Consumer packaged goods (CPG) companies are going through a transformation. Unforeseen worldwide events and changes in the technological and societal landscape are changing consumers’ behaviors and, in turn, are altering business as usual for the CPG industry.
As a customer-centric industry, the CPG sector is especially vulnerable to rapid changes in consumer demand. Consumers now have many more options than ever before; the entry cost for a new brand is lower than in previous years, and smaller brands with original offers are eating away big CPGs profits. Also, private labels have eroded margins for CPGs, and many retailers have also become competitors. Consumers are becoming more ecologically and culturally aware; they demand sustainable products from their favorite brands, and also expect products that represent their cultural traits or their health concerns. All these factors put pressure on planning, forecasting, and fulfilling the consumer in a way that the industry has not faced before.
Navigating this new landscape requires a less reactive and more responsive approach. To make this transition from reactive to responsive, companies need timely information to adapt and respond to the demand quickly; but this is more easily said than done.
The information you need is on the shelf
Most of the information a CPG needs to understand consumers’ behaviors is at the store. Even though there is a significant shift towards online channels, forecasts state that e-commerce will account for only 10% of CPG sales in 2022. Stores are still a major source of consumer information.
Currently, many companies rely on syndicated data to fill their information gaps. In some cases, the retailer also provides CPGs with consolidated data so that CPGs can have an idea of how their brand is doing. The problem with this information is that it is usually weeks behind.
In order to have recent information, companies need to look at what is happening on the shelves. Their retail execution divisions have the potential to stream fresh information to the business about consumer behavior, competitor’s execution, and local trends. But that brings two challenges that were hard to overcome until very recently. First, is how to collect detailed information without requiring more staff or decreasing the performance of the sales rep team. Second, is how to gain insight from the vast amount of data collected from thousands of shelves around the country or worldwide.
Why retail execution will be a differentiator in the years to come
Most CPG companies have pigeonholed retail execution into a purely operational role, and it is not hard to see why. Until recently, few significant improvements could be done beyond ensuring clear processes and giving sales reps basic tools, like scanners, to expedite counting facings and filling checklists.
On top of that, retail execution is under pressure from the business that expects sales reps to handle the increasing complexity of more products, more promotions, more stores, and less personnel with the same basic tools.
Retail execution needs to transition from a purely operational role to a data intelligence role to help the business compete in the following decades. That transition will not be fulfilled by demanding sales reps to fill bigger checklists. Bringing retail execution to the next level requires more than process optimization: it needs new technology.
Machine learning and image recognition are the two most important artificial intelligence (AI) technologies for CPGs to this date. Available AI solutions now allow sales reps to turn photos from a shelf into information for the business in seconds, using mobile devices like a cell phone or an iPad.
At its most basic level, we are talking about an operational improvement: AI can do out-of-stock detection without the need for the sales rep to count the product facings one by one. But the benefits of this technology do not stop there; here is where retail execution transitions to a competitive advantage. AI can also provide information about share of shelf, competitor products, and newcomer brands automatically.
AI can find patterns and correlations in millions of data points and provide the business with predictions based on past information. For example, it could suggest an assortment for a store based on data from the brand’s products, their out-of-stocks, the demographics of the store’s location, the season, and local festivities or events.
On the other hand, sales reps will be working with AI-powered tools that suggest, right on the spot, actions to take in order to avoid out-of-stocks or to stock other products based on past observations. Compare that to what they have available right now: a static checklist that does not represent the demand’s dynamics.
AI solutions can also help CPG companies that cannot do retail execution in certain stores due to retailers’ restrictions. It provides a way to still monitor their brand and profit from on-the-shelf data.
As we meet with clients and prospects, we see that retail execution is becoming a major differentiator for CPG companies. Great CPG companies will be those who leverage their retail execution teams to provide them with the information that will keep them competitive in the years to come.
If you are curious about how AI can level up your retail execution, drop us a line at firstname.lastname@example.org.