With a sea of data, from sale to customer support, marketing, etc., your CRM system has the potential to reach new heights – if you can generate the right understanding of the large quantities of data. If you add ML into the equation, you can improve the revenue from your customer base, while ensuring that you retain your customers as much as possible with Customer Retention.
Machine Learning in CRM
CRM systems generally focus only on historic data and, therefore, only provide insights into previous customer patterns. ML is not limited to working with historic data like CRM systems; it can also offer a qualified prediction about the future. This way, you can optimise how you manage, understand and help your customers now and in the future.
The technology works like an intelligent layer on top of your existing CRM system, which draws on the company’s data so that you are told the whole customer story. The technology lets the machine perform tasks based on the company’s historic data, and among other things, it analyses previous purchases to predict the future actions of customers. The technology will only improve with time, once it is re-trained and has more insights from the continuously growing pool of data, which will enable it to learn new sales patterns. With insights from ML, you will be able to process your customers as individuals with different needs, and they will be able to sense your interest in them, which is more important than ever.
Increase the value of your CRM investment in these three areas
1. Look into your future
As mentioned, CRM systems focus on historic data. However, ML offers something entirely different: a future-oriented and predictive look at your customer base. Every interaction that your customer has with the company will be examined, and on this basis, the technology will provide recommendations for how your next interaction with your customer should take place.
ML develops together with your company – if there is a change in your data due to new products, employees, etc., the technology can change automatically. This lets you avoid the manual work of creating and maintaining rules, which is typical to CRM.
2. Demystifying the why question
With CRM systems, it is possible to collect all customer data in one place, but there is a lack of insight into why specific interactions between the customer and the company take place. It is possible to manually mark a customer who is at risk of “churn”, but it takes a long time to investigate the reason for this. If the ML model is re-trained, it can perform work for you and uncover the reason behind specific actions using its self-learning system. Furthermore, the technology can take into account many more data points than the human brain is able to comprehend, and on this basis, it can determine motives you do not already know. This way, there is a greater chance that you make the right decisions for you and your customers.
3. Consistent use of unstructured data
Your CRM system is only as good as the data that it works with. CRM is at its best when it handles structured data – but this is just one side of the customer story. The other, and at least equally important, side consists of unstructured data, such as email correspondence, feedback, meeting notes, etc. This consists of text that ML can optimise and process as data using NLP. Unstructured data, together with structured data from your CRM system, can act as fertile soil for useful information that can be used to make even better data-driven decisions. In the end, you simply get better results.
Listen to Martin Bagger talk more about the use of NLP in this video.
You are always welcome to contact us if you want a non-binding meeting on how your CRM efforts can be improved through the use of Machine Learning.