Productivity in business-to-business (B2B) sales is simply defined as the outcome of a sales team taking into account all direct costs and performance. Two trends that dramatically affect sales productivity are sales analytics in general and predictive sales analytics in particular.
Sales analytics has long been an efficient method of measuring what works and what does not work in sales. This is then used to compare performance in order to increase sales.
The ability to make predictions in B2B sales will radically affect the productivity of sales teams. According to McKinsey, companies using sales analytics improve their sales & marketing ROI by 15 to 20 percent. Predictive sales analytics provides an additional boost to accelerate sales performance.
B2B sales productivity & analytics - watch the numbers
B2B companies have always been under pressure to meet sales targets, discover new opportunities and maximise productivity. Today, however, in an increasingly competitive, changing and globalised marketplace, it is essential for sales leaders to optimise the use of limited sales resources.
Andris A. Zoltners describes this challenge in his book, "The Power of Sales Analytics"and gives the following advice:
"As sales leaders contemplate the challenges of structuring, sizing, deploying, hiring, developing, motivating, informing and controlling their organisation, they are working with a new generation of technology-savvy employees and an explosion of data and technology that has several components:
– Gigantic amounts of informatione about customers, sales transactions, market potential, competitors, sales activities, and salespeople.
- More powerful and rapidly changing computer, storage and mobile communication technologies.
- Always more advanced models and analysis tools."
Most companies today have the sales data they need. Data is one of the most powerful assets a B2B sales team can have. To find sales insights and to plan and predict sales, managers who Analyse ERP sales transactions and CRM sales activities.
Sales analysis plays a crucial role in identifying customer segments, activities and opportunities that can enhance sales efficiency.
Today, this analysis is however, mainly manually performed, using Microsoft Excel, QlikView, Tableau or another data visualisation tool. Several studies estimate that managers spend approximately 25 % of their time on these tasks.
And that is often where the confusion lies. Data visualisation without business intelligence (BI) is just another rudimentary form of analytics, and it will not improve sales performance immensely.
New technologies for analytics, such as The Trend Trend Platformare emerging as a important distinguishing factor for top performing sales organisations. Useful sales analyses should provide users with all the information they need to make successful sales decisions. Sales leaders should make it a priority to access crucial sales data with just one click.
Show me the future - Predictive sales analysis in B2B
Sales teams need information that enables them to predict customer behaviour and anticipate successful sales actions.
A B2B account manager typically handles dozens of accounts with hundreds of products. This complexity results in endless possibilities about what to sell, to whom to sell, and at what point in time to sell.
"Sellers invest their time sifting through a lot of opportunities 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 lot smaller.”
Advanced and predictive analytics functions can anticipate the buying behaviour of B2B customers. They are useful for understanding where there might be pricing, sales potential or churn risk.
Predictive Sales Analytics uses predictive algorithms, mathematical models based on ERP and CRM sales data, and significantly improves the productivity of any sales team in a B2B environment. These predictive models reveal sales insights that account managers care about. This includes the best cross-sales opportunities and the likelihood of closing a deal and the estimated potential of a customer.
Sales managers can use Predictive Analytics to optimise customer demand and predict customer potential.
The time saved with predictive sales analytics is 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.
This includes time spent looking for accounts with a better chance of success, time spent investigating churn risks and price inconsistencies, time spent in sales planning meetings, time spent coaching and onboarding new account managers.
Predictive sales analytics also offers performance improvements for the first line of sales: Sales Representatives and Key Account Managers. They benefit from by reducing the total time spent on non-customer-facing activities.
These non-productive activities typically include time spent seeking cross-selling opportunities with existing customers and time spent unnecessarily developing customer loyalty with loyal customers, while monitoring customers at risk of dropping out.
They also include the time spent dealing with price inconsistencies in existing accounts; the time spent discussing sales plans with management and the time spent driving to non-relevant customers or leads.
Why predictive sales analytics is a must-have to increase sales productivity in B2B - Summary
Both sales analytics and predictive sales analytics play a crucial role in improving sales productivity in B2B. They reduce the time sales managers and sales teams spend on unproductive, non-customer-facing activities. Moreover, they provide a decisive competitive advantage in highly competitive industries.
Therefore, sales managers must be able to implement and track the most appropriate sales activities and KPIs. These KPIs should reflect the overall status of current customers, along with segmented sales, profitability and acquisition of new customers.
Sales teams must have access at all times to all the information that is critical to the success of the business: where are the low-hanging fruit, the quick wins, customers who are in danger of dropping out, and additional sales activities with a major impact on sales performance.