How to Use Data to Stop the Inventory Guessing Game

One of the worst feelings a retailer can have is when a customer is ready to buy a product, but you don't have it in stock. Yes, they can be directed to a sister store or check online, but today's shoppers can easily take their dollars elsewhere.

Another real problem is the overbuying of product, leading to cluttered sales racks that don’t generate much revenue. IHL Group estimates that both sides of this “inventory distortion” problem add up to over $1.1 trillion in lost revenue opportunities each year, roughly the size of Australia’s entire economy!

In-store retail technology has seen some exciting additions in the last few years — interactive dressing room mirrors, beacons and mobile notifications all promise to provide a better shopping experience for customers and generate sales for retailers. But to get back to the basics, there’s a bigger, more actionable problem in front of retailers, and it’s hiding in their back rooms.

Using enticing window displays and hot sales may be the best way to attract customers to enter your store or convince them to make a purchase. However, if you don’t have the product in stock when the customer is ready to buy, those marketing efforts become meaningless.

So, how do you make sure key products remain in stock? Predictive analytics. Predictive analytic technology has become vital to inventory management. By analyzing your retail data from a variety of sources, you can assure that your in-store inventory will perform to its best. Otherwise those trips your retail staff make to “see if we have more in the back” are going to be useless.

Sell (Analyze) What You Know
The best thing about a data-driven approach to managing inventory is that most retailers already have data they can drill into for predictive insights; they just need to know where to look. The first place is in a CRM platform. Currently, CRM platforms have expanded beyond their initial applications where sales teams used them to manage customer relationships. Retailers now lean on CRM to segment data on how stores are performing in terms of sales, customer memberships and, yes, inventory management.

One of those segments can be inventory requests, which can provide important insight into what products and stores are performing well, and where supply is needed. Once those trends are discovered you can really begin to hone in on which stores need which products, how many and when.

Even if retailers don’t use a CRM platform, they're sure to have transactional data. Sales data can give an essential look into purchase trends, such as what items are purchased together or are constantly low. Again, all a retailer needs to do is turn the microscope to that data.

Although useful, relying on CRM and transaction data alone poses the same limitation – they're based on historical information. While historical data is useful for noticing trends that can potentially show up again, it's often inaccurate in determining consumer demand and pricing for new products. That’s where crowdsourced predictive analytics comes in, turning customer feedback on upcoming products into actionable insights for strategic retail decisions.

No matter which approach is taken by the retailer, failing to leverage predictive analytics for inventory management is a missed opportunity to improve store and product performance.

All the Cool Kids Are Doing It
As hip retailer JustFab notes, inventory management is an art form and science. Merchandisers are just as vital as design teams, maybe more so, as they're the ones who best manage what styles, in what sizes and quantity, need to be in stores by specific times in order to be a hit with customers.

A data approach can also help streamline the design-to-destination process for retailers, as JustFab’s co-founder Adam Goldenberg notes. “In a traditional retail company, it’s more normal to have a monthly or quarterly review of how things are performing. That information might be at the head of design, but it doesn’t get filtered down to the designers,” he said, noting that this type of transparency allows for faster turnaround on go-to market decisions.

Although this example is of a young, agile company, any retailer can improve its inventory decisions based on predictive analytics. All it requires is the same kind of boldness.

Making the Decision to Use Data
It’s clear that denying the use of data for effective retail decision making is no longer possible. Retailers deal with data every day, and it’s time to make the data work for them. One of the most powerful applications of that data is addressing the age-old issue of supply and demand. Luckily, we’re at a point where analytics can be truly predictive and take much of the guesswork of consumer demand out by polling them directly.

For years, retailers have struggled to figure out consumer sentiment and now, more than ever, they’re able to (and should) listen to it by using predictive analytics. Fast-fashion brands have already shown the possibilities, but it’s up to you to make this approach a reality for your business