The past two years have brought new challenges for businesses globally and for CPG field teams, time spent in-store is also becoming more challenging. Shopper behaviour changed due to the pandemic, shifting to online along with double-digit growth in the shopping basket value. With range reductions and stretched supply chains, keeping products available on-shelf is more important than ever.
Where store-level Scan Data is available, it is easier to develop a prioritised coverage model. However, outside of Coles and Woolworths the task is more complex. This problem is prevalent in Independent and On-trade channels, where no Scan Data can be found, and is even worse in previously unvisited outlets where we have limited information.
So how can Brand Owners apply science to the complicated task of call file design and prioritising coverage?
A large majority of 80 per cent of brands utilise local knowledge and have a good idea of the sales patterns in stores they visit regularly. The team audits the shelf or bar, monitors ranging, logs the share of space, and so on. But what about the stores that are not currently visited? For those, 40 per cent of audit teams are using demographic data, others are utilising warehouse withdrawals, crowdsourcing and so on. However, the optimum solution is to combine of all of these methods using a data science based approach. This approach leads to significant improvements in the ROI of field sales teams. The key is to have a ‘data informed’ model that can identify the true potential of every outlet and allocate the optimum coverage model.
In today’s market, there is a plethora of information available from a wide variety of sources. Data scientists use machine learning and intelligent algorithms to mine the data about each outlet and its environment in order to calculate the likely sales potential of every outlet, regardless of visit history. Furthermore, a store specific optimised range can be identified to maximise sales by outlet.
A well designed AI model can identify the optimal resource, frequency, and duration of visits, delivering an optimised call file with a contact strategy designed to maximise sales revenues.
The end goal remains the same: increase sales performance by ensuring range compliance, maximising share of shelf, and optimising displays and promotions.
Only 10 per cent of brand owners currently use AI to identify the best outlets to visit, yet 60 pre cent intend to adopt this technology over the next two years. This makes StayinFront's upcoming presentation a must-do event! Register here for the Using Data & Insights to Optimise Sales Opportunities webinar on Wednesday, September 8 at 12.30pm AEST.
Presenters of the webinar are Archel; Aguilar, Managing Director of StayinFront Group Australia and Andrew Smith, Head of Analytics, StayinFront Retail Data Insight.