Service businesses provide intangible products to customers. Typically, these take the form of professional services like accounting, banking, consulting, cleaning, or transportation services. To the extent that the "product' these companies sell are services, business operators are reliant on the quality of their service to retain and attract customers.
In this post, I am going to walk through an example of how systems models can help service businesses better understand the impact of service quality on their customer base. The model I will present is stylised, and designed to show the main dynamics of quality, reputation and customer acquisition/retention.
Modeling Service Quality
I start by introducing three variables that capture key aspects of overall service quality on a scale 0-100. Useful metrics might include things like delivery time and customer-reported measures of satisfaction.
These feed into a composite metric of overall service quality - which is the simple average of the three metrics. A more realistic service quality measure might weight a large number of different metrics individually to arrive at an overall service quality measure. This is the appraoch taken by one popular way service quality measure, SERVQUAL.
Impact on Reputation
Reputation - a key metric that the firm monitors - is modelled as a stock. There is a flow into this stock - change in reputation - which modifies the stock as a function of the service quality, with a delay for the time it takes for changes in service quality to impact reputation:
Change in reputation = ("Service Quality" - "Reputation") / "Time for reputation to spread"
This part of the model captures how reputation is affected by service quality, and that in some industries - like the restuarant industry - that information can diffuse rapidly. In others, it may take longer for reputation to be impacted by reduced service quality.
Impact on Customers
Service Quality directly affects the attrition rate, and hence the outflow of existing customers. They will notice increasing or decreasing service quality and adjust their behaviour accordingly.
The flow of new customers, in contrast, will depend on reputation - not directly on service quality. As reputation is a lagged measure of service quality, increases in service quality will reduce the outflow of customers before they have a positive impact on the inflow of new customers.
Decreasing service quality will increase the outflow of customers before it will negatively impact the acquisition of new customers.
For firms focused on customer acquisition, and seeking to cut costs, this dynamic makes it appealing to reduce service quality. The legacy reputation will conitnue to pull in customers for some period of time. Depending on the industry and the strength of the reputation, this can work for a while. Eventually, however, the firm's reputation will suffer, and only a sustained investment in service quality can re-establish it.
For a deeper dive into the trade-off between customer acquisition and retention, you can read the primer on modeling customer lifetime value.
I run the baseline simulation over a period of 24 months. The firm's reputation starts out at 30, with 50 customers. All 3 service quality metrics are set to 60. We see that over the life of the simulation, the firm's reputation rises gradually to 59.86. After a brief initial dip, customer numbers rise to 73, before flattening off.
Increasing service quality
I run a new simulation, as per the baseline simulation, with one small change. Instead of constant service quality of 60, I use the ramp function, to increase service quality over time, rising from 60, by 1 each period, to 84 by the end of the simulation. We can see the impact this has on customers and reputation below:
Slower spread of reputation
Finally, I repeat the same experiment as above, only this time I change the "Time for reputation to spread" variable from 5 to 10 - slowing the rate at which reputation changes by a factor of 2. The increasing service quality above now has less impact over the course of the simulation, as reputation is enhance more slowly. The increase in service quality still materially improves customer numbers and reputation versus the baseline simulation. Were the simulation to proceed further into the future reputation and customer numbers would continue to increase. Changing the ratae of spread of reputation simply delays the effects of increased service quality, rather than reducing them.
Systems models can quickly reveal powerful insights that you can apply to improve key business outcomes. And you can get started today for free.