Know what the market wants before you formulate for it
Preference data, panel results, reviews, social signal, and incoming briefs usually sit in different systems and reach R&D too late to change anything. Consumer Insights connects demand to the bench: segment preference, predict liking, read the market continuously, and turn briefs into work that is worth doing.
One value stream, both industries. The workflow and the platform are shared; the data, constraints, and sensory targets are specific to your business.
Understand demand, then act on it
Segment by preference
Cluster the market by sensory preference rather than demographics, so a concept is aimed at a group that actually exists.
Predict response before the panel
Model liking, expert scoring, and emotional response to triage concepts before physical panels are committed.
Read the market continuously
Reviews, launches, claims, and social signal are tracked as a stream, not sampled once a quarter.
What consumer Insights covers
From consumer preference through to market and portfolio signal. These apply across both Flavour & Food and Fragrance & Personal Care: the models are the same, the stimulus and the descriptor vocabulary change.
Consumer preference segmentation
Segment the market by what people actually prefer sensorially, not just by demographics.
Virtual focus groups & predictive liking
Model how a target consumer group will respond to a concept before running a physical panel.
Predictive expert scoring
Reproduce your expert panel's scoring so early candidates can be triaged without occupying the panel.
Sentiment analysis of reviews & feedback
Mine reviews, complaints, and open-text feedback for what people say about the sensory experience.
Emotional response & biometrics analysis
Combine biometric and implicit-response data with stated preference to see what people react to, not only what they report.
Targeted formulation by consumer preference
Formulate directly against a consumer preference model rather than against a chemist's proxy for it.
Market & portfolio analysis
See where your portfolio is crowded, where it is exposed, and where the category has open space.
Market intelligence
Track launches, claims, and ingredient trends across the category continuously rather than in quarterly bursts.
View more use cases for formulation development
Influencer matching & social listening
Read emerging demand signals from social and creator content before they show up in sales data.
Generated product description & naming
Draft the descriptive and marketing language for a submission, grounded in the actual composition.
Conversational product advisor
Give commercial and customer-facing teams a grounded assistant that answers from your own portfolio and data.
From market signal to a brief worth working on
Unify the demand signal
Panel data, reviews, sales, launches, and social signal are brought into one structured view.
Your own historical briefs and win/loss records anchor the model in your commercial reality.
Model preference and white space
Preference segmentation and predictive liking show which concepts land with which group.
Portfolio mapping exposes where the category is crowded and where it is open.
Route it into R&D
A preference target becomes a machine-readable constraint set, not a slide.
It passes straight into Formulation Development, where briefs are matched and prioritised.
What changes when consumer insights is AI-driven
Earlier
market read, demand signal reaches R&D while the formula can still change.
Fewer
wasted submissions, briefs are triaged before creative capacity is spent on them.
Reused
prior work, existing formulas are matched to new briefs instead of being rebuilt.
Connected
loop, what the market wants and what you can make stay aligned.
Each one feeds the next
Four forces shape almost every conversation we have with biopharma teams. Each one is a documentation and data problem before it is a technology problem.
Frequently asked questions
Everything you need to know about Visium.
How does this offer differ from Visium Data Foundation?
Visium Data Platform Foundation focuses on choosing, designing and securely deploying a data & AI platform inside your Azure tenant (Databricks, ADF, Microsoft Fabric, Azure ML Workspace) — an extension of your infrastructure, whereas the Data Foundation offer accelerates the delivery of data products.
Why is an assessment phase necessary?
Because everything depends on your starting point: do you have an Azure Landing Zone, is it fully operational, does your on-premise Azure connectivity allow a best-practice Databricks deployment? The assessment minimizes the risk for the next steps.
Do you work in our own tenant?
Yes. The offer is designed to deploy infrastructure directly in the client’s tenant, extending the existing estate while keeping security, governance, and control firmly within the client’s environment.
Microsoft Fabric is SaaS: is security really a concern?
Absolutely. Connecting Fabric to sources at the network level, accessing a Key Vault or an ADLS Gen2 with no public endpoint, creating managed private endpoints: all essential security skills, even in SaaS.
Do you also handle AI workloads?
Yes. A Data Platform carries both data and AI workloads. Visium addresses both and helps you choose the most suitable platforms given your goals, priorities and requirements.