According to a new report, marketing departments are only supplying 30 percent of the leads that Sales departments need – and sales is spending a lot of time trying to qualify those leads as likely wins. Can Big Data come to the rescue?
CMSWire.com spoke recently with Brian Kardon, Chief Marketing Officer at Lattice Engines, a Big Data vendor that was one of the sponsors of the study. Conducted by sales/marketing research firm CSO Insights, the study, Sales Performance Optimization: Sales Strategy and Key Trends Analysis, found that nearly half of the companies surveyed believe that the quality and quantity of marketing-generated leads needs improvement.
Enter Big Data
The report also found that, even as leads are not as good as they need to be, revenue targets at the vast majority of surveyed companies increased 16 percent in 2012 over the previous year — the largest yearly increase since the 2008 recession. But fewer than two-thirds of sales reps hit their own quota last year, and only about 57 percent of companies reached their targets for revenue.
The CSO study also found that sales is “wasting time trying to figure out which customers and prospects to call.” Forty-two percent of sales reps say they do not have the information they need before they call, 45 percent want help on which accounts to prioritize, and 20 percent of sales reps’ time is being spent doing this kind of qualifying research, instead of selling.
The standard practice, Kardon pointed out, has been that marketing pushes a lead to sales, and sales then researches and qualifies the lead, possibly makes a pitch, and tries to close. But the complexity of qualifying if a lead is worth the effort has increased, taking up more non-closing time for sales people, and companies are being driven by a need to keep their pipeline full — and beating their competitors to the sale.
From the "Sales Performance Optimization" reports
Enter Big Data. Across the landscape, companies are tapping into external data as well as the reams of internally-generated information they accumulate, to find patterns that can inform decisions. It’s happening in sentiment analysis of social media, in evaluating marketing ROI, or in protecting companies’ reputations, among other arenas.
Lattice Engines is sifting Big Data for patterns that can be used to find leads, qualify them and tailor the deal to the customer’s profile.
Take Juniper Networks, one of Lattice’s customers. Kardon said that Big Data sifting can tell which companies “might be ready to buy Juniper’s switchers and routers, because they’ve just moved into a new building” and have to get their networks up and running. Lattice sifts through publicly-available real estate contracts to see which companies have recently signed for new space, in order to find and qualify a target list of leads.
Another example: Staples. A key indicator is an increase in job listings for a given company. Kardon said Lattice’s engines crawls through publicly available data, and, “if a company goes from two job listings to 20” in a short period of time, it’s staffing up — and will need desks, chairs, staplers and office supplies.
Prioritizing Sales Calls
The key is finding that trigger. In order to see which changes might signify a ripe sales target, Lattice analyzes the client company’s internal data, including purchase histories of its previous customers, to find patterns. Did customers’ new governmental contracts, for instance, lead to new orders from this client?
External data, purchased or publicly available, can similarly be analyzed for hundreds of attributes, such as credit rating, funding wins or new lease agreements. “If our client is a security company,” he said, “you could be looking for companies with a recent security breach,” which you can find by crawling through NexisLexis.
Social media data can tell if a company is failing, such as when the number of followers has been stagnant or even dropping, or if other social activity is falling off. Kardon said that this information points to the potential lead probably not being worth much sales effort.
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