Data: A Flashlight to Sales Innovation
All organizations understand the power of using data to gain insights into performance and to maximize returns. The power of data-driven insights is familiar to most sales executives who manage data-intensive CRM systems, analyze revenue linearity, or eye deal-close rates.
Yet few organizations have considered applying the same approach to finding the most profitable homegrown sales innovations to accelerate growth in new product categories or build industry communities without a large investment. Recent research and case studies, though, show that the same data-driven approach can serve as a flashlight to illuminate these innovations.
The principal challenge is not a shortage of ideas. Within every sales organization, there are almost certainly a number of innovative ideas, processes, and structures that are being employed on a limited scale. Rather, the barrier to these taking root across the organization is uncertainty: Which ideas are worth the most and have the highest likelihood of succeeding?
In our previous article, “Innovation Can Be Scaled” (The Tipping Point, June 2008), we outlined a critical four-phase approach to resolving that uncertainty. Here, we’ll examine how to use this data flashlight in the initial two phases—gauging available data in the organization to discover which of the existing sales innovations already produce measurable performance differences.
The foundation of this approach is variability analysis—that is, analyzing distributions of performance across multiple dimensions (product, sales-rep achievement, geography, solution) to identify significant variability. Using internal (not external) benchmarks companies can discover which ideas are already doing disproportionately well and build on them.
Variability analysis applies to any level of data quality, meaning even companies without an extensive investment in data capture can use it to uncover performance variation. It is not necessary to have a wealth of customer account, industry, or product data—or to rely on intuition or partial information in making decisions.
In one example, a company lacked reliable customer data on bookings and purchasing mix due to legacy systems that only tracked product data. Variability analysis still helped identify and explain pockets of outperformance, including specific solutions with high uptake among small and midsize customers, and what account practices resulted in cross-selling of over four products at a time. After further qualitative validation, a handful of ideas were funded for broader implementation.
Where data quality is high, companies can capture even more value by applying more-sophisticated tools. For example, regression-based statistics can create greater certainty and precision about what the prime variables are that drive performance. Isolating variables such as rep characteristics, product bundles/features, and discounts allows sales executives to make finely tuned adjustments to their sales models. In turn, this drives better results including faster cycle time, higher close rates, higher attach rates, and better-qualified sales opportunities.
The brightest flashlight beams come from the use of statistics-based engines, particularly those that use optimization algorithms to improve the granularity of opportunity estimates, or to generate predictive models.
These hold the potential for radical change in sales force management by letting companies predict the expected change in productivity when they modify different variables—such as adding systems engineers, or dispersing industry experts in specific accounts, or changing the rules on exception-handling. Such approaches let sales executives quickly determine exactly where and how to invest their budgets to create the best returns.
Beginning with variability analysis is a low-cost starting point that does not require re-architecting your back office, or investing in hours of statistical training for your sales operations team. This avoids the typical frustration felt by many sales executives faced with large technology dependencies, the cost of technology investments, and their own lack of experience in using more-sophisticated statistical tools. Once variability analyses uncover innovative ideas that work, companies will be more comfortable making new investments in data sophistication to drive additional productivity.
In McKinsey & Co.’s High-Tech Sales and Marketing Practice, Lareina Yee (firstname.lastname@example.org) is an associate principal; Eric Kutcher (email@example.com) and Tom Stephenson (firstname.lastname@example.org) are principals.
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