In sales, as everywhere else in business, there is a buzz about big data and analytics. Vendors hype tools and mobile applications to help sales forces make sense of it all, while touting case studies that generated impressive improvements in sales force effectiveness.

Companies are anxious to capitalize on the opportunity. While some jump in, many are reluctant to move forward. Some will remember or hear stories of failed projects – big investments to give salespeople tablet computers, to develop data warehouses, and implement CRM systems that ended up racking up huge costs, while generating little value for customers and salespeople. We also hear concerns such as “the technology is too new — let’s wait until it matures,” or “we don’t want to invest in something that becomes outdated in a year.”

These are valid concerns, but here is the crux of it all. It’s not the data and technology that matter. What really matters is how technology, data, and analytics can help salespeople, sales managers, and leaders improve fundamental sales force decisions and processes.

Consider a few examples.

Helping salespeople. Consider account targeting. Traditionally, salespeople decide which customers/prospects to spend time with by examining a list of accounts in their territory and figuring out which ones to focus on to achieve a territory sales goal. But far too frequently, salespeople end up spending too much time with easy and familiar accounts, demanding customers with urgent needs, and friendly prospects. Ease and urgency trump importance.

Approaches that use data and analytics, structured around frameworks that capture the dynamics of customer/prospect needs and potential, help salespeople target the right accounts and spend time more effectively. Such an approach involves:

Identifying profile characteristics (e.g. type of business, number of employees) that predict account potential and developing an estimate of potential for each customer/prospect.

Using techniques such as collaborative filtering to identify customers/prospects with similar needs and potential (the “data doubles”) and suggest the best value proposition and sales approach for each account.

Closing the loop by providing an assessment of how effective account targeting was so as to inform better future decisions.

Helping sales managers. Analytics can help sales managers have higher impact as coaches and make more-informed decisions about issues such as sales territory design, goal setting, and performance management. Traditionally, managers rank salespeople on criteria such as territory sales or sales growth, and tie rewards or corrective consequences to these rankings. But if territories don’t have equal potential, the rankings don’t reflect true performance. Salespeople with rich territories have an unfair advantage while those with poor territories are demotivated.

Data and analytics enable performance metrics that account for territory potential, so that sales managers can reward the best salespeople, not the best territories. Such an approach involves:

Developing measures of customer/prospect potential, using company and third-party data sources (e.g. business demographics) and sales force input.

Identifying the true best performers using techniques that separate the impact of territory potential from the impact of a salesperson’s ability/effort on performance.

Rewarding the true best performers, learning what they do that’s different from average performers, and sharing the learning across the sales team.

Helping sales leaders. Analytics can help sales leaders improve decisions about issues such as sales strategy, sales force size and structure, and the recruiting of sales talent. Consider how analytics can help sales leaders design a sales incentive compensation plan. Traditionally, incentive plans are designed by surveying salespeople about their satisfaction with the current plan, benchmarking against industry and company historical norms, and checking past incentive costs versus budget. This retrospective approach can blindside sales forces with undesired consequences in terms of sales force effort allocation and financial risk.

A better plan results when companies use data and analytics, structured around frameworks that link plan design to projected costs, sales force activity levels, and fairness under varied market conditions. Such forward-looking approaches improve the odds that despite an uncertain future, an incentive plan will motivate the sales force to focus effort on the right products and customers, and be fiscally responsible. Such an approach involves:

Using analytics to test the consequences of proposed plan designs, compare alternatives, and reveal unwanted side effects and financial risks.

Monitoring payout distributions and metrics showing a plan’s strategic alignment, motivational power, and costs.

Proactively making adjustments to keep the plan on track.

It’s not about the technology or the data. Investments in sales data, technology, and analytics can only live up to their promise when sales forces focus first on understanding the dynamics of the fundamental decisions and processes that salespeople, sales managers, and leaders are responsible for.