Salesforces' recent acquisition of RelateIQ signals the start of a new wave of data science and sales convergence. How will other players in the industry react?
I'm not sure how many readers of ClickZ caught the recent news of the RelateIQ acquisition by Salesforce. I believe that this is a bellwether deal in what is sure to be one of the most significant acquisitions for Salesforce since Radian6. And it marks the blending of predictive analytics across the domains of sales and marketing in the enterprise.
Salesforce will pay almost $400 million for the Palo Alto, California-based start-up, which provides "relationship intelligence" based on data science and machine learning. RelateIQ will become a Salesforce subsidiary. RelateIQ says it's built "the world's first Relationship Intelligence platform" that redefines the world of customer relationship management (CRM).
Basically, the platform captures sales data from email, calendars, mobile calls, and social media to provide up-to-the-minute dynamic insights - like Tinder for sales guys looking to hook up with deals. Since its founding in 2011, the company managed to raise $69 million in venture capital, with $40 million in Series C finalized just this March.
RelateIQ co-founder Steve Loughlin says, "RelateIQ is pioneering the next generation of intelligent computing through data science and machine learning. Looking ahead, Salesforces' acquisition of RelateIQ will extend the value of their CRM apps and platform with a new level of intelligence across sales, service, and marketing."
When an industry leader pays through the nose for a company that is barely known outside of Silicon Valley, you can be sure that we're in the early innings of another huge shift in the market, a move to dynamic analytics-driven action for sales reps.
If you can't beat them, buy them. By integrating RelateIQ to its portfolio of cloud-based applications and services, Salesforces' Marketing Cloud - which I've written about here - could bring "relationship intelligence" to its installed base, and perhaps drive RelateIQ to an industry standard position and, at the same time, spawn a list of intelligent mobile, CRM competitors. It will be fun to watch the other cloud players, IBM, Adobe, HP, Oracle, and Microsoft, react. As CRM is mostly broken, there are dozens of hungry start-ups looking to disrupt the old guard while the old guard watches and scoops up the early leaders in defensive moves.
Driven by big data, this is a revolution taking place around the mobile sales process that is every bit as big as that one that was driven by marketing automation a decade ago. Perhaps bigger.
While marketing automation systems used big data to drive marketing process, from the top of the funnel down, these new systems will use a system of record, a database coupled to a delightful mobile app that reps want to use to close more business, bottom up. RelateIQ says a key strength to their technology is that it works "almost entirely without human interaction."
One of the great things about RelateIQ is the platform's ability to capture and analyze activity data automatically as the rep performs day-to-day tasks "process" tasks - sending and reading emails, scheduling calendar events, attending meetings, and making phone calls.
The problem is that CRM sucks and is only as good as the data and content it contains. Sales people hate using CRM, and in order to ensure use of any system, sales reps need to be delighted about the entry process. Seventy-five percent of a rep's time is spent doing non-customer-facing activities like data entry and research. When a rep uses a mobile app as an interaction layer, coupled with back-end systems, actionable insights unfold and everyone wins; the rep closes more business and management builds process.
A rep needs to know, "What action can I take right now to significantly increase closing this critical deal?" Like marketing automation systems that provide a score based on engagement metrics, these new systems will be able to capture rep process data, or another form of engagement from the phone, and "score" their "next best actions" in order to convert leads to revenue. The back end will push a campaign back to the rep, just like a marketing automation-based campaign to a prospect, this time however its content is targeted to the rep. The content is a tactical rep action that moves the deal along to close, win. It's very clear to me.
Sales management will have complete transparency into team performance and forecast - which deals are likely to close, and which are at risk? The problems in B2B are tactical, but can be addressed by combining data science and predictive insights, and a highly engaged rep experience as data is being generated from the rep and the device, while the back-end system, using predictive analytics, pushes actionable "targeted" content back to help them close more business based on a score.
Salesforce understands this convergence. That's why they paid so much for a company the mainstream market hasn't heard of. Now comes the hard part: integrating a next-generation platform and user experience with a mature infrastructure that was built in a different decade.
We're still in the early days of data science and sales convergence. The eventual outcome of this marriage will be game-changing - using predictive insight to figure out exactly what actions drive reps to exceed quota, and which tactics will be most leverage able across the organization.
Image via Shutterstock.
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Rand Schulman is a pioneer in digital marketing and new media. He was founder and CEO of one of the first SaaS-based web-analytics companies, Keylime Software (Yahoo) and lead products and strategy at Webtrends; was CMO of WebSideStory(Omniture/Adobe) through its IPO; was general manager of Unica's Internet Division (IBM); and is a co-founder and board director, and is an emeritus board director of the DAA. He's been a trustee of the Direct Marketing Educational Foundation and is an executive-in-residence at the University of the Pacific for New Media and Marketing and was managing partner at SchulmanThorogood Group.
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