The bid landscape is the distribution of bids across a programmatic auction — how many buyers bid at each price point, what percentage win, and at what clearing price. In CTV, reading the bid landscape tells you whether your bids are competitive, whether you are overpaying, and where the real competition for inventory lies. DSPs expose bid landscape data in their reporting; knowing how to interpret it is one of the most underused skills in programmatic CTV buying.
What is bid landscape data
Bid landscape data is a histogram of auction outcomes across a defined time period and targeting set. It shows:
- The distribution of bid prices across all auctions you participated in
- The clearing price (what the winner paid) at each bid level
- Win rates — the percentage of auctions won at each bid range
- Estimated impression volume available at each price tier
Most enterprise DSPs — DV360, The Trade Desk, Amazon DSP — surface some form of bid landscape data. The Trade Desk's Koa and DV360's bid strategy tools use bid landscape data under the hood to power automated bidding. Buyers with API access or advanced seat access can often pull raw bid landscape reports.
Key bid landscape metrics
| Metric | What it tells you |
|---|---|
| Bid rate | % of eligible auctions where you submitted a bid. Low bid rate = targeting too narrow or bid too low to pass DSP pre-filtering |
| Win rate | % of submitted bids that won. <20% means you are likely underbidding; >80% may mean you are overbidding |
| Average clearing price | What you actually paid per impression. Should be compared to your max bid to assess headroom |
| Bid-to-clear ratio | Your max bid ÷ average clearing price. Ratio of 1.0–1.2 suggests efficient bidding; >1.5 means you have room to lower bids |
| Impression density by price | Volume of impressions available at each price band. Shows where the inventory market sits |
Reading bid landscape patterns
Clustered clearing prices: If 70%+ of impressions clear within a narrow ₹50 CPM range, the publisher has a consistent floor and limited competition above it. You can bid just above this cluster and win reliably without overpaying.
Wide distribution: Clearing prices spread across a large range (e.g., ₹200–₹800) indicate variable competition — some slots are contested (live sports, primetime) and some are not (off-peak, non-premium content). Targeting adjustments can shift you toward the less contested inventory.
Bid floor spike: If a large percentage of your lost bids are lost "below floor," the publisher has raised their floor since your last optimisation pass. Adjust your floor acceptance or move to a PMP for predictability.
Competitor pressure: A rising trend in clearing prices over time (without a floor change) means more buyers entered the auction. Common during festive season (Diwali, IPL), when advertiser competition for India CTV inventory intensifies significantly.
India CTV bid patterns
India CTV bid dynamics differ from Western markets in important ways:
Lower average competition: Compared to US CTV, India CTV auctions have fewer active DSP bidders per auction. This means win rates are higher at lower bid multiples — you typically don't need to bid aggressively above floor to win standard inventory.
Seasonal spikes: IPL season (March–May) and Diwali (October–November) see 40–80% increases in clearing prices on major OTT platforms. Budget planning needs to account for these spikes explicitly — flat CPM budgets will under-deliver during peak periods.
Day-part variation: Indian CTV viewing peaks sharply in the 8–11pm window. Clearing prices during this period are typically 2–3× daytime rates on the same inventory. Bid landscapes stratified by hour will show a clear primetime premium.
Publisher concentration: JioHotstar commands a disproportionate share of premium CTV inventory. Bid competition is highest there. Alternative publishers (SonyLIV, Zee5) often clear at lower prices with similar content quality.
Using bid landscape data to optimise
Find the efficiency threshold: Plot win rate against bid price. There is usually a knee in the curve — a point where small bid increases produce large win rate increases, above which further increases produce diminishing returns. Bid at the knee, not above it.
Segment by day-part: Use separate bid strategies for primetime and off-peak. Off-peak inventory at ₹150–200 CPM often reaches the same household as primetime at ₹400 CPM, with frequency driving effectiveness rather than premium placement.
Use bid shading: On first-price auctions, bid shading algorithms use historical clearing price data to submit bids just above the expected clearing price — not at your maximum. Ensure bid shading is enabled in your DSP settings for CTV line items.
Monitor floor changes: Publishers adjust floors quarterly or around content events (new seasons, sports rights). A sudden drop in win rate without a targeting change usually means a floor increase. Check bid landscape for floor-loss percentage before investigating other causes.