The AI-Enabled Field Sales Force

Stage 3: Coach

How much more would your field team sell if every alert told them what to do, and what it was worth?

By Sam Barclay, Chief Growth Officer, StayinFront

The Process

If you are just joining the series, each stage of the StayinFront Retail Optimization Platform makes every store visit count for more. Opportunity calculates the Size of Prize for every SKU in every outlet and dynamically routes representatives to the highest-value stores. On arrival, In-Store Conditions captures the realogram and delivers instant insights into on-shelf availability (OSA), share of shelf, and quality of shelf. Pitch turns the recommended action into a store-specific case and moves the buyer’s agreement directly into an order. Measure and Goal Set then proves what each action was worth and resets goals store by store.

This post covers stage three, Coach, where all of that data turns into a decision the representative can carry out in store. It shows the problem, the possible solutions and why they work, and the action worth taking first. It is also the stage where most retail execution platforms stop short.

The Alert Graveyard

Most consumer goods field organizations find themselves lost in an alert graveyard.

Somewhere in the system, dozens of exception rules fire daily into a representative’s task list, generating a steady stream of alerts for out-of-stocks, price gaps, compliance failures, and distribution voids. The rules work and the alerts are accurate, but adoption is still somewhere south of forty percent. The trouble starts when a representative sees eighteen alerts on a visit, has time for four, and has no way of knowing which four matter most.

An alert can only tell the representative what is wrong in the store. What they need to drive value is a decision that spells out what to do, why it matters, what to say, and what the action is worth. Picture a representative walking into a store with an alert that reads: “SKU 4471 is out of stock.” The alert is accurate, but it leaves the hard part to them. They have about ninety seconds with the store manager to make their case and come to an agreement.

Nothing in that alert tells them how to make that happen.

The Coach stage gives them the right action, what to say, and the reason the store manager will agree before that conversation starts.

At this stage, the platform does four things, in this order. It shows the problem and describes it, then shows the possible solutions and why they work. It recommends the best course of action, and it weights the value of each action so the representative’s limited time goes to the highest-return item first.

Organizations often skip the second step, explaining why a solution works, but it is the step that turns the platform into a coach. A representative who is only told what to do will follow along for a while. A representative who understands why it works starts applying it to situations the system never anticipated. That is how coaching builds capability across the team, where instructions on their own only build dependency.

A Coaching Story with a Number Attached

A leading global snack maker in Canada ran into a problem that will be familiar to anyone who has managed trade promotions in a major grocery banner.

After a promotion, stores can be left holding excess inventory. Store managers in major grocery banners are measured on inventory control, so they reduce target days of cover on the affected SKUs to fix their inventory metric. That can cut into on-shelf availability and future sales for months afterward, and the promotion you paid for ends up depressing baseline performance. No one intends this to happen, but it does when two organizations are measured on different metrics.

StayinFront RDI built a long-stock coach that identifies store-SKU combinations sitting above an agreed threshold, calculated against pre-promotion rate of sale. The alert itself was only part of it. The team built it into the representative’s routine, so the representative checks the stock, finds a full-margin placement opportunity for the excess, and works it out with the store manager. He gets help with his inventory problem, and the conversation stops being about defending shelf space.

That is the Coach stage at work. It identifies the problem, explains why it matters, and recommends a solution with its value already attached.

This is the clearest ROI case in the series, and the numbers break down like this:

• When a representative acts on a long-stock alert, the estimated forward-looking POS impact is $270 per instance, measured over the following 28 days against a matched cohort of stores where the same action did not occur.
• Approximately 400 stores in a major grocery banner are in scope.
• If 10% of stores carry long stock after a promotion and representatives resolve half of those, that comes to roughly $5,000 per promotion.
• Across 20 promotions a year, that is approximately $100,000 in annual benefit from a single coaching action, in that banner alone.

That matters more when you consider that roughly 25 cents on every dollar of promotional activity goes into trade spend. Improving execution against that spend moves one of the largest lines in your P&L.

It is also worth saying how we got there, because the method is what separates this from a vendor claim. The $270 impact is measured against a matched cohort over the following 28 days, comparing stores where the action happened with similar stores where it did not. That makes it a causal design, not a simple before-and-after chart. The measurement happens in stage five, Measure and Goal Set, which is built into the same platform that generated the alert. That is what lets us stand behind the results from stage three.

Coaching at the Assortment Level

Coaching also goes beyond exceptions. A leading confectionery and snacks manufacturer in Mexico applied the same principle to assortment.

The manufacturer was using one broad must-stock list across thousands of convenience and independent grocery stores, each with its own demographics, trade area, and shopper behavior. A blanket list like that is a coaching failure dressed up as a merchandising policy. It tells every representative to do the same thing in every store, so it gives them no real guidance on the store in front of them.

RDI’s Must Stock List of 1 used machine learning, predictive analytics, store clustering, historical sales-in data, demographics, geography, and comparison against similar successful stores to build a unique must-stock list for each individual outlet. The representative then used that recommendation to show the store owner which underperforming products to swap out for higher-potential ones, backed by evidence from that store and others like it.

The pilot delivered sales uplift of approximately 3.5% to 6.5% in pilot stores, averaging a little over 4.2%.

The Question Worth Sitting With

Pull your last month of field alerts and ask two questions. What proportion were acted on? For those that were, could the representative have told you in one sentence what the action was worth?

If the answer to the second question is no, your team is getting notifications without the coaching that makes them pay off. The good news is that the alerts you already have are a strong place to start, because each one can be given a value.

What This Means for Your Field Team

The Coach stage takes data and turns it into decisions your representatives can act on in the store. Because every action has a value attached, your team knows what to do first, you can measure what each action returned, and you can build a business case by stacking up use cases that each carry a number, instead of resting it on a single hopeful claim. It is also the stage where ROI first becomes visible, and over time those decisions build lasting capability across your field team.

Next in the series is Pitch, covering why the same fact lands differently in every store and how to tailor the message for each one at scale.

Want to know which of your alerts are worth the most? Sam Barclay, Chief Growth Officer at StayinFront, would be glad to model your eight highest-value alerts, and what each one is worth, using your own data. Get in touch.

Know More. Do More. Sell More.