Last-click
Credits the final touchpoint before conversion. Usually a branded search or a retargeting ad. Systematically under-credits discovery, consideration, and upper-funnel influences.
Full-Journey AI
Beyond last-click. Beyond next-click. The whole journey.
Deep Stream Data is prescriptive, not descriptive. You don't just see what happened — you understand why, and what to do about it. Across weeks. Across competitors. Across every step that mattered.
The problem with today's attribution
Last-click is a lie. Next-click isn't much better.
Real customers don't make decisions in a single click. They spend days or weeks comparing, lurking, reading reviews, discussing with friends, changing their mind, and returning. Every traditional analytics tool collapses that reality into an impoverished single-point attribution.
Next-click
Shows the next page in a session. Useful for UX, useless for understanding a week-long decision process that moves across 40 different URLs and 3 devices.
Modelled MTA
Better, but still bounded by what you can instrument on your own properties. The most interesting moments — time spent on a competitor's product page — are invisible.
What's missing: the shape of the journey. Not "the last thing that happened," but the arc — what triggered interest, what deepened consideration, where defection happened, what pulled them back, and which competitor owned which stage.
The Deep Stream Data answer
Prescriptive journey intelligence.
Deep Stream Data combines the panel's behavioural truth with an AI reasoning layer that decodes journeys into narrative, identifies drivers, and recommends next steps — grounded in evidence, not opinion.
A real 4-week journey (illustrative)
Week 1 · Trigger
reddit.com→
google.com/search→
news-site.com
Week 2 · Research
competitor-a.com→
competitor-b.com→
reviews.com
Week 3 · Consideration
your-brand.com→
competitor-a.com/pricing→
your-brand.com/pricing
Week 4 · Decision
youtube.com/review→
your-brand.com→
your-brand.com/checkout
Deep Stream Data doesn't just reconstruct the path — it identifies that the YouTube review in week 4 was the inflection point, that Competitor A captured 62% of similar segments at the pricing-page stage, and that the biggest untapped opportunity is review-creator partnerships.
Capabilities
What Full-Journey AI unlocks
Journey archetype detection
Automatically cluster the millions of individual paths captured by the opt-in panel into a small number of archetypes — the "researcher," the "price-driven switcher," the "brand-loyal comparer" — and track how each segment is evolving.
Competitor analysis in narrative form
Ask "why are we losing 25–34 metro females to Competitor X?" and get a journey-grounded, evidence-weighted narrative. No more stitched-together dashboards.
Inflection-point identification
Deep Stream Data surfaces the specific moments in a journey where decisions flip — the review article, the price check, the social post — ranked by causal weight.
Prescriptive recommendations
Not just "here's what happened" but "here's what to do": content investments, partnership opportunities, page-level changes, campaign emphases — each tied to the journey evidence.
Counterfactual analysis
"If we'd won 10% more of the review-site impressions in week 3, how many more of the defecting segment would have ended up on our checkout page?" Deep Stream Data models the counterfactual on real behaviour, not media reach assumptions.
Emerging-pattern alerts
Proactive alerts the moment something shifts — a new entrant suddenly appearing in the consideration set, an adjacent category absorbing share, a content format overperforming.
How it works
From descriptive to prescriptive
| Capability | Deep Stream Data Full-Journey AI |
|---|---|
| Journey visibility | Full cross-site capture plus archetype clustering |
| Attribution | Causally-informed, counterfactual-aware multi-touch |
| Competitor analysis | Narrative explanations plus ranked inflection points |
| Recommendations | Specific, evidence-weighted actions tied to journey evidence |
| Query interface | Conversational, multi-turn analyst — ask in plain English |
| Alerting | Pattern anomaly and emerging-trend surfaces |
| Temporal window | Full purchase-cycle length — days to months |
Questions
About Full-Journey AI
How is this different from traditional multi-touch attribution?
Traditional MTA weighs the touchpoints you've instrumented on your own properties. Deep Stream Data attributes across every URL the consented panelist visits — yours, your competitors', reviews, social, search — and uses AI to weight causal contribution rather than simple position in the path.
What does "prescriptive" mean in practice?
Deep Stream Data doesn't stop at "here's what happened." It surfaces the specific content investments, partnership opportunities, or page-level changes that would most likely shift the outcome, each tied to the journey evidence so you can see why.
Does this run on the same panel as the rest of Deep Stream Data?
Yes. Full-Journey AI uses the same opt-in, privacy-compliant panel and the same web-log stream. The AI reasoning layer and the conversational query experience sit on top.
How does Deep Stream Data handle counterfactuals without bias?
Counterfactual reasoning is grounded in the real observed distribution of panel behaviour — Deep Stream Data models the alternate-world scenario by resampling similar panellist-weeks, not by guessing. Assumptions are always surfaced alongside outputs.
Can I try it on my own category?
Yes — book a demo and we'll pull a sample of real panel behaviour from your competitive set, with Full-Journey AI narrating the result.
See Full-Journey AI run on your category.
A 30-minute walkthrough with a sample of real panel behaviour from your competitive set.
Book a demo →