Sales representatives manage a diverse portfolio of accounts, each with unique needs and sales dynamics. Determining the right product mix and order recommendations for every customer is a complex challenge. Traditional practices — such as simply recommending replenishment of what sold last time — fail to capture the full picture.
Factors like seasonality, promotional calendars, shelf space constraints, competitor activities, and evolving consumption patterns should all influence the next recommendation. Historically, sales teams have relied on experience, intuition, and rules of thumb to make these decisions. Today, data and AI make it possible to move from everyday guesswork to everyday precision with evidence-based recommendations integrated seamlessly into workflows.
The Cost of Sales Order Lists That Miss the Mark
When order recommendations don’t align with market realities, the consequences compound quickly across the CPG value chain.
On one side there are the opportunity costs of out-of-stock situations. Consumers frequently opt for a competitor's product when their preferred item isn't available. Every empty shelf is lost revenue. Despite improvements to supply chains, out-of-stock rates for fast-moving consumer goods still hover between 5% and 10%. CPG companies that reduce these rates to below 1% can increase sales by 2% to 4% (PwC, 2024).
On the other side, overstocking hits the balance sheet directly. Excess inventory ties up working capital, increases the risk of obsolescence, and often leads to costly markdowns or waste. For products with limited shelf life, the pressure intensifies.
And in the middle are strained retailer relationships. When a CPG sales rep consistently suggests assortments that don't match store-level demand, retailers lose confidence. That erosion of trust makes future negotiations harder and puts shelf space at risk.
Why Sales Reps Struggle with Order Preparation
The key challenge isn't a lack of expertise. It's the complexity of the decision combined with how reps actually spend their time.
According to Salesforce, sales reps spend just 28% of their time selling. The remaining 72% goes to administrative tasks, travel between accounts, data entry, and other activities that don't directly generate revenue. Within that limited selling time, they need to build relationships, handle objections, and do everything else necessary to close deals.
Among all these workflows is the process of preparing draft orders, but it often becomes a "check the box" exercise. There simply isn't enough time to manually analyze consumption data, review promotional calendars, assess inventory levels, and factor in dozens of other variables for every single account.
From Regional Forecasts to Outlet-Level Intelligence
Your organization likely already holds the data required to generate highly accurate order recommendations. Most CPG companies possess extensive insights into customer-specific sales histories, purchase behaviors, promotional calendars, pricing trends, and more. What’s often missing is a seamless way for sales teams to access and use this data in the field for fast account-level analysis and decision-making.
Advances in AI and machine learning are bridging this gap. Unlike traditional demand forecasting tools designed for central planning, modern AI-powered systems are built for field execution. They go beyond predicting “how much will sell in a region or channel’’ to address the practical realities of sales execution: who is selling, to which outlet, and how.
Suggested Order from Lingaro: Addressing Variables Traditional Demand Forecasting Systems Miss
Suggested Order is a solution from Lingaro enabling CPG companies to make accurate outlet-level assortment suggestions. Its three key modules help sales teams execute informed strategies to ensure that the products on each store's shelves align with consumer needs:
- Micro-Segmentation: This multi-dimensional approach provides insights applicable across various scenarios, enabling highly customized decision-making and strategic planning at the outlet level.
- Suggested Assortment: Rather than merely optimizing inventory, this module ensures that assortments reflect consumer needs for each outlet and identifies cross-sell opportunities. Machine learning-powered predictive models guide these recommendations.
- Suggested Quantity: This module maximizes sales by identifying upsell opportunities for SKU quantities while minimizing the risks of both out-of-stock situations and overstocking.
The three modules of Suggested Order work together to optimize outlet-level recommendations.
Rather than forcing reps to find time they don't have for manual analysis, Suggested Order processes vast amounts of information and automatically surfaces recommendations. It excels at addressing the operational nuances and messy realities of sales that traditional demand forecasting tools don’t, in particular:
- Sales execution capabilities: Different reps have varying strengths, relationships, and track records. What one rep can successfully execute may not work for another. The system incorporates these operational factors to ensure recommendations are practical and actionable from a sales perspective, going beyond pure predictive accuracy.
- Sales visit frequency: Some accounts receive weekly visits while others are monthly or quarterly. The system accommodates this operational reality, adjusting recommendations based on the time between interactions while maintaining the data aggregation required for smooth, scalable processing.
- Sales rep rotation: When different reps handle the same accounts over time, outlet dynamics change. The maturity of relationships, buyer preferences, and negotiation history — all influence what recommendations will be accepted and successful. The system uses validation techniques and feedback loops to continuously refine suggestions as these dynamics shift.
- Outcomes from past recommendations: When certain product combinations result in higher or lower sales, the system identifies those patterns. While measuring adherence to recommendations presents challenges, it creates opportunities to implement feedback loops that enable gradual improvement over time. Recommendations become more accurate and tailored to each account's operational reality.
Scaling the Impact: From Suggested Orders to Smarter Sales Execution
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 not yet engaged. By analyzing demographic patterns, competitive dynamics, and consumption trends, sales teams can target new accounts most likely to generate strong returns.
Making Intelligent Order Optimization Work with Lingaro
Technology is only part of the equation. Getting meaningful results from intelligent order optimization depends on several foundational elements — and how well your implementation partner helps you navigate them:
- Clean data. Your sales data, inventory information, and account details need to be accurate and consistently structured before any AI model can generate reliable recommendations. “Garbage in, garbage out” remains true regardless of algorithmic sophistication. Lingaro's implementation process begins with analyzing your current data landscape, identifying gaps or inconsistencies, and establishing the data infrastructure required for accurate modeling. We work through interactive analysis to understand your specific data challenges before building solutions.
- Business rule integration. AI recommendations must respect your operational reality: minimum order quantities, promotional timing windows, account-specific agreements, and strategic priorities. These constraints vary by customer type, region, and product category. We facilitate collaboration between your sales leadership, category management, and data science functions to ensure recommendations align with how your business actually operates, not just what the data suggests in isolation.
- User adoption. Even the most sophisticated system creates zero value if your reps don't use it. That means recommendations need to be easy to access in the field, simple to understand at a glance, and practical to act on during customer conversations. Reps also need visibility into the reasoning behind suggestions, not black-box outputs they're expected to trust blindly. Our software engineering approach prioritizes user experience alongside analytical rigor, delivering optimized solutions with clean interfaces that fit into existing workflows.
- Governance framework. Your organization needs clear guidelines: When should reps follow AI recommendations? When should they override based on account-specific knowledge? How do you handle edge cases? We help you establish decision frameworks and regular review processes that examine when overrides were justified and when the AI was right. These feedback loops drive continuous improvement and build trust in the system over time.
Throughout implementation, we align our work with your strategic objectives. That means confirming scope and expected outcomes upfront, then applying robust data science methodology alongside high-quality software engineering practices. The goal is to tailor Suggested Order to your specific commercial context, not a one-size-fits-all deployment.
Your Next Step
Gartner research projects that by 2027, 95% of seller research workflows will begin with AI, up from less than 20% in 2024. The shift from manual analysis to AI-augmented decision-making is accelerating across sales functions.
For CPG field sales, AI-powered sales orders represent a practical, high-impact starting point. The question "What should this customer order?" gets asked thousands of times every week. Answering it better, faster, and more consistently creates measurable value.
If you're ready to explore how Suggested Order can work within your sales operations, we'd welcome a conversation about your specific context and goals.
