CRM Analysis: Get the most out of your data!

CRM Analysis: Get the most out of your data!


Truly understanding the customer, collecting and using this information is easier said than done. CRM analytics are important for the evaluation and prediction of customer behavior and thus also for long-term customer loyalty. Nevertheless, many companies only use their CRM as a better address database and thus miss out on great business opportunities. Optimizing your CRM analyzes brings decisive added value to your company. anchor

What is a CRM analysis?
A CRM analysis (also: Analytical CRM) includes all procedures for the systematic analysis of customer data available in the CRM database. It provides information about customer behavior and needs. The goal of CRM analysis is to make faster business decisions and derive recommendations for customer relationship management.

What customer data is important for CRM analysis?

Basically, collect the data you really need for your CRM analysis and from which you can derive relevant information. With the unstructured accumulation of data, it’s easy to lose sight of quality. The more unused data you have, the greater the effort to evaluate it at any given time.

Better to start the problem from the back: ask yourself which CRM analysis is useful to you and which data you need for it.

What are the prerequisites for CRM analysis?

Ultimately, it’s about putting individual data into a customer-centric form and creating connections between information. This is how insights are created. For example, if it was not a victory if you die Determine the order probability based on your previous data and thus work more efficiently in sales?

This is just one of many examples of what you can get out of your CRM analysis. In order to use it for greater commercial success, however, these four requirements must be met:

1. A well thought out strategy

In order to be able to analyze your data in a targeted way, you must first know your questions. Some examples: Where do we gain customers and where do we lose them? What products do customers buy most often? How is the quality of the products? How many service cases do we have per customer group? You should be able to answer these and other questions using your customer data through CRM analysis so you can derive appropriate actions.

2. Flexible system

Why is a certain metric getting worse and worse? Rigid reports in PDF format do not answer this question. Connections can only be recognized and corresponding conclusions drawn with a system that enables multidimensional and in-depth analyzes of the data.

3. High data quality

Data quality is the basis for meaningful CRM analytics. Data quality should play an important role as soon as the system is set up. Data quality must then be ensured through regular data maintenance throughout the customer life cycle. This is the only way to run meaningful CRM analytics.

4. Central data management

Consolidate your data silos. Customer data is generated in a wide variety of places: website, social media, via email or phone, in person at trade shows and events. For the best possible overview, you should consolidate all data in one CRM system to avoid annoying double calls from multiple employees and channels. In addition, duplicates that enter the system from different sources should definitely be cleaned.

How to make good use of customer data for CRM analysis?

Intelligent CRM analytics gives you valuable control tools in customer management and provides your business with tremendous business benefits. Here are some examples:

  • Lead segmentation: Which customers are particularly important to your business, which have high or low potential? Customer segmentation provides information about customer structure and buying behavior. Sales and marketing must tailor all actions to customer groups.
  • profitability: With a CRM analysis, you can determine exactly which customers generate the most profit over time and align your sales activities accordingly.
  • Process improvements: How long do customers need from the first incentive to buy to the point of sale? What are the obstacles that cause interested parties to jump? You can clarify questions with a CRM analysis and thus optimize your processes.
  • Probability calculations using artificial intelligence (AI): Thanks to AI, structured data can be used to calculate probabilities that take your marketing and sales to the next level: as already mentioned, order probabilities can be calculated in CRM analytics, for example. The basis for this is your previously won or lost sales projects. It is also possible to calculate the probabilities of the products a customer is likely to buy and determine the potential for cross-selling and up-selling.
  • Back to advertising measures: Measure direct returns on your marketing campaigns. What content works? How many people clicked? Who converted?
  • Purchase: Which products are popular with customers? When should the goods be reordered?Based on daily updated inventory and sales data at the point of sale (POS), goods can even be ordered automatically and availability at the POS can be guaranteed.
  • Customer service: Are your customers satisfied? What channels do they use? When you are available? These questions can also be answered with a CRM analysis and contribute significantly to customer retention.
  • personalization: Instead of working according to the principle of the watering can and addressing all customers and prospects in the same way, you can reach your customers more specifically on the basis of the data evaluated and the probabilities calculated by the AI. Thanks to CRM analysis, you know which products your customers are interested in and how likely they are to buy them.
  • Marketing automation: Use the results of CRM analysis to make your marketing more effective through automation. Read also the article: From lead to deal by Norbert Schuster.

CRM analysis helps you better understand and retain your customers

The cornerstone of CRM analytics, customer data is already available to most businesses. Now take the next step: group and structure them in the CRM and use CRM analytics to win, understand and retain customers.

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