If you thought the coolest job in retail involved design or buying, we’ve got news for you. It turns out that data scientists have become the hottest thing in retail over the past few years. All you need to know is that one search of ‘Data Science’ on Amazon’s career website turns up more than a thousand results. If the ‘Zon is doing a big hiring push in an area, that tells you a whole lot. But Amazon isn’t alone, brands including Target, Nike, Nordstrom, and Burberry, all have teams of data scientists unraveling and making sense of the seemingly endless pile of data that modern retail offloads. In fact, with companies like Amazon and Facebook showing that the data is driving the business, as much, if not moreso than a product or service offering, there’s no doubt that the future (survival) of retail pins on getting data science ingrained into the organizational decision-making process.
With the advances in machine learning, the process of harnessing unstructured data sets has been largely democratized, so it’s no longer just for the big boys (and girls) with deep pockets and armies of data scientists and engineers. We’re here today to discuss how retail organizations who are in the more nascent stages of figuring out how to utilize data can build a sustainable and impactful ecosystem.
Design Metrics From The Top Down
Okay, yes, having data is interesting, but without being linked back to the organizational goals, what’s the point? If you’re just grabbing whatever is already out there and hoping someone internally finds it interesting, you’ve just wasted time and resources that could allocated elsewhere. What drives customers to your brand? Is it getting the latest designs, the best price, or having a seamless omni-channel experience? Whatever that core value or USP is, be sure to prioritize and tie your data straight back to these organizational metrics. Moreover, this ensures that the data is woven into the fabric of every level of the organization.
Make Sure You Have Data In Place Before You Need It
One of the challenges of designing a data ecosystem is corralling data that is of good quality before you need it. Though it’s not unusual for companies to augment gaps in their data sets with that from external sources, in order to predict tomorrow’s behavior, you need to harness relevant current and historic data. Identify the who or what (which segments of customers or categories of products), the where (which geographies or places), and when (a specific point in time or series of time) that will guide your analyses and outcomes.
And by starting small, that doesn’t mean there’s not a major benefit to the business. Remember that data science requires not only the right data sets but the right manpower and business support in place to drive it. When you focus on the previously surfaced idea that data be tied back to the most important organizational goals, you are able to focus and allocate limited resources. Moreover, smaller, less disruptive efforts that reap clear results give larger initiatives more internal traction.
So now that you’re thinking about where to start, what are the areas of the business where it can have the greatest impact?
When you think about demand prediction, it’s helpful to bucket it three ways: what’s happening right now, in the medium-term, and long-term, while also recognizing the synergies between each. Let’s take an example in the present, like the weather. Completely out of our control (as of this writing as least), yet it can have a massive impact on what does and doesn’t sell. One retailer successfully utilizing this particular type of data is Tesco, which logs the weather forecasts and adjusts sales forecasts locally three times per day. In fact, they have been able to save more than £6M a year by getting in front of this very type of demand pattern. By continually adjusting for these environmental factors, retailers can avoid a catastrophic pile-up of excess stock they need to get rid of...after it’s too late. Who said mother nature has to have the last word?
Pricing + Promotional Strategies
Price is undoubtedly one of the most important factors in consumer decision-making, and it’s equally critical to a retailer’s bottom line. And while we have seen increasing interest in developing a structured strategy around pricing, it is still viewed by many retail organizations as a ‘one-off’ and dealt with in a reactionary manner. It’s more often than not, an ‘oh sh*t, this isn’t selling, we had better take a big markdown to move stock’ kind of move. But the reality is, as one BCG report cited, ‘30 to 50 percent of promotions have no positive impact on sales and margins. Even worse, many of them reduce profits without leading to additional sales.’ Just take a moment to consider all the different levers you have in your toolkit, including starting price point, promotions, and discounts. Once you start tracking these metrics (and those of your competitors) you are able to see precisely when and how much you should be pricing and promoting, so that you’re not leaving money on the table.
You Can Deliver A Personalized Experience
As a consumer, we are all generally aware of how much data online retailers are collecting about us. (Those product recommendations can be eerily on-point.) Indeed, tracking online behavior is one of the lowest hanging fruits of the data analytics world, and armed with this, many retailers are taking their customer experiences to the next level with this business intelligence. Retail darling Warby Parker is a prime example of the disruptive nature of a business built on analytics. Take for example, their home try-on program, which guides users through the product selection process taking into account variables around face shape and frame size. Their calibrated smart recommendations ensure a great customer experience and identify future product development and marketing opportunities. As their head of Data Science, Carl Anderson, stated, ‘Our goal is to have data embedded into all of our business processes as much as possible. A data-driven culture is objective, tests assumptions, and challenges new ideas to prove themselves.’ It’s this foundation in data that has, in no small way, driven Warby Parker to its valuation north of $1B.
It goes without saying that there are many other areas where big data can reap huge benefits, whether that is supply chain optimization, store design, or inventory management. No matter where you place your first efforts, start small, iterate, and share insights throughout your organization. Learn more about how analytics can transform your business here.