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Predictions Series 2022: Interview with Anders Lithner and Alessio Fattore

Predictions Series 2022: 03Welcome to our Predictions Series 2022. To better understand how the evolving media landscape poses complex challenges to marketing and advertising technology (Martech/ Adtech) customers, we are hosting insights from Anders Lithner, the CEO of Brand Metrics and Alessio Fattore, Analytics & Insights Lead at Publicis Media Italia.

Today, we are discussing how Marketers can effectively plan and execute their omnichannel marketing campaigns by accurately measuring cross-media effectiveness. In order to prove successful with any cross-media marketing strategy, marketers should be able to understand and analyze the whole evolving media landscape.

Hi Anders and Alessio, welcome to our Predictions Series chat. Could you please tell us about your role and company?

Anders Lithner (CEO, Brand Metrics): Thank you. I’m Anders Lithner, the CEO of Brand Metrics, a global technology company that works with publishers to demonstrate the effectiveness of digital advertising. I’m based in Sweden.

Alessio Fattore: Hi, I’m Alessio Fattore, Analytics & Insights Lead at Publicis Media Italia, and I focus on helping Publicis and its clients use and activate data to deliver more effective marketing. I’m based in Milan.

In today’s complex advertising environment, what is the core challenge around measuring cross-media effectiveness?

Anders Lithner: To be honest, anything that involves ‘cross’ throws up plenty of challenges. If we’re looking at cross-media and including analogue, linear and digital, that’s a considerable challenge. Even just measuring digital across different domains is difficult.

But it’s still essential as campaigns aren’t restricted to one channel. However, measuring cross-media effectiveness requires standardising between the channels, and currently, we lack a single connection between how people engage across the different channels.

Alessio Fattore: It’s also not just the measurement that’s hard. It’s also how you think about cross-media effectiveness. Often, it’s tempting just to take what you already know in one channel and then scale it across all your media environments.

When you talk about a cross-media environment, there are many elements at play, with different channels driving different levels of engagement.

And, another of the complexities faced is that we lack a way of understanding how to effectively allocate budgets across channels to deliver the maximum possible results.

With multiple channels at play, it’s not surprising that some will appear not to be performing as well as others because they’re being measured incorrectly. How should we be addressing this measurement misalignment?  

Alessio Fattore: The most common way is through modelling. We can use the campaign data to understand the correlation between responses and the various channel inputs probabilistically.

Since digital became a mass-market channel, there’s been the hope that attribution modelling could offer a solution. And, the idea we can track everything at a granular level is very seductive. But measurement complexities, GDPR, the walled gardens and market fragmentation are all hindering this.

We find using cross-channel probabilistic methods provide better insights. In our current environment, with attribution modelling being incomplete, there is always the danger that using it can result in misleading insights.

Ultimately whatever model is used, it relies heavily on the quality of the data, and modelling can’t compensate for quality. Much work still needs to be done to address these data quality issues.

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Anders Lithner: Some of the misalignment around measurement comes down to not having an honest discussion with a client at the outset around campaign objectives and what it is meant to deliver. If it’s a top of the funnel objective, like brand awareness, then conversion data should play no part in judging channel success. Likewise, for instant sales, awareness and uplift are irrelevant. Media must be measured on the metrics relevant to the objective; otherwise, we’re distorting campaign performance. And in this performance-driven world, it’s essential to recognise the goal is not always an immediate sale – that’s not how marketing works.

So, when it comes to measuring campaign success, how do you decide what is the best metric to use?

Alessio Fattore: For any campaign, it depends on which aspect of the campaign you need to evaluate as a priority. While typically, a campaign starts with the business objective, for example, to sell more or gain market share, this then translates into messaging objectives and the associated metrics to measure this. But then there are the media objectives – what reach do we need or what channel mix? – and behavioural metrics to consider, which all need to be recognised when making measurement decisions.

We’re hearing a lot about the rise in the attention economy. What impact will this have on measurement going forward?  

Anders Lithner: We first have to be mindful that it’s a huge step to go from data around campaign delivery, for example, impressions, to data around actual conversions, because a lot happens in between. And just because you’re exposed to an ad, it doesn’t mean you paid attention to it. Even if you did, it’s not a given it had a positive impact.

To truly understand what’s going on over the customer journey, we need more data. It doesn’t matter if you have perfect data in one step of the journey – you need data for every step. Only then can you gain the necessary insights into what is happening overall, and brands can then identify how their marketing efforts impact sales.

In terms of metrics, attention is a crucial step in understanding the impact of communications. But it’s not a measure of campaign effectiveness. It’s a measure of whether somebody was paying attention to an ad, even if they hated it. So, while it’s an important step, it doesn’t solve everything.

The contribution Brand Metrics delivers is one step on from this, where we’re helping show the effect an advert has. But again, we are just one component.  We can say if a campaign had a positive or negative impact, but we can’t fully identify why it was effective.

What we have done, however, is to introduce standardisation to our measurement. While there are many ways to measure impact, you’re only building knowledge if you can compare across channels, devices, campaigns, markets, and publishers. When it comes to technology, complexity kills, so we have taken a standardised approach that’s simple enough while enabling insights to be accumulated and acted on to inform marketing decisions.

Alessio Fattore: In how we think about the attention, we’ve fallen in love with a concept that seems to make intuitive sense. But we must recognise our brains work in different ways.

For example, one advertising model is based on low involvement processing, with the brain still learning, even when a person is not consciously concentrating. This means advertising may not actually work in the way it was intended to. The idea of attention then becomes more complex and requires more careful consideration than simply saying we must measure if someone is paying attention to an ad.

As we approach the end of the year, we’ve seen measurement continue to evolve. So, looking forward into 2022 and beyond, what do you feel will be the key trend around measurement and analytics?  

Alessio Fattore: For me, it’s all about developing an effective probabilistic way of attributing effects to channels and people’s behaviours. By using all the learnings we have, we can create better ways of modelling response, rather than just focusing on counting.

Anders Lithner: I agree that this will be a crucial advance next year. There is also a challenge around trust and the fact it’s easier to trust what you know and has experienced rather than something you don’t understand. It’s tempting to stay comfortable with current measurement approaches because effectively they are just counting and therefore easy to understand. But there are many advances we can use that will make it more accurate but at the same time more complicated. To take advantage of these advances, we’re all going to have to trust algorithms that are more modelled and advanced but, ultimately, are better for measuring advertising.

Sudipto Ghosh: Thank you gentlemen for some really useful insights. I am sure your predictions would help the future-focused Martech and adtech users in leveraging advanced capabilities to optimize media management mix across channels.

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