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In the Search for New Customers, Analytics are Key

It’s never been easier to build clean leads lists from scratch. Top sales teams rely on business development platforms for up-to-date information on potential clients, including contact details for decision-makers. Teams that embrace sales acceleration technologies can hit the ground running and close deals more quickly than the competition.

Sales analytics, sometimes known as sales intelligence, can serve various pertinent sales and marketing functions. Most platforms identify, model, and predict trends, sales outcomes, and consumer behavior, which can greatly improve sales activities, including lead generation, customer targeting and segmentation, and forecasting. Yet some firms fail to see the value in such a system or are unable implement one effectively. While sales analytics can be a significant investment, firms who take the leap tend to experience improved operating efficiencies and significant increases in sales.

B2B Sales Analytics Examples

When done correctly, employing a sales analytics platform can dramatically improve productivity, increase the quality of conversations between salespeople and leads, shorten the sales cycle, and make your sales strategy more competitive than ever before.
 

A great salesperson today is expected to go into a sale with all the right knowledge. They are required to know any and all information concerning the product and are expected to have a certain amount of information about the buyer before even making contact. All too often, however, this knowledge-gathering process is time consuming, costly, and incomplete, allowing viable leads to fall through the cracks. Sales analytics technology optimizes the sales process by automating the collection of information and pointing salespeople toward customers with a high propensity to buy. Specifically, sales analytics tech can enhance customer success and lead generation activities in the following ways:

 

  • Predictive lead and account scoring – Whereas traditional lead scoring is conducted by a marketer personally analyzing the behavior of inbound leads, predictive lead scoring applies a predictive analytical model to this task. It analyzes past data from your CRM, maps a pattern based on your company’s past conversions, and predicts the likelihood of that account becoming a sales qualified lead by assigning it a score. The value added here is that sales analytics technology can take a maximum number of factors into account while minimizing room for error.
  • Prescriptive sales activities – Building on the identification of patterns used for scoring leads, sales analytics technology can prescribe the correct sales technique based on the prospect’s behavior. For example, based on contact and conversion data, an email (rather than a phone call) could be predicted to generate a more positive outcome with a particular buyer. Some platforms even automate messaging or predict the number of calls and the timeframe it will take to get a sale to close. According to InsideSales.com, prescriptive sales activities can shorten the sales pipeline by up to 20%.
  • Predictive opportunity scoring – This scoring technique prioritizes sales opportunities by using predictive modeling to analyze account history and activity in your CRM and determine which opportunities are most likely to be winners. This allows your sales team to focus on higher-scoring prospects to close deals more efficiently, instead of wasting time and energy on prospects that are unlikely to buy.
  • Pipeline management – A sales pipeline is a visual representation of every stage in the sales process from lead generation to close, giving a complete picture of where each prospect stands in the buying journey. This allows salespeople to determine how far they are from closing and what activities and deals will help move prospects along the pipeline. Some platforms also allow you to automate selling and post-selling activities using their pipeline management tools. Managers can use this tool to monitor their teams’ progress.
  • Predictive forecasting – Sales analytics can automate accurate forecasting, taking a greater number of factors into calculations and predicting revenues with greater speed and accuracy than traditional methods.

Sales Analytics Best Practices

Sales analytics and insights can do a lot to augment the sales activities of your organization. However, to achieve maximum return on your investment, follow these best practices to make sure that your software is operating as effectively as possible.

  • The technology should be integrated with your CRM platform so that your existing customer data can inform the predictions and activities prescribed by your sales analytics.
  • Behavioral data should be categorized by stage of the buyer’s journey it maps to. This ensures that your sales team can take appropriate action with precision.
  • Salespeople should be provided with resources and scripts for each stage of the buyer’s journey. When an action is prescribed by the software, your sales team will know exactly what resources will be most effective.
  • Data should be updated in real time or in as close to real time as possible.

Make sure that whatever sales analytics platform you select can efficiently meet all of these criteria in order to attain the best results. When done correctly, employing a sales analytics platform can dramatically improve productivity, increase the quality of conversations between salespeople and leads, shorten the sales cycle, and make your sales strategy more competitive than ever before.

Learn more about how Dun & Bradstreet can help your company use analytics to empower your sales team.

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