It doesn’t matter if you’re in the physical or ecommerce retail space; and it doesn’t matter what kind of products are in your inventory. If you’re a retailer, you need to be making optimized merchandising decisions. In the past, this process was simple – all you had to do was make the best guess based on recent performance. However, with the proliferation of retail analytics tools, there’s now more information at the disposal of retailers than ever before. Here’s how you can use retail analytics to make better merchandising decisions.
Know How Much Inventory You Will Need
Inventory level is probably the most basic issue when it comes to merchandising. Buying too much of a product can be disastrous if you can’t sell it. However, not having enough of a certain item in your inventory lead to missed opportunities, and could turn customers off your brand. This is where retail analytics comes into play. With a data program, retailers can get a much better idea of how many units they should purchase of a certain good. This is because data can hide many interesting insights that otherwise might be invisible to even the most experience retailer. For example, slight deviations in season, price, or overall market trends can radically swing the desirability of a product. It’s essential that retailers utilize all information available to them when making merchandising choices.
Utilizing Search-Driven Analytics
Search-driven analytics is one of the best tools to hit the retail analytics market. Without a doubt, this development will completely change how companies go about purchasing products. Search-driven analytics allows companies to generate highly specific data-driven insights in mere seconds. How is this possible? Unlike traditional forms of data analysis, which require dedicated analyst teams, search-driven analytics works much like a Google search bar — just for data instead of celebrity gossip. Users can input strings of key phrases, and the program will output a synthesized analysis based on the company’s structured data pool. There are two main reasons why this can be hugely advantages for merchandising decisions:
- Make Decisions Much Faster: Sometimes you simply can’t wait for your analysts to give you answers. Search-driven analytics provides immediate feedback. So, if you’re on a tight timetable, it’s still possible to make insightful, data-driven purchasing choices.
- Allow More Employees to Utilize Data: It might not seem like a particularly good idea to give too many employees access to sensitive company data. However, consider this: Your employees will know more about day-to-day decision-making issues than you or your data team. Giving them the power of instant insights can greatly improve your operations. Plus, you can control who has permissions to the search-driven analytics tool.
Keep Track of KPIs
Your company’s key performance indicators (KPIs) related to merchandising are crucial to getting the most out of retail analytics. These are a few of the most important KPIs to keep in mind when dealing with merchandising:
- Average Purchase Value: There are a few ways this metric can play a key role in your retail analytics. First, the average price of goods sold can show how much customers are willing to spend on a per-item basis at your establishment. This can help you better price other goods to get them to move faster. The total value of how much customers spend on each visit can also help you determine how to cross-sell related items.
- Sales Volume: Your rate of sales will help you determine if your goods are set at the right price point, or if you should raise or lower the price.
- Cost to Purchase Wholesale: You need to ensure that your margins are large enough to justify costs in other areas of your business. Keeping track of how much you’re spending per unit on wholesale purchases will help your business stay in the black.
There are many potential uses for retail analytics in the merchandising realm. Don’t be afraid to dive into this sphere, as your business will falter if you fail to stay competitive on the data front.