CRM Analytics – The Qymatix most effective three tips for B2B sales

Sales Analytics is changing the way sales teams work in B2B. Both Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems offer pro and cons for sales analytics. Some teams are already using machine learning for Predictive Sales Analytics from their ERP data; most are just implementing analytic in their CRM.

There are several mistakes to avoid while applying analytics – or predictive analytics – to a CRM system. Based on our experience of the past years they are: overloading teams with no-actionable data visualisation, implementing a data science and data mining but forgetting key accounts managers are not scientists, and sticking to a set of sales KPIs regardless of changes in the situation.

Zoltners, Sinha, and Lorimer, Authors of “The Power of Sales Analytics” offer three useful tips for providing a sales force with great sales analytics. Inspired by them we present here our three most helpful tips for companies considering analytics from their CRM systems.

The successful implementation of Analytics in a CRM, predictive or not, depends on critical choices. Moreover, if a company implements analytics, but do not modify sales behaviour or uses the right set of sales KPIs, sales results will not improve.

I want to make my CRM analytics easier.

Tip “Numero Uno”: Less is more.

In CRM analytics, less is always more. The most efficient sales analytics methods use as few key performance indicators (KPI) as possible.

Sales leaders should define what key performance questions they are facing and then implement analytics in a CRM. Especially in complex sales situations, focusing on the critical aspect of a sales challenge provides a crucial difference.

Furthermore, to be useful, sales analytics in a CRM should emphasise causes, not on results. Otherwise, analytics risks overloading sales managers with information that is either unusable or lagging behind.

Tip Number Two: Speak a sales language.

Key Account Managers and sales leaders are not data scientists and should not be. CRM Analytics is nowadays a mined field. The advent of powerful new cloud technologies and the increasing investment in data science made sales analytics a risky endeavour. That is why, if sales leaders want CRM analytics to be beneficial, it should speak a sales language, not a data scientist dialect.

First, the data science: data mining methods from CRM activities and ERP sales data. Second, the interpretation of the analysis A sales force should be presented with actual results, not with the analytical methods.

Tip Number three: Be flexible.

Not every sales situation needs the same approach. No B2B sales organisation faces the same challenge for long. Sales leaders need to reflect on the kind of problem they are regularly confronting.

Standard CRM analytics is helpful, but not always enough to provide valuable insights. Zoltners explains that an analytic “turnkey” service is a good way to enhance adoption, usage and return on investment. Being able to consider different sales KPIs from a pool of output, input and advanced offers a critical advantage.

CRM Analytics – Three tips, summary:

Sales Analytics and Predictive Sales Analytics are changing the way B2B sales teams work. Analytics in CRM is becoming increasingly popular nowadays. For CRM Analytics to be successful implementing within B2B sales, there are three key points to follow.

First, focus. Sales leaders should use as many KPIs as needed, as few as possible. Second, analytics should be presented in sales terms, not in technical ones. Finally, not every sales situation requires the same approach and the same sales KPIs.

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