Use Case / 05 · Engagement & Recommendations

Take content and platform engagement metrics to new levels.

Neuralift helps streaming, publishing, sports, app, and marketplace teams personalize to content affinity by surfacing the segments most likely to deepen sessions, lift feature adoption, and power smarter recommendations across owned surfaces.

Activates across · CMS · App · Email · Recs
Fig. 01 · Ranked Actions · Engagement

Deepen every session.

Neuralift reads your session logs, content metadata, and feature events, and maps content affinity across your entire audience: what each cohort reaches for, when, and what would deepen the habit. The highest-upside opportunities come back as Ranked Actions like the one below.

Deep learning × reasoning

Every cluster and artifact the deep-learning run discovers feeds straight into reasoning models. The Actions they write are grounded in what the network actually found, far more tuned to your outcome than an LLM pointed at a table.

Macro plan, micro tactics

Each Action is a comprehensive plan for hitting the KPIs you named, scored for strategic fit, expected impact, and executability. Beneath it sit segment-specific tactics: customer reach, recommended offers, channel mix, and a dated goal.

Export · Warehouse · MCP

Take every plan as an export package, write it back to your warehouse, or serve it over MCP to fuel AI chains and agentic workflows.

app.neuralift.ai/actions Actions 1 2 3 4 5 6 SEGMENTS 11 CUSTOMERS 3.41M 100% Lift session depth with second-screen picks for weekend co-viewers STRATEGIC FIT 0.92 EXPECTED IMPACT 0.88 EXECUTABILITY 0.93 sessions_per_user TARGETS KPI sessions_per_user, content_depth SEGMENT-SPECIFIC TACTICS Companion-content rail for co-viewing hours for Second-screen co-viewers · Weekend streamers · Single-genre loyalists Reach 1,604,910 (47.1%) Offer Channels Goal +18% sessions / 90 days
▣ Value illustration

Lift engagement at scale.

10M MAU · 70% baseline at low/medium engagement · 30% credibly liftable · +20% engagement signal → +2.1M users moved up tier

+2.1M
Users at deeper engagement
+20%
Engagement signal lift
1.7×
Engaged-MAU multiplier
Uplift by segment · Engagement
Deep-session bingers RANK 01 Recs tuning
+20% ENG
Feature-adjacent users
+14% ENG
Single-genre loyalists
+11% ENG
Weekend-only viewers
+7% ENG
Push-responsive dormants
+4% ENG
▣ How it works
  1. 01

    Discover content-affinity and engagement segments

    Surfaces user segments by content affinity, session depth, and feature-adoption signals.

  2. 02

    Map each segment to the right engagement play

    Recommendation tuning, feature nudge, content surfacing, deep-content path, or reactivation prompt.

  3. 03

    Activate across CMS, app, email, and recommendations

    Push activation-ready IDs into CMS, in-app surfaces, email, and recommendation engines.

▣ Next step

Start with Engagement & Recommendations.

Book a 30-minute discovery call to scope this use case with our team.