Proving marketing effectiveness in the age of data fragmentation

Episode transcript:

Sarah:
Thank you. Let me just get the slides up. And Nicola, do you want to take it away with an intro?

Nicola:
Sure. Hi, everybody. I'm sure you're having a wonderful day. Thanks for bearing with us at the end of the day. My name's Nicola Daniel, and I'm the Managing Partner of the Data Strategy and Planning team here at McCann. I'm absolutely a slave to our industry. I absolutely love advertising. The ad that lured me into this industry was the Wrigley's TV ad with the Should I Stay or Should You Go soundtrack. It's really my fascination of people that's really driven me to having a career in media. And that career has taken me to London and time in Australia and New York. So I feel like I've got to understand lots of different people from all around the world. And funny enough, I used to really hate maths when I was at school and now I'm obsessed with using data to crack new and old challenges in a really fresh and creative way. So that's a little bit about me, and I'm looking forward to sharing what Sarah and I think might be quite helpful for you today. And over to you Sarah.

Sarah:
Thank you. Hi, everyone. I am Sarah. I'm Senior Data & Strategy Director at McCann. One of the things I'm really passionate about is understanding the why behind human behavior, why people behave the way that they do online, in-store, whatever it might be. And so I use a combination of behavioral science as well as data and insights to craft strategic business solutions that drive growth both for now and in future. And I'm really excited to take you through what we have today.

Nicola:
So hopefully McCann doesn't take very much introduction to many of you. Sarah and I work in the media team at McCann under the UM banner. We operate a really open architecture model here at McCann, which means that we get to access the breadth and depth of all of the IPG network. Some of the logos of the wonderful partners that we work with that we cherry-pick talent and craft from is on the screen here. But it really means that we've got access to all of the global network benefits while really maintaining boutique service for our clients.
And as you can see from the name that's above the door of our session today, we're here shining a light on a topic that already gets quite a lot of light shined on that for really good reason, and that's all about how we prove marketing effectiveness in the age of data fragmentation. The reason why we picked this session is, marketing effectiveness is really what we are all here to do. If you're working at an agency, we are here to drive value for our client's business, and really proving the effectiveness of marketing is the currency that really quantifies that value, and it's ultimately what all businesses want to see. So that's why we've chosen marketing effectiveness. But the tail end of our session about having a fragmented data world is that we are surrounded by data all of the time, and it's only multiplying. So what we're here today is to help you see or reconfirm how we can make sense of some of that value to make sure that we're extracting as much value out of the many sources of data that we're surrounded with.

Sarah:
And so before we get into the bulk of the content, I just wanted to cover up a little bit of housekeeping. So myself and Nicola are going to be sharing learnings and experiences, a lot of them based on real client examples that we're going to take you through. But that doesn't mean that it is the only way to tackle effectiveness and data fragmentation. Whether you are brand side or agency side, hopefully there'll be something here that you can take away and can help to bring together your streams of data. The other thing that it's worth mentioning is that we're in a tough landscape at the moment. Marketing landscape is tough, budgets are being cut, so it's okay not to know all the answers, but hopefully some of this will help you.
To begin with, I can see you all messaging in the chat, but I would love to know how many of you are being asked or thinking about the following questions. So give me a yes, give me an emoji, whatever it is, but it is things like these questions, so how much did that campaign drive or contribute towards business performance? Which metrics should I be looking at to show success? Can you show me how this campaign contributed towards awareness? And I don't care about the awareness impact, show me the impact on sales.

Nicola:
Keep the emojis coming for how many times you've come up against these type of stumbling blocks, you've got multiple sources of data and you just don't know how to agree on a single source of truth, or you're fed up of not having the right type of data and can we make sure that we can have it, please? We know that there are some things that are better than others, but we're not sure how to prioritize and how to work with what we've got or we just have got some data, but not all of it. It's lovely to see all the emojis, so it's good. I think we've got the right content for you today. So we're going to explore really how we can help prove the marketing effectiveness even when your data streams might be isolated or indeed incomplete.

Sarah:
And so we're going to start with some of these challenges and ultimately start to think about why is it difficult to prove effectiveness. It's no secret that marketing measurement is becoming more complex. I think we all see that every day. So if you do a quick Google search for marketing measurement, this tells you what you should and shouldn't be tracking. But all of these articles are different, and they're not always aligned. So there isn't one guide that you can use that shows you exactly what you should be tracking.
And even if you could find that one article, the challenge with that is that there are new articles popping up every day with recommendations about what you should and shouldn't be doing. So you'd have to constantly stay on top of these new updates to make sure that you were on top of it. The challenge that we've got is that even when marketers are measuring effectiveness, it's not always being done enough. So 31% are only doing it on an ad hoc basis. Only 13% have ongoing tracking for things like brand health and effectiveness. And 75% have openly said that they know that they need to expand their marketing effectiveness capabilities.
When we look at the sheer number of channels, platforms, and data sources, this means that the challenge itself is really, really clear. As marketers, the challenge that it gives us is that we're all feeling a little bit overwhelmed by the numbers that we've got available, because we're being surrounded by all of this information. That means one of two things. It means that we are either constantly chasing our tails trying to keep up with new updates and the latest in what measurement should be. Or it means that we're drowning in all of the information that's available and we're not sure where to start. And so we might not have started moving towards effectiveness or we might just be doing the same things with the same data as we have done previously.

