Fixing the Prioritization Problem in Automotive with Machine learning
Lukas Moshuber
Product Manager at Anyline
Companies in all industries struggle with targeting the right customers for their products and services. In the automotive industry, that is more common than not. Knowing who to target will result in better branding and a higher return on any marketing effort. Machine learning can offer some help in this way.
Identifying the right customers
When the machine learning program is fed the right data, it can provide an incredible amount of valuable insights. These insights can tell the user which customers are in need of new products, ensuring they can spend more time where there is a need and less time on customers that may not be ready to buy.
Customers who use outdated products, for instance, might be approached by a vendor who sells a newer model of these products. As a result, they are already familiar with it and capable of having a conversation. With these types of insights from machine learning, you do not have to guess any longer which customers might be ready to buy and which are open to new products. The data is already present.
Following this, businesses can give greater priority to those customers who are more valuable to them because they are prepared to use the new product. As a direct result, machine learning enables businesses to recognize the true potential value of a target customer. Then, they can mobilize a sales team to prioritize those accounts. They can even target the most profitable of those customers, like those that may be most likely to buy or make the largest purchase. They can evaluate customers based on many factors, including term size (not just company size) as well as current projects and goals, expansion-minded buyers who are most likely to engage, and other factors. This enables companies to put more toward their growth potential.
Increasing efficiency and accuracy of sales
Most companies spend some time having their sales teams identify the right customers and prioritize the most important ones. However, the process is done mostly manually, which takes time and resources to make happen. For example, companies may need to research who the right person is to make a decision based on their job description. Only then can they target that professional and close the sale.
Machine learning is a necessity for any company that wants to increase sales and improve its ability to prioritize the right customers. Machine learning works with data, though, and that means automotive companies must have a way to gather that data. These are some of the new technological solutions that are already on the market and can help you.
Tire Sidewall scanner
A mobile tire scanner that can capture the entire data set on the tire sidewall enables the salesperson or retail worker to instantly digitize all of the relevant information from that tire into an existing management system for later use. Future product selection and more strategic decision-making are made possible by this real-time data collection.
VIN scanner
A mobile VIN scanner is a data capture solution that allows the user to scan the 17-character VIN number from a vehicle in seconds. Data insights on that vehicle and important notices like recalls and part information become available much faster than ever before thanks to the direct integration of this instantly digitized data into a database of information.
Tire tread scanner
While still very new to the market, a mobile tire tread scanner is proving to be a powerful machine-learning backed automotive solution. It is enabling drivers and service operators to get instant and real-time tire tread information from a passenger tire. This data allows for a a much more proactive approach to tire serving, replacements, and ultimately, road safety.
