How to Increase Sales Discussion Time for Retail Sales Associates at the Convenience Store?

Sales associates have to work against the clock to maintain inventory records, visit retail stores, and generate orders. They are burdened with analyzing increasingly complex customer preferences and expectations. The primary aim, however, is to correctly understand upcoming trends to guide retailers towards what they should stock. Consider two sales agents—Bob and Linda—who work for different beverage distributors. Bob works for Mountain Beer Distributors, whereas Linda works for Fiona Beverages, which distributes soft drinks to convenience stores across the city. On one of their usual work days, they walk into a convenience store to take stock of their respective products. 

Sales Associates at a retail convenience store meeting the store manager

What Linda and Bob are doing is taking stock of the products to estimate the quantity of products to be stocked for the next week. There are several methods that sales agents use to do inventory at the store level. More traditional methods include manual counting, spreadsheets, customer surveys, and statistical methods. Advancing technology, however, allows sales associates to use applications and tools to make the process much easier. 

When sales associates like Bob and Linda are faced with the dilemma of either overstocking or understocking, it is not easy for them to reach a middle ground. Diverging too much from the middle can lead to lost sales or loss of revenue. Finding the perfect balance has a far-reaching impact on supply chain planning, inventory holding cost, and profitability. Efficiently planning the supply and demand chain is the backbone of any retail business, and it begins and ends with demand forecasting or predictive order management. The more accurate the forecast and plan are, the higher the chances of customer satisfaction and growth. 

Demand forecasting is one of the most critical tasks for retailers, as various operational decisions depend on it. It is aptly said that “Retail is detail in large scale”. Since the supply and demand for products are not uniform throughout the year and change with season and time of the year, forecasting daily demand for different product categories at the store level becomes a challenge. Whether they are omnichannel retailers or small brick-and-mortar stores, these forecasts act as an input while making production and ordering decisions. To ensure smooth operations, retailers must stay on top of goods flow for each store location on any given day. 

Bob, accustomed to his way of manual stock-taking and unsure if modern technologies suit his purpose, enters values against each product onto his notepad to be later entered into a spreadsheet. 

Sales associate counting SKUs at the convenience store to place orders

The traditional approach not only makes it time-consuming to manage inventory but also difficult for Bob to glean insights from the data. 

Also Read: Advanced AI In ERP Systems: The Future Of Beer & Beverage Retail

The Challenges of Demand Forecasting

There is no uniform solution when it comes to predicting customer demand. However, some methods and principles are often used to anticipate product demand. The choice of technique is also complicated, as each situation might require a different method. Different factors favor one method over another, depending on the order management situation. Under stable conditions, predicting demand can be quite simple, however, the retail market is rarely stable. With retail being inherently dynamic, there are several variables that sales representatives and demand planners struggle to consider, which include – 

  • Constantly changing baseline demands depending on the day and seasonal/occasional variations
  • Internal decisions affecting promotional methods, price adjustments, or display
  • External factors such as changes in demographics near the consumer store, local events, festivals, competition, or even the weather

It becomes humanly impossible to consider the full range of potential factors. However, artificial intelligence and machine learning tools make it possible to consider every imaginable factor at a detailed level. This is why several retailers have now transitioned to AI/ML-powered demand forecasting solutions. Including Linda who has been using a smart application that enables her to place upcoming orders in just a few minutes. 

Sales associates counting SKUs at the convenience store to place orders

OrderSmartTM – AI-powered Predictive Order Management Solution by Quantiphi

Though Linda and Bob share similar workloads, it took considerably longer for Bob to finish his task.  The secret to Linda’s speed – OrderSmartTM

Sales associate counting SKUs at the convenience store to place orders with OrderSmart

Quantiphi’s proprietary predictive order management solution, OrderSmartTM, is a future-ready, AI-powered solution, engineered specifically to help retailers generate highly accurate forecasts of future demand for their products. With evolving customer demands, OrderSmartTM enables retailers to position the right amount of products at each location. 

OrderSmart - AI/ML powered Predictive Order Management Solution for the convenience retail industry

OrdersmartTM is the product of a partnership with CONA designed for the CPG and retail industry. It has a proven track record of transforming the sales order general process at thousands of convenience stores. With years of learnings in various Coca-Cola Bottlers environments, OrderSmartTM has made accurate one-click-order-generation a reality. It can be smoothly deployed in the client’s existing technological ecosystem and is highly customizable to suit various store, SKU, and category combinations. OrderSmartTM produces tangible results when it comes to sales order generation –

  • There is a significant reduction of dependency on sales representatives and manual processes, significantly reducing the number of sales visits to convenience stores.
  • OrderSmartTM also reduces operational expenses annually by decreasing the redundant sales visits to convenience stores, therefore, increasing savings in operational expenses.
  • Apart from sales representatives, contractual labor dependency around public holidays, peak sale periods, and time-offs is also reduced.
  • The most significant advantage, however, is the tremendous reduction in order generation time, which can be diverted to more productive or value-adding services.  

Executing a successful demand forecasting strategy is highly challenging. Retailers can no longer afford to plan inventory management using traditional methods. By leveraging modern predictive order management systems, retailers can gain a competitive advantage and meet future customer expectations with ease. Quantiphi’s partnership with CONA Services LLC has delivered an innovative AI-enabled predictive order management solution to bottlers and distributors worldwide. If you are interested in making demand forecasting an integral part of your decision-making process, reach out to our experts.

Written byKraig Alexander

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