Ampd Signal Aims to Solve Social Commerce's Billion-Dollar Blind Spot

SEATTLE, WA – January 05, 2026 – Commerce media technology firm Ampd today announced the public launch of Ampd Signal, a solution engineered to bridge a costly gap between social media advertising and online retail sales. The platform aims to provide brands with direct, actionable line-of-sight from campaigns on platforms like Meta into consumer purchases on retail giants such as Amazon, Walmart, and Target, tackling a problem that has led to billions in underperforming ad spend.

For years, brands have invested heavily in social media to drive product discovery, only to lose visibility the moment a potential customer clicks through to a third-party retailer. This “data vacuum” has made it nearly impossible to measure the true return on investment (ROI) of social campaigns, leaving marketers to optimize based on proxy metrics like clicks and engagement rather than actual sales.

Ampd Signal is positioned as a first-to-market technology that transforms this disconnected process into a cohesive, high-performance growth engine. “This is a turning point for social commerce and brand media,” said Joshua Gebhardt, CEO and Co-Founder of Ampd, in a statement. “By capturing deterministic signals and enriching them with our purpose-built MMM, we are providing brands with a synthesized and actionable dataset that was previously out of reach. Ampd Signal transforms disconnected media into a scalable growth engine, integrating retail intelligence back into ad channels to finally give retail brands the same algorithmic advantages that have historically fueled DTC commerce success.”

The Multi-Billion-Dollar Measurement Challenge

The problem Ampd seeks to solve is rooted in the explosive but fragmented growth of digital commerce. Retail media advertising is on a staggering trajectory, with industry projections estimating the market will hit $140 billion in 2024 and surpass traditional TV advertising revenue by 2028. Simultaneously, social commerce has become a titan, with some analysts valuing the market at over $1.2 trillion in 2024.

Despite this massive scale, brands, particularly in the Consumer Packaged Goods (CPG) sector, face immense challenges in justifying their spend. A primary obstacle, identified by 62% of retail media buyers in an Interactive Advertising Bureau report, is the lack of standardized measurement. Each retail media network—from Amazon to Walmart—operates as its own “walled garden” with unique attribution models and reporting metrics, making apples-to-apples comparisons nearly impossible.

This creates significant attribution complexity. Marketers struggle to definitively prove whether a social media ad directly drove a sale or if the customer would have purchased the item anyway. Traditional attribution models that rely on last-click data often fail to capture the nuanced, multi-touchpoint customer journey, which frequently begins with discovery on a social feed. This can lead to flawed budget allocation, with channels that close the sale receiving undue credit while top-of-funnel discovery platforms are undervalued.

A New Engine for Privacy-First Attribution

Ampd Signal’s architecture is designed to provide clarity without compromising consumer privacy, a critical consideration in a landscape shaped by GDPR, CCPA, and the impending deprecation of third-party cookies. The platform’s “Sequential Engine” operates on a continuous, three-step feedback loop.

First, it captures what the company calls high-fidelity, “deterministic commerce signals” from retailer websites. Crucially, this process is described as entirely privacy-safe and non-PII (personally identifiable information), focusing on aggregated shopper behavior and outcomes rather than tracking individuals. This approach aligns with the industry-wide pivot toward privacy-preserving measurement techniques like data clean rooms.

Second, these aggregated signals are processed through Ampd’s proprietary Media Mix Model (MMM). Unlike traditional, high-level MMMs, this model is specifically engineered for offsite-to-retail measurement. It uses sophisticated data science to analyze the raw behavioral data, identify performance trends, and calculate the true incremental lift generated by specific ads, creatives, or audience segments. With a weekly refresh cycle, the model adapts dynamically to changes in campaigns and budgets, offering more timely insights than static quarterly models.

Finally, the enriched data science outlook is integrated back into the native social advertising platforms. This allows brand marketing teams to use their existing tools on Meta or other social channels to see which campaigns are driving retail outcomes like “Add to Carts” and sales. It unlocks native reporting and enables automated, algorithmic optimization toward real commerce goals, all without exposing any raw user-level data.

Leveling the Playing Field for Retail Brands

The strategic implication of this technology is a potential rebalancing of power between direct-to-consumer (DTC) brands and traditional retail brands. DTC companies have long held an advantage due to their ownership of the entire customer journey, from ad impression to final sale. This gives them a wealth of first-party data to fuel precise, algorithm-driven optimization.

In contrast, CPG brands selling through partners like Target or Amazon have been forced to treat much of their social media spend as an “awareness-only” play, with performance judged on vague brand lift metrics. By closing the measurement loop, Ampd Signal promises to equip these brands with the granular, performance-oriented insights needed to compete on a more even footing. According to Ampd, early adopters among large CPG firms are already using the technology to reallocate spend with greater confidence, improve campaign efficiency, and unlock incremental sales across major retailers.

This shift allows brands to treat their retailer-bound social spend as a direct sales driver, optimizing for efficiency and market share gain. As one analyst firm noted, the ability to manage campaigns in near real-time with actual sales data is a “huge win” for manufacturers looking to justify their marketing budgets and prove tangible ROI in an increasingly competitive digital shelf space.