Cross-sell conversions up 38%.
Churn down 4%.
A predictive AI platform that reads customer, product and transaction data to tell sales teams the right product, at the right time, at the right price — before the customer has to ask.
and up-sell conversion
from pricing precision
across global markets
automated price approvals
Client
A confidential chemical and ingredients distributor running traditional, reactive commercial practices across multiple countries.
Goal
Move sales from reactive to predictive — identifying the right product, at the right time, at the right price — to lift customer lifetime value and reduce churn.
No app told sales teams what to do next.
Commercial teams worked in a volatile environment with inconsistent order patterns and little visibility into when a customer would next reorder. Sales engagement was reactive by default, and that reactivity showed up as missed revenue, customer down-trading, and churn nobody saw coming until it had already happened.
No single application existed to put sales intelligence in front of reps during their day-to-day work, and the data needed to build one was scattered across SAP, Oracle, home-grown systems and disparate CRMs — inconsistencies that had to be resolved at scale across multiple countries. On top of that, there was no reliable way to measure performance gaps between sales teams.
Three constraints were non-negotiable:
- Reorder timing had to be predicted from inconsistent order histories, not guessed at
- Sales intelligence had to sit inside the workflow reps already used, not in a separate report
- Data fragmented across SAP, Oracle and home-grown CRMs had to unify into one layer, safely, across multiple countries
Predictions embedded where reps already work.
A scalable decision-intelligence platform combines machine learning with commercial workflows to produce real-time recommendations. The engine continuously analyses customer, product and transaction data to generate order-window predictions, churn and win-back predictions, next-best-product recommendations, dynamic pricing signals, first-order triggers and value/risk segmentation.
The best sales tip isn’t a report a rep has to go find — it’s a nudge inside the workflow they’re already in.
An app built for laptops, desktops and mobile devices puts these signals directly in front of sales teams — for example: ‘Customer X is likely to reorder Product Y in the next 10 days at price Z.’ Two dashboards sit alongside it: one gives regional presidents a view of performance against target, the other lets sales leaders assess team performance, surface top performers and spread best practice.
Underneath it all, an AWS-based data platform integrates data from SAP, Oracle and home-grown ERP systems, plus the available CRM systems, into a single unified layer where the predictive models are built and hosted. Pricing approval workflows were automated on top of this, and sales triggers were prioritised so reps spent their time on the highest-value interactions first.
From reactive selling to predictive, in every market.
The distributor’s operating model shifted from reactive selling to predictive, AI-driven commercial execution. The unified data platform and decision-intelligence layer gave the business a repeatable approach it could run across every market, dashboards let leadership close performance gaps between teams, and customer engagement and NPS rose alongside the revenue gains rather than at their expense.
* Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.
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*Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.