Predictive analytics & data science and advanced AI analytics

Predictive analytics & advanced analytics

Examples of projects:

Predicting out-of-stock events for a multinational beverage brand:
  • Built SKU-store-day-level models predicting stockouts 14 days in advance, integrating syndicated demand signals and warehouse dispatch data.
  • Used ensemble ML models (XGBoost + LightGBM) and embedded business rules around promotional uplift and seasonality.
  • Triggered alerts into the client’s BI tool for planners and sales teams, allowing preemptive replenishment.
  • Developed a hybrid model combining NLP topic modelling of previous launch performance with time-series decomposition.
  • Incorporated retailer-specific cannibalization rates, shelf space constraints, and substitution elasticity at UPC level.
  • Automatically updated forecasts weekly based on real-time sell-through and syndicated basket data.
  • Built classification models flagging at-risk loyalty customers using recency-frequency-monetary (RFM) scoring and segment-level decay curves.
  • Integrated lifestyle and shopper mission data to create predictive personas at 94% accuracy.
  • Deployed a scoring dashboard with tiered action plans for re-engagement based on churn drivers (price vs. variety vs. promo fatigue).

Value we have created

+15% forecast accuracy improvement

Compared to previous rolling average models, our ensemble launch forecast models delivered 15 percent more accurate predictions. This helped reduce overproduction and markdowns.

-22% reduction in stockouts

The beverage client reduced lost sales due to stockouts by 22%, saving an estimated $3.1M across 10 focus SKUs in 3 months.

+19% increased repeat rate in churn cohorts

Re-engagement of churn-prone loyalty customers resulted in a 19% uplift in repeat purchase rate over 60 days.

+6pts retailer satisfaction score

Improved demand signals and replenishment consistency boosted satisfaction from retail buyers, especially in mass and club channels.

2 hours saved per planner per week

Predictive insights auto-published in their dashboards reduced manual scenario work, freeing up analyst time for strategic initiatives.

Why our predictive analytics is scientifically better

1

Tailored to retail-SKU-channel hierarchies:

  • Our models are built to reflect how your business thinks — not just product → brand → category, but also shopper segments, channels, banners, pack roles, and velocity classes.
  • We dynamically switch model types based on SKU volume tiers and their promo sensitivity, using meta-learning to optimize.

2

Augmented with domain-encoded features:

  • We don’t just throw raw data at models — we embed domain knowledge through engineered features like price thresholds, event triggers, and competitive intensity indices.
  • Feature generation is aligned to category realities — such as seasonality curves for beverages vs. price elasticities in snacks.

3

Real-time refresh with business context:

  • Forecasts are not frozen — they refresh with every POS feed, WOS change, or planogram reset, while still preserving business sign-offs and overrides.
  • A/B tested forecasting pipelines ensure business teams remain in control, not just data scientists.

4

Explainable models with confidence bounds:

  • Our outputs are not black boxes. Every forecast comes with driver contribution graphs, confidence intervals, and causal validation options.
  • Business users can drill into “why this changed” using natural language outputs aligned to brand and retailer language.

5

Built for human-in-the-loop collaboration:

  • Designed to slot into your planning cadences, S&OP calendars, and promo planning tools — not disrupt them.
  • We co-develop insights with your teams, ensuring they trust the models and understand the assumptions behind each prediction

AI planning & optimization applications

Examples of projects

Promo planning optimization for a snack manufacturer

  • Built a multi-objective optimization engine that simulates promo depth, timing, and mechanics across banners and channels.
  • Optimizer considers incrementality, baseline cannibalization, retail account distribution efficiency constraints, and promo execution lag.
  • Results are packaged into decision scenarios and auto-integrated into JBP negotiation playbooks.

Store clustering engine for route-to-market planning

  • Designed a clustering engine using K-Prototypes to segment 18,000 stores based on POS, demographic, and visit patterns.
  • Integrated outputs into sales territory design, helping reroute field teams and direct-store-delivery schedules.
  • Created "store DNA" profiles used to inform assortment, promo, and merchandizing strategy by region.

Pricing optimization platform for a beauty brand

  • Developed an elasticities-based price optimizer for D2C and retailer-distributed channels using Bayesian hierarchical modeling.
  • Model incorporates price ladders, shopper thresholds, and competitive reaction curves.
  • Deployed UI that allows brand and channel managers to simulate outcomes of any price action and export into business cases.

Value we have created

+12% ROI on promotional spend

The snack client reallocated 40% of its trade budget based on our optimizer, leading to a 12% greater ROI in its annual plan.

Clustering and route redesign cut down on in-person sales rep travel, freeing up 1.4 FTEs and reducing cost-to-serve.

Pricing engine helped the beauty client raise prices with minimal volume drop, boosting gross margin in high-end SKUs.

Our simulation models improved the reliability of predicted uplift vs. actuals during annual retailer negotiations.

Commercial teams now simulate 100s of scenarios on the fly, saving days of manual Excel work during planning season.

Why our AI optimization is scientifically better

Multi-level planning logic:

  • Our platforms don’t treat planning as linear — they integrate top-down goals (e.g., revenue) with bottom-up executional constraints (e.g., DC fill rates).
  • We use mixed-integer programming and custom solvers to reflect your exact execution logic, down to the last DC gate.

Constraints-aware modeling:

  • We go beyond theory — our models ingest your actual constraints: trade spend caps, ship windows, field coverage, and DC load balancing.
  • These constraints are not add-ons; they’re baked into the core logic of how plans are optimized.

Designed for brand-retailer collaboration :

  • Outputs are structured in the language of retailers: uplift estimates, margin impact, and execution calendars.
  • Planning UI is built to support co-creation with buyers and instantly export scenarios into pitch decks.

Elasticity precision at segment level:

  • Price and promo elasticities are not generic — they are dynamically calculated by sub-brand, pack, banner, and shopper mission.
  • We use Bayesian hierarchical modelling to balance robustness in sparse data environments while capturing micro-level nuance.

Built for the nuances of FMCG:

  • Whether you’re in beverages, beauty, snacks, or OTC — we capture the difference in replenishment cycles, promo responsiveness, and price ladders.
  • Our AI planning adapts to your velocity tiers, lifecycle stages, and even historical compliance rates across banners.

Get in touch

Our friendly and efficient team is here to discuss your ideas. No pressure, just solutions.

Contact us

What happens next?

  • Our team will reach out to you to schedule a 'no pressure' call to help understand your objectives
  • We'll provide relevant demos and examples. You then confirm to us that you have a formal mandate to purchase.
  • We will provide the best-in-class proposition, tailored to all your nuances.
Revenue under management
$ 0 B+
Locations globally
0
Capital deployed
$ 0 m+
5 year RFP win rate
0 %

Send us a message

Do not delete
0
Edit Template