Multi-touch attribution (MTA) distributes conversion credit across all the ad touchpoints in the buyer's path, rather than giving everything to the last click. For CTV, MTA is more accurate than last-touch in principle — but only if the attribution system can actually see the CTV exposure and connect it to the eventual conversion. That connection requires cross-device identity resolution: knowing that the person who watched the TV ad is the same person who later converted on mobile or desktop. In India, this identity infrastructure is limited, which constrains how well MTA actually works for CTV campaigns in the market today.
This article defines the MTA models in common use, explains the specific technical barriers CTV creates for MTA, reviews vendors with CTV MTA capabilities, and assesses what this means for India advertisers.
What is multi-touch attribution?
Multi-touch attribution is a measurement framework that assigns fractional credit for a conversion to every tracked touchpoint in the path, rather than a single touchpoint. The total credit always sums to 100% or one conversion — it is distributed differently depending on which MTA model is used.
MTA assumes you have a complete view of the conversion path — that every ad exposure the user encountered is logged, timestamped, and linked to the same user ID. In practice, the conversion path is always incomplete. Channels that lack click mechanisms (CTV, linear TV, radio, outdoor) are invisible to most MTA systems unless the vendor has built specific integrations to capture impression-level data from those channels.
Common MTA models
Linear attribution
Credit is distributed equally across all touchpoints. If there were four touchpoints in the path — a CTV impression, a social video view, a search click, and a direct visit — each gets 25% of the credit. Simple to implement and understand. Does not account for the actual influence of each touchpoint on the conversion decision.
Time-decay attribution
Touchpoints closer in time to the conversion receive more credit than earlier touchpoints. A search click that happened the day before the purchase gets more credit than a CTV impression from two weeks earlier. This model reflects the intuition that recency correlates with influence, but it systematically underweights awareness channels like CTV that operate early in the funnel.
Position-based attribution
Also called U-shaped or W-shaped attribution. Position-based models give elevated weight to specific touchpoints — typically the first touch (awareness) and the last touch (conversion) — with the remaining credit shared across middle touchpoints. This can reflect CTV's role as an awareness driver if the CTV impression is the first touchpoint, but it depends entirely on the system being able to see the CTV impression.
Data-driven attribution
Algorithmic models (often using Shapley values from game theory, or machine learning) assign credit based on analysing which touchpoints actually correlate with higher conversion probability. Data-driven attribution is theoretically the most accurate, but requires very large datasets to produce stable models — typically hundreds of thousands of conversion events. Most MTA implementations in India do not have this volume in CTV specifically.
Why MTA is difficult for CTV
Three specific barriers make MTA harder for CTV than for digital channels:
The identity bridge problem
MTA requires connecting events across a conversion path to the same user. The CTV ad is served to a device in the living room — identified by IP address, device ID, or ACR data. The conversion happens on a mobile phone — identified by a mobile ad ID (GAID or IDFA). Connecting these two events requires a cross-device graph: a mapping of which mobile IDs correspond to which household TV devices. These graphs exist but are probabilistic (based on shared IP, behavioural inference) rather than deterministic (based on confirmed login identity), which introduces error into every attribution calculation.
CTV impression visibility
Most MTA systems were built for digital channels with strong impression tracking. CTV impression data needs to be fed into the MTA system through a specific integration — typically via the DSP, the CTV platform's measurement API, or a third-party measurement provider. If this integration does not exist, the CTV impression is simply missing from the attribution model, and the conversion path appears to start at the first digital click.
Walled gardens
Major CTV platforms — JioCinema (which uses a Reliance-owned infrastructure stack), YouTube connected TV, Samsung Smart TV — do not share user-level impression data with third-party MTA vendors. The impression data stays inside the platform. What gets reported to the advertiser is aggregated, not user-level. This makes it impossible for an MTA vendor to include these CTV exposures in a cross-channel path analysis.
MTA vendors with CTV capabilities
A small number of measurement vendors have built integrations to include CTV impression data in multi-touch attribution models:
- Neustar (TransUnion): Offers cross-channel MTA including CTV. Uses a deterministic and probabilistic identity graph. Strong in the US market; limited India-specific deployments as of 2026.
- Analytic Partners: Combines MTA with MMM (an approach called unified measurement). Has been active in India for large-brand engagements. Best suited to large advertisers with consistent long-term data.
- Nielsen ONE: Nielsen's cross-media measurement platform integrates with CTV publishers to capture impression data. India availability of the full suite is limited.
- AppsFlyer and Adjust: Mobile measurement platforms that have added CTV attribution capabilities specifically for app install measurement. More relevant for app-first brands measuring TV-to-install journeys than for general MTA.
For India specifically: the MTA vendor landscape for CTV is thin. Most Indian advertisers do not have access to a vendor that can run true cross-device MTA incorporating CTV impression data from Indian streaming platforms.
When does MTA make sense for CTV?
MTA is most useful for CTV when:
- The advertiser is running CTV alongside digital channels and needs to understand the relative contribution of each channel to the same conversion event.
- The brand has a large digital footprint with robust conversion tracking, giving the MTA model enough non-CTV data to anchor the model.
- The advertiser has access to a CTV-capable MTA vendor with integrations into the specific platforms they are buying — not just a generic MTA system that will miss the CTV impressions.
- The brand is primarily focused on digital conversions (app installs, e-commerce purchases) where conversion tracking is strong.
When MTA does not make sense for CTV
- When the CTV impressions cannot be imported into the MTA system (most Indian streaming platforms).
- When the advertiser's primary conversion event is offline — store visits, phone calls, dealership appointments — where digital conversion tracking is weak regardless of channel.
- When the brand is below the scale threshold for data-driven attribution models (typically under 100,000 monthly conversions).
- When the measurement goal is brand lift or awareness, which MTA is not designed to measure at all.
India context: MTA and CTV in the current market
India's CTV market is dominated by a handful of large platforms — JioCinema, Disney+ Hotstar, YouTube, Samsung Smart TV — none of which provides open user-level impression data to third-party measurement vendors. This makes true cross-device MTA incorporating CTV essentially unavailable for India-based campaigns at scale.
Indian advertisers running CTV should treat MTA as a supplemental signal at best, and should prioritise incrementality testing or branded search uplift measurement as primary indicators of CTV performance. As the Indian identity ecosystem matures — driven by UPI, Aadhaar-linked services, and OTT platform login data — the feasibility of MTA for CTV will improve. But that improvement is measured in years, not quarters.