This week’s post is from guest contributor, Tullika Tiwary. Tullika is a Senior SEO Analyst at Intelligence Node, and passionate about working with data and analytics – and the role they play in making intelligent business decisions.
It’s every retailer’s dream—getting all the right products onto the right shelves in all the right stores, at the right time. Talk to any retail chain executive, and you’ll see how many resources are spent on things like inventory management, resource allocation, and replenishment of sold-out products.
A retail chain’s likelihood of success, in essence, depends primarily on the way it handles its inventory management. It boils down to simple logic, really: if products are on hand when a customer comes in looking for them, you achieve good sales targets. If products aren’t available where and when they are needed, sales are going to take a hit, as are your brand’s reputation and customer trust.
Why Retailers Need Predictive Analytics
While the logic is simple, inventory management is by no means something retailers can achieve with consummate ease. There’s a lot that goes into inventory planning, which is why retailers now rely heavily on things like big data and retail merchandising software to get things done.
This is where predictive analytics comes into play, helping retailers study trends in consumer behaviour and shopping habits to effectively plan their resource allocation and replenishment strategies.
To see clearly why predictive analytics are so crucial to retailer success, you’ll need to understand how exactly this technology affects inventory planning.
The ‘Ideal’ Retailer Situation
If you asked the average retailer what their ideal situation would be, they’d probably describe something like this:
Inventory is purchased from various sources, and specific selections of inventory are then allocated to different stores along the retail chain. This allocation would be designed in such a way as to entice customers entering each store to buy as much of the inventory as possible. The inventory would be transferred from the source to the store via a DC, or a Distribution Center.
In an ideal world, the DC would replenish products such that sales are never affected by the lack of stock in the store.
Why This Situation Never Happens In Real Life
The IHL Group—a global research and advisory firm specializing in the field of retail—has actually coined a term to describe what goes wrong: inventory distortion.
This is what happens when stores in your retail chain are either out-of-stock (running out of products to sell) or overstocked (having too many of a particular product). Usually, it’s a combination of these two—while one store has too much stock it’s simply not pushing, another has to explain to customers that it’s run out of the product they came in to buy.
What happens next is predictable:
- Retail executives at stores that are overstocked resort to marked down prices in a last-ditch effort to clear stocks.
- In those stores that are experiencing an out-of -stock situation, potential sales are lost simply because you don’t have the product your clientele is looking for.
- Because of the previous situation, your customer base may shift loyalties to a nearby competitor who fulfils the sale.
- Customers you turned away because you didn’t have enough stock start thinking that the product is a low-demand one, which means that sales take a hit. You’ll then have to reduce stocks at the store, starting a vicious cycle.
According to research carried out by the IHL Group, global losses due to inventory distortion cost retailers a whopping $800 billion annually.
How Predictive Analytics Fly To Your Rescue
In a nutshell, predictive analytics lets you forecast consumer buying behaviour, helping you devise allocation and replenishment strategies accordingly.
The technology enables retailers to implement strategies tailored to their specific business processes, and their customer base. Using modern software solutions, it also improves demand forecast accuracy, which is crucial to marketing and merchandising strategies.
Another great facet of predictive analytics is inter-store inventory balancing.
Balancing inventory across a chain of brick-and-mortar stores—or an omnichannel retail chain—involves a number of factors. Using predictive analytics, you can address the ‘inventory distortion’ issue head-on. You can set in motion an optimal inter-store transfer process and schedule, effectively moving excess stocks to stores that have run out of them.
This means that you avoid losses at both stores experiencing a surplus and those facing lesser demand. And it’s not all wishy-washy theory—the numbers back this up. Retailers using this technology have reported up to a 40% decrease in inventory costs, and increased sales effecting a 3.5x increase in turnover.
With such advantages such as inter-store inventory balancing, predictive analytics are taking the world by storm. And with these kind of results, it’s easy to see why.