How CPG Brands Can Harness the Power of POS Data

For Greater Product Performance Visibility
and Improved Sales & Demand Planning

Consumer Packaged Goods (CPG) manufacturers operate in an increasingly competitive environment, where the ability to access and analyze timely, accurate data can make or break a company’s success. Among the various data sources at their disposal, point-of-sale (POS) data stands out as a crucial one.

POS data provides valuable insights that can lead to increased sales, better customer satisfaction, and sustainable market growth in the fast-paced retail and e-commerce sectors.

Best Practices for Gleaning Insights from POS Data

The value of POS data lies not just in its collection, but in how manufacturers turn that data into actionable insights. To truly harness the power of POS data, CPG manufacturers must implement systems and processes that translate data into strategic improvements in sales, marketing, and operations.

Gaining Access to the Right Data

The first step is to gain access to the data. Retailers typically make this data available through proprietary portals, which may vary in terms of data granularity and update frequency. Manufacturers must ensure that they have access to data that is detailed enough to provide meaningful insights but not so granular that it becomes overwhelming to analyze. Once the data is obtained, it must be exported and consolidated. Depending on the retailer, this can involve manually downloading files or using automated tools to pull the data into a centralized system.

Normalizing and Harmonizing the Data

After collecting POS data from various retailers, manufacturers must clean and standardize the data to make it usable for analysis. This process is known as data normalization and harmonization. Each retailer organizes their data differently. They may sell a manufacturer’s product using different naming conventions.  They may include varying levels of detail in their POS reports.  Their report formats may be inconsistent. And if they have store closures, their manufacturing partners face the daunting task of realigning locations within the POS data.

Normalizing data ensures that manufacturers can compare product performance across retailers accurately. This process, however, is labor-intensive and can require significant technical expertise. For some companies, it may be necessary to hire data scientists or invest in specialized software to manage this process effectively.

A supplementary approach is to use a data hub-driven platform like Silvon’s Stratum solution, which consolidates data from multiple sources and can be used to resolve issues related to duplicates, formatting inconsistencies, and missing information. The end result is an integrated, normalized and harmonized hub of POS and other operational information that can be leveraged by CPG brands to drive key performance insights – from high-level trends down to store-level details.

Driving Greater Insights to Product Performance

Once POS data has been collected and normalized, the next step is to conduct analyses that support business goals. Some of the most common real-world applications of POS data analytics include:

• Sales Analysis to identify trends in customer behavior, assess the effectiveness of pricing strategies, and evaluate the success of marketing campaigns.

• Inventory Optimization based on actual sell through to streamline inventory management processes, reduce stockouts, minimize excess inventory, and improve overall supply chain efficiency.

• Pricing Strategy Optimization to understand product performance at various price points and subsequently adjust wholesale pricing based on demand elasticity.

• Distribution Growth and Retention to monitor the growth of product distribution across different retailers and retail locations to better address performance issues, product reorders, promotional strategies, even retailer contracts themselves.

• Product Rationalization that showcases performance to retail partners based on sales trends, customer preferences and inventory management to ultimately secure additional orders, better product placement, and in-store product line expansion.

• Chargeback and Deduction Management to better track and understand the root causes of deductions and identify areas where costs can be reduced, trade spend efficiency improved, and profit margins protected.

• Trade Promotion Evaluation to track how customers respond to discounts, coupons, and other sales incentives and fine-tune promotional strategies over time more effectively.

• Product Innovation to determine how existing products are performing and to identify gaps in the market – better enabling CPG brands to develop new products that meet emerging consumer needs.

Using POS Data for Improved Sales &

Demand Planning

By leveraging POS data, companies can additionally (and accurately) forecast future sales, which is crucial for demand planning. This process ensures that sales and operational teams can align their strategies, anticipate consumer demand, and avoid overstocking or understocking situations. More importantly, POS data-based demand planning enables manufacturers to project future growth with greater precision, helping them meet market demands efficiently.

POS-driven demand planning has been highlighted by many researchers as a supply chain best practice because of the revenue gains it facilitates. These gains stem from improved demand visibility, higher perfect order rates, reduced inventory levels, and faster cash-to-cash cycles. The ability to anticipate demand more accurately enables manufacturers to reduce inefficiencies across their supply chains, thereby improving overall business performance.

