CQuotient, With $3M from Bain, Looks to Help Retailers Adjust to Customers’ Buying Behavior
There’s certainly no shortage of intriguing new tech startups around town. One stealthy company that has come to light in the past week is CQuotient, a Belmont, MA-based developer of analytics software for retailers. The startup confirmed it recently raised $3 million in Series A financing from Bain Capital Ventures.
Founder and CEO Rama Ramakrishnan is a veteran of analytics as applied to business and retail. He was formerly chief scientist and vice president of research and development for ProfitLogic, a price-optimization software company that Oracle acquired in 2005. Joining Ramakrishnan at CQuotient is chief operating officer Graeme Grant, who was most recently the CEO of Allurent, the now-defunct online shopping firm. Grant and Ramakrishnan previously worked together at ProfitLogic (and then Oracle).
CQuotient just started in July, so the company isn’t giving too many specifics about its technology or business strategy. What’s interesting is that it’s riding some big trends in consumer tech, such as personalized shopping and recommendations, more targeted and mobile advertising, and increasing amounts of data on exactly which items people buy, and when and where they buy them. A company that can help retail stores make sense of all those details—and translate them into selling more stuff—stands to do pretty well.
Here’s a transcript of an e-mail chat I had with Ramakrishnan:
Xconomy: Can you describe the genesis of CQuotient?
Rama Ramakrishnan: During my tenure at ProfitLogic, and later in my analytics consulting work with retailers, I became increasingly convinced that looking at the world through a “customer lens” was the answer to a number of challenges retailers face.
While “looking at things from a customer’s point of view” sounds obvious, in reality retailers rarely do it. Instead, they look at high-level product sales and inventory data (e.g., we sold 100 units of Product Y in December) to make decisions. They rarely look at what their individual customers are doing (e.g., those 100 units were sold to 40 customers who each bought one unit and never again, and 20 customers who bought one unit and then returned to buy it twice more on future trips).
I was blown away by what I saw as the untapped value in customer data and started CQuotient in July 2010 with the idea of helping retailers infuse customer insight into every important decision they make. One of the retailers I pitched to found CQuotient’s vision compelling and became our first (and paying) customer.
X: What’s the main problem you’re trying to solve?
RR: Competition in retail is incredibly fierce, and has become even more so since 2008 when consumers slashed their spending. In the new retail landscape, growth is going to be driven by winning more of the purchases from the customers you already have, not by constantly getting new consumers and churning through your old ones.
The only way to win this “share of wallet” game is to understand your customers better than the competition: what they buy, when, how, which channels. But understanding isn’t enough. You need to flow this understanding into all the decisions you make that affect the customer (pricing, offers, assortment, service, etc). And you need to do so at scale (hundreds of stores, thousands of products, and millions of customers).
What products/platforms/services exist today to help retailers do this well? NONE.
X: So what are you actually building, and what is unique or special about your technology or business approach?
RR: We’re building a cloud-based data-and-analytic-app platform:
—Customer-level behavioral (what they buy, and when/how they buy) and other data will be the “raw material.”
—Optimization apps will crunch this data and generate recommendations for action by retail execs.
—The apps will leverage proprietary data-mining/machine-learning models of customer behavior.
CQuotient’s secret sauce:
1. Our models of how individual customers will behave when exposed to different marketing and merchandising actions.
2. Our ability to combine big data, sophisticated math/computer science, and retail domain knowledge to build products that are wicked smart but “speak” retail.
X: How do your and Graeme’s experiences at ProfitLogic, Oracle, and Allurent inform your new strategy? Can you share a specific lesson or two that you learned?
RR: Graeme and I met at ProfitLogic. So we have both seen first-hand the value analytics can provide to retailers. ProfitLogic was very focused on adding tangible, measurable financial value to retailers and we have the same ethos. Too often, analytics companies produce “insights” and some poor decision-maker at the customer has to scratch their head and figure out what to do with the insight. CQuotient’s focus is concrete, actionable recommendations so it is easy for the recipient to act on it.
But, our experience means that we have also seen the limits to the approach ProfitLogic and other firms have taken (using only sales and inventory data). By focusing on customer-level data from day one at CQuotient, we are opening up a whole new set of value for retailers. And by leveraging new developments in cloud computing, we are able to process the huge quantities of data required to truly unlock this value.
X: In basic terms, how is the landscape of retail optimization evolving? What are the big trends you are riding? And who are the established companies to be disrupted (e.g., IBM, Oracle, SAP, Amazon)?
RR: The leading edge of the retail optimization landscape (particularly for bricks-and-mortar retail) is the move towards more granular data, i.e., customer transactions. We have squeezed out a lot of value from high-level sales and inventory data. There’s not much juice left. The next chunk of value will come from analyzing customer-level data.
The biggest trend we’re riding is customer-centricity—a focus on what your customers want, and then aligning everything you do around that. The retail world is moving to a “share of wallet” game and if you don’t infuse customer-centricity into everything you do, you will likely not survive.
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