“Every company is a data company”. Data Strategy for B2B Predictive Sales Analytics

How to develop a winning data strategy in the artificial intelligent era.

Having read the most recent publication of the author, strategic business and technology advisor Bernard Marr “Data Strategy: How To Profit From A World Of Big Data, Analytics And The Internet Of Things”, we would like to present our ideas regarding predictive sales analytics in business-to-business (B2B).

Marr is an internationally best-selling author and futurist,

addressing the implications of new technologies such as artificial intelligence, big data, blockchains, and the Internet of Things. In his book, Marr provides plentiful of ideas and examples regarding the usage of data in companies.

Data is gaining in relevance across many industries, with industrial distribution and manufacturing being no exception. As the German market leader in B2B predictive sales, we have analysed more than a billion sales transactions with our predictive sales analytics software. This results in some (hopefully interesting) details that we add to the discussion.

First, we would like to extend the insights provided in the book with our thoughts about data strategy in a predictive sales analytics’ context. Second, we will discuss how predictive analytics is relevant to a company’s data strategy. Third and lastly, we will briefly address how to know if your company possess relevant data for predictive sales.

Let’s review each point in turn.

What is a data strategy in a sales analytics context?

Every strategy starts with an assessment. Questions such as: what kind of data records do we have? How good is their quality? What is the business goal we are pursuing with our data strategy? Come first. Starting with an assessment of a company’s business goal is critically essential in sales analytics.

Companies successfully developing a data strategy always keep their business goals in focus. Sales analytics is only a tool serving a company target. Furthermore, technology is changing at a pace that makes hard to follow. Having a reliable assessment of the current situation and the business goals is paramount and can make a huge difference in your financial results.

As Marr put it: “The challenge in creating a robust data strategy is to identify the best, most accessible, most achievable analytic approach for you. Having said that, advances in analytics, AI and machine learning are moving so fast that it is safe to assume that new and improved ways of extracting value from data will emerge very, very soon”.

Having defined your goals and an assessment of your current status, the following point of your strategy should include guiding policies. Successful business leaders see policies as the “rules of the game”. They answer the central question of “how do we want to achieve our goals”?

Successful business leaders see policies as the “rules of the game”.

Finally, a good data strategy in the context of sales analytics should include specific actions. These actions are the consequence of having set clear business goals and working policies. Examples of strategic activities of a successful data strategy include improving data quality, establishing new processes for data gathering and implementing predictive sales analytics.

Beware of the costs of each measure. Let’s say a B2B sales team consists of 10 employees. Including salary, commissions and related expenses, only reporting sales can cost the company around 250,000 euros a year. Successful data strategies reduce the costs of reporting and controlling sales. Here you can find out more about the ROI of your sales team.

Why is predictive analytics relevant to your data strategy?

As part of sales analytics, predictive sales analytics offers B2B companies a look into the future. Predictive analytics represents a cost-effective way to improve sales activities, increase customer satisfaction and lifetime value.

There are, however, unique implications of predictive analytics regarding your data strategy. Predictive analytics relies on data – from its quantity to its quality.

Successful sales leaders understand data quality and quantity as two separate yet interrelated working packages. The former is an iterative process, where predictive analytics helps to prioritise where to improve it. The latter starts with an assessment of the current data that the company possess, for every B2B company has sales data.

Implementing predictive sales analytics and machine learning using ERP and CRM data helps sales executives to focus their data efforts where it matters most.

Do you have the data you need for analytics in sales?

We finally come to the last point of your data strategy regarding sales analytics: how can you know whether your company has the data it needs?

Assessing whether your company owns the data sets it needs is a critical element of your data strategy.

This evaluation mainly depends on the information that the company expects to extract from the data and the available software for sales data mining.

Successful executives clearly distinguish between information and data. As per Peter Drucker, information is “data endowed with relevance and purpose.” Determining what information the company is after, rests on the points we described in the sections above, chiefly your business objectives, decision-making process and available technology. Allow us to quote Bernard Marr once more. Marr explains that: “Raw data, such as customer retention rates, sales figures, and supply costs, is of limited value until it has been integrated with other data and transformed into information that can guide decision making. “

Information is “data endowed with relevance and purpose.”

The second point to consider is whether your company commands the software solutions that can extract information from your data, depending on your business goal.

If your goal is to sell more, reduce marketing costs, and increase customer lifetime value, you need a software that analyzes your data. Most B2B companies already have the raw data that such software requires. These datasets include sales data and transactions (e.g. ERP data) as well as CRM data.

Artificial intelligence and machine learning make today possible to transform these sales data into valuable information. Modern predictive sales software is a cost-effective way of turning data into relevant information.


Data Strategy for B2B Predictive Sales Analytics – Summary

We question whether every company will soon become a data company, as Maar arguments. We can certainly agree that ERP data mining and sales analytics are today of enormous relevance for industrial distributors and manufacturers.

Analytics is critical to increase sales and reduce costs. With the help of predictive sales analytics and data mining, the sales team can look into the future and become substantially more productive.

Gains in productivity is the ultimate reason why a data strategy is relevant to predictive analytics. Moreover, sales objectives, available data sets and sales analytics software are critical components of any fruitful data strategy.

Based on our solutions and experience, we finally presented some ideas about the kind of data that successful companies need to implement a data strategy.

Do you have further ideas or questions about your data strategy? Please write to us today! We are happy to discuss them with you.


Further Read:

Data Strategy: How To Profit From A World Of Big Data, Analytics And The Internet Of Things

Also Interesting:

If Your Data Is Bad, Your Sales AI Tools Are Useless

Predictive analytics – how much data do you really need?