Decision Model & Data Strategy
From dispersed signals to accurate judgments
NTD's data architecture and decision model
PULSEPPL: LAYER OF PROGRAMMATIC PRECISION
Purchasing media focused on value rather than volume
Scoring for qualitative impact
NTD’s exclusive programmatic layer, PulsePPL, rates each impression according on how likely it is to meet the campaign KPI:
- Unique algorithms that get knowledge from the KPI (awareness, sales, leads, visits, etc.).
- Based on signals (geo, device, affinity, intent, attention, frequency, etc.), each impression is given a score between 0 and 1.
- We purchase value rather than just inventory; to optimize ROI, the bid is modified in proportion to the anticipated score.
- Inventory quality and brand safety are assured.
PULSEPPL viewers
Own campaign data
From the advertiser, GA4, and CRM.
Partnership information
Affinity, intent, and contextual information supplied by experts.
Contextual and open-data source
Pre-bid attentiveness combined with
semantic context
Environments with a higher probability
of generating meaningful attention.
Cohorts with predictive intent:
audiences created through the stages of the funnel (discovery, consideration, conversion, loyalty).
Advanced imitators:
growth from top-performing audiences and exclusions due to low profitability or saturation.
By integrating several data sources into a single framework, this method allows campaigns to expand and optimize.
The PulsePPL programming approach
Looking ahead
Contextual + intent + predictive cohorts.
To increase consideration and encourage new, qualified visits, we employ high-impact formats like Rich Media, Display, and Video.
Retargeting
Qualitative effect on users who have already engaged, using stage-specific creatives and regulated frequency to produce incremental conversions.
A constant layer of insights is provided by PulsePPL’s predictive and generative AI, which determines which audience, format, and context combinations enhance the KPI and offers optimization hypotheses for upcoming waves.
GA4, CRM, and campaigns:
First-party audiences, navigation routes, engagement, and conversions.
Context and third-party data
Affinities, real intent, browsing context, and sociodemographic and psychographic factors.
Clean Room of Data
A safe setting where we may model and ascribe outcomes, turn on matching and lookalikes without disclosing personally identifiable information, and exchange findings in complete privacy compliance.
Unification of IDs
We create a single, actionable audience view that offers “precision at scale” with minimal latency and ongoing data-quality control (taxonomy, deduplication, and round-the-clock monitoring).
Model of decision-making: Fusion of NTD IMPACT
Fusion IMPACT uses three levels of comprehension to feed continuous learning and offers the operational layer to optimize ads and segments with AI:
Marketing Mix Modeling (MMM)
A statistical model that accounts for external influences and uses aggregated data to assess the contribution of each media and channel to the outcome (searches, leads, sales, visits). It offers the “big-picture” perspective.
MTA (Multi-Touch Attribution):
This type of attribution allocates credit to eachtouchpoint in the trip at the user/impression level.
It discloses the “operational detail” of every format and channel.
Geo-experiments
A/B tests by zones or segments: we measure the incremental difference in KPIs (searches, qualifying sessions, leads, sales) and activate or modify spending in “test” vs. “control” areas. They are employed to scale only what actually works and validate theories.