Tracking On-Shelf Availability using Sales Force Automation
The importance of shelf presence for retail products, especially fast-moving consumer goods (FMCG), cannot be overstated. Products that are visible to customers are more likely to be sold. As a brand owner, it is important to have answers to pertinent questions such as:
- Do you have enough real estate?
- Is someone else occupying your property?
- Is the space prominent enough to grab customer attention?
- Are you getting the share of shelf you deserve?
To track these parameters, it is essential to enable your sales team and merchandisers with a cutting-edge Sales Force Automation (SFA) solution that has a mobile app with a flexible workflow to capture any required information and a powerful Business Intelligence (BI) engine at the back-end to process the information and provide you with the metrics you need to monitor.
Parameters to Track
While there is no universal list of parameters to track, it is bound to vary depending on the industry, overall market position, and many other factors. Based on the experience of providing insights to numerous clients over the last few years, the top three parameters to track are:
It is essential for FMCG companies to track the shelf share of their key brands against the competition. They often set a benchmark that the share-of-shelf of any brand must be higher than the market share published by leading market research companies. If there is a declining trend in a particular region, state, or channel, it is a cause for concern and requires immediate attention.
Are your key brands present in the most 'happening' aisles of the store? Are the products placed at the customer's eye level or in the 'Hot Zone,' as it is commonly referred to by many companies? There is a good chance that your product is not even noticed by the customer if they have to move their neck significantly upwards or downwards.
You would want to place different SKUs of the same brand as close to each other as possible. If a customer has reached the shelf where 2 KG Surf Excel FrontMatic packs are available while searching for the 1 KG pack, they would expect it to be available in the same shelf. There is no reason why they will put extra effort to find out where the 1 KG packs are available.
How an SFA Can Help Track Parameters
There are different practices followed by different companies depending on data volume, expected accuracy level, and lag time they can afford. The top three methods are:
Manual Data Entry Through App
Assuming that the mobile app workflow of your SFA solution is flexible enough, the merchandisers can provide the count of facings for your brand and the competition at a highly detailed level. They can also report prominence, proximity, etc. for your key brands. All this data can then be rolled up to generate a dashboard where you can check all the parameters relevant to you. While this method will ensure that you get quick and real-time visibility of your 'shelf health,' you have no control over the accuracy of the data and are completely reliant on the precision and good faith of your field team.
Backend Audit Team
This method involves merchandisers taking pictures of shelves or aisles and submitting them through an SFA app. The backend solution of the SFA should have a provision to show the actual images (with zooming functionality, etc.) and then digitize the information as per the parameters you want to track. All you now need is a backend audit team who will go through the images, count the facings, and other parameters and enter the details. In this method, you will get very high accuracy, but there will always be a lag of at least 3-4 days before you get to see the data you want. Also, there is additional cost involved in the manual audit process.
Thanks to technological advancements, image recognition has become a reality, although it is not yet widely adopted. Some independent companies offer image recognition APIs that can be used with SFA solutions. If your SFA solution is integrated with one of these APIs, you can easily capture shelf images using the SFA app and let the image recognition technology retrieve the parameters you need to track. These APIs can provide the number of packs and their coordinates, which can be used to develop a logic that determines prominence and proximity. While this is a great use of artificial intelligence with quick turnaround time, there are few other things to consider. The biggest question is the accuracy level. While many claims to have achieved 90-95% accuracy, it comes with many disclaimers such as packs must be rectangular in shape, there should be enough light, and so on. In the actual retail scenario, many of these conditions may not be met, and that is why there has not been great adoption of Image Recognition, especially in India. Having said that, we believe that the speed at which technology is advancing daily, very soon, these constraints will be taken care of, and image recognition will become the preferred approach to track your 'shelf parameters.'