The following is a guest contribution from Bridge Mellichamp of StitchLabs
The year is 2025, and you are in need of a cocktail dress for a housewarming party. Luckily, your house is smart enough to clean itself, and your programmable personal assistant has food and decorations under control, leaving you with time to shop.
You step into your favorite department store and check your smartphone. From the screen, four dresses pop up into a virtual display; all curated especially for you based on past purchases in the brands and price range you favor. You pop into a dressing room, select your two favorites, and look into the mirror to see each projected upon you. You click the blue one – no need to select size, the store already knows – and a drone swiftly delivers it to your dressing room.
Upon taking the dress from the drone, you’re automatically charged with no exchange of money or buttons clicked, while rewards points are accrued and relevant loyalty discounts are applied. You take your dress and are transported home in your driverless car.
The above scenario is not such a distant reality. The more technologies continue to evolve, and devices become connected, the more data intelligence can help retailers understand what customers want, when they want it, and how they want it delivered.
From pre-selecting dresses based on the buyer’s previous purchases to contactless payments and intuitive promotions, we’re not so far off from completely personalized shopping experiences...and data is paving the way.
The Emerging Data Landscape
Image via Seth Familian on SlideShare
Today's businesses have more data than ever before. Quite literally, since 90% of the world’s data was collected in the last two years alone.
Leading ecommerce businesses collect data on customer buying habits, shipping rates, marketing operations and tactics, and just about everything in between. Advanced retailers use algorithms and reporting to forecast demand and predict trends.
But while most businesses understand their data can help them make more intelligent business decisions and maximize their profitability, most struggle to derive truly actionable insights from the overwhelming amount of data they’re presented with on a daily basis.
The fact is, there is simply so much data that knowing where to start can be even more challenging than implementing a highly technical business intelligence or machine learning solution (we’ll get to that later).
Why Data Intelligence Is Hard for Everyone
Whether or not you utilize it, you already have more data than you realize. You probably also feel like you aren’t tracking enough and are missing potentially valuable data.
Image via KISSmetrics
One of the most difficult components of making data-driven business decisions is getting all of the data you need in one place and knowing how it fits together.
Data often lives in disparate systems. For example, web traffic may be tracked in one system, with sales data in another system and your inventory, fulfillment, and shipping metrics are in another. When this information is siloed, it can feel impossible to see how all the interdependent pieces fit together to reveal existing inefficiencies or opportunities for operational or tactical improvements.
Take the problem of people abandoning the items in their online shopping carts. If you could see your shopping cart abandonment rates in conjunction with your data on shipping rates, maybe you’d realize if you lowered the shipping cost by 20%, you would see 40% fewer abandoned carts.
What would a 40% reduction in shopping cart abandonment mean for your company’s bottom line?
The First Step to Tackling Your Data
In addition to siloed data, the sheer abundance makes it difficult to know where to begin. So, start by asking yourself: What questions do I want to be able to answer? What decisions do I want the ability to make?
For each of these questions, what information and metrics are necessary to get answers, and do you have access to that data? If you do have access to that data, where is it currently located and when can you start analysis?
If it’s in separate systems, is there a way to integrate them? Integrated systems will help you increase efficiency, reduce errors, and provide you with more robust insight.
If you do not have access to the information you need to answer those questions, then what tools do you need to put in place to gain access to that data? Whether it’s better utilizing your Shopify Plus or Google Analytics data or implementing a new system like Kissmetrics or RJMetrics, the first step is identifying the data you need and the tools that will help you get there.
The next article in our series will tackle specific use cases for data, but for now, let’s look at a few real-world scenarios in which overcoming the challenges of using data, combined with asking the right questions, resulted in huge success for retailers:
Data Knows What Customers Want: Ever since the designer eyewear company, Warby Parker, was created in 2010, the business has relied heavily on data to drive decisions and learn more about its customer base. One of its most popular programs, “Home Try-On,” in which customers are sent five eyeglass frames free of charge with the opportunity to send back those they don’t want, is possible because of data.
The impact? Using data based on previous purchases, the company’s algorithms predict demand as well as the styles a customer might want. Warby Parker’s Director of Data Science, Carl Anderson, told Data Science Weekly they learned through this program that their colonial monocle has “an extremely high conversion rate,” leading them to “tweak our basket analysis algorithm specifically to account for it.”
Subscription Services Inform New Product Development: From fisherman’s tools to makeup to dog toys, there seems to be a subscription box for everything these days. That’s because data is enabling these companies to know what their customers want – all while gaining more information with each new subscription.
According to Harvard Business Review, “When consumers sign up to receive goods via subscription, retailers can gain access to a rich source of consumer purchasing and preference data. They can also use subscription services as a vehicle to test products prior to launch.”
Beauty, wellness, fitness, and fashion subscription company, FabFitFun, recently used customer data from their subscribers to launch a new makeup line, called ISH, with celebrity makeup artist Joey Maalouf. FabFitFun Co-Founder and CEO, Michael Broukhim, said, “ISH is a perfect manifestation of us listening, seeing whitespace, and launching something novel and special.”
- Data Keeps You Competitive: According to IBM, 62% of retailers report that the use of big data and analytics is creating a competitive advantage for their organization. Knowing what your customer wants and when they want it can be available at your fingertips – you just need the right tools and processes in place.
Take Vera Bradley, for example. The popular quilted handbag and accessories company spoke at the 2014 National Retail Federation’s Big Show, sharing how they use customer data insights to better segment and target their marketing emails. As a result, they managed to send 63% less emails while achieving a 101% increase in click through rates and a 275% spike in conversion rates.
Make Your Data Work For You
Data is everywhere, but being able to use it effectively is a competitive advantage. In our five-part blog series, you’ll learn:
- how to use data to create a personalized customer experience and drive digital optimization,
- how to track data trends to inform new product development, and
- how data can highlight which of your operational processes need improvement.
When data is used effectively, it has the power to improve efficiencies, increase productivity, boost revenue, and ultimately save businesses money. Taking the time to more effectively use your data to make decisions isn’t just a nice-to-have; it’s a must-have.So be sure to come back in the next few weeks to learn about our recommendations to help retailers extract actionable insights and implement improvements through smarter data reporting and intelligence.
About The Author
Bridge Mellichamp is the Director of Data Science and Special Projects at Stitch Labs. Numbers excite her more than you can imagine; at the core, she’s driven by helping Stitch and its customers make sense of their data so they can make incredibly smart business decisions.