The Promotion Optimisation Institute and Gartner similarly introduce Retail Activity Optimisation (RAO) as the leveraging of EPOS data to help field sales forces identify which stores represent the highest turnover opportunities and what the required actions in-store are.
EPOS is a great starting point when considering the importance of incorporating various data sources into retail execution activities, particularly in modern grocery channels. However, if we consider all opportunities to maximise in-store execution activities, there are many sources of information beyond EPOS data which can help us to maximise sales effectiveness.
How and why, therefore, might we want to expand the scope of Retail Activity Optimisation to include a range of capabilities and data sources beyond EPOS?
In nearly all routes to market, field teams are confronted with two fundamental questions:
1. Which stores should I visit?
2. What actions should I take in-store to maximise my impact?
Let’s look at the optimisation of execution activities at each level.
Where to go?
Put simply, we want to send reps to stores representing the highest incremental turnover opportunities in the market. Analysis of EPOS data can help us to identify and measure the value of out of stocks, generating a lost sales value (LSV) which, if swiftly corrected, may represent a significant opportunity. We can also use these insights to direct field sales teams to problem SKUs within the store.
RAO ensures that field reps know where to go, what to do and how to do it, helping them to prioritise visits, optimise in-store execution and increase in-store selling effectiveness. However, if used in isolation, LSV as a proxy for opportunity is likely to lead to a situation of continually directing precious field resource to fix the issues of underperforming stores; a kind of moral hazard and not a sustainable long term strategy to reducing cost to serve and improving standards in store. A more balanced and blended mix of data sources, beyond EPOS, including "sell in" priorities as well as "sell out", provides reps with a richer understanding of the “where, what and how”.
Furthermore, in emerging markets where traditional trade accounts for nearly half of all grocery sales, EPOS data is hard to come by and territory focus is likely to be determined more by distribution opportunities and other trade dimensional data sources.
Once the base distribution opportunities are identified, information on the store’s performance in comparison to others in the rep’s territory, the area’s demographics, local events and even weather forecasts, can all be used to prioritise territory and in store product focus. Imagine you're a sales rep for a protein drink brand, and consider the difference between knowing that
- store X does not sell your protein drink brand
and knowing that
- store X is located in close proximity to a couple of gyms and that its nearby competitor (store Y) has outperformed it in the soft drinks category by selling your specific brand of protein drink, particularly popular with 18-25 year-olds, who make up over 50% of the area’s demographics.
You don't have to be a salesperson to know which pitch is the stronger one.
In the instance of traditional trade channels, CPGs firstly need to gather information on distribution opportunities. What is the total outlet universe? Out of all the identified stores on the territory, how many are selling the company’s products? Out of the stores not currently selling any of the comapny'sproducts, which represent the best opportunity?
For example, when selling ice creams in the summer, you may want to know about opportunities located near a school. Once the distribution opportunities to maximise sell in are identified, additional insight can be gleaned from observing in- store activation standards and their link to sales. If we find a store near a school, where we're not currently available, with good activation compliance, bingo - we've hit the jackpot.
What to do?
Field reps can identify the required corrective actions on a SKU level with EPOS data. Zero sales, for example, can indicate that the given product is not on the shelf and needs to be replenised. However, incorporating additional data points, enables reps to go beyond merchandising and add further value to each store call. For example, data on current and upcoming promotions, loyalty schemes customer behaviour in-store, customer product reviews and even social media mentions all enable data-driven conversations with store managers, influencing their decision-making on re-stock levels, new displays and the execution of promotional campaigns.
Similarly, in traditional trade channels, CPGs can use data on outlet-level transactional history, area demographics and store assortment to inform cross-selling recommendations. CPGs can also predict stock-outs based on the estimation of selling rate and closing stock, incentivising outlet owners to proactively replenish shelves and increase assortment and on-shelf availability.
How to do it
Data and digital tools should support the main objective of RAO to clearly direct reps to the greatest opportunities in every route to market and optimise their time and effort in the field. However, given the pressures on field reps' time, complex data dashboards do more harm than good. Similarly, all the digital tools aimed at assisting field reps’ retail execution efforts, quickly lose their usefulness if reps need to constantly switch between different interfaces and applications. The answer lies in providing field reps with a friendly modern user-experience which gives them access to all the relevant insights and digital tools, integrated in one intuitive interface.
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