{"id":11098,"date":"2026-08-21T14:31:11","date_gmt":"2026-08-21T12:31:11","guid":{"rendered":"https:\/\/www.htt.it\/?p=11098"},"modified":"2026-08-21T14:42:36","modified_gmt":"2026-08-21T12:42:36","slug":"marketing-attribution-mta-lift-tests-marketing-mix-modeling","status":"publish","type":"post","link":"https:\/\/www.htt.it\/en\/marketing-attribution-mta-lift-tests-marketing-mix-modeling\/","title":{"rendered":"The Holy Grail of Attribution"},"content":{"rendered":"\n\n\n
\n
\n
\n
\n

How to measure marketing without losing your mind?<\/h2>\n

\u201cHalf the money I spend on advertising is wasted; the trouble is I don’t know which half\u201d. This famous quote, attributed to retail pioneer John Wanamaker, sums up the historical dilemma of our industry. <\/p>\n

Today, despite technology, the issue of attribution, namely understanding which channels and campaigns are actually generating results, has become a titanic challenge. Many professionals chase the illusion of a single source of truth<\/em>, searching for a magic number that indicates the perfect ROI<\/abbr> for every euro spent. But, as we have also seen when discussing the relationship between ROAS and real profit<\/a>, in operational reality that perfect number does not exist. <\/p>\n

\n

At HT&T Consulting, we address this challenge by starting from the very nature of our services, which are characterized by a high degree of intangibility. To make the value of our work visible, we apply a \u201cMolecular Model\u201d<\/strong>: we surround the intangible core of strategic consulting with tangible elements such as data, reports and Data Visualization dashboards and tools<\/a> that allow the client to visualize the benefits of the offering. <\/p>\n

Reading these data, however, requires the right lenses to avoid disastrous strategic decisions. <\/p>\n<\/p><\/div>\n<\/header>\n

\n

Why is attribution an inherently imperfect problem?<\/h2>\n

The problem is not only technical, but structural. Today’s purchase journeys are fragmented across different devices and channels, making it almost impossible to track every single step. <\/p>\n

Added to this is the isolation of so-called Walled Gardens<\/em>, where each channel monitors only its own ecosystem and tends to attribute the credit for every conversion to itself. <\/p>\n

\n

\u201cI have been complaining for a decade about a fundamental flaw in Web Analytics tools: they encourage a one-night stand rather than engagement aligned with the customer’s actual intentions\u201d. <\/p>\n<\/blockquote>\n

As Avinash Kaushik points out, this short-sightedness has fuelled the Last-Click<\/strong> paradigm for years, assigning 100% of the credit to the last click before a purchase. <\/p>\n

\n

\u201cCompanies remain stuck on last-click attribution because it has been the standard for too long. But it is counterproductive for two simple reasons: except for trivial purchases, no one arrives and buys immediately, and above all it encourages renting traffic rather than building a relationship with the consumer over time\u201d. <\/p>\n<\/blockquote>\n

Rewarding only the final interaction pushes companies to cut budgets for Awareness<\/em> channels, namely those intended to generate brand awareness and demand, causing demand to collapse in the medium term and CAC<\/abbr>, the cost of acquiring a customer, to soar. <\/p>\n<\/section>\n

\n

The 4 fundamental methodologies: the importance of triangulation<\/h2>\n

Since no method provides the absolute truth, the most rigorous strategy is triangulation<\/strong>. <\/p>\n

Triangulation in services marketing is not simply an aggregation of data, but a cross-validation process necessary to eliminate the structural biases of each individual measurement method. <\/p>\n

Since every attribution model is, by definition, a partial and imperfect representation of reality, triangulation acts as a system of statistical checks and balances<\/strong>. <\/p>\n

\n

The three pillars of triangulation<\/h3>\n
\n
Pillar 1<\/span> <\/p>\n

Observation<\/h3>\n

Native Attribution and MTA.<\/strong> It is based on direct or probabilistic user tracking. It tells us what happened (a click, a view) along the purchase journey. <\/p>\n

It is essential for day-to-day operations, but suffers from privacy issues and the platforms’ tendency towards \u201cself-attribution\u201d. <\/p>\n<\/article>\n

Pillar 2<\/span> <\/p>\n

Experimentation<\/h3>\n

Incrementality Testing.<\/strong> It is the application of the scientific method. Through control groups, it isolates causality. <\/p>\n

It does not simply tell us what happened, but whether it would have happened anyway even without the marketing intervention. <\/p>\n<\/article>\n

Pillar 3<\/span> <\/p>\n

Modeling<\/h3>\n

Marketing Mix Modeling.<\/strong> It is a top-down<\/em> approach that uses statistical analysis on large historical datasets. <\/p>\n

It ignores the individual user and looks at correlations between total investments and sales, including macroeconomic factors, seasonality and offline channels. <\/p>\n<\/article>\n<\/div>\n<\/section>\n

\n

The strategic value of the process<\/h3>\n

Relying on a single pillar exposes the company to critical decision-making risks:<\/p>\n