Pharmatic Mindset is the AI core of Pharmatic. It interprets available audience signals, connects content themes to categories and products, accounts for relevance and recency and helps build media segments. This is a probabilistic reading of context, not a diagnosis, medical advice or a guarantee of purchase.
How it works
- 01Raw signals
- 02Topic interpretation
- 03Relevance and recency
- 04Interest profile
- 05Segment and activation
From signal meaning to a segment hypothesis
- 01SignalExploration of a topic or category
- 02MeaningInterpretation of thematic relevance
- 03ContextRelevance and recency of interest
- 04SegmentScale check and media activation
Inputs remain distinct
The system can use thematic content, search and interest context, platform signals and permitted partner data. Fiscal and telecom sources retain different meanings.
From signal to segment
A signal receives a thematic interpretation and a recency window. Related signals form an interest profile; the resulting segment is checked for scale and activation.
What the technology does not claim
It cannot infer why someone viewed a medicine leaflet or diagnose a condition. Audience hypotheses need campaign validation and measurement.
What a signal suggests — and cannot prove
- interest in a topic or category
- stage of product exploration
- relevance of a message
- a diagnosis or health status
- why a person viewed a page
- guaranteed purchase intent
Why meaning matters beyond keywords
The same term can appear in news, a product instruction, prevention content or active comparison. Mindset considers topic, context and recency to distinguish broad exploration from a more specific category choice.
A brand workflow
The brand defines its product, category and campaign goal. Relevant topics and signals are selected, recency is checked, and segments are built for different decision stages. Messages, frequency and measurement are agreed before activation.
How to assess segment quality
Before buying media, review explainable criteria, scale, overlap and message fit. During the campaign, assess response and frequency by segment, then adjust signals or recency windows as needed.
Mindset in the data stack
Contextual signals help reach people before a purchase exists. Permitted fiscal and telecom signals can enrich this layer. The output is an actionable media segment with a clear business role, not a medical assessment.
From raw signal to relevance
Permitted observations of category content and product exploration are interpreted in pharma context. Relevance and recency are assessed before several consistent signals form an interest profile; one accidental visit should not carry the same weight as sustained research.
From profile to activation
The profile becomes segment rules: required signals, supporting signals, exclusions and validity windows. After a scale check, the segment moves to an agreed media channel and results inform the next iteration.
Limitations
Available sources and model settings depend on the product configuration and partners. No unverified accuracy claim is made here.
Questions
How does Mindset differ from contextual targeting?
Contextual targeting looks at a page or placement. Mindset interprets the meaning and recency of available permitted signals to build audiences for a decision stage and brand objective.
What is needed to configure it for my brand?
Start with the category, product, audience and goal. Relevant topics, exclusions and recency are then defined; available sources are confirmed for the project.
Can a new product use Mindset without sales history?
Yes. Initial audiences can use category and content interest; purchase signals and campaign results can refine them later.
How often are segments refreshed?
That depends on source, category and campaign duration. The key is to set a validity window for each signal and review it as demand changes.
Can Mindset's contribution be tested?
Segments can be compared on agreed media KPIs. A claim about incremental sales requires a separate Sales Lift design and purchase data.