When Kobie Fuller was hired as chief marketing officer at Revolve Clothing, in July 2011, one of the major challenges he faced was how to encourage customers to become repeat buyers.
He was sure that the answer lay somewhere in the mass of customer-order data the 160-employee online apparel retailer collects daily. But when he arrived, that data was stored in a difficult-to-navigate database. Pulling sets of data meant asking an IT guy to run a special query. And even figuring out where to start was tricky. Revolve's data reached back to when the company was founded, in 2003. "I wanted ready access to key statistics without begging someone to mine the data for me," says Fuller.
So Fuller turned to Custora, a company that specializes in data analytics. For $1,000 per month, Custora copies data from Revolve's database, crunches it on its servers, then groups Revolve's customers by purchase date and calculates their "lifetime value" down to the dollar. Almost immediately, some major revelations emerged. Custora confirmed that shoppers who made repeat purchases within 90 days of first patronizing Revolve were particularly profitable. Fuller tested retention e-mails at the 30-, 60-, and 90-day marks and found the 90-day messages most effective. He says the e-mails have already generated 30 percent more second-time purchases. "We were able to take the data, trigger action, and see what worked, all within one comprehensive tool," Fuller says.
Thanks to affordable storage rates and intelligent software, companies now have access to a staggering amount of data about their customers, operations, and markets. This information, and the industry that has blossomed around it, has come to be known as Big Data. Even small businesses now have terabytes of information (1 terabyte equals 1,000 gigabytes) at their fingertips, material that includes customer names and transaction histories as well as Facebook likes. The data often contains key insights, but unearthing them can be pricey. Recruiting data scientists and building analytics tools in-house, as some large corporations do, can cost more than $1 million. Hiring a big-name software provider or consultancy can be costly as well.
To fill the gap, a handful of start-ups have emerged that deliver a version of Big Data analytics to small and midsize businesses. Some, like Custora, emphasize customer forecasts. Others focus on data aggregation and reporting (InsightSquared, RJMetrics) or data visualization (SumAll). Not all are recognized as Big Data specialists--the definition of the term is a topic of some dispute--but all help businesses evaluate large amounts of data for less than a few thousand dollars a month.
A low-cost solution was exactly what Justin Winter, co-founder of Diamond Candles, was looking for. The 13-person online retailer sells scented soy candles with jeweled rings hidden inside. The company has been compiling masses of Web data since its February 2011 launch. Initially, it managed this information in Excel spreadsheets and an application offered through its e-commerce platform, Shopify. As that data piled up, Winter realized he needed a better evaluation method. He balked at the cost of hiring a consultant and soon discovered SumAll's analytics software for online merchants--free because it is still in beta. SumAll imports Diamond Candles's data and feeds it back through a dashboard that tracks customer and sales trends. In particular, Winter has used SumAll to determine the effectiveness of offering daily-deal discounts. By tracking the results of a recent promotion on the daily-deal site LivingSocial, Diamond Candles was able to determine that the promotion increased orders from new customers 14 times compared with its existing sales channels. Given that information, Winter decided to continue using daily-deal sites as a way to broaden his customer base.
"I can visually comprehend things now, instead of just staring at a large data set," says Winter. The company recently spent the modest sum of $50 for a year's premium access to SumAll.
A more heavyweight platform was needed for ThinkNear, a Los Angeles-based company that matches mobile ads with consumers on the basis of their location, activity, and local weather or traffic. Since May, ThinkNear has been reviewing terabytes of this situational data each week through a dashboard from analytics provider Metamarkets. ThinkNear CEO Eli Portnoy pays about $500 a month for the software; he says his ability to quickly evaluate billions of data points has doubled his ads' click-through rates. "It's been a massive help to our business, because we're providing better value to our clients," says Portnoy. "Data ultimately wins in our business. The companies that have the best data and the quickest way to manipulate it succeed."