In this webinar, we’ll explore how leading CPG teams are achieving peak performance and maximum efficiency in the field. In this session, you’ll learn:
Repsly is a retail execution solution that drives teams to achieve peak performance, and maximize their impact on sales. As I mentioned earlier, more than 1,000 brands around the world use Repsly to get visibility, control, and most importantly, real-time data and analytics about their performance in the field. Repsly empowers those teams to take more proactive, intelligent actions in the field and maximize their impact on store-level sales.
At a high level, here’s how Repsly works: our mobile app empowers brand reps with the tools they need to work smarter in the field and collect data about their execution at the point of sale.
The manager’s dashboard equips territory managers with real-time data from their team in the field, and, gives them the tools they need to identify opportunities, and deploy their teams to take the right action in the store.
We work with more than a thousand high-performing field teams, but here are just a few pieces of great feedback we’ve gotten so far. From the team at UNREAL Candy: “Repsly’s real-time data is a game-changer.”
The team at Purity.Organic loves the intuitiveness of our mobile app: “It’s really easy to use,” they said. “Like when my reps use Instagram. It’s very natural. It doesn’t feel like a chore.”
From the back office out to the field, high-performing teams love using Repsly.
Whether you’re a contender fighting for the top spot in your category or an established brand looking to protect your program spend, retail execution is the key to your success in the store.
For example -- featured displays can lift CPG sales by an average of 193% when executed according to plan. BUT, despite the huge upside of these promotions -- not to mention the investment they take from brands -- less than half of all displays are executed as planned.
And display compliance is just one piece of the larger picture. To ensure flawless execution, brands have to prevent and correct other retail execution errors -- like out of stocks, distribution voids, misplaced or damaged products, and more.
All told, poor retail execution can cost brands as much as 20% of their total retail sales.
That’s why you’ve invested in a field team. And not only can they help prevent losses from poor execution, but your team can help grow your business potential in the store as well.
That could mean anything from securing secondary placements and cutting in new SKUs, to expanding your facings and building new displays. Between growing your sales opportunity and minimizing losses, your retail execution team can have a massive impact on your point-of-sale performance.
With so much of your success in the store riding on your retail execution team, achieving peak performance out in the field is key. It’s critical brands are able to direct their teams to take the right action in the right store at the right time to have the biggest possible impact on sales.
High performing field teams have two key things in common. First and foremost, they commit to using data to drive their strategy and decision making. Secondly, they apply that data to a cycle of continuous improvement, which they use to identify and execute on the highest impact activities in the field.
So, first thing’s first: what exactly does becoming “data-driven” look like? While everyone certainly takes into consideration some amount of data, high performing field teams understand all three types of retail execution data: observational, activity, and sales data.
By Observational Data, we mean the in-store conditions your reps are observing and reporting from the field. This paints a picture of both your execution in the store as well as opportunities your reps uncover during their visit. Sample metrics include stock levels, number of facings, competitive activity, promotional compliance, and more.
Next up is Activity Data. By Activity Data we mean the specific actions your team is taking in the field. How often are they visiting accounts, how well are they able to cover their territory, which actions are they taking most frequently in the store? Activity Data paints a picture of the specific actions your team is taking to improve execution in the store. Measuring and tracking these actions is key to understanding which have the highest impact on sales.
That brings us to the third key data type -- Sales Data. Sales Data is simply how much of each product is sold over a certain period at certain store locations. This one is straightforward, and likely one your brand is already tracking. But it takes on new meaning when associated with your team’s Observational and Activity Data -- giving you a sense of which activities and store conditions lead to the highest sales.
So to recap, there’s Observational Data -- the in-store conditions your reps report from their accounts, Activity Data -- the actions your reps are taking in the field to drive sales, and Sales Data -- the volume of your products moving off the shelf.
Fully embracing all three types of retail execution data empowers field teams to answer basic questions about their retail operations with data, rather than anecdotes or gut feelings. For example, here are a few questions you can answer simply by understanding your team’s Observational, Activity, and Sales Data.
Clearly, taking a data-driven approach can help make your retail execution strategy more scientific. But, high performing field teams go a step further.
High performing field teams apply a data-driven framework onto a cycle of continuous improvement and constant iteration in the field to ensure they’re always having the highest possible impact on sales. Here’s what that cycle looks like.
As you can see, the three main components are Insights, Planning, and Actions. At a high level, every action in the field starts because of some Insight. An Insight is simply an indication from your data that there is a problem or opportunity in the field that you can address to have an impact on sales.
