From No Customer File to a Scalable, In-Market Audience

How Allant helped an affordable wireless solution company identify likely buyers using transaction intelligence, compiled consumer data, and digital behavioral signals.

Why It Matters

This engagement demonstrates an important Allant differentiator: a brand does not need a prepared customer file to begin building a highly specific acquisition audience.

Allant can use transaction intelligence, multi-source compiled consumer data, identity resolution, and digital behavioral signals to create and validate a proxy customer population, then analyze and identify the unique characteristics that distinguish likely buyers from everyone else.

That is the difference between purchasing a broad third-party segment and engineering a Precision Audience:

• Multiple sources corroborate one another.
• Advanced analysis identifying what predicts action.
• A scalable audience kept fresh as the opportunity changes.

The Challenge

In the highly competitive pay-as-you-go wireless market, broad telecom segments provide reach, but little confidence that the people being targeted are actually likely to purchase.

An affordable wireless solution company needed to identify consumers likely to buy its SIM cards. The challenge was that the company did not have a customer file available to use as an audience seed. Without that foundation, a traditional provider might have relied on generic demographics, broad category affinities, or an off-the-shelf wireless audience.

Allant took a different approach: build a reliable proxy for the company’s customers from its own multi-source data environment.

The Allant Solution

Allant combined multiple forms of independent evidence to create a Precision Audience from the ground up.

1. Started with observed purchase behavior
   Allant identified transaction activity associated with relevant SIM card purchases. This provided a behavior-based starting point grounded in actual market activity, not assumptions about who might be interested.
   
2. Connected transactions to likely consumers
   Because the transactions were available at a geographic level rather than directly tied to individuals, Allant analyzed the demographic and geographic context surrounding those purchases. That information was compared against multi-source compiled consumer data and Allant’s identity resolution foundation to probabilistically identify the consumers most likely connected to the transactions.
   
3. Validated the audience with digital signals
   Allant then overlaid brand and category-related digital behavioral signals. The likely transactor population showed a strong relationship with those digital signals, creating two independent points of validation:
   
   • Evidence of relevant purchase activity
   • Evidence of corresponding digital interest and behavior
     Together, these sources created a high-confidence proxy for the customer data the company did not have.
     
4. Identified the signal combinations that mattered
   Allant analyzed how the proxy population differed from the broader market, ultimately identifying approximately 150 differentiating signals and the interactions most strongly associated with likely purchasers.
   
   Rather than targeting everyone with a general wireless affinity, Allant engineered an audience around the overlapping behaviors, characteristics, and signal patterns that indicated a greater likelihood to act.

The Impact

The initial Precision Audience was developed in a matter of days and delivered for programmatic activation.

In market, the audience generated the engagement and conversion signals needed for the agency’s media algorithms to continue learning and scaling. The clearest indicator of performance was the agency’s repeated request for additional audience volume and fresh records.

Allant subsequently established a weekly audience refresh cadence, with each refresh completed quickly and seamlessly using the audience specifications already built.

The result was not a one-time list. It was a repeatable supply of newly qualified prospects that could keep pace with the campaign and changing consumer behavior. Because no customer conversion file was available, direct sales matchback was not possible. However, agency-reported engagement, continued scaling, and the request for weekly refreshed audiences provided strong in-market evidence that the audience was working as intended and exceeding expectations.