Sales data analysis can be a goldmine for your business, helping you control your inventory more effectively, understand demand better, and become more aware of your customers’ purchasing behavior. But sales data analysis is not without its pitfalls, especially if you’re just getting started with it. In fact, it’s quite easy to misinterpret sales data, which in turn can lead to costly decisions you may come to regret later.
In an article published earlier this year, Jeff Lackey of Salesvue highlighted three critical sales data mistakes that businesses should avoid.
Using a Small Sample
The first of these mistakes is using a statistical set that is simply too small to bring you accurate results. Small businesses can be especially prone to making this mistake, which can also have negative repercussions on a sales forecast and inventory control planning.
An example of this mistake would be to reach the conclusion that if 25 of your total weekly customers (which amount to say 50) have bought products Saturday, then half of all your sales occur Saturday. That is of course not true, and Jeff cited an important study that shows that the smaller your sample size is, the greater the margin of error.
Using Biased Samples
Before taking your sales data analysis results for granted, it’s important to consider the potential factors that may influence it. This is the second critical mistake of sales data analysis — not taking into account relevant data that can affect the outcome. One safeguard against this is to import old statistical data into your new sales data analysis tool. Old data still has value, although that value may not be obvious right away. Such data can help you better understand who your customers are.
Neglecting Variables
As Jeff observed, businesses have to account for variables when doing sales data analysis. The same is true for a sales forecast. Variables can include the geographic location of customers or shopping habits before the holiday season. Your sales analysis has to be localized and focused, so that as many variables as possible are eliminated.
Eliminating sales data analysis variables, as well as avoiding small sample or biased sample issues becomes a lot easier with a solution such as DataQlick, a sales and inventory software for small business which comes with a dedicated sales analysis component enhanced with charts and graphs. With DataQlick you can view profit margins for individual products, see who has ordered what, compare sale performance during custom intervals, and more. Learn more about DataQlick.
