Sales data usually refers to the recorded purchase transaction of a product between a brand and a consumer. Yet the sales process is rarely straight forward. There are typically several businesses involved with getting products into the hands of consumers. Usually, distribution does not occur between a manufacturer and the end-users. There are intermediaries like wholesalers, distributors, and retailers connecting a manufacturer’s product to the target market. This logistical path makes analyzing sales data rather complex as different businesses have varying information. As someone in charge of analyzing sales data, it’s essential to know what kind of data you are examining and its implications on marketing trends. Below I will discuss different types of sales data and what they refer to.
Manufacturing data comes directly from the manufacturer itself and will be the most complete reporting you have access to. However, this will not reflect consumers’ trends as they usually sell to a distributor of some sort. Also, the company’s buying directly from manufacturers typically buy in large volumes to leverage quantity discounts. An example would be Samsung electronics selling stock directly to the major electronic distributor Synnex, which sells to partnering wholesalers.
Intermediary Sales Data
Intermediary sales data is collected through channels like wholesalers or distributors. It can become complicated as the information would be collected from different businesses based on how many intermediaries are involved in the logistical path. This data is affected by discounts, buy-ins, and internal inventory adjustments. However, it is the second to the best type of data for determining consumer trends. For example, Synnex sells to wholesalers like 5GStore, who then sells to like Best Buy.
Retail data is collected from retailers and is the best type of data for uncovering consumer trends. It’s also called the point of sales data and directly reflects purchases by consumers. With this data, market category information can be derived like price elasticity for products and consumer purchase motivators. When analyzing retail data, it’s important to note that it will only determine regional trends as it’s limited by geographic location. When wanting to get a sense of national trends pulling information from varying regions is necessary. An example of retail data would be Best Buy reporting to Samsung how any Galaxy S20 smartphone consumers purchased.
Knowing what type of information you are analyzing is essential to accurately represent the data and build credibility. Misunderstanding where the data comes from can cause business decisions to be made on misrepresentations and can be damaging to a company.
If you haven’t yet, read my last blog on Tableau Filters.