All companies want to be as efficient as possible and spend their marketing budgets effectively, but the ultimate goal is "to make relationships better with customers," AgilOne CEO Omer Artun told InformationWeek in a phone interview. "It's about understanding the sentiment of the customer both today and over time."
Most of AgilOne's customers know they have valuable data but either haven't yet built a marketing model or have already tried to integrate various data sources but "failed or have a subpar solution," Artun said.
Artun not only has a background in data science (he holds a Ph.D. in computational neuroscience and physics from Brown University), he also served a consulting stint at McKinsey & Co. and held senior positions at two retailers: CDW/Micro Warehouse, and just prior to founding AgilOne, Best Buy, where he was senior director of B2B marketing at the company's Best Buy For Business division. "I started AgilOne based on my own experience as a frustrated marketer," Artun says in a company press release.
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"The commonality of all our customers is they're all highly transactional, high-volume marketers," he said, adding that huge volumes of dirty data mean the "signal to noise" ratio is low for these enterprises. AgilOne is working with some 40 customers already, including brands like Bosch, Shazam and PetCareRx.
For PetCareRx, a 14-year-old online pet health and wellness products retailer, the issue was growth and the need to look at diverse marketing "touches" over time to understand the lifetime value of each customer. "[Building a predictive model] in-house was not where we wanted to put our resources," PetCareRX CFO Mark Ellis said in a phone interview.
With 20,000 SKUs, hundreds of thousands of active customers and millions of transactions, understanding the purchase patterns of customers is a complicated business, Ellis said. Before AgilOne, the retailer had to take a one-size-fits-all marketing approach, such as offering a set calendar of discounts and similar promotions to everyone. But now the company has much more insight into each customer, Ellis said.
Likewise, AgilOne helped the company create much more finely grained financial models and business projections, which are now updated monthly. "It's remarkably accurate and incredibly valuable," Ellis said.
Cloud-based big data solutions or "appliances" that rely on machine learning to quickly create a predictive model make sense in some scenarios, said Forrester research senior analyst Rob Brosnan. "Look, a hand-tuned model developed by super-smart data scientists will beat a machine-generated model most days of the week," Brosnan said. That said, he went on to note the common wisdom that data scientists aren't easy to hire or retain.
Interestingly, Brosnan doesn't expect the likes of IBM, SAP or Oracle to follow AgilOne into this space. Rather, he anticipates a wave of acquisitions in two or three years, either by traditional database companies or the largest marketing automation companies.
AgilOne's Artun, meanwhile, expects a shakeout this year. The rush to create big data infrastructures and "so many people building interconnectivity," he said, means "a lot of immature solutions will collapse. Absolutely."
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