Mining the Insights Just Below the Surface Could Change the Way You Approach Your Business
Brenda Hoskins, who leads direct marketing at HFA member store RC Willey, asks the question every home furnishings retailer is wanting the answer to: “As an industry we spend a lot of money to bring a customer through the door,” says Hoskins. “What do you do when they are one and done? What do you do when they haven’t been back in six months?”
For Hoskins, the answer is as simple as paying attention to RC Willey’s customer database, and utilizing an automated program developed specifically for the company that uses point-of-sale data and analytics to motivate the customer to come back and shop again. “Acquire, activate and retain are my three favorite words, and that’s the big data that I hone in on,” she says. “If you really take care of the customer and keep them engaged, the customer base just grows and grows. The return is sales.”
As tech term du jour, “big data” refers to the large volume of information that inundates businesses on a day-to-day basis. For retailers to effectively glean the insights necessary to improve critical functions like marketing and advertising, merchandising and store location, the sheer amount of data at hand demands that it must be analyzed computationally to reveal trends and associations. And that gives rise to one of the most ubiquitous buzzwords in business today: Analytics.
“We’ve got the capacity on our computers now for every piece of information that can be learned about anybody to be stored and analyzed,” says Jerry Epperson, founder and managing director of investment banking and research firm, Mann, Armistead and Epperson. “When you make any purchase, anywhere, it is recorded, whether you use your credit card or you are applying for a mortgage.”
Along with capturing information at point-of-purchase, websites—where consumers may visit page after page, click on something and back out, and move to another page—also can provide retailers a wealth of data for collection and analysis. As do social media sites like Facebook and all other facets of a retailer’s business, including customer service, shipping/receiving, human resources, facilities maintenance, transportation, and so on.
“Every time you sell something, you’ve made an impression on that person,” Epperson says. “If you sold them a bedroom and a mattress, but while they were in the store they looked at a home office, you know they are still in the market for the home office. And you need to be able to use that information to reach out to them. Unfortunately, when most people come into a store, they buy and they leave and we just tally it up as a sale.”
He points to a long-standing promotion at HFA member Art Van Furniture as one example of how to maintain a connection with customers. “Art Van would send out keys to a treasure chest,” Epperson says, “and one lucky key would get to open that chest and you got $5,000 worth of Art Van furniture. That’s a lot of free furniture. But before each participant got to try their key, they had to write down their name, their address, their phone number and nowadays their email, and answer questions like ‘What are you looking for?’ Those cards weren’t just thrown away; they were kept and analyzed. And when salespeople weren’t busy on the floor they were calling those people back and saying, ‘I see you were in the market for a dinette and we’ve got something really special, come on in.’ Those kinds of things have been done for years. We just didn’t call it big data.”
Today however, “the sheer proliferation of data and the blossoming speed at which that data is coming in has required that new tools be developed to handle the volume,” says Mike Knight, president of Customer Analytics, which helps retailers like RC Willey manage their data. “The more complex the organization, the more allegiances you have, the more data that’s flowing through. Analytics really is just gathering all that information and trying to find patterns in the data to chart a way forward.”
Increasing competition and shrinking margins mean more home furnishings retailers are searching for the best ways to stay in front of their customers,” says Amitesh Sinha, president of iConnect, a technology solutions-provider in Virginia. “Retailers need to come out looking intelligent and sharp. The majority of consumers today are very tech savvy. They know they can pull up the sofa you are trying to sell them for $X on their phone and get five competitive prices right away. The best way to remain in front of them is to get on top of your analytics and understand what is working and what isn’t.”
Still, Knight admits the promise of big data is out of reach for most small independents. “But,” he points out, “there is a long runway of things small retailers can do—call them small data techniques—that can provide a serious amount of lift and optimization and efficiency to an organization.”
The Promise of Small Data
“To be successful anymore you have to have metrics, and where the big guys are killing you is they have the manpower and the money to get the data they need and want,” says HFA member Lael Thompson, chief operations officer at Broyhill Home Collections in Denver. “I just redid the digital infrastructure at our store and what I’ve been seeking out is how to get these insights on customer behaviors into our business, but on a mom-and-pop level.”
“The challenge we have is that there are some very basic things that independents can be doing, but a lot of the mom and pops bought into this narrative that they are too little, that they could never afford it, never understand it, so they just don’t try,” Thompson says. “If I had the magic wand, I would want the average mom and pop to get ahold of some basic key performance indicators (KPIs). Without getting crazy deep into the data, I guarantee if they had a clearer vision of what their key performance indicators are it would be very impactful in terms of truly understanding their closing ratios or their true demographic. Instinct is powerful, but it’s even more powerful when it’s backed with a foundational knowledge of what’s really happening in a business. A lot of furniture retailers have sharp guts and great intuitive sense. How much more powerful could they be if they actually had a base level for these metrics?”
Given the importance of understanding conversion ratios, one of Thompson’s tips for starting down the small data path is investing in shopper counting technology that harnesses artificial intelligence for facial recognition. If that sounds complex and expensive, take heart.
