Lund, 2018-06-05
Division of Production Management
Customer Adoption of Data-Driven Services:
A Model for Customer Prioritization
OLLE GEMFORS & EMIL PERSSON
It is widely acknowledged that setting priorities between customers and creating relationships with the most profitable ones is important for a company to maximize their profitability. Many models for how this prioritization should be made exist, usually based on past profitability and expected future profitability for the customers. Recent developments in technology have created new possibilities for offering after-sales services based on the collection and analysis of data. For such new and unproven services, a customer prioritization model based on profitability is not sufficient, since e.g. the customer’s willingness to adopt such services also is important when deciding which customers should be prioritized. Thus, existing customer prioritization models are not sufficient for the prioritization of customers when implementing a data-driven service offering.
New opportunities for service offerings
The emergence of new technological advancements such as the Internet of Things has created new opportunities for original equipment manufacturers (OEMs) to collect and analyze large amounts of data to offer new after-sales services. An example would be to use data from sensors to understand when maintenance should be performed on it to prevent breakdowns, an offering called predictive maintenance. There are many more examples of such data-driven offerings that an OEM can develop, but they are rather
complex and hard for their customers to understand. Therefore, when prioritizing which customers to sell the new offerings to, it becomes important to incorporate more aspects than those traditionally used in customer prioritization models.
Advancing customer prioritization
One way of tackling the problem of insufficient customer prioritization models for OEMs selling data-driven services, is to let the prioritization be multi-dimensional. That way it is possible to incorporate many relevant factors affecting a customer’s decision to adopt data-driven services. This is precisely the logic behind the model suggested in this article.
Two factors affecting customer adoption
On a high level, there are two important factors affecting a customer’s decision to purchase data-driven services:
● their potential benefits from the offering
● their ability to value and realize these benefits
To measure these factors we introduce two concepts: Potential and Receptiveness. A customer has high Potential if their potential benefits from the offering are high. Similarly
a customer has high Receptiveness if the customer has a good ability to value and realize the benefits . Both of these need to be measured in order to tell how likely a customer is to adopt data-driven services. For example, just knowing that a customer has high Potential is not enough for prioritization, since there are several other factors that could still make the customer decide to adopt the offering: the customer might not be willing to extend the relationship with the OEM or their production processes might not be mature enough for the benefits to be realized. Such things are meant to be captured by measuring the customer’s Receptiveness, which is why both the Potential and the Receptiveness need to be measured for the customers in order to make a prioritization between them.
Measuring the customer’s Potential
To assess the Potential of a customer, five areas should be scored, weighted, and summarized:
● Maintenance potential - the current maintenance costs of a customer’s installed base
● Cost of standstills - the total cost for the customer when equipment breaks down
● Equipment uptime - how often the customer’s equipment breaks down ● Efficiency of operations - how skilled
the customer is in running their production efficiently
● Environmental benefits - the potential for the customer to reduce their environmental footprint
Measuring the customer’s Receptiveness
When assessing the Receptiveness of a customer, there are also five areas that should be scored, weighted, and summarized:
● Relationship - how close the relationship is between the OEM and the customer
● Maturity level - how mature the customer is in their production and service strategy
● Risk aversion - how willing the customer is to pay to avoid risks ● Innovation adoption - the customer’s
general attitude towards new products and services
● Competitive situation - how fierce the customer’s competition is
Visualizing the prioritization
With the help of these areas and concepts, a matrix can be constructed where the customers are to be placed based on their Potential and Receptiveness scores. This can then be used for prioritizing the customers, as it will visualize which customers got a high score in both.
References
Gemfors. O & Persson. E. 2018. Customer Adoption of Data-Driven Services: A Model for Customer Prioritization. Lund University: Division of Production Management
