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Why you should embrace sales analytics tools

Many sales reps still take a traditional approach to sales, and they reject sales data. But here's how sales analytics can make organizations more efficient and improve sales.

When the topic of business analytics comes up, the first target mentioned is, generally, sales -- and for good reason, since sales is a hybrid of numbers and people, where analytics can excel.

But what makes sales fertile ground for the application of analytics is more than optimizing numbers and gaining better knowledge about your customers: Sales analytics tools can not only enhance the results of sales reps and managers, but they can also transform the roles of all of the members of the sales team.

While sales teams can be resistant to bringing math into the art of selling, sales analytics can help make tasks such as sales forecasting, sales pipeline management and marketing campaign management more efficient. Sales analytics can also enable greater transparency about the sales process by answering questions like, "How healthy is the pipeline for new prospects and customers?" and "How close is the rep to making quota?" Some companies have even used analytics alongside gamification to make sales competitive among teams, inspiring reps to outdo one another by making it easy for them to see how their peers are faring.

The upside of introducing sales analytics tools is that the process is easy. The downside is that transitioning roles is not always so easy.

Getting in step with customers

The truly competitive sales department walks in step with a customer from start to finish in the customer journey; from when a customer is a new prospect to when he is a loyal buyer, or even a recurring subscriber. In each stage of that journey, analytics has a role.

From noise to prospect. Historically, sales and marketing have been in an ongoing tussle to define the ideal customer; the most likely prospect. Descriptive analytics is the key here, exploiting internal data (historical data profiling of the most loyal customers) and external data (metadata about those loyal customers, gathered from social media and other sources) to present not only an ideal customer portrait, but also a context that defines the greatest opportunities to get the attention of such customers.

Basic marketing and sales analytics use tactics like audience segmentation to better target subcategories within an audience based on attributes such as common demographics, lifestyles or other defining characteristics. This enables more targeted messaging by marketers and more successful acquisition of new prospects. This level of precision and focus ensures that sales reps put their efforts into prospects that have the greatest potential. In modern marketing, applying analytics to target these most promising prospects is sometimes referred to as account-based marketing.

From prospect to pipeline. Making effective contact with key prospects and converting them into customers is partly about finding the best possible channel for making contact (whether that's on a mobile device, store website, physical store or other channel). Analytics can deliver here, as well, with customer profile data matched to social media monitoring, a technique for "listening" to a customer and measuring his degree of engagement. Once this is achieved, that channel of communication can be maintained as a touch point for later encounters in the customer relationship.

From pipeline to customer. Sales analytics tools can do more than segment the potential customer audience according to the likelihood of buying, or even by channels, preferences and demographics (all of which are important): They can also be used to personalize product presentation to specific customers. Sites like Amazon try to anticipate what you'll want next, and then have that product pop up everywhere. That's an example of analytics at work.

From customer to brand loyalist. Customer retention is as much a part of the journey as ever -- treating previous customers' needs as equally important to sales as finding new customers. And this process also benefits from analytics: Those channels of communication that helped sales get to know the customer better in the first place, along with social media monitoring, bring in new information based on what the customer says to others in online reviews, on social media and so on. This information is actionable, both in crafting a follow-on message and in improving service to the customer. And all of this activity serves to introduce improvements in the customer acquisition process.

The impact of sales analytics tools

The sales/marketing tussle over how to define the ideal prospect is ultimately a compromise, one that shifts the mission of each to some degree. Marketing traditionally focuses on the number of leads generated for sales; sales traditionally works with what it is given, and copes with the fact that lots of these prospects may be wrong for the pipeline (because they aren't ready to buy or never will be). That's wasted effort on both sides.

By cooperatively altering this process and defining shared metrics through descriptive analytics and predictive analytics, sales and marketing teams can get much closer together, improving performance for both. This, in turn, improves general management by providing better information on target markets and specific market sector success potential.

Impact of analytics on sales teams

Sales teams have traditionally lived and died in a mashup of targets and numbers, so they are no strangers to data-driven processes. But, in recent years, everything has changed: the internet, online reviews and social media have made buyers smarter, more aware of their choices and more in touch with trends. It's easier for consumers to evaluate and choose before buying.

By cooperatively altering this process and defining shared metrics through descriptive analytics and predictive analytics, sales and marketing teams can get much closer together.

The focus for sales teams, then, is no longer about selling products; it's more about providing greater value to a more discerning customer. This can happen through broader education about products and the market, targeted discounting and so on.

How does analytics help? Members of sales teams no longer need to work from common customer data. They can now be guided on a deal-by-deal basis with real-time analytics insights that account for a specific deal's context. With different members of the team able to take different analytic views into their process at different points in the sales cycle, the entire team's chances of closing increase.

Sales leaders, in turn, benefit from seeing team performance in context with data from other teams, such as marketing and operations. Integration provides new metrics to improve communication between groups. Tactical improvements and coordination between departments become not only possible, but also convenient.

The impact of analytics on sales managers

Traditionally, the sales manager's role has been fairly simple: First, generate sales forecasts for each sales rep based on the rep's reports of pending sales and likelihood of closure. Then, herd reps to predestined finish lines, improving performance as needed and when possible.

The analytics-based sales manager, however, has a different role, though the relationships are the same. Sales analytics tools create a rich enough body of data for sales reps to have far greater control over their own pipelines, with easy-to-use software supporting them and keeping them focused on priority sales targets and actionable goals.

Analytics and the customer

This is the first article in a three-part series focusing on the role of analytics in sales, marketing and customer service. 

Since reps can do their own forecasting, the sales manager can become an aggregator of those forecasts (for upper management's use), and put the significant savings in time to better use, coaching sales reps to greater performance and catching and preventing errors sooner. 

Next Steps

When predictive analytics fails

Tips to improve your sales forecast accuracy

Tools for sales insight

This was last published in January 2017

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