MarTech Interview with Alexander Igelsböck Co-Founder and Chief Executive Officer of Adverity

Alexander, please share a bit about your background and what led you to found Adverity in 2015.

I’ve been fortunate to experience, and power, multiple waves of technological evolution. Starting my career amid the initial   boom, I helped shape key internet infrastructure services and the emerging mobile space, including co-founding two mobile commerce firms: all about apps (aaa) and Ciuvo. After Ciuvo was successfully acquired, a drive to support wider tech progression led me into the advisory and investment space, which afforded the broad oversight needed to spot an increasingly challenging business problem: disorderly data.

Marketing teams in almost all sectors were struggling to handle fast-growing volumes of data, with many unable to extract vital insight and use it to inform important decisions – often due to heavy reliance on inefficient management practices and outmoded spreadsheets. Adverity was created to solve these issues by making it simple to collect, connect, analyze, and use data. The goal was to ensure every choice could be steered by up-to-date, accurate data, within marketing departments and across entire organizations.

 With 20 years of experience in the tech industry, what were the key moments or influences that led you to focus on data-driven efficiency?

I have a long-running passion for data, not just for its own sake, but because of what it can do. At any company, maintaining well-oiled operations and steady growth ultimately depends on efficient data use; with the right people able to harness relevant insights that can be applied to guide smart moves, at the right time.

This view was originally validated by my early product management days, as I saw first-hand the value of basing product development on solid data about specific customers and global markets. Since founding Adverity, countless situations have further illustrated the value of robust data orchestration in unlocking emerging opportunities and tackling challenges for the companies we support — with each one reaffirming why data-driven efficiency is a universal necessity.

How has your focus on data-driven efficiency shaped Adverity’s product offerings and overall strategy?

With a founding mission to power data-led effectiveness, Adverity’s product and strategic development has continually focused on providing solutions to serve the needs of teams that run on data. In essence, the consistent guiding aim is to simplify complex processes so that businesses can effortlessly scale and streamline their data operations. Over the last nine years, we’ve strived to achieve that by constructing a complete end-to-end system designed around making the full data lifecycle easier to manage, at every stage.

For instance, our extensive range of API connectors allows companies to effortlessly integrate information from an array of sources, as well as apply refined governance in line with internal policies and external data regulations. We’ve also built automated cleansing, de-duplication and syncing they can apply to achieve a comprehensive overview of marketing performance, before putting this single source of truth into action via bespoke analysis and transformations.

Why do you believe that GenAI is no longer just a buzzword for marketers? How is it addressing the challenges they face, such as data quality and access?

From a buzz perspective, the simple answer is that GenAI has not only become a part of the modern marketing toolkit, but it’s also delivering practical value. According to recent research from the Boston Consulting Group (BCG), 80% of CMOs feel their implementations are already improving automation, speed, and productivity.

My take on why GenAI tools are worth the hype lies in their advanced capabilities. Today’s generative solutions can process larger quantities of data, coordinate a host of tasks at once, and most critically, produce novel outputs from inputted information in various formats. This makes them perfectly suited to both generating content and assisting with data analysis.

For example, analytics solutions driven by sophisticated large language models can now allow users to instantly assess vast data sets and receive summaries of key findings. Those fueling smart evaluation on well-orchestrated and accurate data are helping to democratize insight-driven success by bringing friction-free, high-quality analytics within the reach of more users, even if they don’t have specific expertise.

Can you describe the new features Adverity released last year to help marketers maximize the value of their data? How have these features impacted your clients’ marketing strategies?

In short, the Enrichment Copilot enables simple, fast, and accessible self-service analytics. At the GenAI level, there are two main components. Firstly, our data transformation assistant fuels made-to-order data configuration. Users can enter details about how they want their data to be formatted and the GenAI tool will instantly produce custom code, alongside clear step-by-step instructions on how to implement it.

Secondly, natural language analytics allows users to apply a similar approach to answering data-based questions. Individuals can enter plainly worded prompts — such as “which ads have driven the highest click-throughs over the past six months?”— the AI will run SQL queries that retrieve relevant data, before translating it into understandable visuals.

In terms of broader AI updates, we’ve also introduced features that make it possible to quickly add custom API connectors of the user’s choice, automatically monitor for changes in data sets to catch potential discrepancies, and smart naming conventions to sustain data quality.

