How Mystery Shopping Can Help Companies Make Better Decisions
Data is everywhere and we make the most basic decisions based on the data we gather. Take myself for example. In the morning when I get off the train to get to work, I take the gate that is less used by others simply because it’s less congested. Even though the journey is slightly longer. I stop by the convenient store near the gate I exit from even though I know that the prices are at least 5% more compared to another store 200 meter down the road. Why do I do this although I know the journey is longer and the prices are higher? The data that I collected was only used to help make the best decisions for myself. I enjoy the longer walk and therefore it suits me, on top of which its less congested. I buy the slightly more expensive artificial coffee because it allows me to enjoy the coffee on that longer journey.
Humans are Emotional Creatures, Data Isn’t
See how I made decisions based on the data. What suited me wasn’t the shorter walk time, but the longer less congested journey. The extra 5% is worth it to me to enjoy the coffee earlier. The main difference in how we use data in our everyday life and a Mystery Shopping Program is the emotional connection to it. In the example above, by all logical and rational accounts, it makes more sense to take the shorter route and have a cheaper coffee. However, humans are emotional creatures and make decisions based on emotions. Above all my objective was not to arrive at the office as fast as possible, however, to ease the process of my mornings.
Mystery Shopping is all about data collection. There is always a predefined objection when setting up a program. Whether it’s to check the service level of the staff or compliance level, there are many other objectives that could be set by the clients. The clients always want to check something, to make sure that everyone is performing on par with company guidelines and values.
These companies can check these internally for sure. However, by asking a third (external) party, the data is impartial. Therefore objectified.
How Mystery Shopping Programs and Visits are Executed…
Before we go any further let’s discuss how a Mystery Shopping Program and Visit is executed. First, the program is set up and designed based on the guidelines and objectives of the client. The surveys are then designed and modelled into different sections with different scores. More often than not, a specific scenario is given to the shoppers (people who perform the visits) to follow. This is done to ensure that the shoppers know what to look for in an objective manner and how to report it.
There are occasions where we have to reject the survey. These are what we call what we call “Failed Visits” – visits that did not meet the criteria of the project. For example, if the visit is a purchase scenario, however, the shopper failed to make the purchase. This visit would therefore be rejected. Because if we were to accept the data, it would askew the data of the controlled variable. If you think that example is funny because well why wouldn’t you follow the scenario right? Well believe me, it happens a lot.
Projects are also set to a specified sample size (e.g. 50, 150, 1500 visits etc.) and could be on a nation, regional or global scale.
So, you can imagine the amount of data gathered from a simple Mystery Shopping Program. However, just as the example in the first paragraph, the data needs to be analyzed based on an objective approach.
What are we looking for?
What can be done with this data?
What Insights and improvements can be suggested?
We have the data and know the objective. All that is left is to analyze…
We highlight sections that performed well and what went right, and vice versa with the poorer performing sections. Pretty basic and on the surface, right? That’s just the starting point. Many of our clients have stayed with us over the years and conducted several “waves” or periods of programs. This allows us to spot trends and patterns. We deep dive into the data depending on… you guessed it… the objective of the client.
Of course, the whole process of analyzing the data is rather complex. We analyze difference sections and deep dive into those. We highlight different stores, country performances vs global performance, read through comments from the shoppers and calculate Net Promoter Scores.
The Main Objective
In this regard, our main objective is to help our clients get the most out of the data. It’s often a longer journey combing through the data and checking different sections. For example, if we noticed all countries had a drop-in score in a particular month, we would deep dive and find out why. What was happening during that period? Was it a sale period? Were there internal changes that effected performances in store? Or could it be something on our end of things?
Because we are looking to give our client’s the best out of the data, we sometimes go beyond the objectives of the client to look for interesting and actionable insights to share.