"Standard firmographic data isn’t very helpful if you’re trying to determine whether a company is ready to buy your software. A printing company in Seattle with 100 employees might look virtually identical to one of similar size in Charlotte. Yet one may be technologically advanced and eager to digitally transform while the other is content with traditional solutions and won’t budge. What we needed was a way to distinguish a promising lead from its peers.” - Nicolas Facon
“My team needed to narrow its sales funnel to include only those companies for which it made sense to reach out, understand their challenges, and offer Microsoft technologies as a solution,” said Facon. “But the sheer size of the market—coupled with a lack of insight into what really drives a particular company to buy at a certain time—proved to be a highly challenging situation.”Facon’s team started by studying their existing customer base to develop an ideal customer profile for each of Microsoft’s various cloud-based products and services. Their thinking was they could identify similar companies using standard business databases and then score them against the ideal customer profile.
"The machine learning engine is always on,” Facon said. “We keep refining our models, and they keep getting better all the time."The bottom line for Facon’s team is they can now target small- and medium-sized business prospects with the kind of accuracy and personalization that was previously available only for major accounts. And they do it on a truly massive scale. Microsoft has witnessed significantly high results, including:
“SMB customers don’t expect Microsoft to engage with them directly and are even more surprised when the sales person already has a deep understanding of their business and what value digital transformation can bring to them specifically,” said Facon.