Field sales organizations and brand marketers act based on a “see-all, know-all” ability powered by AI technology.
Until now, brands have managed store coverage with limited visibility, relying upon a combination of syndicated data, retailer sales reports, and store level spot checks. Inconsistent or ‘sampling’ based visibility at retail, prevents a brand from making optimal trade promotion decisions and executing impeccably in-store.
Imagine if you could attain complete visibility of your brand’s retail actions across tens of thousands of
stores in an instant. For field sales, such instant retail visibility will help them diagnose execution issues
at the store level as and when they happen. For brand marketers, availability of instant data across
100% stores will help them make better, more granular and faster trade-promotion decisions.
The wait to get such data is over, thanks to AI image recognition technology which converts photos of
store shelves into data instantly. Consumer Goods brands are embracing this paradigm shift in data
availability to get ahead.
Empowering field sales organizations to see more and do more in-store.
Field managers at direct-delivery brands like snacks, tobacco, and beverages have long wished for objective, real-time, 100% visibility of in-store conditions. Traditional methods of measuring in-store execution are too time-consuming and subjective to be useful. Without a way to measure objectively, field managers do not have a way to keep retailers and field reps be accountable for improving execution.
AI image recognition provides instant retail visibility at every store visit by measuring all aspect of in-store execution from product (out of stocks), promotion/price compliance and planogram compliance. This brings a new level of diagnostic power to field merchandising due to the fine-grain data that is collected and the rapid turnaround.
With instant retail visibility, field managers can quickly focus on high priority issues as they are happening. They can diagnoize quickly if an issue has to do with retailer, field rep, supply chain etc, and have store photos to back it up. Within the field management hierarchy, regional managers gain visibility into team performance and a way to benchmark this against objective standards. Field reps – your heroes on the front lines – also gain visibility into their own effectiveness, including the ability to compare the quality of their merchandising visits against company standards.
Brand marketers make optimal decions with complete, quick and detailed in-store data
To illustrate the limitations of current sample-based data, consider the convenience store channel which has more than 150,000 stores across the U.S. Sheer scale and variety of this channel is an immense opportunity for a brand, but a management challenge. Syndicated data provides sampled data from a set of participating c-store retailers (often only larger chains) and delayed by a month. In c-store channel where single store owners represent more than 60% of stores, sampling-based data coming from a few large chains is not adequate to represent the variability across stores.
Instant retail visibility brings data from 100% of stores, and is both representative and granular. The granularity allows brand marketers to zoom into ‘local trade areas’ (such as regional or zip code level), with full representation of the variety. Combining local trade area granularity with demographics unlocks powerful insights and trade promotion decisions.
Better store-level metrics become accessible too, allowing measurement of promotion effectiveness based on actual execution. For top managers, this opens the door to more detailed performance evaluation of programs across brands and local trade areas.
Results from AI are broad enough to serve needs of multiple stakeholders including revenue optimization, category management, national account teams and brand marketing. The combination of speed, quality and coverage of data creates a game changing opportunity for superior trade promotion decision making.
Get the picture?
The gold standard would be audit-level data capture of shelf conditions – every item in every store, on every visit. That was once a big ask, but it is no longer beyond reach. The difference maker is the availability of reliable AI image recognition technology. This enables a field rep to point a mobile device camera at a shelf set and capture photos that can be rapidly analyzed by a cloud-based intelligent AI recommendation engine. Within minutes AI returns recommended merchandising actions based on measured shelf conditions, merchandising standards and other in-store data.
The ability to track shelf conditions and merchandising actions with high accuracy provides multiple stakeholders with a full view of performance in each store visit without the blind spots that are often present in their business scorecards.
This paradigm shift in retail data availability is already delivering meaningful ROI for vendors in the snack foods, beverages and tobacco categories who service convenience stores. It has reached a tipping point due to advances in AI image recognition and analytics developed by Snap2Insight.