Analytical CRM - Maximizing the Potential of Your Own Customers

1. What is analytical CRM?
2. The expansion stages in analytical CRM
3. What are typical use cases of analytical CRM?
4. What are the key components of analytical CRM?
5. What are the goals and benefits of analytical CRM?
6. Conclusion

Data is the oil of the 21st century, if you know how to process, analyze and interpret it. It can provide a crucial added value and reveal considerable potential for cost reduction and revenue increases. As a result, buzzwords like data-driven marketing, artificial intelligence or marketing automation hover above many marketing departments. But what do the first steps look like? What prerequisites are necessary and how can algorithms support the work?

Analytical CRM is the key to this.

What is analytical CRM?

Analytical CRM is one of the pillars in CRM - short for Customer-Relationship-Management. Analytical CRM refers to the systematic evaluation of customer data. The goal is to make customer-focused and more economical decisions in marketing, sales and customer service. Analytical CRM thus extends the operational CRM to the processing and especially evaluation of the data collected in the operational systems.

CRM data includes all collected information about customer contacts, actions and reactions, as well as the associated master data of each customer.

With the help of analysis tools and a CRM analysis large amounts of customer data are evaluated to recognize and understand patterns and trends in customer behavior and in their purchasing habits. Of course, the mere evaluation of customer data is not an end in itself, but aims at the value-adding use of the analysis results, e.g. by optimizing marketing efficiency.

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The expansion stages in analytical CRM

Analytical CRM has different expansion stages, which answer further questions.

Expansion stages in analytical CRM

1. What is happening?

The first and the most important step is the creation of data-analytical transparency. Counts and defined KPIs support you in creating a clear picture of your customers. The question 'What is happening?' is the basis, but also the endpoint - and thus the control - of all analytically controlled actions.

2. Why is it happening?

In the second step you can find out why certain things happen by combining different information. Who and what triggers, for example, the behavior of your customers? Are they external factors that cannot be influenced, are they the customers themselves or was the behavior of the customers influenced by a specific advertising measure by the company?

3. What will happen?

In the third step, the question is answered, what will happen in the future. Predictive models allow predictions about future needs of each individual customer or about which customers are no longer satisfied or are at risk of churn.

Another example is the predicted Customer Lifecycle Value (CLV). The predictive CLV indicates how much turnover the customer will likely make in the coming period. Whether we look at this on a monthly, quarterly or annual basis depends entirely on the company and the typical purchasing frequency. If you now know that your customer Mrs. Mustermann will make 50 € in sales next month, that's exciting information, but this information alone is not yet value-adding. For this information to be value-adding, you need to use this knowledge and apply it specifically in the operational CRM, this followes in the fourth step.

4. How can the future buying behavior of your customers be specifically influenced?

In the fourth step, the optimization, it is now a matter of integrating the collected information and analysis results into the operational business in such a way that they are profitable. Often, different generated results and information are combined to derive measures that specifically influence the future buying behavior of customers in a positive way.

What are typical use cases of analytical CRM?

A big advantage with the use cases in analytical CRM is that you can start with small steps and each use case is a valuable measure in itself. Examples where analytical CRM can be used profitably are:

• Churn-Prevention:In order to win customers as loyal customers in the long term, it is relevant to identify success-critical changes in buying behavior at an early stage and specifically prevent churn.
• Ad Affinity:For measuring marketing efficiency it is not relevant whether the customers buy, but how the advertising measure influences the buying behavior of the customers. Who would have bought anyway and now just took the offered discount. Customer behavior that not only can be observed at Black Friday. So it is relevant to identify the customers who have actually generated additional turnover and especially profit through the measure.
• Customer Segmentation:The formation of data-based customer segments makes it possible to interact more individually with the customers. The content or the channels can be specifically aligned for a segment. In doing so, wastage is reduced and customer satisfaction is increased. More on Customer Segmentation you can find in our article.
• Assortment Affinity:The calculations of affinities for each individual customer allow a targeted approach and efficient exploitation of cross- and upselling potential.
• Acquiring New Customers:Via which acquisition channel can you gain the most valuable customers? Are there differences in subsequent purchasing behavior? For example, it often turns out that customers won through 'customer recruit customer actions' are much more loyal than customers who are won through competitions. Analytical transparency in acquiring new customers is an important factor for the orientation and budget planning in acquisition.

What are the key components of analytical CRM?

Optimally, the basis of analytical CRM is a central database for the collection, enrichment and preparation of customer data.

A data analysis tool is then used to access the data. The data analysis forms the heart of analytical CRM. Through calculations, from simple to highly complex, the customer data is analyzed to recognize trends and patterns.

Through targeted visualizations of the data and the analysis results, complex facts can be presented in an understandable manner, so that in the best case scenario direct recommendations for action can be derived from it, which are also actionable for non data-affine departments and employees.

From Data to Action Cycle

Basically, there are different possibilities to combine the components of the analytical CRM. Various software products on the market offer solutions that bring all components together. These complete packages include not only data storage but also pre-built, integrated algorithms and visualizations. User-friendly interfaces enable the use of complex models with just a few clicks and settings. With such complete solutions, you have to worry little about interfaces and seamless data flows.

These products often sound very comfortable, but even with these solutions you should be aware that different handling of outliers for example or missing data can have considerable effects on the results. A basic understanding of statistics should therefore also be present with these software solutions. In addition, the given analytical possibilities often do not answer all questions. It is relevant here to know which algorithm can be used for which question.

So there are CRM systems that can answer many analytical questions in CRM with a robust decision tree in the background, but for example, do not provide a cluster analysis for the formation of personas.

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Software solutions that combine the components of analytical CRM include for example:

Salesforce Sales Cloud

Microsoft Dynamics 365 Sales

SAP Sales Cloud

You are less restricted with the independent creation of the analyzes. The stored customer data can be accessed with common query or programming languages.

So there are, with Python or R-Studio widespread and free programming languages with which all kinds of calculations are possible. Also analysis tools like SAS® Customer Intelligence 360 or IBM SPSS Modeler can be used for exactly these analyzes. SAS and the IBM SPSS Modeler are powerful statistical programs, but here, in contrast to R or Python, additional license fees apply.

Often, further profitable ideas and approaches arise from answering individual questions, which can then be directly depicted. Clear advantages of creating the analyzes yourself are higher flexibility and lower costs for the software solutions. However, this usually also leads to more resources and internal know-how being needed. Alternatively, this can also be specifically purchased to use the knowledge and resources for internal development or to have the algorithms created and carried out externally.

The choice of software depends on the specific needs and requirements of a company. Therefore, it is advisable to evaluate various options and choose the solution that is best suited to the CRM strategy. A combination is also often possible, as many providers allow the integration of external code, such as scripts written in Python, or interfaces exist.

What are the goals and benefits of analytical CRM?

The goal of analytical CRM is to derive insights from customer and sales data in order to make better decisions in terms of marketing, sales and customer service. These insights help to improve customer relationships and increase business success. Along the customer relationship phases of acquisition, loyalty and churn, a deeper understanding of customer needs and preferences allows a personalized and relevant customer experience. Marketing campaigns can be designed and executed more targeted. Customers now expect personalized approaches and offers and it is clearly shown that customers are more willing to buy and more loyal when these expectations are met. In addition, manual effort can be reduced and efficiency increased by implementing automated processes.

Conclusion

In order to remain competitive, it is becoming increasingly important to fully exploit the potential of your own customers. The key to this is understanding the customers and identifying their needs and preferences. This is exactly what analytical CRM enables. Analytical CRM allows many topics to be set up independently of each other. Even with small steps, a lot can be achieved and every step brings you closer to your customers.