Singapore – Adjust, a measurement and analytics business, has announced InSight, a machine learning and AI-powered measurement tool that gives marketers a data-driven perspective on campaign efficacy.
Marketers may now use incrementality analysis to gauge the effect of particular marketing initiatives, including budget increases, on return on investment (ROI) because of the launch of Adjust’s InSight in the INSEA area and other markets worldwide. This facilitates ROI-positive decision-making by allowing marketers to evaluate these marketing initiatives against desired KPIs and ascertain if they yield incremental lift, cannibalise organic traffic, or have no effect at all.
When incrementality analysis reveals the actual worth of new advertising channels, campaigns, budget adjustments, and seasonality—values not found in previous marketing initiatives—it can augment a marketer’s existing arsenal for measuring.
Speaking about the launch, Katie Madding, chief product officer at Adjust, said, “As our industry moves towards a more privacy-centric, aggregated approach to measurement, marketers are faced with even more complexity in understanding the true impact of their efforts. This new era demands new innovative approaches that unlock real visibility. Without it, optimising campaigns and allocating budget becomes a guessing game and marketers could be highly misled by relying solely on short-term measurement.”
She added, “Adjust is deeply committed to delivering next-gen solutions that answer the most critical question on marketers’ minds: ‘Is a campaign having a positive impact on my business? With InSight, marketers are no longer in the dark. Our models can accurately predict ‘what could have happened if this marketing action hadn’t taken place’, delivering results with a 95% confidence interval, which is the highest on the market.”
Meanwhile, Jay Christian, performance marketing manager at Pret A Manger, stated, “Adjust’s incrementality measurement brought us insights into iOS campaign optimisation once thought was no longer possible. With their machine learning models doing the heavy lifting and analysing our historical aggregated data, insightful outcomes and advanced incrementality metrics are available at the push of a button.”
