How Sales Analytics Can Drive Productivity

The volume and complexity of sales information that resides in CRM, ERP and other business systems is increasing with greater velocity to become one of the most valuable productivity assets of an organization. The challenge is turning that data into actionable intelligence.

Analytic applications to optimize sales are emerging as potent weapons that can provide the competitive edge needed to survive and prosper.

Here are a few ways how sales analytics can be used to drive productivity:

Improve Sales Effectiveness, Productivity, and Cycle Time

A first challenge that sales managers face is simply to understand high-level dynamics behind sales performance. As fundamental as it is, sales managers at many organizations find it difficult to access reliable, timely information on key sales performance metrics. This is largely because of the complexity of multiple order entry and customer service systems.

In such an environment, it’s not easy to understand which issues are most important and how to determine priorities. Analytic applications start with a high-level approach that gives managers at-a-glance readings on key performance indicators (KPIs). Tracking sales performance by key metrics is a crucial first step towards improving sales effectiveness and productivity. The metrics available in an analytic application (as well as trigger-driven alerts on problems or anomalies) enable managers to work proactively and respond to issues as they develop. Such metrics monitor a number of things, including:

• Key changes in forecasts and their projected impact on quarterly results
• Sales trends and revenue fluctuations
• Sales performance gains and losses by sales reps, products, and key customers
• Actual sales productivity vs. goals

Analyze Key Sales Drivers, Trends and Root Causes

Metrics are only the tip of the iceberg. Beneath them is a wealth of data that when analyzed will invariably yield insights that managers can use to fine tune sales performance, and greatly improve their ability to develop accurate forecasts. A deep understanding of revenue drivers and cost considerations is essential to evolve sales performance to a higher level.

Analytic applications excel at delivering those insights through sales-specific reports and queries to commonly asked questions. They can also provide a capability called guided analysis that helps streamline cause-and-effect scenarios to determine what’s causing issues to occur. And they enable managers to explore data on their own, with ad hoc query that can unearth correlations not discernable in a typical business transaction system – or spreadsheet, for that matter.

This deep analysis is valuable to any organization, but particularly so for mid to large organizations with multiple data sources. Analytic applications work from a common set of data aggregated from ERP, CRM, financial, and other sales information sources to ensure that managers work from a single version of the truth to examine:

• Sales by multiple dimensions (e.g., product, customer, sales rep, channel)
• Under-performing sales reps, products, channels, customers
• Effect of discounts, special offers, sales incentives on revenue

At the bottom line, significant productivity improvements can be achieved as a result of having better sales insight. Add the insight that’s also possible by extending analytics to other strategic areas of the business and the ultimate impact on productivity can be epic!