There are creative elements to succeeding in retail, of course, but they’re all rooted in extensive data analysis. By recounting and parsing the facts of the past, you can forecast the future, and make appropriate alterations to your strategy — setting stock levels, tweaking layouts, and making major decisions about the positioning of your brand.
In ideal circumstances — provided you have the resources, the time, and a plan for separating the wheat from the chaff — you’ll bring in data from all possible sources (your internal analytics, market research, and customer surveys, to name just a few). But how should you structure the analysis efforts? Not every area or metric is worthy of granular investigation, after all.
Well, there’s a whole host of elements that warrant attention, but here we’re going to look at purchase data (everything you glean from the orders your customers have placed). Analysis of this data is absolutely essential to your operation — here’s why:
It will help you adapt to consumer habit changes
The consumer market moves forward, sometimes slowly, but never falling entirely static. New products are released, existing products pick up steam due to promotion or word-of-mouth recommendation, and previous hit items fall out of favour or are discontinued. The more rapidly you can adapt to the changes (and even predict what’s just over the horizon), the more strongly you can take advantage of them.
These changes, of course, don’t happen out of nowhere. A product won’t generally go from popular to written-off overnight — instead, it will gradually lose its popularity before eventually reaching a tipping point at which it might as well no longer be sold. By analysing the purchase data of your customers, you can discern which products are rising or falling in the ranks, and draw inferences about what product types are gaining or losing value.
You might see that sales of skateboarding accessories have risen steadily throughout the last year, for instance, in which case you’d have good reason to think that it might be a niche worth investigating — and if you check Google Trends and discover that interest is rising across the board (for whatever reason), you could make a concerted effort to target skateboarders.
Patterns support enhanced marketing tactics
Marketing is a core concern for retailers, particularly in the online realm where you can’t rely on people merely happening upon your store and wanting to shop there. The more efficient you can make your marketing, the better its ROI will become — you’ll sell more, and spend less to win those sales. And since digital marketing is inherently suited to complex targeting (relying on smart segmentation of customer sets), customer trends are hugely valuable.
For example, if your purchase data shows that shoppers from a particular region or country have spent 35% more on food products, you can dig deeper into that stat to figure out some tweaks to your marketing. Could you market other food products to those people? Is there a specific reason why that area is so concerned with food? There could be a new hit cookery show, or a big trend towards healthy eating.
If you identified the latter as the culprit, you could take advantage by marketing other health products to people in that area (you could even use a product discovery tool like Product Hunt to find more). The more you know about customer habits, the more sophisticated you can get with persuading them to spend even more with you.
The data is relatively straightforward to collect
Some types of data (e.g. customer feedback) are somewhat laborious to collect, because you need to incentivise them somehow and then actively chase them — and even then, you might have a dataset that’s both limited (most people prefer not to comment) and biased (the people most likely to provide feedback are those either very happy or very angry with your service).
Purchase data, though, requires next to no effort to collect. No matter what platform you use, you’ll end up with basic purchase records (they’re necessary for customer accounts and support requests). What’s more, if you use something with rich native data (the Shopify ecomm service has GA’s Enhanced Ecommerce features running out of the box, for instance), then you’ll have tagged funnel stages at your disposal — possibly without even realising it.
Will any ecommerce solution give you a flawlessly-parsed analytics dashboard from the outset? No, of course not — that’s why you’re best served finding someone to help you get everything usefully tagged and categorised. But all the data is there and ready to use, not costing you anything or causing you any strife. Why wouldn’t you make the most of it?
Loyal customers deserve additional effort
Customer loyalty is of paramount importance to sellers of all kinds, but particularly ecommerce sellers. It’s tough to stand out through pricing or product selection in the digital marketplace, and winning someone’s loyalty will give you the edge regardless, so you need to prioritise it. But how do you know which customers to focus on? Simple — your purchase data will tell you.
By checking your customer histories, you can pick out those who have placed the most orders and/or spent the most money with you, then make an effort to serve the needs of those customers. You can reach out to them for direct feedback, ask them what improvements you could make, and even offer them incentives to spend more and/or bring you referral customers.
When you don’t pay much attention to what your customers buy or how they shop, you can’t go the extra mile to cater to those with the most value. Keep loyal customers happy, and you’ll reduce your churn rate, convert on a more frequent basis, and achieve remarkable stability.
For all of these reasons, putting in the time to closely analyse the purchasing habits of your customers is a highly-effective way to improve your marketing, adapt to changing circumstances, and cultivate immense customer loyalty. If you’re not doing this type of analysis already, what are you waiting for?