Predictive-Sales-Analytics

Predictive Sales Analytics is a Game-Changer in B2B.

Productivity in business-to-business (B2B) sales is simply defined as the output rate of a sales team, considering all direct costs and performance. Two trends are drastically affecting sales productivity: sales analytics in general and predictive sales analytics in particular.

Sales analytics is since long an efficient method to measure what is working and what is not working in sales, to compare performance and to increase revenues.

The ability to make predictions in B2B sales will radically affect the productivity of sales teams. Per McKinsey, companies using sales analytics improve their sales & marketing ROI by 15 to 20 per cent. Predictive sales analytics offers an extra boost to accelerate sales performance.

B2B Sales Productivity & Analytics – Mind the numbers

B2B sales organisations have always been under pressure to hit sales targets, uncover new opportunities, and maximise productivity. But nowadays, in an increasingly competitive, ever-changing and globalised market, it is essential for sales leaders to make optimal use of limited sales resources.

Andris A. Zoltners describes this challenge in his book, “The Power of Sales Analytics”, and gives clear advice:

“As sales leaders ponder the challenges of structuring, sizing, deploying, hiring, developing, motivating, informing, and controlling their sales organisations, they are working with a new generation of technology-savvy workers and an explosion of data and technology that has several components:

• Escalating volumes of information on customers, sales transactions, market potential, competitors, sales activity, and salespeople.
• Social networks such as Facebook, LinkedIn, and Twitter.
• More powerful and fast-changing computer, storage, and mobile communication technologies.
• Advanced models and analytics tools.”

Most companies have the sales data they need. Data is today one of the most mighty enablers a B2B sales team can have. To find sales insights, to plan and forecast sales, managers should analyse ERP sales transactions and CRM sales activities.

Sales analytics plays a crucial role in identifying customer segments, activities and opportunities that can drive sales efficiency.

However, nowadays, this analysis is mainly being done manually, using Microsoft Excel, QlikView, Tableau or another data visualisation tool. Different studies estimate that managers spend around 25 % of their time undertaking this task.

Do not be confused, data visualisation without business intelligence (BI) is just another rudimentary form or analytics, and it will not improve sales performance immensely.

New technologies for analytics are emerging as a core differentiator for top performing sales organisations. Useful sales analytics should present users with all the information they need to make successful sales decisions. Sales leaders should be able to get access to critical sales data with just one click.

Show me the future – Predictive Sales Analytics in B2B

Sales teams need the information that enables them to predict customer behaviour and anticipate successful sales actions.

A B2B key account manager typically handles dozens of accounts, with hundreds of products. This complexity results in endless possibilities on what to sell, to whom and at which point in time.

“Salespeople are investing their time poring through a heap of possibilities to find the good ones,” writes Eric Siegel, author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. “If sales is a needle in a haystack, analytics can make the haystack a whole lot smaller.”

Advanced and predictive analytics functions can foresee the buying behaviour of the B2B customers. They are useful to understand where there might be pricing, selling potential or churn risk.

Predictive Sales Analytics uses predictive algorithms, mathematical models based on ERP and CRM sales data and represents a considerable enhancement to the productivity of any sales team in B2B. These predictive models reveal sales insights that key account managers care about, such as the best cross-sales opportunities, the likelihood of closing a deal and the estimated potential of a customer.

Sales managers can optimize customer demand with Predictive Analytics and forecast customer potential.

The time saved using predictive sales analytics represents one of the key drivers of sales productivity. For example, a sales manager can significantly reduce the total time spent on sales analytics activities and activities that can be optimised using analytics.

They usually include time looking for accounts with better chances of succeeding, time spent in researching churn risk and pricing inconsistencies, time spent in sales planning meetings, time devoted to coaching KAMs, and time spend on onboarding of new Key Account Managers.

Predictive sales analytics also offers improvements in performance for the first line in sales: sales representatives and Key Account Managers. They benefit by reducing the total time spent on non-customer facing activities.

These non-productive activities typically include the time spent looking for cross-selling opportunities with existing customers and the time unnecessarily spent in developing customer loyalty with loyal customers, while overseeing those at risk of churning.

They also cover the time consumed dealing with pricing inconsistencies in existing accounts; the time spent discussing sales plans with management and the time spent travelling to non-relevant customers or leads.

Why is predictive sales analytics a “must-have” to increase sales productivity in B2B – Summary

Both Sales Analytics and Predictive Sales Analytics play a critical to improve sales productivity in B2B. They reduce the time sales managers and sales teams spent in unproductive non-customer-facing activities. Furthermore, they also provide a decisive competitive advantage in highly competitive industries.

Therefore, sales managers should be able to implement and track the most appropriate sales activities and KPIs. These KPIs should reflect the overall status of present customers, together with segmented turnover, profitability and new customer acquisition.

Sales teams should have all critical information predicted at any time: where are the low-hanging fruit, the quick-wins, customers at risk of churning and additional sales activities with high impact in sales performance.

 

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Predictive analytics is the technology that enables a look into the future. What data do you need? How do you get started with predictive analytics? What methods can you use?

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    Further Read:

    The Power of Sales Analytics (English Edition)

    Data Science For Business: What You Need to Know About Data Mining & Data-Analytic Thinking, by F. Provost & T. Fawcett

    Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

    Applied Predictive Modeling