My First Workforce Management Discovery: Conversion Rate Modelling

by | Feb 7, 2012

Note from Bob Clements:  I’d like to take a moment to introduce Andrew Gates.  While Andrew’s voice is new to Axsium’s Retail blog, he brings a great deal of relevant workforce management implementation experience to the discussion.  He has been part of Axsium’s European team for three years, and he’s been in the industry for six years. Andrew is based in Milton Keynes, United Kingdom.

When asked if I’d like to contribute to Axsium’s retail blog, my initial reaction was that this sounded great.  I could use this platform as a place to share the things I have learnt, the things I have seen and as a place to explore what the future of this industry might look like. My second thought was, now I have to write something – where to start?

After some deliberation I decided that there was only one logical place for me to start. I had to start by covering the first workforce management topic that really grabbed my attention and originally that got me thinking about the wider picture of what impact this industry can have: conversion rate modelling.

I came across this topic about five years ago when I was approached with an opportunity to work on a very small, but very interesting proof of concept project. At the time, I was working for a WFM software vendor and I was told that I needed to captivate an unconvinced, high end fashion client to the capabilities of workforce management. This was definitely one of the more challenging introductions to a project I had received, but how was I going do that?

When the project kicked off, we agreed that the first phase of the project would be to review the existing operational processes and the available operational data to determine how a workload model could be built. The high-end fashion industry is very dependent on actively selling to customers. The sales assistants within the stores behave as personal shopping assistants, helping the customers select their items, getting the appropriate item sizes until finally processing the customers at the till. As a non-designer label-wearing member of the public, this showed me for the first time how these customers expected to be treated when they shopped here , and it was clearly part of the expected experience.

This got me thinking, could this active selling approach be built into the workload model I was about to build? What if I took the number of customers and applied it to the number of sales transactions, this could create a whole new business driver, and one that tells a very different story. This business driver goes by the name of a conversion rate and it was immediately clear to me that this was how I could captivate the client.

 Once the decision was made about how I would make the workload model, I started collating and processing the footfall and transactional data. Quite quickly, I was able to shape what the varying conversation rate looked like based by day of the week and time of the day. By using weighting techniques and other adjustments, I was confident in the accuracy of the model.  Now, I needed to take this model and turn it into something tangible, something useful.

By analysing the output of the model, I was able to focus on the periods of time where customer sales were low in relation to the high number of customers in the store: I named this analysis the profile of missed opportunities. By comparing this analysis against the employee shift patterns I was able to identify periods of time that were being understaffed and periods of time that were being overstaffed, relative to the missed opportunities. I decided to be bold with these findings.  I approached the business with a proposal to adjust some of their employee shift patterns for a two week period in accordance with my new workload model to prove the concept. They agreed, and after the two week period sales were up. It worked, one client successfully captivated.

Since this project, I have really opened up and embraced the power of conversion rate modelling. By simply analysing a business and using their existing data, it was possible to increase their sales without increasing their wage bill. The mind starts to wonder: what if I further increase the accuracy of the weighting methods used to build the model? What if the employees had fully flexible availability to suit the model? The possibilities are endless.

Of course, this example is not applicable to all corners of the market. Many retail businesses do not have an “active” selling model. I mean, you do not need or want a personal shopping assistant when you are in a supermarket looking for your weekly fruit and veg. However, since my first experience of conversion rate modelling I have found a relationship between different data types in a wide range of business practises and industry verticals. I have found each time that the relationship between the data types tells a new story, and one that can be used in a proactive way to improve a new or existing labour model. This is the power of conversion rate modelling and the reason why I consider it one of my most powerful tools as a workforce management professional.

Do you have any experiences or examples of conversion rate modelling? I am always keen to hear from others, so feel free to email me at


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