From there, you enter the Planning stage -- analyzing past performance to decide when, where and with whom you will address that opportunity, and exactly what steps they should take in the store to deliver the highest impact.
Once you’ve deployed your team, we’ve entered the Action stage. Your reps execute your plan in their targeted accounts, and report back data about the in-store conditions they encountered and were able to affect. The data those activities generate feed back into your data set, and inform the next set of Insights that kick the process off again.
This cyclical process drives peak performance because it allows field teams to constantly improve. Every action they take in the field is informed by real-time data, both about current store conditions and the results past campaigns have had on sales. The cycle of continuous improvement empowers teams to take the right action in the right store at the right time, maximizing both their efficiency and the impact they have on sales.
Now that you understand how the cycle works at a high level, let’s take a closer look at each of the stages and provide a bit more context around how it might look in your organization.
Let’s start with Insights. At this stage, you’re analyzing the three types of retail execution data (remember: observational, activity, and sales) to uncover problems or opportunities in the field.
For example, you might try to answer any of the following questions:
If your team has committed to understanding the three types of retail execution data, you should be able to answer these questions and more. To go one step further, teams who organize this data in a dashboard should be able to see at a glance when certain metrics are significantly above or below average. In this best-case scenario, your Insights practically raise their hand and make themselves known to you, indicating an area your team can focus on to drive sales.
Let’s set up a hypothetical to show this cycle in action. Looking at your Sales Data, as well as Observational Data from your team in the field, you might see a pattern of out of stocks at Target for one of your most popular flavors. We’ll use that Insight to inform the actions our retail execution team takes in the field.
After uncovering an Insight, you enter perhaps the most important phase of the cycle of continuous improvement: Planning. This is where you use historical retail execution data to come up with a fix to the problem or way to take advantage of the opportunity. You need to decide who will go where, when they’ll go, and what they should do once they get there.
Also, part of your planning process should be determining whether there’s any additional data that would help you solve the problem - or take advantage of the opportunity - more intelligently. By identifying that ahead of time, you can have your reps collect the data while they’re at the store, feeding the cycle for more intelligent action the next time around.
Let’s look back at our example, where one of our most popular products has been out of stock lately in Targets. Now it’s time to figure out how we’ll combat that and make up for those lost sales. Looking further at our reps’ Observational Data, we can see that we only have one facing in those stores, and the managers have refused to let us change the planogram to add another one.
With that in mind, I’d like to deploy my retail execution reps to install dump bins just in the Target locations that have been out of stock in the past 6 months. That will allow us to get additional stock in the store to keep up with demand and capture those lost sales.
Finally, the Action stage of our continuous cycle of improvement takes us down to the store level. During each account visit, not only is it important that your reps execute the plan, but that you get clear data about what they were able to accomplish in each store. That way, you get visibility into what actually happened in each store, and can then attribute any sales lift to the appropriate activities. This Activity Data, together with any Observational Data your reps report from the field, combine with that store’s Sales Data to inform a new set of Insights, and the cycle begins again.
So back to our example, our reps are now responsible for taking Action, and executing a dump bin promotion at the Targets where we’ve historically been out of stock. Having real-time Activity Data allows you to see where they have visited so far, and in what percent of stores they were able to successfully execute the promotion. Taking that a step further, when you combine that Activity Data with Sales Data from those stores, you can see exactly what kind of sales lift you got from the dump bin promo, putting a dollar value to the activity of your field team.
So to recap, retail execution teams can achieve peak performance by working in the framework of a cycle of continuous improvement. The cycle starts with an analysis of the team’s Observational, Activity, and Sales Data to uncover an Insight, or an opportunity to drive sales in the field. From there they en ter a Planning phase, performing additional analysis to determine the specific actions their team should take to take advantage of this opportunity and maximize their impact on sales. Finally, their team takes Action in the store, and the results feed another set of Insights.
Repsly’s retail execution solution was built specifically to support the cycle of continuous improvement, empowering brands with the data and tools they need to achieve peak performance in the field.
Repsly’s powerful reporting dashboards bring sales, activity, and observational data together, fueling intelligent, real-time insights.
From there, Repsly offers managers the visibility and control they need to effectively plan their team’s next best move, and deploy their reps to take targeted action to impact sales at key accounts.
The Repsly mobile app gives retail execution specialists everything they need to execute effectively in the field. From customizable data collection tools to smart routing and scheduling, Repsly empowers every rep to achieve peak performance in all of their accounts.
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