Admetrics Launches Marketing AI Assistant Newton to Advise Marketers on Growth Opportunities

Admetrics, a marketing intelligence company that supports advertisers with data science solutions to gain actionable insights into their marketing data, announced the release of Newton, a new capability that enables companies to run always-on experimentation for insight generation at scale.

Newton represents a shift in marketing intelligence software, providing marketers a simple way to turn any marketing data into countless experiments running in parallel to learn, optimize and iterate at scale by continuously analyzing data and making recommendations for growth. In this capacity, Newton functions as a 24/7 expert and advisor, drawing from deep domain expertise to convert data into knowledge, proactively identifying insights and actions to drive positive business outcomes.

The ability of Newton to evaluate options and alternatives, determine cause-and-effect relationships and predict highly probable and profitable outcomes positions marketers to bridge the growing disconnect between information and insights. Research firm Forrester puts the scope of the problem into perspective, observing that a massive 60 to 73% of data companies collect is not analyzed.

The outcome is a vicious cycle in which marketers invest massive resources to gather tremendous amounts of data from an ever-increasing number of sources and channels—and yet lack the capabilities to turn that information into action, according to a 2019 survey of CMOs and brand marketers reported in Inc. magazine. This also dovetails with Admetrics internal data that shows the majority of brands (57%) have “access to sufficient data resources,” but, at the same time, critically lack capabilities to address and execute data-informed tasks including testing, reporting and bidding model optimization.

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Newton was architected to address this issue, providing marketers the capabilities to automate and scale data analysis in order to maximize insight generation and increase cost efficiency. To achieve this, Newton leverages Quantify, the Admetrics experimentation engine released in March, to provide highly accurate statistical results at the highest possible pace. This enables quicker time to value and dramatically faster data-driven decision making than traditional testing methodologies by empowering marketers and campaign managers to continuously learn from ever-running experiments.

Leading data-driven companies across categories have developed their own experimentation platforms in order to outpace their competition. It’s a competitive advantage that used to demand significant investment and data science expertise, but this is changing with the advance of next-generation solutions, according to Markus Repetschnig, CEO and co-founder at Admetrics.

“The release of Newton, which was purpose-built to supercharge experimentation and data analytics by suggesting actions marketers can take to optimize growth, marks a turning point in what all companies can achieve—even if they lack big budgets and large data science teams,” Repetschnig explains.

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“Newton helps advertisers and agencies minimize missed opportunities for growth and stop losses as early as possible,” Repetschnig says. More importantly, Newton is also one of the first in a new breed of augmented analytics solutions, a cutting-edge category of data capabilities Gartner has identified as a top 10 strategic tech trend for 2019. In practice, Newton harnesses AI models that are tailored to specific advertising value and attribution chains to generate insight and provide actionable recommendations at scale, while minimizing the human component previously required to find issues and opportunities hidden deep in the data.

Advertisers and agencies that want to leverage the new capability do not need to change their existing infrastructure as Newton works with any kind of marketing data and can be easily plugged into any tech stack. To date, Newton offers expert models for programmatic advertising and social platforms like Facebook, with additional models in the pipeline to address other marketing use cases.

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