How Big Data and Advanced Analytics Are Revolutionizing Sales in B2B, and What Managers Should Know About It.

B2B customer buying experience is radically changing. This trend has led to dramatic changes in the sales profession over the past decade.

“Nothing is so painful to the human mind as a great and sudden change”, wrote Mary Wollstonecraft Shelley in Frankenstein. Sales management in B2B is experiencing an abrupt and rapid transformation.

The change in the way that B2B customers buy has produced an explosion in the amount of data available to sales teams. Modern, powerful computers and machine learning algorithms can learn from this data.

This tsunami of sales data has several sources. First, there are the cloud ERP and CRM systems, their mobile usage, e-commerce, emails, own products connected to the internet (popularly known as IoT), and social media, among others.

In around two years, companies will make half of all B2B purchases directly online. Businesses can track and analyse the buying patterns of their customers with extreme precision – and they can predict these journeys.

Any smartphone today is a million times more powerful than the computer that brought the man to the moon. Computing power, combined with artificial intelligence, enables B2B companies to gain competitive advantages, improve efficiency and develop new business models.

[bctt tweet=”The Whole B2B Buying Experience Is Changing.”]

Companies struggle to develop an integrated approach to this new reality. Strategic choices should be contemplated together: big data, e-commerce, and predictive analytics. Let’s review each point.

What is Big Data and what does it mean for B2B sales?

We produced approximately 90 % of the world’s data in the last two years. This exponential growth in data – described as Big Data – has shaken the business world. Sales in B2B is not an exception.

Nowadays, most if not all, companies have digital resource planning (ERP) and customer relationships (CRM) systems in place. Around three-quarters of B2B enterprises are already setting up a web-shop (E-Commerce). Manufacturing companies harvest data from their products (IoT). Lastly, social media is nowadays part of most B2B marketing strategies.

Thanks to social media usage, ERP and CRM, the amount of sales data available has exploded. Successful companies are investing heavily in data analytics. Interestingly, 80 % of SMEs are already doing it.

Big data dramatically change the job of a sales leader. Their primary goal is to increase the efficiency of their sales team. Achieving this objective is impossible today without the use of data.

In other words, key account managers nowadays are more efficient if they can rely on data-insights. These insights typically include finding customers with cross- and up-selling potential or at risk of churning and lead-scoring and account prioritisation.

Sales analytics and software tools help sales managers to generate information based on Big Data and so to profit from it.

Simply put: E-Commerce in B2B is a game-changing technology for Sales

Nowadays, B2B sales will not work without the internet. The company website of a provider is the most used source of information by buyers. Industry forums are also prevalent among customers and utmost influential.

[bctt tweet=”Effective Sales Force Remains A Critical Key To The B2B Customer.”]

In two years, half of all B2B sales transactions will take place without human intervention. Automation and digitisation are reaching a critical threshold.

E-commerce not only opens a new sales channel and produces not only more sales data. It fundamentally changes the role of salespeople and the commercial structure of big organisations.

There is, therefore, the need to retrain B2B salespeople for this new reality. If companies automatically fulfil 50 % of their sales and predictive analytics tells reps which customer to visit, what is their role?

Furthermore, from a marketing perspective, Social Media is being accepted prevailingly to communicate with B2B customers. It is still nevertheless the last prominent criteria in the buying decision.

There is also the risk of digital and “real-world” sales not working together. Aligning digital buying and personal selling should be a daily exercise, not a one-shot deal. Sales and marketing in B2B can only go along.

Sales managers in B2B should apply tools and process that allows a continuous and productive controlling based on real-time sales data.

I want to know how can I use technology to increase sales.

Only Artificial Intelligence brings a modern approach to B2B sales.

Big-Data seems by now a foregone conclusion in B2B. How does a company create value out of its sales data? By applying predictive analytics methods to improve sales efficiency.

Mining data alone offers no value unless used to increase sales and reduce costs. Which technology can create useful predictions using big data, dynamic pricing, or reduce customer churn? Artificial intelligence.

Artificial intelligence in general and predictive analytics are the key to lasting improvements in sales efficiency. Their importance is due to sales productivity remaining crucial in B2B.

Key Account Managers are currently B2B scarcest resource. Technology should help to make them more efficient. This field salesforce visits the customers, communicate value, and are responsible for making optimal use of available opportunities. However, artificial intelligence can automate a big part of their job.

If robots are the future of sales, where does the job of a successful sales leader fit? Sales managers in the future will earn their salary by gathering the right sales data, hiring the right salespeople, and coaching them using predictive sales analytics. In other terms, they will be responsible for creating value across the sales organisation using Big Data.

Sales managers are forced to efficiently manage and control their sales teams using the best technology available. Moreover, they are gaining in importance, at a staggering speed. Each of them is – over the last four years and on average – responsible for 68 % more reps.

How B2B Data Analytics is Changing Sales and What Managers Should Know About It – Conclusion

There are three critical aspects regarding big data that today significantly influence the job description of a sales manager in B2B.

Three things any sales manager in B2B should know today about big data. Big data and data mining can offer valuable competitive insights. E-commerce is changing the way customers buy and the role of sales representatives. To get value out for an increasing amount of data, sales managers need artificial intelligence applied to predictive analytics.

[bctt tweet=”Sales in B2B are dramatically changing. Big data and Advanced Analytics offer valuable competitive insights to navigate these changes.”]

Sales in B2B are dramatically changing. E-Procurement is gaining pace, and B2B web-shops have become indispensable. Companies are increasingly relying on predictive sales analytics to create value out of sales data from cloud ERP and CRM systems. Social Media opens new communications channels with customers while adding to this data tsunami.

Controlling a sales team is critical because, regardless of improvements in technology, a competent sales team remains critical to the B2B customer. This group, however, needs new skills and competencies to navigate the new B2B landscape.

Despite what you often hear, no single tactic — e.g., a selling methodology, big data analytics, or B2B e-commerce, or predictive analytics — will address this new Business-to-Business reality. Make sure you have a holistic approach and be ready to improve it continuously.

Do you have any further questions about Big Data Analytics in B2B Sales? We are happy to help!

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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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free eBook How to get startet with Predictive Analytics

 


Further Read:

Gandomi, A. and Haider, M. (2015) Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management. Volume 35, Issue 2, April 2015, Pages 137-144