The Importance of Predictive Analytics in the Marketplace

The Importance of Predictive Analytics in the Marketplace

The Importance of Predictive Analytics in the Marketplace

May 03, 2021
Nisha Jagmohan

The past two years have brought new challenges for businesses globally and for CPG field teams, time spent in-store is becoming more challenging. Shoppers 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, in markets and channels with no such data, the task is more complex. This problem may arise in independent and on-trade channels, where no scan data can be found, or in previously unvisited outlets where we may have limited information.

So how can brand owners apply science to the complicated task of call file and prioritising coverage?

Local knowledge? Demographic data? Warehouse withdrawals? Crowdsource? Similar store sales? Well, it’s not just one, it is a mix of all of them. With years of experience, StayinFront Retail Data Insight (RDI) is helping organisations break through the barrier of established working practices with Predictive Analytics using Data Science & AI, enabling them to improve the ROI of their 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.

How it Currently Works
Brand Owners with Field Sales teams have a good idea of the sales in stores they visit regularly. The team audits the shelf or bar, monitors ranging, logs the share of space, and so on. Solutions such as StayinFront Digital Merchandising can make this activity more efficient and accurate, but what about the stores that are not currently visited?

What is Predictive Analytics and How Can it Help?
In today’s market, there is a plethora of information available from a wide variety of sources. Predictive modelling uses machine learning and intelligent algorithms to mine the data about each outlet and its environment 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.

Predictive Analytics segmentation technology can identify the optimal resource, frequency, and duration of visits. Not only does the Brand Owner have an optimised call file, but they also have a contact strategy designed to maximise sales revenues.

To maximise the ROI by visit, field reps need to be clinical in the execution of their visits and optimise their activities in-store.

The end goal remains the same: increase sales performance by ensuring range compliance, maximising share of shelf, and optimising displays and promotions.

There has never been a more important time to guide field reps on which stores to visit, at the right time and the right length, using the optimal journey plan and maximising ROI. In this modern-day and age, we hear data is power, and it is data that can help field reps: know more, do more and sell more.

StayinFront is a Corporate Partner of the Drinks Association.



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