• Improved Forecast Accuracy

Since POS data reflects real consumer purchases, forecasts based on this data are more accurate. This accuracy is especially important for new product launches, where early sales data can indicate future demand patterns. For example, one CPG manufacturer using Silvon’s Stratum solution monitored daily POS data to spot early demand trends for newly launched products. By focusing on these initial sales trends, the company was able to forecast future demand with a high degree of accuracy. This granular forecasting also allowed the company to avoid overstocking or understocking and ensure that the right products were available in the right retail locations during critical sales periods.

• Responsive Forecasts to Demand Shifts

When leveraged for sales and demand planning, POS data allows companies to create highly responsive forecasts that take fluctuations in demand driven by promotions, discounts, or seasonal changes into account. Because POS data is collected at the store level and in real time, companies can adjust their forecasts quickly, ensuring that they meet demand surges without overwhelming the supply chain.

Traditional forecasting methods, like those based on shipment or order history, often miss real-time market dynamics. For instance, shipment data reflects orders sent to stores rather than actual consumer demand. This lag creates inherent biases in the forecast, whereas POS data provides a more accurate and timely demand signal. POS-based forecasting reduces reliance on lagging indicators and ensures that CPG manufacturers can respond proactively to shifting consumer preferences.

• Localized Demand Patterns and Demographics

POS data allows CPG manufacturers to factor in local demand patterns more effectively. Consumer preferences often vary significantly from region to region, and even store to store. By analyzing localized POS data, manufacturers can develop forecasts that reflect these differences more precisely, ensuring that each location gets the right amount of inventory based on real demand patterns.

This level of granular insight is particularly useful for adjusting strategies in markets with diverse customer demographics. For instance, if a particular product sells well in urban areas but underperforms in rural regions, POS data enables CPGs to tailor their distribution and marketing efforts accordingly.

• Reduced Latency and Eliminated Biases in Other Demand Streams

Traditional forecasting methods, such as relying on order or shipment data, introduce latency into the demand planning process. The time it takes for products to be ordered, shipped, and stocked in stores doesn’t always reflect actual consumer behavior. In addition, shipment data can obscure the real picture because it’s affected by out-of-stocks, bulk orders, and retailer-driven buying tactics.

POS data eliminates these issues because it captures demand at the point where it matters most: when the consumer buys the product. This direct visibility into consumer demand allows companies to make more timely and accurate forecasts, minimizing the negative effects of latency and biases that can distort other demand signals.

• Rich Sales and Marketing Insights

Because POS data provides a clear view of what’s happening on the retail shelf, it’s an invaluable resource for evaluating the effectiveness of sales and marketing initiatives. Manufacturers can analyze how promotions, discounts, and marketing campaigns impact product performance and then make adjustments in real time. POS data also reveals which products resonate most with consumers, allowing companies to refine their product offerings and better meet market needs.

For example, if a particular promotion leads to a spike in sales for a specific SKU, manufacturers can use this insight to plan similar promotions for other products or regions. By continuously analyzing POS data, sales and marketing teams can stay agile, optimizing their strategies based on consumer response.

• Enhanced Collaboration with Retailers

POS-driven forecasting also improves collaboration between CPG manufacturers and their retail partners. When both parties share POS data and forecasts, they can work together more effectively to optimize inventory levels, promotions, and product placements. This level of collaboration is particularly valuable for managing high-demand periods or new product introductions, where accurate forecasts can prevent stockouts and maximize sales opportunities.

By sharing POS data with their retail partners, manufacturers can also improve trust and transparency, strengthening their relationships and positioning themselves as strategic partners rather than just suppliers.

• Aggregated Forecasting for Broader Insights

While POS data can be analyzed at the store level for highly localized insights, it can also be aggregated at regional or global levels to generate broader forecasts. This level of aggregation allows manufacturers to streamline their demand planning processes while still maintaining the accuracy provided by POS data.

For instance, another customer of Silvon’s Stratum solution aggregated weekly POS data at the retailer distribution center (DC) level to create more manageable forecasts. Even at this level of aggregation, the company achieved excellent in-stock performance, shipped orders on time and in full, and increased sales by aligning supply with true market demand.

The Evolving Retail Landscape: Adapting Strategies with POS Data

The retail landscape is constantly evolving, driven by changes in consumer behavior, technological advancements, and market dynamics. CPG manufacturers must be adaptable in their sales, marketing, and supply chain strategies to stay competitive in this environment. Whether it’s improving forecast accuracy, evaluating promotion effectiveness, or tailoring products to local consumer preferences, POS data gives CPG companies a competitive edge in a rapidly evolving market.