February 1, 2014
By Sarah Sluis,

Sales Management Tools and Trends to Watch

observes. According to the 2013 Key Trends Analysis in Sales Management Optimization conducted by CSO Insights, over the past four years, the average number of representatives per manager increased from 5.9 to 6.9.

Collaboration allows for decentralized learning and sharing, improving performance and freeing sales managers' precious time. Salesforce.com's Chatter and Microsoft's Yammer make it easier to exchange information. "There used to be this black market of sales intelligence, where people asked, 'What did you hear about this competitor?' In the black market, you've got to give to get. That information is now exchanged, and can be captured and shared with [social technologies such as] Chatter," Galvin says, attributing some of the change not to the technology, but to millennials' openness to sharing and collaboration.

Predictive Analytics

"In sales management, we need a lot more metrics and a lot less hunches," Dickie admonishes. Sales managers have two goals when it comes to analytics: analyzing performance and using data to figure out how to have better outcomes in the future. They want to acquire customers, and retain those customers. Most sales teams use rudimentary analytics that Dickie compares to an abacus. Sales forecasts, for example, are no more likely to be right than a bet on a game of craps, according to his 2013 Key Trends Analysis. Yet there is a multitude of data sources, from CRM systems to email content to social networks, ready to be analyzed to provide insight.

"From a sales manager's standpoint, there is a new skill set, which is, 'How do you harness all this data, and have it make sense?'" Galvin says. The power of predictive analytics is still largely uncaptured, despite the wealth of data available to salespeople. "I was talking to one software company, and they called CRM data the WORN database—Write Once Read Never. If you go in and start analyzing all that information, it would tell us a ton of stuff, such as what steps to use in the sales process, what language to drop, etc., and what competitors we do better against, or if we don't fit based on price or geography," Dickie adds. That power is still largely uncaptured, at least for now. Javier Aldrete, senior director of product management at Zilliant, reports that most of the time, his company's price optimization and sales effectiveness tools replace basic spreadsheets. The company's field is a "totally underpenetrated" area with "a ton of market potential," he confirms. However, the market is heating up.

"The influx of big data, which is where companies like Datahug and Lattice Engines come in, has put a huge amount of pressure on sales and IT organizations to leverage the information that's available," Stakenas says.

The field is small, but growing. Zilliant is a vendor that grew out of the dynamic pricing space. Zilliant's SalesMax product launched in 2011, and provides B2B salespeople with information that helps them determine which customers are likely to defect and to identify cross-selling opportunities. The solution is an extension of the company's MarginMax technology, which helps distributors and suppliers optimize pricing for large numbers of SKUs.

Lattice Engines uses internal and external data to score leads and help fast-growing companies make sure their sales representatives are following the strongest prospects. That includes CRM data as well as external, unstructured data from places such as LexisNexis, LinkedIn, and Experian, according to Lattice Engines CEO Shashi Upadhyay. Information about government grants, a new hire on LinkedIn, or the author of a research article may signal a likely customer. Upadhyay says that he has a whole team devoted to finding new sources of data that might help existing or potential clients. Lattice Engines' product, which is integrated into a CRM system, will show the top reasons a lead has been scored, giving the end user some insight into the process. Upadhyay says one of the company's recent "big windfalls" is that consumer-designed recommendation engines have helped make their product more understandable to end users.

Datahug, which Stakenas placed alongside Lattice Engines on his 2013 "Cool Vendors in CRM Sales," also mines data for insights. The code scans internal email traffic and contact lists to find unknown connections to potential clients. The idea is that you may find another person in your organization with a connection to a target that helps facilitate an introduction. In that sense, Datahug is an algorithm-enabled form of collaboration. The company is also trying to use email to illuminate sales forecasts. A deal may be on the rocks if a rep is sending tons of emails to a prospect and not getting any responses. By graphing email traffic, a manager could see if a sales rep had neglected a lead, dropping off email contact until right before the end of a quarter.

Most B2B companies are selling to very specific, narrow audiences, and big data can help find these buyers. Dickie offers the example of one of CSO Insights' clients, which sold medical products. "They first did an analysis of their best customers, their advocates in the marketplace, and then asked if there was anything in common with these guys. They realized a significant number of people were doing research in this one area, and that's why they were using the spectrometer," he relays. Armed with their findings, "they pointed the big data engine at all the medical journals, and found all the doctors and researchers in that area and started contacting them, and found a lot of very likely prospects."

Where to Next?

Sales has long been tied to performance. Salespeople succeed or fail based on their numbers. But sales managers seeking to solve a pain point in their organization have plenty of technology investments to choose from. They can motivate their employees better, creating more dynamic and accurate compensation plans. They can urge them to complete training sessions through gamification. They can provide them with collaboration tools to share and standardize knowledge. And they can give their salespeople better leads with algorithms that distill unstructured data into actionable findings, all with the promise of creating a more profitable, efficient sales force.

Associate Editor Sarah Sluis can be reached at ssluis@infotoday.com.

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