CRM Data Analysis & Business Intelligence

Do you still make business decisions atwithout data analysis?

Even without CRM data analysis & business intelligence: With the increasing digitalization of business processes, many companies are accumulating more and more data. This is a real treasure trove that should be used profitably.

According to the IWD (Institut der deutschen Wirtschaft), 84% of the 1,100 companies surveyed as part of the IW panel can still be classified as newcomers to the data economy. This means that they are still hardly using data as a resource relevant to value creation.

Why use data at all?
Making data-based decisions - but how?
The right tools
Conclusion: First the right questions, then the right analysis

Why use data at all?

The fact that we need to collect data and evaluate it with CRM data analyses in order to remain competitive is no longer seriously questioned. However, it is not always clear what this is all for.

In fact, business data can be analyzed profitably on many levels and serve as a meaningful basis for important decisions. For example, managers can use the right data and evaluations to gain multi-layered insights into everything from departmental performance and individual employees to specific measures. Targeted strategies and methods can then be derived from this. Data analyses also provide support for employees at lower levels.

Sales as an example

Let's look at a sales department, for example: it analyzes the incoming data in the CRM software, from website tracking and telephone notes, through visit reports and quotations, to sales transactions and further correspondence. This allows numerous patterns and trends to be identified or previously unknown correlations between individual factors to be recognized and contextualized.

This can serve as a basis for the sales manager to define the appropriate methods for a strategy to achieve the set goals.

Reminders and suggestions based on CRM data analyses can help sales staff in their day-to-day work - for example, who should be contacted urgently or which customer might have a potential need. The following video introduces you to the exemplary "Customer Journey Monitoring" function.

Holistic view

If these data pools and CRM data analyses are systematically linked with those of other departments such as marketing and service, the holistic view of the company data also provides a valuable basis for overarching strategic decisions and alignments at managing director level.

At the same time, the CRM data analyses can also provide insights for a possible new business model. For example, can you derive an additional service from the data that has already been collected? Think of automated maintenance based on the data collected by a WLAN-enabled washing machine or similar.

Making data-based decisions - but how?

Quality over quantity

If you intend to view your data as a resource relevant to value creation and are now asking yourself how exactly you should approach this, it is best to follow the credo "quality before quantity".

Collecting all kinds of data and analyzing it for all it's worth can certainly provide you with valuable and sometimes completely surprising information, but it is also time-consuming and costly. First of all, answer the question posed above specifically for your company:

Why do you want to analyze data?
Where are the action points in your business model that could be better served by meaningful data analyses?

Derive the analytical questions from the entrepreneurial questions that arise from this. Then ask the "how" question.

What data do you need to analyze in order to answer the questions?
Are you already collecting it?
What external data might you need to contextualize your data?

Finally, develop an evaluation framework for the analyses, for example in the form of KPIs (Key Performance Indicators), i.e. performance figures that you can use to identify specific (failures) successes. This is where it can be extremely helpful to work with experts. They have a lot of experience of where it is worth measuring and analyzing and what framework makes sense for this.

Looking ahead and looking back

One of the great advantages of data-based decisions is the ability to assume trends based on the available data or to simulate the impact of different decisions in the future - predictive analytics. Of course, this still means that no decision is infallible, but it does increase the probability of making the right one.

At the same time, it is also important to establish mechanisms that track the actual impact of your decisions and measures. This allows you to make timely adjustments and improvements if necessary. After the analysis is before the analysis, so to speak.

The right tools

Once you know why, how and which data you want to use, you will of course need the right tools.

With the increasing popularity of data analysis, high-performance BI tools (BI: Business Intelligence) are also gaining in popularity. This certainly makes sense for complex evaluations of large amounts of data. However, they are not a must.

Depending on which data you want to evaluate and to what extent, far less expensive tools will do.

CRM data analyses & reports

For example, your company will need certain tools, such as a CRM system, to collect the necessary data anyway. Most CRM software, on the other hand, already comes with some analysis and reporting functions that allow you to perform a variety of evaluations.

For example, the CRM system itself often includes ready-made reports on the sales funnel, pipeline, forecast or conversions. These usually come with clear graphics and can be flexibly arranged on dashboards as required. This ensures that important KPIs are recorded quickly.

In addition, it is usually easy to create individual reports that are then tailored precisely to the key figures that are relevant for your company. Based on such information, fundamental, forward-looking decisions can already be made.

When does a BI tool make sense?

If the amount of data increases or more complex analyses are required, a BI tool may be necessary. This is the case, for example, if you rely on big data and want to discover previously unanticipated insights by comprehensively analyzing all available data.

The BI tools are of course much more specialized and powerful for this. Ideally, they are equipped with corresponding standard interfaces (APIs) that make it easy to dock onto the existing software landscape.

However, you should also bear in mind that any patterns that emerge can only be interpreted in the context of other data and information if they are to form the basis of your decision.

Conclusion: First the right questions, then the right analysis

Whether you are using CRM data analysis or a BI tool, you should always ask yourself the right questions first and always think systemically:

What business questions should be answered by data analysis?
What data do I need to analyze?
How can I analyze it?

Also, don't forget to weigh up the cost-benefit factors carefully before making any expensive purchases.

DASHBOARDS & REPORTS
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