Retail analytics is much more than just tracking sales over time. Here’s what retail analytics is and how it can help your business thrive.
The path to success in the retail industry may feel more like an art than a science. But in this era of data, running a modern business takes less guesswork than ever before in history. That’s because every item in your inventory, every store, every customer, and every sale creates a datapoint. And together, this data tells a compelling story about what works and what doesn’t, and can even be used to deduce what you should do in the future.
But knowing how to collect, analyze, and understand this data takes quite a bit of know-how. That’s where retail analytics come in, and they’re a must for the modern retailer. Here’s a run-down of what they are and how they can help your business.
Retail analytics is the process of analyzing data from a retail business in order to glean insights from patterns and trends. Retail analytics solves challenges like understanding what customers want, knowing what price points customers are willing to pay, matching inventory to demand, and more. It’s an incredibly helpful tool for all types of retail organizations, from the smallest to largest operations, to those with physical stores, an online presence, or both, to brand new businesses or centuries-old mainstays.
There are a number of benefits to using retail analytics for your business. The short version is that they drive better outcomes when managing your operation. A 2020 report by McKinsey found that consumer companies leading the industry in digital and analytics had 1.6 times the total returns to shareholders than those who lagged. But the ways that analytics accomplish that benefit may be less intuitive. Here are just some of the ways retail analytics can benefit your business.
Retail analytics give all levels of employees a better understanding of operations. In-store sales floor associates can watch for sales and inventory patterns, not just from stores but also the online marketplace, sharing their knowledge of trends with customers.
You should always trust your gut — so long as your gut doesn’t go against well-documented evidence. Decisions like which inventory to stock in which stores, which customers to target with which marketing campaigns, or even what to charge for different items should be based on data. The more data you have, and the better retail analytics system you have, the more confident you can be in your decision-making.
When your retail analytics are all in one place, and widely available to employees, it creates a single source of truth that keeps everyone on the same page. This clarity can be particularly valuable in larger organizations with multiple departments that focus on different aspects of the business. And it can help employees across the board maintain focus on the bigger picture.
For example, with a single source of truth, a company might realize some customers buy products like garden fencing and dog food together, which could be a sign someone is a new pet owner. This reveals an opportunity to sell other pet-related products like leashes and dog bowls, providing a target audience and strategy for marketing and advertising campaigns.
Having a more optimized business that always has the right products in stock at the right times and the right number of employees available to meet customer support demands is a sure-fire way to improve customer satisfaction with your brand. Whether you’re tracking customer satisfaction indirectly through sales or directly through surveys and feedback forms, retail analytics can help track — and improve — customer satisfaction over time.
In fact, according to a 2020 research report from the Harvard Business Review, 69% of business leaders who had invested in analytics for their front-line workers reported a boost in customer engagement and satisfaction.
You’ve got a product people want to buy — but your bottom line depends on how many you can sell and how much you can sell them for. Retail analytics is a great way to take the guesswork out of this foundational sales issue, helping optimize pricing in order to maximize revenue. Advanced retail analytics software can even give automated concrete suggestions for how to improve your margins.
Retail analytics don’t just watch trends at the point of sale, they can also incorporate information from entire supply chains. Predictions and prescriptions based on sales trends are only helpful if the existing supply chain can accommodate them. That’s why pulling in information from suppliers and the broader industry can make your own supply chain more resilient.
No one loves analytics more than your marketing team! Retail analytics are the best way to track marketing campaign performance. Ad clicks and website pageviews are great, but sales are the real end-goal of any campaign. Understanding how trends in sales track with efforts from your marketing team will be the evidence they need to understand what works and what doesn’t for your customer base.
Whenever you’re dealing with a wide variety of products, and especially if they’re products that expire or go out of season, optimizing your inventory becomes a key facet of your business. You never want to run out of popular items, nor do you want to be left with unsold goods you’re forced to unload at cost (or worse). But retail analytics can track and manage your inventory, too, helping you optimize based on trends, seasons, and more.
“Retail analytics” is a blanket term that can be used to refer to a lot of different types of analyses. If you’re using data to look at trends and learn from them, and you’re in the retail sector, you’re using retail analytics.
The most basic form of retail analytics is to watch past and current trends in broad strokes. For instance, nearly every manager in the retail sector will be aware of sales per day or per month, or the relative popularity of a new item. These types of descriptive analytics are what businesses have done manually for centuries and on computers for decades. Today, software can automatically produce these reports, no analyst needed.
As you’re watching what’s happened in the past and what’s happening now, the intuitive next step is to extrapolate those trends into the future. Perhaps sales of a certain product surge every summer, so it’s safe to estimate that they’ll surge again this year. Or perhaps your business has seen steady overall growth — or decline — in month-to-month sales over the past few years, and you expect the trend to continue at the same rate.
This information is all incredibly helpful when it comes to decision-making for your business. But it would be even more helpful to know why these trends are what they are — that is, what’s causing them. Although it’s hard to prove cause and effect without some well-designed experimentation, looking at correlations between possible factors can provide very meaningful insights. Some factors influencing customer behavior might be more obvious, like price or availability. Others might be less so, like competitor behaviors, current events, or even unexpected changes in the weather.
Slice and dice your stats any way you want them: by store, by region, by type of item, by age of customer… the options are endless. Track sales by day, month, quarter, or year. Does store square footage impact the average sale?
Does item cost correlate with number of items sold? If you can ask the question, retail analytics can answer it, especially if you're using a tool like ThoughtSpot.
There are many ways that managers can use retail analytics to boost business. Here are just two examples of how analytics used well can improve performance.
A shoe store wants to increase sales back to pre-pandemic levels, and suspects the shift of their customers to online retailers is the main reason for their losses. They’ve had a website for years, but always focused their advertising and marketing toward in-store customers, through initiatives like sending coupons in the mail.
The business looked at trends in their sales over the past few years, and confirmed that online sales had been steadily rising, while in-store sales had plummeted. They identify the stores that consistently perform in the bottom 10%, and consider closing their doors, while allocating new resources to marketing campaigns directing customers to the website.
They launch a marketing campaign, increasing their efforts across social media platforms. They increase the number of posts as well as directly promoting their website and online shopping. After 8 weeks, they find that users coming from Instagram were 50% more likely to make a purchase than users from Facebook, and they were 20% more likely to buy multiple items. Using this knowledge, they focus future efforts on Instagram, and their sales steadily grow.
A company that sells jams, jellys, and preserves relies on a number of different suppliers to obtain their fruits. Their specialty is fresh, in-season, local blueberries. One year, a late frost in the area threatens the crop of their favored local berry vendor, which they find out about a few months before the usual harvest.
The jam company looks at their supply chain analytics for previous years, identifying which other blueberry vendors they had used in the past, and how well those products had sold. They identify a clear standout, and contact them immediately to see if they’d also been hit by the frost. It missed them, and they expect a good crop this year.
Their retail analytics software automatically analyzes how many blueberries from the new vendor would be needed to cover expected losses from the original vendor. At harvest time, jam production proceeds as usual.
In this era of big data and digital-everything, using retail data analytics to improve your business is a no-brainer. A better understanding of past trends, current situations, and predictions for the future is the best way to make sure you’re using evidence to make smart business decisions.
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