Predictive AI Boosts Cross‑Sell 38% and Cuts Churn 4% for Chemical Distributor

Context

A confidential chemical and ingredients distributor operating in traditional, immature commercial practices sought to drive revenue, retention, and sales effectiveness. The business needed to move from reactive selling toward predictive, insight-led commercial execution: identifying the right product, at the right time, at the right price to enable proactive engagement, boost customer lifetime value (CLV), and reduce churn across multiple countries.

Challenges

Commercial teams worked in a highly volatile environment marked by inconsistent and unpredictable order patterns and limited visibility into reorder timing. Sales engagement was predominantly reactive, causing missed revenue opportunities, customer down-trading, and churn. There was no single application where sales intelligence could be embedded into daily workflows. Data was fragmented across ERPs, legacy home-grown systems, and disparate CRMs, and this inconsistency needed to be scaled safely across multiple countries. Poor prioritisation and lack of timely signals left sales teams unable to focus on high-value opportunities or proactively retain at-risk accounts.

Implementation

A cross-functional project team and the Fractional Head of AI designed and deployed a scalable decision intelligence platform that combined machine learning with commercial workflows to generate real-time recommendations. An AWS-based data platform integrated transactional and master data from ERP systems (SAP, Oracle, and home-grown) and available CRM sources into a unified data layer where predictive models were developed and hosted.

The predictive engine continuously analysed customer, product, and transaction histories to generate signals including Order Window Prediction, Churn & Win‑back Prediction, Next Best Product, Dynamic Pricing Signals, First Order Triggers, and Value/Risk Segmentation. These signals were embedded directly into an app built for laptops, desktops, and mobile devices to act as the sales portal. The application presented intuitive interfaces and integrated recommendations within sales workflows, delivering timely, actionable prompts such as: “Customer X is likely to reorder Product Y in the next 10 days at price Z.”

Two dashboard sets were delivered: one for regional presidents to measure overall success and identify performance gaps versus targets, and a second for sales leaders to assess team-level performance, elevate top performers, and share best-practice actions. Pricing approval workflows were automated to accelerate decision-making, while prioritised sales triggers focused enablement on high-value interactions.

Results

The implementation delivered measurable commercial impact and an operating model shift from reactive selling to predictive, AI-driven commercial execution. Key outcomes included:
– Revenue growth with a 38% uplift in cross-sell and up-sell conversion as sales teams executed timely, personalised offers.
– Improved pricing precision and margin capture, yielding pricing outcomes that were on average 33% more profitable.
– Global churn reduction of 4% through early detection of at-risk accounts and targeted win-back actions.
– Efficiency gains of 35% via automated price approvals and prioritised triggers, freeing sales capacity for higher-value conversations.
– Higher quality and value of sales interactions driven by actionable signals and prioritisation, which contributed to increased customer engagement and improved Net Promoter Score (NPS).
– Demonstrated ability to prevent churn and recover lost accounts by surfacing win-back opportunities and next-best-product recommendations at the point of contact.
– Enhanced sales leadership capability through dashboards that identified performance gaps, highlighted top performers, and enabled knowledge transfer to lift weaker teams.

Sales teams reported significantly improved prioritisation; customer interactions became more data-driven and impactful. The unified data layer and decision intelligence platform provided a repeatable, scalable approach across multiple markets, enabling the distributor to sustain predictive commercial performance and continuously refine models and workflows as the business evolves.