“With shoppers now making their buying decisions in just a few seconds, consumer goods companies need a fool-proof system for winning in the store” – observes a report by Bain & Co. CPG brands need a reliable tool to enable faster and accurate store checks to create the Perfect Store.
The growing number of consumer goods is responsible for the scarcity of shelf space. To ensure your brand stands out in the market, understanding in-store shelf KPIs such as share of shelf, planogram compliance, out-of-stock incidents has become important. Retail execution is key for your market success. Keeping a track of it is often the first step to achieve your perfect store execution.
A typical retail aisle is full of immensely rich data. The information it contains is sheer and dispersed into shelves carrying hundreds of similar looking products. Although the presentation is highly visual, extracting valuable insights from it regarding retail execution can be both daunting and prone to human errors. Thanks to Artificial Intelligence, you can now easily keep track of your shelf KPIs. Here is how
Why tracking retail execution is important
It is crucial for brands to understand that product availability and placement on shelf play a fundamental role in buying decisions made in-store by shoppers. According to a study, “out of stock, overstock, and shrinkage contributes to nearly USD 1.1 trillion in lost revenue for retailers worldwide”. Consumers either jump shop to buy their preferred brand or choose a competitor in case their desired product is not available.
In this competitive market space, retailers & brands cannot afford to lose customers because products are misplaced or unavailable. This is exactly why tracking retail execution is important. Such studies generate insights into the factors affecting product sales in any retail store. It has been noted that actual retail execution often falls short of what has been originally planned. The best possible solution to overcome these shortcomings is by tracking retail execution.
Gaps in the present retail execution tracking methods
The present planogram compliance assessments are often time-consuming and difficult to manage around work-peak. Meticulous manual input is required to guarantee that products on the shelves match with the planogram.
Traditional tracking methods are also prone to errors. Ensuring data quality can be difficult with manual auditing. A study revealed that “with manual field reporting combined with disparate information on sales results and back-stock inventory, managers and executives may have to make decisions and act based on inaccurate, incomplete, or incoherent data—when they should be using valuable insights to drive company-wide resourcing, best practices, and campaign and investment decisions.”
The lack of visibility and up-to-date data prevents consumer goods organizations from tackling problems proactively. During a crucial sales period, lack of data can lead to suboptimal decisions.
But today’s technological advancements, mainly in the field of AI, have successfully enabled highly efficient and innovative retail execution. Using object detection technology, brand reps can conduct a thorough and systematic scan of the store to check their compliance with the planogram.
Tracking retail execution using Shelfwatch
Karna has leveraged the power of AI to create Shelfwatch, a retail execution tracking tool that empowers reps to collect photos with a click of a button and generate real-time in-store insights. With its state-of-the-art technology, Shelfwatch efficiently overcomes all the shortcomings present in the manual retail execution tracking methods. It is reliable, free of human biases, and speed and cost-efficient. The algorithm controls the data quality at the collection level to bring out the standard and objective analysis.
Shelfwatch in action
Case I: Automated auditing solution for a leading Indian Consumer Giant
The client, a large Indian FMCG firm, had to conduct regular shelf audits in order to validate their retail execution. Their approach involved traditional tracking methods. The reps visited retail outlets across India and filled an audit form, and as a proof collected in-store images every time they visited a store. The client was sitting on a goldmine of data but had no way to extract value from them.
Shelfwatch leveraged its AI-powered image recognition technology to analyze thousands of images to automatically detect SKUs/POSM. We trained our AI from a single example image and identified an 80% gap in planned vs actual retail execution.
Such studies usually take months to conclude, whereas ShelfWatch took a few days to derive the insights. It also gave a complete view of the retail by mapping the retail execution of the outlets using their geo-data collected by our in house data collection mobile application.
Shelfwatch’s de-duplication algorithm improved data quality by detecting fraudulent activities. It accurately identified and removed duplicate or irrelevant images tagged under different location or shop name. And finally, it built a real-time management dashboard with custom features to track monthly and quarterly audits. It managed to induce a 90% reduction in overall research turnaround time.
Case II: Visibility across vending machines in Japan for a Beverage Company
The client wanted to get accurate visibility results about its retail products and market presence in the vending machines all over Japan. Accounting for about 2% of the population, vending machines are an integral retail channel in the country.
As data were not already available, we crowdsourced vending machine images using our in-house data collection mobile application. Shelfwatch automatically identified and removed all spam/duplicate images, ensuring high-quality data collection. Our AI immediately detected the SKUs and POSMs, to give real-time insights such as share of shelf, planogram compliance, and out-of-stocks incidents all in a single dashboard.
How it works
Using Shelfwatch is pretty straightforward. It is a simple two-step process. First, share one image of the SKUs you want to track retail execution for and second, ask the field reps to take images of the retail outlet’s shelves using our mobile application. Shelfwatch algorithm automatically analyzes the images to give out a competitive analysis like share-of-shelf, and planogram compliance.
In this competitive market, shelf space continues to shrink as the number of products rises. Driving retail execution to perfection is decisive in market success. Traditional retail execution methods have proven to be incompetent in terms of speed, and cost.
But now you can tackle these challenges easily with Shelfwatch. It is a powerful and hassle-free tool capable of running on a wide spectrum of retail channels. Collecting and analyzing data is quite elementary. The technology is equipped to process millions of images instantly to give quick analysis and real-time insights and is extremely cost effective.
Found this blog useful? Read this blog to know more about how Karna products provide effective solutions to traditional retail execution methods to improve brand presence and visibility.
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