Every organisation needs to “decode” how its particular chain works, which means understanding the links between:
Internal service quality.
Customer orientation of its staff.
External service value.
How McDonald’s Chain Developed
For McDonald’s the chain worked as follows – committed leaders created more competent and confident staff (they have a young demographic and a record of building social mobility).
Once engaged, the staff created a simple, easy, enjoyable restaurant experience (measured through metrics of food quality, service and cleanliness) which would be predictive of customer visits, and these customer loyalty business metrics were predictive of business growth measures of sales and profits.
They began by simply “linking” the metrics – and there was good news. Where staff were engaged, customer visits were 66% higher and sales 28% higher. But correlations are not causal.
McDonald’s were aware of this, and used our centre to bring more advanced statistical know-how to their data analysis. The data helped HR learn about what we called the performance recipe. How exactly did aspects of people management really create business success, and how did the business metrics really link to each other.
The Operations Director saw what was going on and the data got more visibility and usage in the organization. HR was helping the rest of the organisation learn about some of its measurement and control systems.
What Did The Data Reveal?
Well, it dispelled some myths that existed. First, it was clear that staff could be dissatisfied, but actually still display a strong customer orientation. Also, it became clear that whilst store leadership was a very important predictor of engagement, just having managers who were seen as strong leaders was not enough.
Although leadership correlated with engagement, there was a missing ingredient. It only actually caused engagement to go up if the leaders also made employees believe that they had the resources to do their job – so HR spotted the need to build the provision of important resources for employees if the engagement-customer service link was going to work.
And then it could be seen that staff customer orientation did indeed lift food quality, service and cleanliness and subsequently, profitability, whilst employees knowing they had the resources to do the job properly, increased productivity and therefore lowered costs.
You Might Be Tempted To Think “Job Done”…
Building an analytics team enabled HR to unravel how store profitability and service perceptions were very staff dependent, and they deciphered how one has to manage the subtle ingredients that led to a chain of performance.
But it got better.
Once they had a culture and a system of analytics in place, they were able to extend their insight. But there was a niggling question. Although the engagement data could be seen to be useful and predictive, there seemed to be a link between some of the demographic qualities of their stores and performance. Age seemed to play a part, but how and with what consequence?
They then ran some controlled tests on the data.
The team were able to compare the performance data of 178 company-owned McDonald’s restaurants where one or more members of staff aged over 50 years of age is employed with the performance data of 239 company owned McDonald’s restaurants where nobody over 50 years of age is employed. To their amazement, the stores that had one or more 50 year olds in the teams had a 22% higher level of customer service.
They were already able to show the financial utility of that kind of performance improvement – massive. Moreover, the customer service uplift could be created in both poor and already high-performing stores. The data was telling them not that older employers were better, but that the demographic mix of their teams did change the store dynamics and ultimately customer experience.
As the Financial Times summed it up,“the kids are alright but they need help”.