TV Measurement at Tatari
Tatari is a performance TV platform that provides outcome-based measurement across streaming and linear television using proprietary attribution and incrementality models.
Television advertising has evolved from a brand-awareness medium into a performance channel. Tatari’s mission is to make that performance measurable with the same rigor as digital marketing. Tatari holds several patents for advertising measurement and we continue to innovate.. This deep dive explains how Tatari’s measurement stack works across streaming and linear TV, details the models and metrics used to link exposure to outcomes, and highlights how Tatari integrates with third‑party tools to provide a full picture of impact.
Understanding these elements will help brands maximize return on ad spend (ROAS) and uncover the true value of TV across the funnel.
Data Collection and Privacy Infrastructure
Tatari offers both Pixel and Server‑to‑Server Options (no pixel needed). Tatari measurement begins with accurate signal collection. Advertisers can implement either a browser‑based pixel or a server‑to‑server (S2S) integration via Vault:
Tatari Pixel
A lightweight tag that records page visits, app installs, or purchases and links them back to specific ad exposures. Its simplicity makes it ideal for e‑commerce brands, and Shopify merchants can deploy it in a few clicks.
Vault Server-to-Server
A privacy‑first clean room solution that tokenizes user events before matching them with ad exposures. This method completely avoids client‑side tracking and ensures PII never enters Tatari’s environment. It allows accurate matching across devices while maintaining compliance.
Both options feed the same attribution models, giving advertisers flexibility while ensuring consistent data quality. Vault’s ability to link multiple devices in a household further enhances attribution accuracy
Privacy & Clean Room Controls
Tatari’s data infrastructure uses strict filters to remove shared or communal IP addresses, reducing false matches. The platform also integrates natively with Vault’s Data Clean Room, allowing brands to ingest matched results into their own BI tools or data warehouses. This approach provides complete transparency into assumptions and baselines without exposing PII.
Convergent TV Measurement
Tatari measures TV holistically across linear broadcasts and streaming (CTV) to reflect modern viewing behavior. The platform isolates the true incremental impact of TV by predicting expected traffic and attributing only the net lift to the campaign. Models built for TV’s specific dynamics—such as delayed response, cross‑device households, and passive viewing - ensure accuracy.
Outcome‑Focused Measurement
Tatari tracks the metrics that matter to both performance and brand advertisers:
Cost per Visit (CPV), Cost per Install (CPI) and Customer Acquisition Cost (CAC) – These metrics tie spend directly to outcomes like website visits, app installs or purchases, enabling advertisers to optimize toward efficiency.
Return on Ad Spend (ROAS) – Calculated from revenue (or gross margin) attributable to TV divided by media spend, giving a clear indicator of profitability.
Reach, Frequency and Impressions – Used to evaluate brand lift and awareness; Tatari combines these with performance metrics for a complete picture
Tatari’s dashboard lets you customize reports by network, creative, DMA or platform, and filter by attribution model, window or response timing. If you prefer to work in your own environment, raw event and attribution data can be exported directly to S3 for integration with BI tools
Tatari offers three distinct attribution models to fit different objectives.
Tatari View‑Through is the only view‑through model built for TV
Incremental Lift provides causal lift without view‑through assumptions
Digital View‑Through mirrors standard digital attribution for cross‑channel comparison.
Attribution Models Built for TV
Tatari View‑Through
The only view‑through model designed specifically for TV. It uses a proprietary device graph and filters out shared IPs to provide accurate household‑level attribution, accounting for multi‑device households and delayed actions.
Digital View‑Through
Aligns with digital platforms by using IP matching and shorter attribution windows, enabling apples‑to‑apples comparisons between TV and channels like Meta or YouTube.
Incremental Lift
Isolates net‑new conversions by comparing observed performance with a predictive baseline. It answers the question “What would have happened if we hadn’t aired?” and is not a view‑through model.
DragFactor: Timing Response Curves
Tatari’s DragFactor tool reveals exactly when conversions occur—whether seconds or weeks after the ad airs. By analyzing how sales decay over time, marketers can set appropriate attribution windows (from one day to four weeks) and avoid crediting sales that would have happened naturally.
