Value-Based ACV over Vanity Metrics

Measuring value delivered, not reach

"We are not chasing just monthly active users," Alex Mashrabov says of Higgsfield, the AI video company he built. "The MAU number can be inflated through some viral effect and doesn't really speak to the frequency of the usage and the value delivered. So for us really daily active users and average ACV, those two metrics are the most important ones."¹ The pattern is a refusal to let a headline growth number stand in for the health of a business: he names two metrics as the ones he watches and one he deliberately sets aside.

The pairing is deliberate. Daily active users capture frequency: whether the product earns a place in someone's daily workflow, which a viral spike inflates in MAU but not in DAU. Average contract value captures how much of the value created is actually captured. Mashrabov describes the internal calculation at Higgsfield as an estimate of "how much value we believe we provide," validated through user interviews, cross-checked against how much the company charges users annually. In his account, the two numbers together describe a real business in a way a single reach metric cannot.

Pricing anchored to the outcome

The metric discipline implies a pricing stance. Mashrabov describes a target customer profile that is neither a free-tier land grab nor a small set of enterprise mega-contracts, but a large middle: on his framing, tens of millions of high-intent professional users willing to pay on the order of two thousand dollars a month. The enabling condition he cites is product quality high enough that "it kind of sells itself," so that price can be anchored to delivered return rather than to a competitor's markup or a cost-plus formula. This is the value-capture logic behind the 0 to $1M in 90 Days pattern he describes: roughly eighty thousand dollars a month is reachable from a small base of high-ACV users rather than a large base of non-paying MAU.

In a later interview Mashrabov names where he believes this logic points. He describes value-based pricing evolving toward outcome-based pricing, saying Higgsfield "by the end of the decade should be a platform integrated in the marketing technology stack so that eventually Higgsfield helps to drive the outcomes," paid for results rather than per use.² He is explicit that he does not consider this possible yet, "because the models don't have the reasoning engine to connect the dots between the data, the visuals, the generations." In his telling, ACV and DAU are the discipline available today, and outcome pricing is the destination the same value-capture reasoning reaches once a system can perceive and attribute the outcomes it produces.

What the metric assumes

Mashrabov presents the choice as fitting the metric to the usage rhythm. Daily active users is the right frequency measure for a tool people are meant to open daily; he does not claim it generalizes to every product. The framing also carries a large market assumption. Tens of millions of users at roughly two thousand dollars a month describes an aspirational profile of the best customers rather than a literal reading of the whole base, and Mashrabov offers it as a target for who Higgsfield's most valuable users could be rather than as a current count. The claim that a product can be "so good it sells itself" is one he states as a condition to build toward, not a measured fact, and it is the load-bearing assumption underneath the decision to price against value rather than against a competitor.