Man at window, title: predictive marketing automation part 1

In his previous interview, Mario Pufahl, CSO at ec4u, talked about cloud-based CRM and how it can help sales processes. Today, we talk about predictice analytics and marketing automation and what it means (or can mean) for sales and customers. 

„Artifical intelligence and predictive models help using time more efficiently.“

The transcript of the interview part „predictive Marketing Automation part 1“:

How can predictive methods help improve sales?

Predictive methods and tools can help improving sales and making it more efficient. A lot of time is spent with collecting and processing data in the sales units. Precious sales time is wasted.

As an example:

When I, as a sales representative, prepare a customer visit, I plan what I can offer my customer and consider what he needs. What did the customer experience in the time that has passed?
If he had many complaints for example, there’s a higher risk of him to churn. This can be applied to every industry excluding end-to-end work and working with final customers in B2C as well as online selling.
In many sales teams, this is part of the daily routine and takes a lot of time. This time could be used more efficiently by providing and using tools and methodologies in form of artificial intelligence and predictive models. Valuable data that has been collected by the sales representative can be used efficiently with these methods. We collect all this data nowadays but hardly ever use it.

As a sales employee you often get to know new customers. A new customer has different characteristics:

  • he has a certain number of employees,
  • he belongs to a certain industry,
  • he moves in a certain region, etc.

These characteristics are saved in the database and can be compared to the characteristics of previous customers. The software then can show you what previous customers with the same characteristics as your new customer have purchased.

The reality in sales looks different, though. Sales representatives collect and look at everything manually. You can get rid off this tedious work thanks to predictive models.
The sales representative gets forecasts and support during in real-time.
For example, he can get proposals, which are based on statistic probability, regarding his customer who has characteristics from previous customers. These proposals include how he can access a customer in the best way and how high the probability of closure is. Is it even profitable to invest sales capacities and valuable time of the sales representative? A predictive analytics result can give this forecast.

Even external data like news sources can be included, which further enriches the knowledge of the sales employee. This happens in real-time. Customers can be supported on a higher level and sales representatives can work more effectively and are therefore more satisfied with their work. In the end, the sales representative and the company are becoming more successful.

How does a customer notice the use of predictive procedures?

As a customer, you can notice the use of predictive procedures indirectly, for example by experiencing a better quality in the collaboration, in communication and in the know-how of the sales representative.

As a customer myself, I often notice that an employee of a company knows next to nothing about me even if I have been a loyal customer for years. There should be lots of data about me. Sometimes, the employee simply does not have access to this data.
However, the decisive point for the customer is that the employee serves him the best possible way in case of a complaint and that he solves the customer’s problem.

The problem can be a malfunction of a device for example. If you use predictive models, you can even prevent the malfunction, for example by using a predictive system that shows you when devices of the same type showed malfunctions. The system can therefore notify you that the device needs a check-up and the possible complaint can be solved even before it arises.   This is a positive moment for the customer (“moment-of-truth”) because the problem has already been fixed before it even emerges.


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