Next week I’m speaking at the National Nutrient Databank Conference. It’s an event that brings together researchers, nutritionists, academics and government agencies with a deep interest in food and nutritional data. I’ll be talking about the importance of granular product attribution -- what it is and how it can help the food industry meet consumer demand for product transparency.
It’s an important topic -- for conference attendees, but also for food brands and retailers in general. Every day I’m approached by companies that want answers to questions like “How should I define clean labels?” and “What attributes do my shoppers care most about?” and “What does all natural even mean?”
The answer to all of these questions can be found in granular attribution. Ready to learn more? Ok, let’s dive in.
What is granular attribution?
Open your pantry at home and pick up a packaged food product. You’ll see the label is filled with data points -- from the product name, flavor and packaging color to ingredients, certifications, secondary packaging and recycling scheme and code. All of these points are basic attributes. In fact, the average product contains between 200 and 300 basic attributes on its packaging.
Label Insight technology captures these basic or “raw” attributes via our data generation process, deconstructs them into millions of hyper-granular data points that we refer to as Base Attributes, only to reconstruct and organize these individual data points and apply context to form Master Attributes. This process allows us to apply our final and, arguably, most useful stage -- data customization -- where we construct Smart Attributes in various ways depending on the perspective a customer aims to present. For example, once the granular attribution process is complete, we can easily identify products that meet the parameters for the American Heart Association’s Heart Healthy, are Whole Foods Ingredient acceptable or Kroger Simple Truth acceptable, have ingredients or claims that support digestive health, or are free of specific ingredients, such as gluten, peanuts or even soy lecithin.
The lifecycle of an attribute during the data transformation process.
How Granular Attribution Helps Retailers
Retailers who get granular with their product data will enjoy three big benefits.
First, these retailers will be able to better serve their customers. With granular data at their fingertips, retailers can quickly respond to consumer questions and demands. I’m allergic to dairy, soy and gluten, what foods do you recommend? How can I find products free of high-fructose corn syrup? Free of peanuts? How about a low FODMAP diet? A restricted sodium diet? When you know what products are on your shelves and what’s in these products, you can personalize and customize your service -- something you can’t do without granular attribution.
Second, access to granular data enables retailers to go beyond “what” and understand “why.” For example, if you know what cereal products are selling well, granular data can help pinpoint why -- is it because these cereals offer 100-calorie servings, are low in sugar, high in fiber? We can help you segment your inventory based on specific attributes and POS data to assess what’s driving performance.
And third, retailers can use this data to differentiate themselves from competitors. Today, many grocery stores offer aisles dedicated to gluten-free or organic products. Others highlight “Dietitian’s Pick” products -- that is, products that meet a set of standards determined by the store’s on-staff dietitian.
Even more innovative uses are on the horizon. Some retailers are using granular product data to diversify -- for example, they look at ailment-based attributes for diseases and issues like diabetes, acid reflux, indigestion, asthma and even acne! Others are using the data to inform their own definitions of things like “minimally processed,” “sustainability” and “clean label” -- topics that matter to their customers.
Granular Attribution for Food Brands
The benefits are just as real for brands, too.
First, it gives brands the ability to understand how stakeholders perceive or view their products. Every stakeholder has its own lens -- a retailer may only be most interested in all-natural products, a consumer may only want peanut-free items, or a regulatory group may only care about the amount of sodium or sugar a product contains. At Label Insight, our SmartSPEC technology enables customers to view their data through “custom lenses,” making it easy to gain a stakeholder’s perspective.
An additional benefit is competitive analysis and benchmarking. Label Insight can gather granular data on multiple brands within a category. From here, a brand can learn things like how its products fit within the category, what claims other brands are making, or what formulations are most popular.
This highlights a third benefit: Identifying reformulation and innovation opportunities. If a brand manufactures products in a category that is responding to shifts in consumer preferences -- for example, lowering sodium or removing artificial colors -- it can use granular data to identify popular reformulations. Alternately, digging into category data can highlight white spaces or gaps that a brand may want to explore.
Granular Attribution at Work
Now let’s take a look at deep attribution in action. These are some of my favorite examples of how we’ve helped brands and retailers use granular attribution to improve their business, meet customer needs and provide greater transparency.
Leading Natural Foods Retailer Redefines “Specialty” Category
Many product categories were standardized long ago. But standards can become outdated. A natural foods retailer wanted to solve for this in the “specialty” category. Traditionally, a product is categorized as “specialty” if the term “specialty” appears on its packaging. This means lots of other attributes that matter to consumers are not considered. We helped the retailer identify a custom set of “specialty” attributes, enabling it to restock this section with a more relevant set of products. We let the data tell us how to categorize, rather than outdated subjective methods.
Retailers can use granular attribution to identify the DNA of a product in order to categorize it. Let the data drive categorization, rather than subjective methods.
Leading Discount Retailer Defines “Clean Label”
A lot of retailers think about clean labels from an ingredient perspective -- how many, are they natural, etc. But it doesn’t need to stop there. Label Insight helped a leading discount retailer develop a clean label attribute set that goes beyond ingredients to include nutritional information like sodium and sugar content, ingredient length and ordering, sustainability practices and absence of artificial ingredients. Once the attribute set was defined, the retailer was able to easily and accurately mine its inventory for products that fit its “clean label” definition.
Bakery Brand Optimizes Its Ingredient List
Many ingredients have lots of variations that may appear on a product label. Salt is one of them (think: sodium chloride, sea salt, hint of salt, the list goes on). A bakery brand wanted to identify which variation of “salt” resonates best with consumers so they enlisted our help. We gathered product data on the category, then integrated POS data to determine how competitors talk about the ingredient and what variation is behind sales.
Have a product data dilemma you’d like help with? You’ve come to the right place. Label Insight is all about helping leading CPG brands and retailers get smart about their product data.