Case Study  |
Food

From Broth to Breakthrough: Rewriting a Category Around Consumer Needs

How a needs‑based segmentation model reset right‑to‑win, innovation, and investment.
The challenge

The Category Was Evolving Faster Than Brand’s Mental Model

A leading North American food company had identified brothas a strategic growth priority within its broader “savory flavor andnourishment bases” portfolio—a space spanning traditional broth and stock,bouillon, bone broth, and emerging sipping and wellness formats.

The category, however, was evolving faster than the brand’smental model. Wellness-led positioning, premium culinary formats, and new usageoccasions were redrawing who they really competed with. Leadership neededclarity on where the brand could credibly win and where to focus innovation,communication, and portfolio investment.

Existing data showed what shoppers bought, not why. Shareand format reports lacked a consumer-led view of needs, motivations, andoccasions. Growth decisions risked being made on product form and price tiersinstead of the jobs consumers were hiring broth to do.  The team needed a consumer-led view of soupand broth needs and occasions.

The Approach

A Needs-Based, Future-Facing Segmentation Fueled by AI Modeling

SIVO built a modern, needs-based segmentation, fueled by AI and grounded in how consumers actually use savory bases, not how the aisle is merchandised.

Category reframed around jobs to be done

We redefined the space as “savory flavor and nourishment bases”—any product used as a foundation for cooking, meal enhancement, comfort, or direct consumption. This grouped broth, bouillon, bone broth, cuisine-specific bases, and sipping broths according to the job they solve, nottheir format.

Behavior-led, multi‑dimension framework

Using mixed methods, we modeled behavior across five dimensions, including flavor philosophy, nourishment philosophy, convenience tolerance, authenticity threshold, and cuisine embeddedness. The segmentation is built on what people actually do in real contexts, not on stated preference alone.

From this, we identified four distinct, needs-based segments that cut across formats and demographics. Demographics profile the segments but do not define them, making the framework durable as populations and life stages shift.

Designed for emerging forces

We explicitly accounted for two disruptive forces:

  • GLP‑1 usage: new use cases around small-protein nutrition, protein without volume, recovery, and meal‑bridging.
  • AI-assisted discovery: how digital assistants are shaping search, recipe discovery, and wellness guidance.

This ensured the model surfaced emerging demand before it appears clearly in sales data.

The Impact

A Shared Operating System for Growth

The segmentation became a shared operating system for growth.

  • Shared language: Brand, insights, innovation, and commercial teams now plan against four clear, needs-based segments and their core jobs.
  • Right-to-win clarity: By overlaying existing equity, the client could see where the brand has permission today and where it must earn credibility, sharpening near-term and longer-term bets.
  • Smarter investment: Innovation and marketing spend now follows segment-specific jobs, occasions, and barriers, rather than assumed white space.
  • Early view of new demand: Specific opportunities were sized early, giving the business a head start on where the category is moving.
  • Durable and transferable: Because it is needs-first and behavior-led, the framework is resilient to trend shifts and can be applied to other fragmenting categories.

Together, this work gave the client a more confident path to broth category growth, grounded not in formats, but in the real consumer needs that savory flavor and nourishment bases are hired to solve.