The 7 areas where you should apply data analytics in B2B sales

The rapid technological advancement paired with an accelerating digitalization of workflows and processes generates large amounts of data. While in the common understanding, data might have a negative or boring connotation, data is nothing else but information.

And information, if processed smartly, is power – and in modern days, for businesses, through data analytics, provides competitive advantage.

But in which areas of B2B sales should you apply data analytics?

The digitalization process and availability of information has changed the B2B customer journey: there are different (and more frequent) digital touchpoints, and the interaction with customers starts long before the first contact between buyer and seller. One of the first point of contact is the company website, where customers collect (but also provide) information. Also, websites have more often e-commerce capabilities, allowing digital transactions also in B2B.

This transformation in the buying process has changed the way sales need to be addressed – and gives sellers the incredible competitive advantage we talked about before: data.

Sellers have also another source of data; company CRM and ERP systems, that record historical data and interactions from customers and their transactions.

Though, data alone is not enough: companies need to process data to learn from it and generate the right insights for growth. This process is made possible through data science and analytics.

Data analytics takes unstructured data and uses them as a source of business intelligence.  Algorithms are deployed for the analysis and are shaped based on the desired results.

Considering data analytics as a tool for historical analysis would be reductive. In fact, machine learning algorithms can learn from experience, in a more effective and efficient way than humans conducting predictive analysis. In a highly automated and structured way they can consider all the data available, identify patterns, analyse different scenarios that they imply and develop solutions in a much more accurate and significantly faster way.

Data analysis is a science that requires expertise and knowledge. At the same time, it is a practice that should be applied to drive decisions and processes at all levels of the organization, therefore used also by non-experts. That is why at Solia Consulting we are focused on developing tools and solutions that semiautomatically analyse data, draw conclusions  and derive improvements from them, delivering to the user a real action plan on what should be done to maximize profit and achieve growth.

A graphical representation of the data analytics process and the steps we automate follow:

The successful role of data analysis in B2B exists not only in theory; research show that there is a positive correlation between the implementation of data analytics in sales related fields and the company’s growth performance.

Therefore, we have outlined 7 areas in sales where data analytics provides a competitive advantage: 

Pricing:

Availability of historical data and their analysis allow a strategy dominant approach to pricing across the organization.

Clustering and segmentation allow to develop optimal pricing and makes value selling possible through costing tools and value calculators.

Customer segmentation:

More efficient and precise customer segmentation is possible through AI – classifying customers based on their buying behaviour, needs, industries (etc.), is critical for the development of effective sales strategies.

Customer portfolio optimization

Customer portfolio analysis facilitates the creation of buyer personas. That allows more targeted offers based on customer needs, thus improving conversion.

Product portfolio optimization

Understanding needs and behaviours of similar customers based on data, allows product portfolio optimization, making targeted up-selling and cross-selling possible, thus generating more sales and customer satisfaction.

Customer relationship management

Predicting customer buying behaviour through data analysis allows optimal customer relationship management. This enables sales being in contact at exactly the right time and helps reducing customer churn.

Sales force management

Prioritization and segmentation empower the sales force in the task of self, time and effort management. Customers are not all the same and the sales department should not dedicate its time equally among them. A data-based diversification allows sales to schedule their week more efficiently, improving personal and organizational performances.

Lead prioritization:

Understanding and predicting buyer behavior to identify and prioritize among leads, thus understanding which ones are hot leads and improving conversion rates

To conclude, the digitalization and availability of information (data) has changed the customer journey, while providing new powerful insights to improve sales performances through data analytics and machine learning.

The 7 main areas where data analytics improve sales processes are:

Pricing

Customer segmentation

Customer portfolio optimization

Product portfolio optimization

Customer relationship management

Sales force management

Lead prioritization

Expect much more on this topic as well as on the use of data analytics in sales! Therefore, don’t forget to sign up to our newsletter!

Also, we are looking forward to your feedback: drop us a comment and tell us your experience on the topic and what you would like to read about next!

“Information is the oil of the 21st century, and analytics is the combustion engine” –  Peter Sondergaard

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