After integrating ExactChoice into CNET.com, my main task was to create something new for CNET. That became Intelligent Cross-Sell, a product used by four of the top ten brands in the Internet Retailer 500, among others.
I was part of CNET Channel, since renamed CNET Content Solutions. Its customers are e-commerce sites that sell technology and consumer-electronics products. Its primary product is a detailed database of products. E-commerce sites use this database to display products and specs in a standardized way. For example, if you see a product page for a computer on CDW.com, much of the page’s content is actually from CNET.Circa 2005, having attracted a large number of e-commerce customers worldwide, CNET was looking for something new to sell them. My job was to determine what it should be and then to build it with my own team.
The industry term for this role is intrapreneur. It can mean anything from “leader of a CEO’s pet-project skunk works” to “random guy building something not elsewhere classifiable on the org chart.” In my case, I was fortunate to have both a specific place in the org chart and a high degree of autonomy. I also had strong executive support.
By choice, I worked as part of a two-person team, with my ExactChoice partner Howard Burrows. We knew how to explore concepts quickly and cost-efficiently, having practiced what today would be called lean-startup techniques since founding ExactChoice in 2002.
At the outset, I talked with dozens of CNET customers about their e-commerce businesses, looking for the pain points we could reasonably address, ranking them by risk and reward. The opportunity that kept winning was a tool to automate cross-selling. Although everyone was familiar with Amazon.com’s “people who bought this also bought that,” tech and consumer-electronics sites could not use it to determine, for example, the right carrying case with a computer.
Among the challenges with “people who bought this also bought that” were:
- If a few consumers mistakenly bought the wrong-sized case for a computer, the algorithm would start recommending the bad combo, causing a slew of returned products.
- It was useless for new products without sales history—no people who bought this, then no people who bought that.
- It left no room for merchandising. For example, as computers began appearing with the Bluetooth wireless standard, cross-selling Bluetooth mice made sense. But how could merchants tell the algorithm to do that when it was only looking backward at the non-Bluetooth past?
Because of these issues, many large tech and consumer-electronics sites were using humans to manually configure cross-sells. These sites had tens or hundreds of thousands of products, changing rapidly. The humans could not keep up. We would later attract two of our early customers—billion-dollar e-commerce sites—by showing them their percentages of empty cross-selling slots.
The beauty of the opportunity was that it played to CNET’s strength. The CNET product database, DataSource, had the size of most computers. It also had the size capacities for most carrying cases. A trivial math operation could prevent a sizing mismatch. This is what the humans were doing in their heads, one product combination at a time. This is what we could do nearly instantly, across an entire product catalog.
In addition to preventing bad cross-sells, we could also enable good ones: Bluetooth mouse to Bluetooth computer? No problem. Match the mouse’s brand with the computer’s brand? Easy. CNET’s database had more than 100 million product attributes to fuel such rules, which would emulate how a person intelligently chooses cross-sells.
Of course, the system would measure itself, so we would have additional data about each product’s sales, its effectivness as a cross-sell, even its behavioral performance in “people who did this also did that.” I liked that, because attribute-driven rules and behavioral data were together likely better than either approach separately.
Finally, the system would need to support hands-on use by merchandisers. Rules would be customizable, in a drag-and-drop way. And reports would link back to rules, so a merchandiser could see which rules caused which numbers.
That was the vision for Intelligent Cross-Sell. We announced the product in February 2006 and released it later that year, with paying customers.
By the first release, we could already see Intelligent Cross-Sell was substantially increasing customers’ cross-selling revenue. We later did case studies with Office Depot and Dell that reported a doubling of cross-sell and upsell revenue. (Upsells are another type of production recommendation that Intelligent Cross-Sell does. Whereas a cross-sell offers a carrying case with a computer, an upsell offers a better computer in place of the one you are considering. When doing this, Intelligent Cross-Sell can automatically generate “pitch text” based on an analysis of each computer’s specs, such as “Faster processor and 50% more storage.”)
Although we hit the market as the housing-bubble-induced recession was starting, we managed to get a decent core of customers in the 2007 to 2009 timeframe. By 2010, among our customers were four of the top ten brands in the Internet Retailer 500’s list of e-commerce sites. We had also gone international, at sites in the United Kingdom, France, Germany, and Denmark. Later, we reached sites in Sweden, Norway, and the Baltics.
As we grew Intelligent Cross-Sell’s revenue, we hired a small team to help evolve and support the product. Things were good in our little world.
But 2010 was a turning point. In the previous few years, several venture-funded startups had emerged as competitors, each with vastly more resources than our small group. They had all started with “people who did this also did that” technology, applying it not just to tech and consumer electronics but to all e-commerce categories. Although Intelligent Cross-Sell was still superior for cross-selling tech and consumer-electronics products, the best start-ups were using their greater resources to offer a broader set of capabilities, with cross-selling and upselling being just one aspect.
We knew the game was changing when, in mid-2010, two customers who had been highly satisfied nevertheless defected to other vendors. The other vendors simply offered more stuff. It was like being a bakery in a town that starts getting supermarkets. Our bread was better, but we didn’t have a deli counter or a produce aisle.
We could have adapted by becoming even more specialized, like an artisanal bakery of cross-selling. But it would have been hard to do within CNET, which had become CBS Interactive when the media giant CBS acquired CNET in 2008. As an enterprise software-as-service player, we were already an outlier of a business within CNET, more so for CBS. I did not want to make us even more marginal. So I concluded that everybody—CBS/CNET, the Intelligent Cross-Sell team, our customers—would be better off if we could partner with one of the other players whose only business was doing what we did.
The right match turned out to be RichRelevance, the personalization company with, by far, the most blue-chip customer base, as well as the most complementary approach to the market. In the partnership, RichRelevance would run the Intelligent Cross-Sell technology and employ the team; CNET would license its data and provide sales collaboration. The best supermarket would now offer artisanal bread.
The deal proved to be a win for everyone. For me, having spent five years on the product, having built a team without ever losing an employee, and having worked directly with every customer, I wanted Intelligent Cross-Sell to continue on the best footing possible. It did, and still is.
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