Data is central to a progressive business function. But in the Consumer Packaged Goods (CPG) industry, assimilating accurate data is double the trouble given the vast expanse of geographies, stores, customer demographics, and compromised transparency of what goes on inside the retail stores. Sales and marketing leaders rely on a bunch of ways to extricate the rawest data straight out of the stores on a monthly basis. The most sought-after ways are as follows:
- Retail Audits: Herein, CPG companies send teams of auditors also known as the field force into stores to gather information on product placement, pricing, availability, and other critical shelf metrics required to analyze and improvise. The auditors physically examine the shelves and record the data using paper forms or portable electronic devices.
- Point-Of-Sale (POS) Data: Many retailers collate POS data via their check-out systems that provide the CPG companies insights on customer behavior, sales volumes, SKU data, and inventory levels. To obtain this information, CPG businesses may work with retailers in partnerships or buy it from outside data vendors.
- Syndicated Data: CPG companies may also put money into market research firms like Nielsen or IRL to purchase syndicated data. Data on market share, prices, promotions, and customer patterns are some of the most crucial areas that market research firms focus on that directly benefit CPG companies.
While all of the above-mentioned ways are good make-shift options, they do entail significant cash burn. Retail audits deploy manpower and runs on human precision, making them vulnerable to an array of transcriptional and estimation errors. POS data involves direct involvement of the retailers, i.e, a big chunk of money flying off of the pockets to get information that more often than not, isn’t tailored to meet the needs of the CPG companies. Furthermore, obtaining POS data out of a general trade transaction is a catch-22. Now imagine the scenario amplified across multiple continents, not a feasible option in the long run. Market research firms on the other hand fetch syndicated data with a population size triple or even quadruple the size of the relevant population size, making the data a lot more dilated.
The Three S of the Solution: Seamless, Sustainable & Scalable
The single controllable source of mainstream data collection for CPG manufacturers is the sales representatives in the field. Couple them with the power of Image Recognition AI, and CPG companies can build an effective and agile workforce and subtract the glint of human error. Let us now look at how Image Recognition makes this significant difference.
Image Recognition AI enables machines to identify objects, people, places, and other visual elements in digital images and videos.
- Image acquisition: The first step in image recognition is to acquire an image or video. This can be done using a camera or by accessing existing images or videos from a database.
- Preprocessing: Once an image is acquired, it undergoes preprocessing to enhance its quality and remove any noise or artifacts. This can involve adjusting the color balance, contrast, and brightness of the image, as well as resizing it to a standardized format.
- Feature extraction: Next, image recognition algorithms use a technique called feature extraction to identify key characteristics of the image. These features can include colors, shapes, textures, and patterns, and are extracted using mathematical algorithms.
- Object recognition: With the features extracted, image recognition algorithms then use machine learning techniques, such as neural networks or decision trees, to match the features to known objects or patterns. This is where the actual recognition occurs, and the algorithm identifies the objects or patterns present in the image.
- Output: Finally, the image recognition algorithm outputs the results of the recognition process, often in the form of a label or tag that describes the object or pattern present in the image. This output can be used for a variety of applications, including search, classification, and object detection.
Image Recognition enabled store audits not only to save millions of manhours but also to slash operational costs, and eliminate erroneous data entry and thus faulty decision-making. Boost the productivity of your merchandisers by upgrading them from manual-store audits to 60-second “Click & Go” audits.
