A rising tide of big data can lift your sales team’s boat, but there’s often a back end to the rosy prospect of combining big info with heavy-duty analytics. Indeed, sales analytics can help the saleshood community of sales reps better help each other.

Bottom line? It’s a technological advantage that first requires significant changes to company infrastructure, it demands new training, and it almost always comes with a hefty price tag. 

While that kind of triple threat can dampen a business’ enthusiasm for the big-data shift, sales managers owe it to their teams to make the case. The revenue prospects are significant.

  • When sales uses data in the right ways, recent research by CSO Insights tells us revenue productivity ramps up by some 17% per sales representative.
  • Additionally, a Crimson Marketing report shows that companies that leverage big data for sales can drive up profitability by as much as 6%.

While some companies are ready to make that leap, some organizations aren’t, or can’t. The good news, though, is that it’s possible to incorporate big-data management ideas into different levels of approach. You can step up your engagement with big-data ideas and then use your initial results to push for next steps. 

Big Data: Strengthening Sales, Step by Step 

For examples of how to bring better client and prospect data strategies to your sales force, start with the following five approaches. From modest moves to the full-on big-data overhaul, these tips can lead your team toward to new and rewarding results. 

  1. Engage with your already available data resources. One of the underlying concepts of a big-data effort is to align different kinds of information in fresh ways. This can happen at a department-to-department level, even before you get into tech and infrastructure changes. For example, enable your customer-service reps to provide you with insight-rich details. Some of the best data comes from customer service; they’re hands-on managing the accounts. “Prioritize this data higher than the standard profile data,” said Andy Brockett, channel business development manager at Allegient, in an interview. Plus, you may well make a big-data discovery or two along the way. As Brockett also noted, “it’s easier to upwell a client than to find a new one.
  2. Be expansive in your search. Even in early stages of shifting your sales team toward data intensive efforts, start to identify and assemble disparate digital sources for information about your clients and prospects. “Collect big data from e-mail, social, etc., and use it to view not only the demographic information about online consumers, but to understand their affinities, habits, and needs,” said Dick O’Brien, director of Product Marketing at Offerpop, in an interview. “Collect that data by using software platforms that can also easily collect, organize, and integrate individual consumer data into CRM and e-mail marketing systems as well. That way your data isn't locked in a silo, but can be used to inform the best practices to sell to a consumer, both online and in-store.”
  3. Create a single home for your different data families. The start of a wider-reaching big-data plan for sales is to work with IT and business leaders to bring data together into a single-pillar system. In other words, lobby for a better boat. Your client information, prospect databanks, order histories, CRM notes, and the multitude of reports, research, and information that surround your sales team’s efforts seldom exist in a centralized space. Especially at legacy companies, data management tools tends to get the retrofit treatment rather than a system wide overhaul. If this is the case for your sales milieu, leverage what you’ve already learned from the first two approaches in this list to open the discussion about overhaul.
  4. Bring the right tools to the job. Putting your data into a central repository opens the door for your team to then apply software solutions that can extract the kind of actionable ideas that separate kinds of information often hide until they’re analyzed. Most often this means a software platform that allows you to tap into, explore, and visualize what your newly gathered data can tell you. Again, bringing big data to bear for sales is about reaching across aisles within the company — to customer service, that’s one example — and illustrating where revenue and development will be fueled by the new opportunities information analytics creates.
  5. Keep your sales-side interfaces intuitive. Algorithms do a lot of heavy lifting when it comes to discovering new avenues to clients and prospects, but sales leaders must ensure that the tools they put in place are, above all, user friendly. “High-level data mining and machine learning algorithms are a must,” said Ryan Naudé, head of the data solutions division at Entelect, in an interview. “A salesperson should be able to pick up influencing factors based on key outcomes, project sales, simple decision-trees, and the like. We shouldn't need actuaries or statisticians to provide sales with this data; high-level analytics should be intuitive for any user.”

When you get to this point in the process, a key move is to then identify long-term goals for the data-work you’re doing, and engage your sales team in the training it needs to reach for those milestones. “Conversely, train up the technical staff to understand the agility required for sales to perform their role,” said Naudé. “The two departments need to understand each other's language.”

And therein is a pattern all its own. 

From small steps to large, bring big data to sales stands to recast your team a both bridge-builder and leader. The revenue boost will make your argument for data-rich sales strategy stronger, but the backbone you build as you deploy the kind of approaches listed above will make your whole organization a stronger one as well.

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