Machine learning can make life much easier for sales staff with targeted and smart predictions. The aim is not to replace the salesperson, but to enable the salesperson to concentrate on the customer in the best possible way. And do what is most important in sales: selling. There are many ways in which machine learning can support sales. Here we list the most common examples of how machine learning can be used in sales.
First of all, the application scenarios for machine learning in sales are very numerous. Second, they are absolutely dependent on the company’s business processes. That’s why we’ll pick the most common scenarios here. Depending on the processes and available data, a wide variety of machine learning models can be used – independently, interdependently or as a basis for each other.
Machine Learning in a nutshell
The goal of machine learning models is the prediction of target variables. Target variables can be events or states or continuous numbers. Examples:
- Purchase of a product
- Churn or termination
- Filling out a form
- Segment assignment
- Customer lifetime value
- Cross- and up-sell potential
Input data is used for the prediction. The type of input data depends on the target variable. Data is required that is assumed to have an effect on the target variable. If a purchase probability is to be predicted, then previous purchases could be included in the model as input data. Examples for input data can be found here.
Machine learning models are trained with data and then evaluated. As soon as (sufficient) new data is available, the models are re-trained. This process is called “learning” in machine learning. Training is carried out using a wide variety of statistical algorithms. A list of frequently used machine learning algorithms can be found below.
Often the terms Machine Learning and Artificial Intelligence are used synonymously. What distinguishes machine learning and artificial intelligence can be found in the following article (german):
We use the term machine learning in this post because it is more appropriate in the context of the application scenarios described.
More resources on machine learning
Segment leads or customers
A frequent application example of machine learning is segmentation using cluster algorithms. Cluster algorithms are one of the methods of unsupervised learning in machine learning. Here, leads or customers are automatically divided into segments based on their characteristics. The segments are maximally homogeneous within themselves and maximally heterogeneous between themselves. An example of simple heuristic, non-machine-learning clustering could be the revenue of a customer. Take the mean value of all sales per customer and then divide it into two groups, depending on whether the customer generates above-average or below-average sales. Whereas, the advantage of clustering in machine learning is that the available information is used for segmentation, allowing patterns to be identified that are not immediately apparent. As new customers or leads come in, they are assigned to the segment they most similar to. Segmentation has the advantage that more targeted marketing and sales actions can be applied due to the homogeneous groups.
Predict closing and purchasing probablities
Salespeople can focus their activity if they already have upfront closing probabilities of leads or customers. The machine learning models provide the closing probabilities for the respective products or services. Based on this, the salesperson can sort his leads or customers according to closing probabilities and contact the most promising ones first. This creates a prioritization that the salesperson can follow, but not have to. It is also possible for a salesperson to market the supposedly most promising product within a customer. Or automated campaigns are run on the basis of the probability of closing a deal. In addition to the pure closing probability, the expected monetary value can also be considered. To do this, the closing probabilities simply have to be multiplied by the average revenues of the products. This would allow sales to prioritize their leads and customers based on Euro values.
The expected values can also represent an adjusted sales funnel for the leads. This does not show absolute values, but is adjusted for the probability of closing.
Calculate Customer Lifetime Value
For targeted marketing and sales campaigns, it is crucial that the most profitable customers are known. After all, a good customer relationship management with them is most important. Often, the 80/20 rule also applies here. 20% of the customers generate 80% of the sales (approximately). However, a pure consideration of the turnover of a customer is too narrow. The costs, which a customer causes, are to be considered likewise. As well as the churn probability. Machine learning models can help to map these complex relationships and calculate the customer lifetime value (CLV). The CLV enables a more holistic view of a customer’s value. In short: The CLV comprises the predicted total profit of a customer over his entire life cycle, adjusted for churn probabilities and discount rates.
Next-Best-Offer (Recommendation Engines)
Recommendation engines are very popular in machine learning. , recommendations for the next offer (e.g., product, service, white paper, mailing, etc.) are determined based on similarities to other people. This enables personalized suggestions for the Next-Best-Offer (NBO). The recommendations can either be delivered automatically or be carried to the customer by the sales person. The advantage is that the recommendations are based on what other people have bought. Possible applications range from online retail stores to newsletter interests, insurance companies and machine components. Recommendation engines are designed to deliver results reliably and quickly, even with large amounts of data.
Tapping cross-selling and upselling potential is an often neglected area in marketing and sales. Simply because the potentials are not known and lie in the dark. Here, too, machine learning models can help uncover the potential. For example, salespeople should naturally prioritize customers where a lot of potential is still open. There are many ways to calculate potential. Often, several machine learning models are combined for this purpose. This gives salespeople an indication of how much monetary potential customers still tend to have, and they can prioritize their activities and contact customers with high potential first.
How machine learning benefits from data integration
The causal chain “data integration-data quality-model performance” describes the necessity of effective data integration for easier and faster implementable and more successful machine learning. In short, good data integration results in better predictive power of machine learning models due to higher data quality.
From a business perspective, there are both cost-reducing and revenue-increasing effects. The development of the models is cost-reducing (less custom code, thus less maintenance, etc.). Revenue increasing is caused by the better predictive power of the models leading to more precise targeting, cross- and upselling, and more accurate evaluation of leads and opportunities – both B2B and B2C. You can find a detailed article on the topic here:
How to use machine learning with the Integration Platform
You can make the data from your centralized Marini Integration Platform available to external machine learning services and applications. The integration works seamlessly via the HubEngine or direct access to the platform, depending on the requirements of the third-party provider. For example, one vendor for standard machine learning applications in sales is Omikron. But you can also use standard applications on AWS or in the Google Cloud. Connecting to your own servers is just as easy if you want to program your own models there.
If you need support on how to integrate machine learning models into your platform, please contact our sales team. We will be happy to help you!