong call faster.

Case 01Retail · Omnichannel

Omnichannel inventory and marketing: improve sell-through, not discounts

Online and offline data lagged each other, clearance leaned on deep bulk discounting that damaged both brand and margin, and conservative buying produced stockouts and dead stock at the same time.

+17%Margin on end-of-season clearance
-38%Stockout rate on best sellers
7.3 → 2.6 daysAverage stockout duration
-45%Weekly residual-value write-down on same-age stock

The problem

An apparel brand running both e-commerce and physical retail, with a catalogue spanning seasonal lines and long-running staples — two very different lifecycles handled by one process.

On the marketing side
, information moved between business units with a lag, and clearing aged stock meant deep bulk discounts on selected lines, at the cost of brand equity and margin.
On the buying side
, risk-averse ordering plus long replenishment cycles meant best sellers went out of stock while ineffective inventory kept accumulating, with no single view of either.

What we built

  1. 01
    A personalisation and dynamic pricing engineTraffic source, purchase history and live browsing behaviour drive the offer type per customer profile — depth of discount, threshold gift, or bundle — with campaigns triggered automatically by product lifecycle stage and stock level.
  2. 02
    Demand forecasting and O2O inventory balancingHistorical sales, market conditions and comparable product performance produce an expected sales curve and pricing band for new lines, with proactive recommendations on reorder timing and quantity. Store and online stock data are joined so inventory can be redistributed by local demand, supporting click-and-collect and store-to-online transfers.
  3. 03
    System integrationacross POS, e-commerce and ERP, so the models run on one live source of truth and close the loop from forecast to campaign to measured result.
When clearance runs on precision marketing, margin no longer gets cleared along with the stock.

The business case

The margin gain comes from better sell-through, not from discounting harder — which is precisely the difference between dynamic, targeted promotion and blanket markdowns.

Case 02Consumer brand · Support

Turning support from a cost centre into a market-sensing function

Support conversations already contain market signal. The hard part is reading it at scale, and making sure what you find actually reaches a decision.

+134%Cross-team follow-through on flagged issues
-82%Average time to resolve a flagged issue
18.7%Of top-selling product improvements traced to support insight

The problem

Support had long been treated purely as a cost centre. The conversations were full of product and market signal, but turning unstructured dialogue into usable insight kept failing at three points.

Manual reading did not scale
It required expensive specialists with real commercial judgement.
Satisfaction data was subjective
Traditional CSAT surveys carry sampling bias and rarely reflect how the wider customer base actually feels.
The information was siloed
What support learned never reached product or marketing.
Market signal never shows up in one place. The hard part is not hearing it — it is collecting it into something usable.

What we built

  1. 01
    System integrationFront-line support channels feed directly into ticketing and resolution, so conversations are structured data from the start rather than something retrieved after the fact.
  2. 02
    Signal extractionLLM and NLP models process conversation records at low cost and high volume, automatically identifying product defects, feature requests and competitive comparisons.
  3. 03
    A satisfaction calibration modelSentiment in actual conversations is cross-referenced against survey responses to assess how representative the raw CSAT score really is, and to recover a truer measure of customer experience.
  4. 04
    Automatic cross-team routingThis is where the project actually turned. Valuable observations used to die in the gap between "we know" and "nobody followed up". Once a signal is classified as significant, the system now convenes the relevant teams automatically, repairing a broken handoff rather than adding another report.

The business case

A faster, more honest source of insight than traditional market research, drawn from what customers volunteer rather than what a survey asks. It complements and calibrates other research rather than replacing it.

Commercially meaningful product signals surface from noisy conversation logs, shortening the iteration cycle.

Support shifts from reactive firefighting to a front-line sensor for product and market strategy.

Need Professional Assistance?

Our team are senior engineers and former executives with 20+ years in commercial product development — from apps serving hundreds of millions of users to B2B SaaS platforms serving tens of thousands of merchants. In recent years the focus has been enterprise AI adoption, from mapping the opportunity through to tuning what is live. Whether that means:

  • Mapping AI opportunities and sequencing them
  • Preparing data flows and integrating existing systems
  • Building forecasting and optimisation models
  • Monitoring and tuning after go-live
  • Test strategy and process review
  • Building and adopting test automation

We can provide professional advice and concrete solutions for teams of different scales and types. If you're facing similar challenges, feel free tocontact our consulting team.