Vitamins and Supplements

Global Company Size: +$1 billion annual revenue

(Glanbia Performance Nutrition portfolio)

Brand Size: approx $300 million 

Team: Brand & Consumer Insights 

Category: Protein Supplements

Challenge

Dymatize faced a classic awareness and perception challenge within the fast-growing $18.9B global protein supplements market. Despite a strong reputation among dedicated athletes, the brand lagged behind competitors like Premier Protein and Orgain in mainstream recognition and emotional connection. 

Google Trends data showed that Dymatize averaged roughly 14,000 monthly searches less than one-fifth of leading competitors while consumer surveys revealed brand awareness of just 1.1% in 2019, rising modestly to 1.4% in 2020. 

The insights team needed to understand why younger (1834) and Asian consumers perceived the brand as less trustworthy, why Midwest loyalty lagged, and how the pandemic’s home-workout boom reshaped consumption patterns. Traditional research approaches couldn’t synthesize such a

Process: 4-Phase AI Insights Sprint

CREWASIS implemented its proven 4-week sprint model, designed to turn complex consumer data into clear business actions within one month. Each week built on the previous one transforming raw information into foresight.

Phase 1 Descriptive Analytics: What Happened? 

The team compiled and visualized two years of Dymatize data: 

● 3,000+ consumer survey responses (20192020) 

● Demographics, geography, and purchase patterns 

● Nielsen sales data for 52 weeks ending February 2021 

Findings: 

● Dymatize sales rose YoY (4.8% → 7.2%) despite low awareness

● Core buyers worked in healthcare and construction 

● Male consumers and home workout users drove growth post-COVID 

Phase 2 Diagnostic Analytics: Why Did It Happen? 

AI text clustering and survey segmentation revealed why awareness and loyalty varied:

● Brand reputation weakest among 1834 and Midwestern consumers

● Females and Asian respondents reported lower trust 

● Low scores for “uniqueness” and “brand I’d pay more for” in younger audiences

● Need for simpler labeling and flavor innovation 

Phase 3 Predictive Analytics: What Will Happen Next? 

Machine learning models forecasted future purchase behavior based on demographic and lifestyle patterns: 

● Continued growth predicted in construction and healthcare segments

● Opportunity for expansion via clean-label and no-artificial-sweetener products

● Younger consumers likely to switch if taste and transparency improved 

Phase 4 Prescriptive Analytics: What Should We Do?

CREWASIS synthesized all insights into actionable recommendations: 

● Simplify nutrition labeling to increase comprehension and trust 

● Launch targeted Midwest campaigns around “clean strength” and “real performance”

● Introduce new natural-flavor protein lines to attract younger and female consumers

● Use bilingual and inclusive messaging to engage underrepresented demographics

Solution

By combining CREWASIS’ data-science sprint with advanced text and sentiment analysis, Dymatize converted fragmented survey, sales, and trend data into a unified understanding of its consumer base. 

Key advantages of the approach included: 

● AI pattern detection across unstructured survey responses 

● Integration of occupation, age, region, and ingredient preference data ● Dynamic PowerBI dashboards for near-real-time visualization 

“We moved from static research to living insights,” said a Senior Brand Strategist. “Each week, we could see the story evolve who trusted us, who didn’t, and what to do next.” 

Results

The AI-powered process delivered measurable clarity and direction: 

● increase in sales (20192020) 

● +0.3% lift in awareness across core demographics 

● Identified regional growth opportunities in the Midwest and West

● Isolated loyalty drivers around taste, label clarity, and natural ingredients

● Created actionable consumer personas linked to occupation and lifestyle

● Translated insights into tailored marketing and product innovation 

Before AI-Driven Insights

The brand had strong formulas and athlete credibility, but limited understanding of emotional and regional perception. Traditional survey tabulations and manual analysis couldn’t capture nuanced trends like why consumers distrusted artificial sweeteners or what “value for money” meant across demographics. 

“We knew consumers liked our performance credentials,” one insights lead shared, “but not how our labels, taste, and tone were shaping everyday

Choosing AI to Amplify Research Capabilities

CREWASIS’ sprint framework allowed the Dymatize team to extract, test, and validate insights every week combining speed, precision, and storytelling power. 

AI pattern recognition revealed hidden correlations, such as: 

● Trust and repeat purchase correlated with occupation and transparency

● Negative sentiment clustered around artificial sweeteners and label complexity

● Higher willingness to pay among younger consumers for “clean” ingredients

Today: Smarter Insights, Faster Action 

Dymatize has embedded AI analytics into its ongoing consumer understanding cycle. The insights are now fueling: 

● Product reformulation for cleaner ingredient lists

● Regional media planning tied to occupation-based personas 

● Packaging redesigns to enhance comprehension and cross-cultural trust ● Innovation pipelines targeting home-fitness consumers 

“We used to rely on static survey decks,” said the Director of Consumer Insights. “Now, we run live dashboards we can see how consumers change week to week and adapt immediately.”

Learn More About These Features

Text Clustering: Automatically grouped 3,000+ open-ended responses by topic and sentiment. Predictive Modeling: Forecasted loyalty and trial probability by region and demographic. Report Generation: Automated weekly summaries that tied metrics to marketing action. 

“AI turned hundreds of hours of manual research into a single story we could act on.” 

Brand Insights Lead, Dymatize 

Looking Forward: Predictive Brand Growth

With the CREWASIS model now embedded in its research workflow, Dymatize is expanding AI text analysis into innovation forecasting predicting how ingredient and label trends will influence future category growth. 

The next phase includes building consumer foresight dashboards that integrate retailer data and social sentiment to anticipate clean-label, functional, and GLP-1-driven nutrition trends. 

By transforming how it reads and responds to consumer language, Dymatize is turning raw data into strategic foresight fueling faster, smarter, and more human-centered growth.