When it comes to hard-nosed business practices like customer segmentation, Eartheasy.com, a Vancouver-based online retailer of environmentally friendly products, isn’t exactly green around the gills. “We learned a long time ago that we needed to have a better idea of our customer base,” says managing partner Aran Seaman.
Eartheasy got into e-commerce back in 2008 and soon grew concerned that its generic email blasts and other direct-marketing forays were missing the mark. Unlike mass merchandisers and online giants like Amazon, the company didn’t have systems to track key metrics such as the lifetime value of a customer and individual preferences based on previous purchases. And, Seaman says, the firm couldn’t find e-commerce consultants who would do this type of analysis at an affordable cost for a small enterprise.
Analyzing online metrics has helped one etailer more than double click-throughs.
Then, last year, Eartheasy retained Canopy Labs, an upstart Toronto firm that is carving out a niche by offering to produce big data-style analytics for small businesses.
Canopy specializes in consolidating a company’s disparate data pools—from e-commerce transactions and web-traffic logs to email newsletter lists and click-throughs—and crunching the information to reveal patterns.
This kind of detailed data analysis has typically been reserved for big-pocketed big business. In recent years, multinationals have developed highly effective techniques for culling insights from the waves of transactional information flowing over the commercial transom, as well as the ever-widening array of online sources, such as Facebook. Armed with analyses of customers’ past purchases, companies have been able to develop precisely targeted marketing. According to McKinsey & Co. research, firms that base their marketing and sales initiatives on data analysis boost the return on those investments by 15 to 20%.
The software tools and expertise needed to do such analyses have been out of reach for most SMEs, but that market gap appears to be closing. Since signing on with Canopy, Eartheasy has been better able to track conversion rates by cross-referencing data on opened emails, click-through patterns and actual purchases, says Seaman. The company can get reports on what specific customer groups tend to buy and then use those findings to send out more product-specific email promotions. As a result, the email-opening rate has jumped from 20% to more than 30%; click-through frequency, in turn, has grown from 4% to more than 10%. “Now, we can do stuff that’s similar to what Amazon is doing,” adds Seaman.
Canopy Labs founder Wojciech Gryc, a Rhodes Scholar with advanced degrees in math, data engineering and international development, says entrepreneurial businesses can significantly boost their sales by finding hidden patterns in their customer data. SMEs may lack the vast troves of transactional information that the likes of Walmart or Apple can tap to create highly segmented demographic profiles, but there are many prÃªt-Ã -porter analytics tools available through Google, Twitter and other online sources that enable smaller firms to analyze web traffic and social-media mentions. Google’s service, for example, provides a wide range of reports on everything from the performance of online ads to a breakdown of what customers search for when they visit your website. Both Facebook and LinkedIn have tools for tracking sponsored pages and groups, while TweetReach, from Union Metrics, creates reports on how tweets travel through cyberspace. Data-driven marketing programs like MailChimp give users the ability to segment email lists and track results.
As well, the big-data consulting sector is growing rapidly, with increased specialization in offerings and target markets. According to a recent provider roundup in PCWorld magazine, some firms will cull and analyze sales data from clients’ CRM systems, others will weed out dead leads from prospect databases, and others will offer to sift through the results of online customer surveys to help generate targeted market research.
Simply Good Technologies, an upstart Toronto consultancy, specializes in analyzing the distribution and effectiveness of electronic coupons as they spread through email and social networks. “You want to find out what offers deliver the maximum return,” says CEO Winston Mok. Some companies use e-coupons to extend their brands, while others’ goal is to provide incentives to new customers. Simply Good’s analytics can also determine if the individual who uses the electronic discount is a new customer (good) or an existing one (not so good). Mok points out that this kind of tracking additionally enables firms to aim their marketing efforts at influential individuals whose product recommendations on deal forums and in social media may be picked up by hundreds of followers.
For Gryc, though, the holy grail in the analytics business is building models that can predict consumer behaviour based on previous purchases. At some small companies, the customer base isn’t large enough to yield a statistically accurate forecast, but Gryc argues that well designed models can work with as few as a thousand existing customers. “We are entering a time when personalizing offers to customers is critical to the success of a business,” he says.
While Eartheasy has seen a noticeable uptick in transactions due to the more-rigorous marketing focus provided by Canopy’s analytics work, Seaman doesn’t think the tools can pinpoint future customer behaviour with precision yet. What he does believe, however, is that his firm benefits from having an analytical edge over its rivals. Seaman subscribes to email newsletters put out by competitors and knows that most of them still rely on the “spray and pray” approach to online direct marketing. Case in point: He receives email blasts from some companies touting women’s products. “Even the demographic data isn’t utilized all that well,” he says.
All this suggests that SMEs that dive into the big-data pools will enjoy an advantage—at least for a while. But as more outfits like Canopy sprout and sophisticated number-crunching techniques become as standard as CRM systems, that analytical edge will disappear.