The headline benefit for clients is the capacity to efficiently extract and leverage useful data to fuel precise, effective activities. Teams are becoming much more data-driven because they have the tech to automate technical tasks — and with increased use has also come a higher degree of confidence in wielding data and eagerness to continue doing so.

How does the AI assistant feature in Adverity’s offerings help marketers who are not proficient in coding? Can you share any success stories?

A critical element of how our AI offering improves efficiency is empowering all users to make stronger use of data, with zero specialist knowledge. Marketers don’t have to be coding experts to transform their data in a certain way; they just need the ability to describe what they need. There is also no longer any necessity to rely exclusively on analysts. Instead of submitting dozens of custom requests and hoping they might be completed in time to optimize current activity, they can answer their own questions.

We hoped to give a broader user base the means to master data. Across the beta tests and early implementations, that is exactly what we’ve seen demonstrated in the feedback so far. Marketing professionals appreciate being able to swiftly find the information they need, even when they don’t know where it’s stored. Analysts not only see the solutions as a great support for new staff to learn on the job, but also an everyday collaborator; in fact, some will now even ask the AI when an enrichment keeps failing.

From your perspective, what are the main reasons for the disconnect between CEOs and CMOs in understanding marketing priorities and impact?

The most significant problem isn’t new, but it is persistent. Many CEOs still fail to recognize the business contribution of their CMOs. Those who have worked inside the marketing machine know that CMOs are an integral cog in any organization’s growth engine: not only fueling sales, but also maintaining the brand equity, affinity, and loyalty that’s paramount to lasting success. A large number of CEOs, however, look at marketing as a cost center, which limits its value at any time but can be especially challenging — and damaging to companies — during times of economic uncertainty.

All of this isn’t helped by further misalignment of vision and goals. In cases when CEOs don’t understand the worth of marketing, they can also find it difficult to connect the dots between marketing strategy and intended results for the business, which makes them less supportive.

According to McKinsey, CEOs who prioritize marketing are twice as likely to achieve significant growth. How do you suggest CEOs and CMOs can better align and collaborate to drive business success?

Such high performance comes when the growth engine is allowed to function at full capacity. Realizing this ideal requires consistent collaboration, and specifically, clear and regular communication across the entire C-suite.

At the start of marketing initiatives, senior leaders must come together and define what success looks like, down to agreeing on set objectives. Discussions likely will involve tackling differences in perspective, such as a common tendency to favor short-term ‘quick hit’ sales on the CEO side and greater interest in indicators of brand strength for CMOs. Working through these issues, however, will help produce a shared vision, with a mix of softer and harder revenue-centric KPIs.

During and after campaigns, accurate and granular reporting on those metrics is also essential to tangibly prove the performance and business results marketing delivers, with data transparency helping to promote better trust.

What trends do you see shaping the future of data-driven marketing? How is Adverity positioning itself to stay ahead of these trends?

The primary factor that always has and will be paramount for data-driven marketing is a solid data foundation. Data-driven culture stems from the top of an organization, and as we’ve just discussed, CMOs can’t move in lockstep with CEOs if they aren’t able to measure, maximize, and validate their actions, which in turn depends on having their data management in good order.

We’re committed to helping companies create the effectively integrated, accurate data pipelines needed for every aspect of their operations, including running existing and emerging tools and processes. Whether we’re talking about marketing mix modelling (MMM), customer data platforms CDPs), or GenAI analysis, the quality and reliability of what comes out depends on what goes in — so, skipping this basic step creates a major risk of layering new solutions over bad data that will lead to inefficient, or even damaging, decisions.

My surest prediction is therefore that cultivating the right foundations will remain vital to facilitate scalability and success.

What advice would you give to marketers looking to become more insight-driven and effectively leverage their data to drive business outcomes?

If companies are starting from scratch, I would advise focusing on small realistic goals that can be set and met using refined data. Taking this route is particularly useful for securing early support from influential leaders — such as CEOs or CFOs — by showing that marketing wins can be easily tracked and optimized, as well as allaying fears around cost risks.

For larger and more established firms, challenges and priorities are more about scale. Trying to implement a data-led way of working through manual project management is likely to be highly resource intensive, both as regards people power and finances. The best call is therefore embracing a strong degree of automation where possible to bolster efficiency.

And once again, companies of any size won’t stand much chance of fueling steady, repeatable results without that core data foundation firmly in place.