How smaller retailers can utilize data as effectively as Amazon
February 24, 2017 11:11 AM
Any information not collected is information lost.
Data is important to maintaining a company’s competitive edge: The more you have, the more you can cater to your customers, and the more you can understand them and create a better experience.
For e-commerce companies, it’s even more of an imperative. Take for example, Amazon, a leader in using a comprehensive collaborative filtering engine (otherwise known as personalized recommendations) and its patented anticipatory shipping model, which uses big data to operate more efficiently. Amazon’s data-driven approach has made it the most influential online retailer around today.
Yet, to be fair, there’s a moral gray area when it comes to harvesting customers’ user and shopping data. How do you gauge how much data collection your customers are comfortable with?
Good messaging on the part of online retailers can help. When informing customers that you want to collect data, they need to know that this isn’t for some sinister purpose. Rather, it allows retailers to create a better, more tailored experience, which can save them significant amounts of time that might otherwise be spent sifting through search results.
So, figure out what data customers are comfortable with sharing. And then, start collecting it. And collect it. And collect it…
Next up is the task of analyzing the data, and fortunately for today’s retailers, there are numerous tools at your disposal to help with this. Maybe a little ironically, in picking one, you too have to decide how much data you want to share, versus how much you want to remain proprietary.
For example, Google Analytics provides a solid platform for data analysis, and it’s free—at least in terms of monetary cost. As you use it, Google collects info about your store and uses it to set benchmarks for other stores. You agree to share data, and in return you get a really good analytics package. On the other hand, a platform like Omniture carries a monthly fee, but it’s even more powerful.
Both options involve costs, though in different form. When you use a free service, it’s a great value, financially speaking. But you’re giving up some of your proprietary data to that analytics company, and that’s your cost.
On the other hand, you can maintain security and proprietary ownership of data on your end and own it 1,000 percent. But it’ll cost you. You may need a team of engineers and data scientists, both of which are much more more expensive than hiring a service. However, owning your own data may be well worth it.
For a lot companies, this is a hard thing to determine. Big retailers have the budget for analysis, data scientists, and the tools they need for that. They just need to figure out what data to collect and analyze, and which tools to use.
However, smaller retailers can get value too. Engineering is at its most interesting when you’re presented with a challenge with lots of constraints. When you have a high budget, you just choose which easy-but-expensive solution you want to use, and you don’t have to think much about it after that. But with a smaller budget, the question for engineers isn’t “what tools do we use?” so much as it’s “how do we build a solution to get this done?”
The platforms and solutions you use should be driven by attributes. Everything has attributes—order items, line items, products, customers, and so on.
So let’s say you’re a $2 million-per-year company that sells T-shirts. To collect more data, you can start by adding more attributes about a product—things like what material is it made of, sleeve length, shape of the neckline. If you start adding that info (which you probably already have from your vendors) into your system, then in a day we can build a report that shows, for example, what sales by neckline look like over the past year, and maybe that there’s a decline in V-neck style because it’s losing popularity. By investing two days of engineering, suddenly you have a valuable insight. You can adjust your product offering, market it accordingly, and bring in more revenue the next year.
At the end of the day, we live in a world of constraints, and this is a great exercise in making progress, growing the business, and learning things along the way that you can apply to bigger data sets. It proves that no matter what the budget is, you should always be able to analyze your data and make educated decisions for business.
SUMO Heavy is a digital commerce consulting firm based in New York and Philadelphia