Advertising has never generated more data. Yet proving its value remains one of marketing’s most persistent challenges.
Every platform produces its own metrics, measurement approaches remain fragmented, and marketers can easily end up optimising for what is easiest to measure rather than what actually drives growth.
For Kelaine Blades, Global Managing Director, Converged Go-To-Market & Operations at Havas Media Network, the answer is not simply more data or another dashboard. She believes the industry needs to get better at connecting media activity to business outcomes, while ensuring marketers can understand and communicate what the data is actually telling them.
Ahead of NDA’s Foresight conference, where Blades will join the panel From Attention to Outcomes: Rethinking the Economics of Advertising, she sat down with New Digital Age to discuss why measurement remains so difficult, how attention fits into the effectiveness equation, the importance of speaking the language of the CFO, and why AI could democratise access to data.
We have more data than ever, so why is proving advertising still so difficult?
It is almost the opposite of a lack of data. Every platform generates an enormous amount of data, but it is measured differently, it sits in silos and it is often telling us more about platform performance than business performance.
Business growth is influenced by many factors beyond advertising, including pricing, distribution, product quality and competitive activity.
We are really good at measuring activity, but we are less good at measuring contribution. At Havas, we are trying to connect the dots between media metrics, audience data and brand signals into a single view. A critical part of that is incrementality: understanding not simply what happened after someone saw an ad, but what happened because of the advertising. That is where we think modern measurement needs to go.
Ultimately, measurement should help us understand how marketing contributes to business growth, which sits at the heart of our Growth, Powered by Desire philosophy. We believe the strongest growth comes from creating brands that people actively desire, and measurement has a critical role in proving which experiences are driving that outcome.
Are marketers optimising for what they can measure rather than what really drives growth?
Too often, yes. The danger is that we start treating easily measurable metrics as if they are the objective rather than the indicator.
Clicks are measurable, reach is measurable and engagement is measurable, but growth is often driven by broader forces such as memory, mental availability, brand performance, preference and customer demand.
The really sharp marketers are asking two questions simultaneously: what happened this week, and what will make us bigger next year? Those questions are not always answered by the same metric.
Does the pressure for short-term performance, particularly with marketers increasingly accountable on a quarterly basis, hold this thinking back?
It does.
It is not an either/or question. You need to be driving performance and growth at the same time.
We are starting to see a little bit of movement there, but it still feels like we have been having this discussion for years. I think it is about marketers convincing leadership that marketing is a growth engine and not just a cost centre.
How does marketing become better at speaking the language of the CFO?
I think marketers are almost raised in different ways. With digital marketing, we were taught that you could measure everything, but that approach and that language were not necessarily part of the traditional marketing toolkit.
You will find very experienced marketers who could still use some help on the measurement side.
We have been saying for years that we need to measure, measure, measure, but we are also incredibly busy. You finish one event or campaign and you are immediately onto the next. But what’s key is focussing on the long-term value to the client’s business.
Attention has dominated the effectiveness conversation for several years. How do we now move from attention to outcomes?
The good news is that the conversation has evolved significantly. We need to understand whether attention created value and link it to outcomes.
That means understanding what types of attention drive memory, what types drive consideration and what types drive action.
We have just published a major study called The Science of Desire. One of the things we’ve learned is that attention creates opportunity, but desire is what drives growth. Attention remains critically important because it is often the first step in creating memory, preference and affinity. The challenge now is connecting those attention signals to the business outcomes that marketers and CFOs ultimately care about.
Attention is not going to be the final score, but it is going to be a key input to effectiveness.
Will the industry ever agree on a common approach to measuring advertising effectiveness?
I do not think we are ever going to have a single metric. Advertising effectiveness is just too complex.
Depending on the industry our clients are coming from, they have different objectives and different buying cycles.
What we are hoping to see is greater agreement around principles such as transparency, incrementality, causality and, ultimately, the connection to business outcomes.
If, as an industry, we can build a framework where different measurement approaches work together, that will take us much further than where we are now.
Are CMOs asking the right questions about advertising effectiveness?
One thing that will help move the needle is when the CMO aligns the marketing metrics with the business objectives at the start. But it is not only about agreeing the KPI. Marketing and finance also need to align on the source of truth and how they will determine the incremental impact of advertising. Otherwise, they can agree on the outcome they want to drive but reach very different conclusions about what contributed to it.
You have your annual plan and your goals. When you get that alignment, the entire team starts thinking around the business objectives and you can have the right conversations with the finance teams.
That will start to move the needle more than treating measurement in isolation.
What needs to change most in the way we measure and communicate advertising value?
We are still all saying, “Let’s create the most beautiful dashboard. How much information can we get in there? What are the metrics?”
But we are not explaining impact. Historically, we have been reporting on what happened instead of why it happened, whether it mattered and what should happen next. One of the opportunities we see with Converged.AI, Havas’ integrated data, tools, and technology platform, is the ability to connect those dots much faster, helping teams move from reporting data to making decisions based on it.
It goes back to the CMO and CFO discussion. Measurement has to become more business-oriented and the language needs to move from media metrics to commercial outcomes.
We do not necessarily need more sophisticated measurement tools. We need to make sure people understand them.
It is really about better translation of the data.
Where does AI fit into the evolution of measurement?
Initially, AI is creating complexity because it is introducing new signals, new models and new ways of analysing data.
But we think AI is one of the biggest opportunities measurement has ever had. It is not necessarily because AI measures better. It is because it is helping people interact with measurement better.
AI is helping reduce the gap between the question and the answer. For example, within Converged.AI Measure, users can increasingly interrogate performance data using natural language rather than needing specialist reporting skills. That allows more people across the organisation to engage directly with insights and spend less time finding information and more time acting on it.
We are also completely democratising data at Havas. We have created training and programmes that are not just generic “here is how you use AI” courses. They are tailored to specific job functions.
A big part of that is AVA, our internal AI environment, which gives employees secure access to multiple AI models in a governed way. It allows teams to experiment, collaborate and build solutions while ensuring data security and responsible AI practices. What is particularly exciting is that many of the capabilities now being used with clients started life as ideas developed by employees within that ecosystem.
That is important because it helps people do their jobs better, but it also significantly helps with change management, which is one of the things that holds people back from fully adopting AI into their workflows.
Getting data into the hands of everyone, in a governed way, is absolutely where we need to go.
How do you make change happen, in terms of AI adoption, across a huge organisation as Havas?
You have to be quite standardised about it. When I joined, people were giving me all these requests for training and I was thinking, how do I do that across 22,000 people?
My team built a toolkit with standardised materials and an effective SharePoint environment where people could access information, share examples and show what they were doing.
We also ran trials with advanced users. We could show how long it took to do something without AI, how long it took with AI, and the efficiency gains.
Then you need to get to senior leadership and make sure they agree with the accountability, so that it flows down through the organisation.
Is there a generational difference in how people are adopting AI and new measurement approaches?
Not necessarily. We found a really interesting pocket of entry-level teams in one of our European markets who were not even using the right technology.
I thought, “I don’t understand. They are Gen Z, they were born digital. Why would they not be using it?”
Their response was that it was complicated and it had not been explained correctly.
So even for generations where you might think they will just adopt new technology, it still needs to be accessible and supported.
