How to plan India CTV campaigns without reliable audience data

India CTV lacks a single cross-platform audience measurement currency. BARC's panel does not measure streaming. Publisher audience figures are self-reported and not independently verified at programme level. There is no Indian equivalent of Nielsen's cross-media Total Audience Report. Planners who work in India CTV navigate this gap through a set of accepted workarounds — not ideal, but workable with the right expectations set from the outset.

The measurement gap

The core problem for India CTV planning is that the two screens a buyer most wants to plan together — linear TV and CTV/OTT — are measured by entirely different systems with no agreed deduplication methodology:

Linear TV: BARC weekly ratings, panel-based, SEC/age/gender/state breakdowns available, used as the transactional buying currency
CTV/OTT: Publisher-provided MAU/DAU figures, DSP impression delivery data, third-party ad verification (IAS/DoubleVerify) for viewability and fraud, and BARC Streaming data (partial, not yet transactional)

The audiences are not the same. India's CTV audience skews metro, SEC A/B, and 25–44 age group. The BARC linear panel represents the full TV-viewing population including rural, SEC C/D, and Tier 3. Any planner who tries to combine them without adjustment will double-count or miss reach.

What data is available for India CTV planning

Before reaching for workarounds, know what legitimate inputs exist:

Publisher audience data: JioHotstar publishes MAU (reported: ~500M+ as of 2025), smart TV MAU, and demographic breakdowns in their media kit. SonyLIV and Zee5 publish similar figures. These are self-reported and not independently audited to BARC standards — but they are the best available audience sizing input for CTV-specific planning.
IRS / NCCS surveys: The Indian Readership Survey and New Consumer Classification System provide media consumption data across TV, digital, and print. These are survey-based and updated periodically rather than continuously, but they can support cross-media reach modelling.
DSP audience data: DV360 and The Trade Desk provide their own reach and frequency estimates based on logged-in user data, device graphs, and modelling. These are not independently verified but are internally consistent within each DSP's universe.
BARC Streaming: Provides platform-level streaming minutes for participating publishers. Not programme-level. Not demographic-level. But useful for relative platform sizing.
Third-party studies: Kantar, YouGov, and Ormax Media periodically publish India OTT audience research. These are not continuous measurement currencies but provide directional audience profile data.

Working with publisher-provided data

Publisher audience numbers in India should be treated as a floor, not a ceiling. Publishers have an incentive to present their largest possible audience figures. Planners should apply the following checks:

MAU vs active viewers: A platform's registered MAU includes users who logged in once in the last 30 days. Active streaming households — people who actually watch content — are typically 30–50% of total MAU. Ask publishers for smart TV MAU and weekly active users specifically, not just total MAU.
Device breakdown: JioHotstar reaches a large audience on mobile, but the CTV-only reach is far smaller. For a campaign targeting TV screens, push for connected TV MAU rather than total platform MAU.
Content adjacency: Publisher audience data is platform-wide. If you are targeting audiences watching a specific genre (premium drama vs news), the relevant audience pool is a subset of total platform reach — typically not disclosed granularly.

Reach estimation approaches

In the absence of a currency, India planners use three reach estimation approaches — often in combination:

1. Publisher-declared universe modelling: Use publisher-provided CTV MAU figures as the potential universe. Apply an estimated campaign reach percentage based on targeting parameters and budget versus publisher rate card. This is rough — accuracy depends entirely on publisher data quality — but gives a directional reach estimate for client presentations.

2. DSP reach forecasting: DV360 provides a reach forecast tool that estimates unique household reach for a given targeting set, budget, and publisher mix. The Trade Desk provides similar forecasts via its Planning tool. These are based on each DSP's logged-in user graph and are the most data-driven estimates available — but they are DSP-specific and don't aggregate across publishers unless the campaign runs through a single DSP buying multiple publishers.

3. Survey-calibrated modelling: Larger agency groups (GroupM, Publicis) maintain proprietary cross-media reach models calibrated against NCCS/IRS survey data and periodic third-party research. These produce cross-TV-and-digital reach curves used in media mix planning. Clients without agency support cannot easily access these models.

Post-buy evaluation without a single currency

Post-campaign evaluation in India CTV is necessarily multi-source. The accepted approach is:

Delivery verification: DSP delivery report (impressions, frequency, device type, publisher) is the primary record of what was delivered.
Viewability and fraud: IAS or DoubleVerify third-party report provides independent viewability rate and invalid traffic percentage. This is the closest India CTV has to independent delivery verification.
Brand outcomes (optional): For campaigns with sufficient budget (typically ₹50L+), a brand lift study via Google Brand Lift (for DV360 buys) or The Trade Desk's brand impact study provides survey-based measurement of awareness and consideration lift among exposed vs unexposed audiences.
Performance outcomes: If the campaign has a performance objective — installs, site visits, form fills — last-touch or view-through attribution via the DSP or mobile measurement partner (AppsFlyer/Adjust) is tracked.

The India practical approach

The planners who navigate India CTV measurement well do the following:

Set client expectations upfront: India CTV reach and frequency cannot be measured with the same precision as linear TV. This is a property of the market, not the planner's competence.
Use publisher data for sizing, DSP forecasts for planning, and third-party verification for delivery accountability — each tool for its appropriate job.
Run brand lift studies on any campaign where the client needs outcome evidence rather than delivery evidence.
Compare effective CPMs against publisher-provided CPM benchmarks to triangulate whether the buy was competitively priced, in the absence of an independent trading benchmark.
Build a first-party data capability: pixel-based site traffic and app event measurement from CTV ad exposure are the highest-quality India CTV attribution signals available today, and they are within the buyer's control.