Industries / 07
Retail & Consumer Intelligence
Forecast every SKU, store, and day.
Demand sensing, segmentation, pricing, and omnichannel visibility for retail and consumer brands.
Overview
How we approach it.
Retail reporting mostly looks in the rearview mirror, while the decisions that matter, what to stock, whom to market to, and what to charge, are made today. Customer data is split across stores, e-commerce, loyalty, and marketing, so campaigns go out to broad segments and are built by hand, and prices and promotions get set on gut feel.
We connect point-of-sale, e-commerce, loyalty, and marketing data into one customer view, then build forecasting at the SKU, store, and day level, segmentation, and price elasticity models on top of it. Recommendation engines and campaign orchestration react to each customer's behavior in real time. For one retail organization, a platform processing millions of customer interactions a day led to measurable lifts in average order value and repeat purchase rates.
Systems we connect
Where your data lives today.
The problem
What gets in the way.
Reporting that only looks in the rearview mirror
Broad segments and manual campaigns
Prices and promotions set on gut feel
What we build
What changes.
- 01
Demand forecasting at the SKU, store, and day level
- 02
Segmentation, lifetime value, and churn scoring
- 03
Price elasticity and promotion ROI
- 04
Omnichannel performance dashboards
- 05
Vendor scorecards and supply chain alerts
In practice
One we built.
Related services
How we deliver it.
FAQ
Questions buyers ask.
01How do retailers forecast demand at the SKU and store level?
Retailers forecast at the SKU, store, and day level by training demand models on point-of-sale, e-commerce, inventory, and fulfillment data connected into one platform. Level60 builds the data layer and the forecasts together, alongside vendor scorecards and supply chain alerts.
02How can a retailer build a single customer view across stores and online?
A retailer builds a single customer view by connecting point-of-sale, e-commerce, loyalty, and marketing data into one customer intelligence platform. For one retail organization, that view supported recommendation engines, dynamic pricing models, and real-time campaign orchestration across millions of customer interactions a day. Customer lifetime value improved in stores and online.
03How do you move from manual campaigns to personalization?
You replace broad segments and batch campaigns with models and orchestration that react to each customer's behavior as it happens. Segmentation, lifetime value, and churn scoring identify who to reach, and recommendation engines work from the same customer view. For one retail organization, this meant event-triggered engagement and measurable lifts in average order value and repeat purchase rates.
Bring us the hard problem.
Other industries