Badass Sales Analytics for B2B-Companies

Get to know 5 Badass Sales Analytics Hacks for large and medium-sized B2B companies.

Do you know the following situation? You work for a B2B manufacturer or wholesaler, and you have some bright minds in your IT department. Data projects are a dime a dozen: in individual departments, across departments (maybe a new CRM?) and some IT people even try to create “THE ONE data analysis”.

Nevertheless, nothing works. The efforts do not pay off measurably, and the analyses wither away in archives.

In our experience, many B2B companies do it this way or something similar.

So, this article “Badass Sales Analytics” is for all strategic sales & marketing managers, project managers, business developers or IT people in large and medium-sized B2B companies who are desperately trying to learn from their data.

What does “Badass Sales Analytics” mean? Ultimate hacker skills or complicated deep learning algorithms? No. Much better: Actionable Insights.

The Qymatix experts have been dealing with AI-based data analysis in the B2B area for seven years now. We have learned when data projects fail and when they are successful. Here are our five hacks that make up “Badass Sales Analytics”.

1. “All on one Table”.

The best preparation for successful predictive data analytics is when you manage to get ALL participants around one table. That is the decision-maker (e.g. managing director), operational and strategic sales manager, IT manager and data analysts. If external service providers are involved, it is best if they are also involved. Why?

The goal of this meeting should be a shared understanding of the objectives of the data mining efforts. There are countless data structures, data sources and even more models. Data analysis can only add value in connection with a specific goal. So, prioritise business cases together.

One example: Let’s assume that your goal is sales growth. You can achieve this goal through various strategies.

– You can increase the customer lifetime value of your existing customers, i.e. get “more” out of your customers, for example, by preventing churn, optimising pricing or realising cross-selling potential.

– Or, you want to grow with many new customers. Keyword: “Lead Management”.

– Or, you want to grow by offering new products that are in high demand.

Many roads lead to Rome. “All on one Table” enables you to find the best roadmap to reach your business objectives. How to get started? Organize a workshop, for example.

2. Classic BI analyses are a thing of the past.

Don’t get us wrong – if you already perform (and use) Business Intelligence (BI) analyses in your company, you doing something right.

But it is not yet “badass”. Why? Business Intelligence requires the time and resources of data analysts. Tableau or Qklick are expert systems that not “everyone” can use.

“Badass Sales Analytics” lets algorithms do all the work.

Automate your analyses with, e.g. predictive analytics software. The algorithms learn from your data and recognise valuable correlations for exact forecasts.

3. Start small, end big.

Have you got a taste for predictive analytics? Very well, then you have already recognised the value of precise, automated forecasts.

Now comes the next problem. Once you have understood the principle of predictive analytics, it becomes clear that there are many areas of application. Trend predictions, predictions about customer behaviour (cross-selling, pricing, churn), inventory forecasts, lead scoring – to name a few.

How and where do you best start? In our experience, the fastest successful data projects are those that start small and expand iteratively. In the first hack “All on one table” you have already prioritised business cases. So, start with a pilot project with your first Priority.

We characterise “Badass Sales Analytics” by the fact that you quickly gain concrete insights. Who benefits from analyses that take forever and are never applied?

I am a Badass Analyst and want to know more.

4. Actionable insights matter.

Of course, it is not only precise predictions through data analysis that are important but also insights.

Let’s explain it by using an example:

Let’s assume that the algorithms detect a connection (correlation) between customer attrition and a particular country. Good. What do you do now? Tell customers to move to another country? Not very actionable.

It is valuable to know that there is a correlation between customer churn and the country where your customers are based, but there might be nothing you can do about it. ctionable insights” are information about situations that you can change.

If, for example, there is an exceptionally high correlation between a specific product (or product group) and customer churn, you could ask your customers how they are coping with this product. And, of course, assist if necessary.

5. Algorithms do only half the work.

The best data analyses and algorithms are “only” valuable, if your teams use them. Insights alone do not change anything. Change requires people.

As Goethe said: “It is not enough to know; one must also apply it; it is not enough to want, one must also do”.

That is what “Badass Sales Analytics” is all about. The interaction of fast and precise findings AND a practical data-based implementation. That also includes a rethink in sales. “Algo-management” is the keyword here. Take your sales team on board early enough.

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