AI is a powerful tool in the fight against customer attrition, but how exactly does it work? How can sales managers implement AI in their company? In this blog, we will guide you through a concrete step-by-step plan.
Identify the right AI solution
There are several AI solutions available for sales managers looking to reduce customer attrition. It is therefore important to choose the right solution that best suits your organisation's needs. Here, an out-of-the-box solution is preferable to custom development because it is less risky, costs less, gives a faster return and you can implicitly use best practices from other companies. Incidentally, there are also solutions available that are specifically designed for certain industries, such as retail or finance.
Create a roadmap
Creating a roadmap for implementing AI is of great importance. This is because roadmaps help monitor the evolution and ensure that the process is streamlined. It is important to describe in this roadmap all the necessary steps to implement AI, including the necessary resources. In addition, it is also advisable to divide the roadmap into several phases, so that the project remains manageable at all times and there is room for adjustments and evaluations.
Besides reducing customer attrition, it is also possible to use AI for other purposes within sales, such as improving customer profiles, lead scoring and cross-selling. Therefore, it is important to also consider these additional opportunities when creating the roadmap. This will allow you to build on the AI solutions already developed at a later date and get even more value out of your investment.
Collect the right data
To use AI efficiently, it is obviously important to collect the right data. Here, we are talking about customer data such as purchase history, customer service interactions and website behaviour, among others. In addition, a good data infrastructure to collect and analyse this data is crucial.
Train the AI solution
Once the data is collected, it is time to train the AI solution. Specifically, this involves using machine learning algorithms to analyse the data and discover patterns. It is important to re-train this AI solution regularly so that it always remains up-to-date and produces the best results.
Testing and validating the results
A crucial next step is testing and validating the AI results before widespread deployment. For instance, it is important to compare the AI solution's predictions with the actual results to ensure that they are accurate. In addition, sufficient data quality and a good data infrastructure is very important, as this ensures that the AI solution can work correctly without the risk of being affected by noise in the data. This can be achieved by regularly performing data quality checks and implementing a data governance framework.
It is also important to monitor how the AI solution is used and how it performs in practice. This is because by monitoring performance in real-time, quick action can be taken when anomalies are detected. This can be done, for example, by setting alerts when performance falls below a certain level.
Finally, it is important to build a continuous improvement process into the AI solution. One way to do this is by regularly training the AI algorithms with new data and testing new models and techniques to further improve performance. This continuous improvement will allow the AI solution to perform better and better and get even more value out of the AI investment.
In short, consistency and efficiency are crucial when implementing AI. Everything starts with collecting the right data, but it is equally important to continuously train, monitor and adjust the AI solution where necessary. Only then can you harness the full potential of AI in the fight against customer attrition.
