Suggested Order

Reimagining CPG Sales Execution

How Suggested Order from Lingaro Transforms Sales and Productivity with AI

In today’s dynamic CPG environment, sales teams are under increasing pressure to deliver more with less time, fewer resources, and tighter margins. Amidst these challenges, artificial intelligence (AI) is no longer a futuristic concept — it’s a practical enabler of smarter, faster, and more effective decision-making.

Suggested Order from Lingaro powered by Databricks Data Intelligence Platform, enables CPG sales representatives to apply the power of AI to a highly practical yet often under-emphasized aspect of their day-to-day workflows: preparing draft orders for customers. With the intelligent, machine learning-driven solution, they can generate optimized, data-backed draft orders before every conversation with a customer.

Our capabilities

Three key modules to help sales teams execute informed strategies to ensure that the products on each store's shelves align with consumer needs:

Value Delivered

Once outlet-level order optimization is in place, Suggested Order’s analytical foundation can unlock broader revenue growth management (RGM) opportunities. The data infrastructure, predictive models, and execution insights that power order recommendations can be applied to adjacent challenges that sales teams face daily:

In-store Execution
Guidance

Beyond what to order, the system can recommend specific ideas to improve category visibility — such as optimal shelf placement, promotional display setup, or merchandising adjustments — that sales reps can share during store visits.

Optimizing Outlet
Visit Frequency

Not all accounts should receive equal attention. The system analyzes outlet potential, order patterns, and growth trajectory to recommend customized visit schedules that allocate rep time where it generates the most value.

Optimizing
Sales Routes

When order recommendations are integrated with territory planning, reps can prioritize visits to accounts with the highest-value opportunities or those at risk of stockouts, turning route planning from a logistics exercise into a strategic revenue tool.

Coverage Expansion
Decisions

The same models that optimize orders for existing customers can identify high-potential outlets that are not yet engaged. By analyzing demographic patterns, competitive dynamics, and consumption trends, sales teams can target new accounts most likely to generate strong returns.

Case studies

Multinational Bottling Company

Challenge:

Apart from the need to predict and optimize outlets sales performance, the client needed to estimate sales potential for each outlet per product category while improving strategic planning, inventory management and resource allocation.

Solution:

This module was part of a larger solution and made predictions of store potential and categorized outlets by product group by assigning a score – gold, silver and bronze.
Analyses were performed using multiple data sources including information on location, demographic and sales data.
Models were trained with approaches including producing metrics, saving to MLFlow model registry.
Scoring model was based on predictions, running postprocessing per model and writing results.

Value Delivered:

More granular and data-driven view of sales potential and sales gap at the outlet and product category level.
Ability to estimate potential for new outlets and regions.
Increased efficiency of stock and production management.
Better allocation of resources

Leading multinational beverage corporation

Challenge:

The company was looking to optimize retail channel sales and marketing activities down to the outlet level using the locally tested optimization tool. They wanted to convert a Proof of Concept (PoC) for ML models into a full production-grade solution, and implement the solution in 28 markets quickly and cost-effectively.

Solution:

Lingaro helped transform the PoC of the retail channel optimization tool into a full-fledged product used in applications at scale.
Data and feature engineering based on multiple data sets and building a consolidated data model.
Accelerated setup and standardized approach for model consumption.

Value Delivered:

Solution cost optimization by performance and tuning – 50% cost reduction on Databricks platform.
40% reduction in processing time and increased efficiency at scale.
• A scalable solution implemented across 28 markets.