20:20 Retail Data Insight
A true expert in sales management and business strategy, Ron Temperley has extensive experience in leveraging innovative solutions for client success. As Managing Director of 20:20 Retail Data Insight, Ron specializes in directing consumer goods field teams to the stores with the greatest potential to improve ROI and operational compliance. Prior to 20:20 RDI, Ron served as CEO of Meridian ISE for nearly 25 years, providing sales consultancy to several world-leading organizations. Earlier in his career, Ron held management roles with L’Oreal and Rank Hovis, where his results-oriented and agile approach delivered proven successful outcomes.
Ron, how did you get started analyzing data for consumer and packaged goods companies and what interests you about their data?
In the early 2000s, we found that our Sales Management Consulting business was increasingly being asked to help consumer goods companies optimize their field sales operations. Of course, that involved handling large and diverse data sets to help understand the costs and impact of sales force activity. In the UK, retailers began to share their EPoS data with their suppliers and this helped us better analyze the impact of the field sales teams, both at a top-line level and at the most granular level. This was transformational because we were able to move from ‘estimating’ the impact of a sales rep building a display in store, to calculating the actual incremental sales generated by such a display. In effect, the business helped our clients move from basing decisions on informed opinion to hard facts. This has been a seismic shift in the industry, and it is this which I find so fascinating about the data-rich environment in which we now operate.
What are some of the advantages of using EPoS (electronic point of sale) data and how have you seen it used to drive sales?
EPoS data facilitates a big shift in ways of working. Even today, many sales teams spend huge amounts of time collecting data about which products are available to the shopper at store level. Time spent conducting these store audits can be dramatically reduced by mining the EPoS data instead. By analyzing the data at a daily, individual SKU level, our software detects which SKUs are selling and how fast. Unlike a rep’s audit which may occur only every seven days, our software checks availability and rate of sale constantly and highlights any gaps between the agreement at Head Office between the retailer and the supplier.
Our software then calculates the lost sales value associated with each gap, prioritizes them and sends alerts around the sales organization so that the problems can be rectified. For sales reps, this means that they can see the problems in their stores and fix them with their local contacts. For Key Account Managers, our software shows patterns of problems, so if a SKU is not selling in dozens or even hundreds of stores, the manager will get an alert so that he or she can intervene with the right contact at Head Office to resolve the issue.
So both managers and sales reps benefit from “near real time” data. Can you share some of the insights and benefits for both types of users?
As I’ve already said, availability issues can be quickly identified and fixed. Our software also helps to maximize a major spend area for suppliers – that of trade spend and, in particular, promotional investments. We know that our clients spend massively to drive their brands in store. They pay for additional display locations, such as pallets at the front of store, Gondola Ends in the main store itself etcetera, and then invest again to support the offer to the shopper in store. If retailers fail to implement promotions on time and in full, meaning starting promptly on the agreed day, displaying in the agreed location and promoting all of the family of SKUs agreed upon, this can reduce the sales uplift the supplier was forecasting. Our software highlights the stores and SKUs which are not achieving the expected levels of uplift and directs field resources to the stores with the greatest lost sales values to fix the problems before any more sales are lost.
Typically, we find that non-compliant stores fail to start promotions on time or simply don’t display the promotion in the full space agreed. If a promotion involves many similar looking SKUs, it is common to find that 8 of the 10 SKUs are on the display and achieving the desired increase in rate of sale, whilst the other two are languishing in the main fixture and, therefore, not attaining the expected uplift. The data alone can’t prove with absolute certainty that the display hasn’t been built in a store, but, by comparing sales in similar stores running the event we can infer the likely root cause and send resources to fix it. Whilst we don’t typically see a true 80-20 Pareto effect, we do find that field sales teams generate 80% of their incremental sales in just 50% of their stores. Knowing where and when to go can really be a game changer for suppliers looking to improve the ROI of their sales teams.
There are other types of data, including syndicated data available in the marketplace. How does it compare?
Other data sources are available and, ideally, should be used in combination with EPoS. For example, AC Nielsen or IRI data can provide market share information as well as comparative data about the level of promotional spend. However, this is only available at the national/regional level, so we can’t make any inferences about store-level market share. Therefore, it is best used as a top-line KPI. The detailed EPoS information can then be deployed to dig underneath any national trends to provide an explanation of what might be happening at a more granular level.
It is also important to point out that there are some pieces of information that can only be provided from ‘eyes in store.’ The nature and quality of displays, the hidden stock in the store’s warehouse, the missing shelf-edge label – all of these items need a rep in store to report the facts back to Head Office. Our software can combine these observations with the EPoS data to really get underneath the headline findings and provide answers.
You’ve worked with many of the largest global consumer goods companies, can you share some takeaways?
While we can’t share specifics due to client confidentiality, there are a few overall facts which are of interest:
- In impulse categories, up to 80% of incremental sales generated by the field teams are driven by boosting and optimizing an existing promotion.
- Across all categories, store level availability issues are worth 2% to 12% of total sales. This varies by category and supplier.
- Up to 25% of in store audit time can be saved by modifying the audit process and using EPoS data, boosting selling time and sales as a result.
- If left unresolved, book stock errors take the longest time before the store ‘self fixes.’
- The field sales improvement that lasts the shortest time is a non-authorized additional facing.
- During new product launches, there can be anything from a 1 week to a 5 week gap between the first store selling a unit and the last. Again, this will vary by retailer and category.
In summary, the trend is for more retailers to make their data available to their suppliers and we see this trend accelerating. At the same time, brand owners are looking for their own suppliers to work data harder, providing them with actionable insight to drive sales.
Thank you Ron for taking the time to share your thoughts with us today.