BI in the Retail Industry: 3 Important Considerations
With better selections, conveniences and experiences than ever, the retail industry as a whole is thriving. Per Statista, global retail revenue is forecasted to reach $24.86 by the end of 2018.
Yet, despite the collective boom in consumer buying, it’s never been more competitive to be a retailer. New brands enter unique sub-niches each day, and countless more become their competition.
This is why business intelligence (BI) in the retail industry is needed more than ever. Let’s examine five important considerations for retailers, and how a smart BI retail intelligence platform clarifies them.
Owning an online business inevitably carries both the benefit and burden of selling to multiple geographies. Successful brick and mortars with various locations know a thing or two about this challenge.
Staying abreast of various geolocation stats help satisfy customers, move products faster and minimize operating costs. This is one area where ThoughtSpot’s BI retail management analytics platform really shines. Merely search for data on specific geos and get detailed answers that show the source and communicates the finding via maps, graphs, tables or charts. A brick-and-mortar might want to know: “January orders by store,” whereas an e-retailer might be interested in learning their “return rates by shipping location.”
Average Order Value
A lot of factors contribute to a retail business’ bottom line. But when it comes to increasing operating margin and profits, improving average order value (AOV) can get you there—without having to slash operational budgets.
Using ThoughtSpot’s retail analytics platform, a retailer can not only easily determine their average order value, but also dig into a variety of metrics that affect your AOV. For instance, simultaneously looking at your revenue per visitor is a good way to delineate who’s spending money, on what, and from where. You might find that only some segments of your customer base are underperforming and that tailoring your efforts to increase their purchasing dramatically boosts your AOV.
Whether you’re paying for real square footage or a warehouse to store your e-commerce products (or both), sell-through rate helps determine the rate at which products are moved over a given time period.
Let’s say you sell apparel. Starting in September, you received your fall and winter product lines. Your sell-through time period is from September to October. If you received 100 jackets of a specific type, and only had ten of them left at the end of October, your sell-through rate would be an impressive 90 percent, much higher than what most retailers average. This of course is just simple math. The hard part is accessing data in real-time to find the answers, especially through insights aided by data visualizations.
ThoughtSpot lets you do this and more to keep your retail business on track and always looking ahead.
Witness ThoughtSpot’s retail data analytics tool in action when you watch our retail demo today!
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