Learning Mechanisms

Both traditional ad tech and Promovolve learn from feedback, but at different levels and timescales. The fundamental difference: traditional RTB learns about users, Promovolve learns about content and creatives.

Traditional: RTB Feedback Loops

DSP-Side

• Bid values: Learn from win/loss notifications what impressions are worth
• Audience targeting: Learn from conversions which user segments are valuable
• Learning happens at the bid level, not the creative level

Exchange-Side

• Floor prices: Learn from bid distributions to set minimum acceptable bids

Limitations

• No exploration: only learns from wins, never discovers if alternative creatives would perform better
• User-dependent: requires cookies, profiles, cross-site tracking

Promovolve: Five-Layer Learning

Promovolve does not run any campaign-side bid optimizer. With quality-adjusted second-price clearing (see Winner Selection), bid shading is counterproductive — the auction mechanism already extracts honest bids. The learning that does happen runs on the publisher and content sides.

Layer 1: Thompson Sampling (Per-Request)

What: Which creative performs best for a given slot Timescale: Every impression updates CreativeStats (1-minute buckets, 60-minute window) How: Bayesian posterior update Beta(clicks+1, impressions-clicks+1)

Before impression: Beta(5, 95)  → mean 5%
User clicks:       Beta(6, 95)  → mean 5.9%

This is the fastest loop — sub-second feedback incorporated into the next serve decision.

Layer 2: Publisher-Side Floor CPM RL (Per-Site, Slow)

What: The minimum CPM the publisher will accept on a given site Timescale: Adjustments evaluated against rolling served revenue How: An RL agent observes bid spread, fill rate, and post-pacing served revenue, and tunes the floor up when the market shows headroom (bid spread > 1.5×) and down when fill suffers

The agent is gated: it only activates when bid spread is wide enough that floor adjustments can plausibly change outcomes. In a homogeneous market where every bidder offers the same CPM, the agent stays put — moving the floor would just collapse fill without raising revenue.

This is the only RL agent in the system. It runs on behalf of the publisher; advertisers see honest second-price clearing regardless of what the floor does.

Layer 3: Category Ranking (Per Auction)

What: Which content categories are valuable for each site Timescale: Updated every auction, 7-day half-life decay, 14-day max age How: Thompson Sampling with Beta(prior_α + clicks, prior_β + non_clicks)

Category "sports" on site X:
  Prior: Beta(1, 1) → weight sampled ~0.5
  After data: Beta(15, 95) → weight sampled ~0.14

Slow loop, learning site-level category affinities.

Layer 4: Traffic Shape (Per Day)

What: Hourly traffic patterns (separate weekday/weekend) Timescale: Daily rollover with dayAlpha = 0.2 blend How: 24-bucket EMA with alpha = 0.1

Adjusts pacing targets to match actual traffic distribution.

Layer 5: PI Self-Tuning (Continuous)

What: Optimal aggressiveness for overpace correction Timescale: Every 20 samples, min 500ms interval How: Overpace multiplier adapts between 1.5× and 5.0× based on persistent overspend detection

Reader-Driven Signals (Not Learning, But Adjacent)

Promovolve also accepts an explicit signal from the reader: the dog-ear. When a reader folds the corner of a creative, the pin lives in their browser and re-encounters of that advertiser surface the bookmarked creative. This isn’t a learning loop in the statistical sense — it’s a direct reader vote, more reliable than any inferred preference. See Why Promovolve?.

Comparison