Unified Measurement & Partner Ecosystem
Television doesn’t exist in isolation. To understand TV’s full impact, Tatari encourages clients to use multiple measurement methodologies and cross‑validate results. Tatari partners with best‑in‑class analytics providers to support several complementary techniques:
Marketing Mix Modeling (MMM)
MMM uses multivariate regression to estimate how channels, promotions and external factors influence sales. Partners like Keen, Measured, Marketing Attribution and LiftLab (podcast here!) ingest Tatari’s household‑level TV data to evaluate long‑term and short‑term impact across the marketing mix. Because MMM doesn’t require user‑level matching, it is resilient to privacy restrictions and signal loss.
Multi‑Touch Attribution (MTA)
MTA tracks user‑level events across digital media and connects them with conversions. Tatari integrates with partners like Northbeam and Rockerbox, which model the full customer journey to attribute incremental lift from TV to metrics like marketing efficiency ratio (MER) or incremental ROAS. These models help performance marketers understand how TV influences lower‑funnel actions alongside digital and out‑of‑home channels.
Design of Experiments (DOE)
Controlled tests such as geo‑holdouts or audience holdouts quantify lift with scientific rigor. Partners like LiftLab run matched-market experiments and agile mix models that go beyond simple geo-lift approaches. DOE outputs include estimated lift, CPA/ROAS and diminishing returns curves by channel
App Attribution & Mobile Measurement
Tatari’s integration with mobile measurement partners (AppsFlyer, Branch, Adjust, Kochava and Singular) ensures that app installs and in‑app purchases triggered by TV are visible in the Tatari dashboard. Coupled with incrementality testing, these integrations show how TV contributes to subscription or lifetime value.
Retail sales measurement: Measuring Amazon & Retail Sales
In addition to app conversions, many advertisers care about outcomes that happen off their own website or app. Tatari provides two approaches to quantify TV’s impact on Amazon and brick‑and‑mortar retail sales:
Amazon Sales Analysis
For brands selling on Amazon, Tatari can isolate the effect of TV by applying its baseline‑lift methodology to timestamped purchase data. Similar to web lift analyses, this method compares observed orders against a predictive baseline within a narrow attribution window (for example, 20 minutes), revealing incremental orders that were driven by TV. Requesting time‑stamped Amazon purchase data allows Tatari to estimate the response curve and determine the appropriate window.
Geo‑Based Retail Testing
To measure in‑store or brick‑and‑mortar sales, Tatari can run geo‑based incrementality tests. By splitting markets into test and control regions and supplying DMA‑level sales data, advertisers can measure how TV influences sales across websites, Amazon, and physical stores. This method works for any channel where sales can be reported by geographic region and requires weekly sales data at the DMA level. Because it relies on aggregated regional data, geo testing does not require PII.
Modeled Retail Conversions
In cases where direct signals are sparse (e.g., purchases through third‑party retailers or Amazon), Tatari also offers Retail Modeled Conversions. This beta capability uses statistical modeling to estimate retail and Amazon conversions and complements deterministic attribution. Modeled conversions help fill gaps when deterministic matching is unavailable, providing a more complete view of TV’s downstream impact on partner marketplaces and brick‑and‑mortar sales.
By combining these methods with its standard attribution models, Tatari can quantify TV’s effect on sales occurring beyond the brand’s owned channels—giving marketers confidence in the halo impact their campaigns generate across Amazon listings and retail shelves.
Best Practices for TV Measurement
Capture Clean Signals
Ensure that event collection, whether pixel or S2S, is properly configured and tested. Linking multiple devices in a household is critical for accurate attribution.
Choose the Right Model
Use incremental attribution as your primary source of truth, then compare with view‑through and digital models to triangulate.
Calibrate Attribution Windows
Use DragFactor to determine when most conversions occur and select an appropriate lookback period.
Blend Streaming and Linear Strategically
Data from forward‑thinking brands reveals that balancing CTV for determinism and linear for scale yields optimal results.
Measure Halo Effects
Monitor how TV influences search queries, social engagement and e‑commerce or retail sales. Brands like Coterie saw immediate spikes in search volume the day TV spots aired.
Use Multiple Methods
Don’t rely on a single “source of truth”. Combine attribution, MMM, MTA and surveys to validate findings.
Iterate and Test
Treat campaigns as experiments. Adjust creative, placement and spend based on real‑time results and long‑term learnings. Bold bets on live events or tentpole programs should be backed by measurement muscle to understand cross‑channel impact.