How Technology Can Improve ‘Product Intelligence’

Gotham Majumdar, chief product and technology officer, SPINS
Gotham Majumdar, chief product and technology officer at SPINS, uses data to ‘improve value to the consumer.’

Gotham Majumdar, chief product and technology officer at Chicago-based SPINS, a wellness-focused data company and advocate for the natural products industry, has worked with his IT team to unify product development. The key, he says, is to better understand “product intelligence.”

Majumdar spoke with StrategicCIO360 about how data builds product intelligence, what it can do for the consumer and the organization, and how the pandemic changed consumer behavior.

What types of strategies have you implemented to unify product development at SPINS?

Initially, we had to align the organization around being product-led, bringing the focus back to strong product management and prioritization to narrow the team’s focus. We defined a small set of “big rocks” that enabled us to “squadify” around the highest value actions and bring focus to the organization. In addition to developing focused squads, we implemented a “three-in-a-box” transformation method—product management, design and engineering—partnering and working in a defined product lifecycle to deliver against our big rocks.

With focused squads, we were able to expand agile product development across the organization to ensure the teams are delivering value through iterative MVPs. There has been a strong alignment and partnership with the commercial and go-to-market team throughout the process to ensure products are being built for market differentiation and clients’ needs.

What is “product intelligence?”

Think of product intelligence as a “source of truth” dataset that documents each and every product on the market across every possible dimension that matters: the product’s imagery, all of its core data (e.g., the product’s nutrition, ingredients, allergens, flavors, certifications, claims, etc.), and computed attributes that look at the underlying product information and algorithmically tag and classify each and every product across consumer preference dimensions, including search terms/trends, lifestyles and diets, ingredient and nutrition preferences, health/dietary needs, allergies/intolerances, etc.

Product intelligence works by extracting and deconstructing on-and-off-label product information, translating that data into a common industry “source of truth,” and then outputting relevant insights in the form of clear product attributes well recognized by shoppers (think keto, low sugar, diabetes-friendly, women-owned business, heart healthy, no milk, etc.).

This creates a source of truth dataset that documents everything that’s meaningful about the product to both consumers and to retail/brand teams. Our job is to make sure we are staying ahead of the market and always have product intelligence covering every possible trend, consumer search term/preference and inventory analysis needs.

How do retailers and brands use product intelligence?

Retailers and CPG brands use product intelligence for all sorts of use cases, including to more granularly analyze the market (e.g. more granular segmentation and analysis in Satori + retail measurement), spot trends (e.g., keto is on the rise, but gluten-free is tapering off), power personalization and next-gen merchandising strategies (e.g., “the keto aisle” or “healthy summer snacks for kids”), help their shoppers find the best products for their health and lifestyle needs, and relay the clear/detailed product information that today’s shoppers rely on when making purchasing decisions.

Product Intelligence makes any nuanced product search or inventory question a simple API call. Example of Pinto/SPINS product intelligence applied to the Whole Foods Market inventory—every search returns all relevant products in a given store ecosystem because PI has been applied across the entire dataset: What are all vegan cheeses? What are all keto snacks that are kosher? What are all low sugar vanilla yogurts? What are all dairy-free pizzas?  What are all unsweetened juices? What are all pastas made with chickpeas?

How are data trends impacting the dynamics of brands and retailers?

Data is truly the new natural resource. Ultimately harnessing the raw/natural data using advanced AI/ML techniques for meaning insights and analytics are differentiators for the retailers and brands.

The top retailers and brands are leveraging actionable data and analytics in a very meaningful way. These insights enable retailers to carry the right brands with the correct assortments and ensure retailers can make timely decisions about their brand mix to not only remain competitive, but to lead. Brands can quickly identify actions to be taken to remain competitive and lead in their category.

What kinds of trends are you seeing within data approaches and IT strategy?

There has been a significant shift in buying behaviors of consumers since the pandemic. Online sales are ever increasing. Understanding the omnichannel data for consumers is truly critical to guide the consumers to buy the right products, enable the right personalization and advise the types of products the retailers should carry.

For example, health and wellness products have been a big focus for consumers since the pandemic. IT systems must be designed to connect consumers with the right health and wellness products and product intelligence in almost near real time. It must be resilient to provide highly accurate product attributes/data to consumers.

A robust, adaptive and scalable infrastructure with accurate, high-quality data delivered with a consumer-focused user experience enables rich insights through AI/ML providing a wide array of opportunities to improve value for the consumer.

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