“It’s a simple Internet camera that costs about $100 a month and it tells me who’s come into the store. It automatically removes my employees and the mailman from the count, and it tells me the number of buying opportunities based on groups.” In other words, if a family of five enters the store, the system recognizes them as a single up.
The system, called Visilytics is available from iConnect. HFA member Alex Macias, vice president of Del Sol Furniture with three stores based in the greater Phoenix area says he looked into the program following a call from Thompson.
According to Macias, “Some of the other traffic counters that had been around were very basic. There were lasers and some of them used cameras, but they really couldn’t tell if it was a group coming in or an employee, salesperson or a rep. So, they all got counted. This has a camera shooting right off the entry of the doors and it snaps a picture of every single human that walks in. But the trick is that it starts to keep a record of who the repeats are every day and it begins to exclude them and automatically count them as employees. It’s not perfect, but it’s accurate enough to get a really good trend and my managers can’t make excuses anymore. When I look at the numbers for the day and I see that we had 40 ups, and the manager says, ‘Well, there weren’t really 40,’ I say, ‘Let’s go through the pics.’ It really holds people accountable because they know I can go into the system at any time and look at the pictures.”
Who’s Your Data?
Melanie Stephens, chief operating officer of Turner Furniture Holding Corp., a licensee for Ashley with 14 locations in six states, needed help pulling together the information hidden in her company’s four databases. “We were running multiple reports over and over again to try to get the information we needed because we had a lot of manual processes in place,” Stephens remembers. “And our higher-level people were spending hours trying to get this information together for us.”
That’s when Stephens turned to iConnect. Turner Furniture now sifts through several KPIs from a store level standpoint and from the sales associate. “Now, rather than having to go in and generate all kinds of reports to try to figure it out, all the information is easily visible on a daily, weekly and monthly basis,” she says.
Automating complex reporting has been a game changer for Stephens. “We prefer that our managers be on the floor, engaging with customers, not spending all their time in the office. And with 14 showroom locations we’ve got different regions with different demographics,” Stephens says. “You really have to dig into numbers to see how things are performing. So much of what we look at also needs to be timely in order for us to act. You don’t want to wait to generate a report for the past 30 days. You’d rather be able to go to a sales associate today and say, ‘Let’s look at what happened yesterday. What happened with this customer?’ ”
Knight has plenty of advice for those setting out on the path to analytic maturity. The key, he believes, is to get to a point where a business is looking forward, instead of back. “If you’re basically PC and laptop based, and you have a point-of-sale system over here and an inventory system over there, and every week the managers put together spreadsheets extracted from these systems, and you’re asking ‘What happened to sales last week? What was our advertising spin? What was our ROI?’ Then everything you are doing is looking backward.”
Knight says small retailers need to move beyond laptops and start putting their data on servers. “That’s when you’re able to create databases off your dataset and start doing some, let’s call it ‘near-real-time’ analytics,” he says. “But you’re still not in real time because you’re still using these legacy systems. The next step might be moving your data onto the cloud where it’s even more extractable because there are tools sitting on top of it and you don’t have to extract the data just to run analysis. So, you might have monitors and sensors, you might have door counters, and you can see some real-time activities. And then the final step—and kind of the promise of analytics—has been simulation, optimization and prediction.”
Knight says smaller retailers can send their data to vendors that can analyze it and provide data such as the exact physical location of a particular household, including age, sex, race, income, education, occupation, the year the house was built, the value of the housing unit, the presence of kids, families, single parents and more. “If you look at a month or six months or a year, and that kind of data at that level of granularity, you’ll find that all these patterns emerge,” says Knight. He says retailers can begin to get answers to questions like who comes to your store only when sales or discounts are offered, or what shopper is more likely to purchase contemporary leather.
“If you get enough data,” says Knight, “you’re able to look at it and say, ‘Every time these three things happen, such as it rains at the ballpark on Saturday, our sales just tank completely,’ or, ‘Every time we purchase this dresser from this manufacturer, we get complaints.’ You’re actually able to look at the data and see what explains or predicts revenue, or a problem that you’re having, and you’re able to optimize your operations to deal with it. Next time you see those three things start to happen, you can say, ‘Let’s jump in front of it and change it.’ ”
Manoj Nigam, chief executive of MicroD, encourages smaller retailers to work with providers who collect analytics on consumer behavior and can offer consolidated information that is normalized and shared by multiple retailers to provide insights on subjects like buying trends by region and pattern.
“At MicroD, consumers are visiting 600-plus websites that we host and manage for our customers, and a hundred thousand orders a day are flowing into our system. You tie them together and say, ‘OK, what’s working online and what are people buying?’ Or, ‘If floral patterns are more popular in my area, why am I showing stripes? How can I use this information to stock the right products in my store?’
“Analytics is nothing to fear, and essential for you to run your business today,” Nigam says. “Don’t be afraid of technology or terms like big data. Just start with the questions you want to ask. And if you know the questions you want to ask, you will find that technology can help provide you with the answers.”