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Bringing data science to sales organizations

In a challenging economy amidst global competition, demanding customers and scarce growth opportunities, companies can't afford a sales organization that doesn't perform.


Compared to other corporate functions such as marketing or supply chain, the use of analytics in sales is still in its infancy. However, the desire to inject more analytics into the sales process is building. The increased availability and sophistication of enabling capabilities (such as analytical tools and data) and simple necessity are driving more extensive sales analytics adoption.

By taking a more scientific approach to selling and applying analytics to key areas across the sales process, an organization can augment its sales force’s experience, judgment and intuition, and enable more effective, fact-based decision making. It's time for enterprises to boost the overall contribution of the sales organization through sales analytics. But, bringing science into selling is not easy. By following seven guiding principles, companies can increase their use of sales analytics and further their journey toward high performance.


Over the last decade, the widespread implementation of enterprise resource planning, customer relationship management and sales opportunity management systems has improved the availability and quality of sales and sales-related data for use in analytics. Facing significant attrition rates, a heavier reliance on tools and analytics helps sales teams maintain effective levels of product and customer knowledge.

In addition, the increasing complexity of many companies’ product and service bundles makes it difficult, if not impossible, for individual sales representatives to make effective decisions about prospecting, customer targeting, cross-selling and other key sales tasks without having a strong analytics capability to support these decisions.

And, finally, customers simply have become more demanding: They expect sales people to understand their needs and to match product or service offerings precisely to their preferences and circumstances.

Bringing Science to Selling: Achieving High Performance through Sales Analytics discusses the opportunities that exist for the use of sales analytics across the end-to-end sales process. It also provides examples of how the application of analytics to specific areas of the sales process has reaped significant benefits. Additionally, it addresses the more practical challenge of making sales analytics happen in your organization by offering seven guiding principles companies should consider when introducing sales analytics.


The seven guiding principles are:

  • Find your starting point. The first consideration is where and how to apply analytics capabilities across the end-to-end sales process. Choose a high-profile problem, follow the value and look at your strategic objectives.

  • Don’t turn your sales representatives into number crunchers. Rather than making them spend time on number crunching and territory analysis, empower them to act on the outputs of analytics.

  • Create a sales analytics support function. It is important to understand the specific requirements of sales analytics as opposed to other forms of analytics.

  • Tie the analytics to specific sales strategies and embed them into the sales process. Analytics should enable a very specific step or set of actions that the sales people are expected to take as part of the selling process.

  • Foster an analytics culture. Ask for facts to back up assertions, reward the use of analytics, set an example, communicate analytics success stories and recognize the changing profile of successful sales people.

  • Don’t be discouraged by lack of data. Acknowledge the lack of data but remember that impactful sales analytics does not necessarily require terabytes of customer data.

  • Incorporate market-sensing analytics into your future vision. Doing so will ensure the analytics journey has direction and is understood by the organization.