Nicola:
We know that we really need to be showing a holistic picture of a customer journey, so what happens from the top of the funnel, the middle funnel, right the way down to the pointy end of the bottom of the funnel and not just be a slave to the attribution that happens at the bottom of the funnel. This quote is lifted from the most recent Grand Prix Award winner at the IPA Effectiveness Awards for McCann, and it's a beautiful quote from the Harvard Business Review that says, "If brands are built over years, then why are they measured over quarters?" And I think it really contextualize the balance of measuring the long and the short of it and making sure that we have these long-range data effectiveness measures, along with those in-campaign metrics that are delivering efficiency.
The other complexity to thinking about how we measure this data and the timeliness of this data is really people don't make it easy for us. Remember I said that I got into this industry because I'm fascinated by people. Our journeys are never linear, they're really complex. Google has coined this as being the messy middle, and the messier the middle is, the more data we have, and the more complex it is, which creates a really beautiful constraint. As Adam Morgan likes to talk about challenges, is that they're just beautiful constraints for us to solve with.
We promise that we're not going to just keep piling on the problems here, we are going to come to some thoughtful options and solutions, but there are just three more challenges that we need to think about when it comes to the challenges that we have specifically with data. The first thing here is all about attribution. I mentioned we don't want to be overly fascinated with what's happening at the bottom, we really do have to think about the messy middle and all the other touch points that's happening elsewhere in the journey. The other challenge with data is we have inconsistent metrics, so different platforms use different metrics, different things mean perhaps different impacts depending on where that data is coming from. So it's really hard to compare all the apples with the right apples, and often we feel like we're actually comparing apples with oranges. It's really, really challenging.
And finally, data is generated and data is visible and data can be used at very different times throughout that journey as well. If we're thinking about quarter to quarter or even if we're thinking about week to week if campaigns are being optimized, some platforms are just not delivering that real time data or, at worse, we might only be able to get partial visibility of that data. So all of this makes for lots of beautiful constraints when we are even in campaigns.
But it's not all bad news. We think that there are ways that we can use data effectively and make sense of it all. And going back right up to what I talked about earlier in terms of why effectiveness is really important, it's the kind of visible value or it's the objective value that marketing can have on businesses. And as marketeers, as the people that work alongside our marketeers, we know that that is the language that businesses, that shareholders, stakeholders really want to see. So it's something that we are becoming very well-versed in and it's no surprise that the global leading award categories that look at how marketing is contributing to business growth and business value is absolutely fundamentally focused on delivering results.
So, how can we overcome these? We're going to get to the solution bit now, so buckle up for the ride of lots of solutions heading your way.

Sarah:
And so before we jump into those solutions, I want to frame them as a bit of a sweet shop. The solutions are a bit of a pick a mix of options, and they give you some selection to choose from. You might have tried some, you might not like some, you might have never thought about trying others, but hopefully there's enough here that something will resonate. It'll be quite snappy as we go through, but it should cover all of the breadth and depth in terms of what you can use. And it's worth noting that the solutions aren't linear, so they don't go through in terms of this is what you should do first, then move on to this, then move on to this. It is really that pick a mix option. So we'll whiz through. If you need any more info and we've got time at the end, we'll answer any questions. But otherwise, feel free to reach out with our contact details at the end.

Nicola:
So first up is to really stay focused on those macro level KPIs. When data is fragmented, try and not get bogged down in all of the micro metrics. Instead, try and focus on those larger overarching KPIs that are going to really matter to a business. Whether that's overall revenue growth, whether it's brand awareness, customer retention, these are the metrics that are going to be easier to track across all channels and provide that clear picture of campaign success. And again, going back to that Grand Prix winner for McCann, what they were able to really prove was price elasticity, which was just really beautiful to see.
Secondly, we should really be thinking about how data is standardized. Like I said about the apples and oranges thing, how can we standardize this across different platforms and campaigns? We've been doing this a lot with our clients recently to be able to really identify where there are big bets to be made in certain areas of the media plan versus others, but it's because of that consistency that we're really be able to identify those insights.

Sarah:
Make sure you have a really solid process for cleansing and naming data. It's definitely not the most exciting or the sexiest tip, but it's really important that you have that taxonomy for campaign naming, UTM parameters and anything else that you might be wanting to report on, because this can really help to unify data across channels, which then automatically simplifies that process of combining the data and then avoiding any discrepancies.
Attribution modeling can be a really powerful tool to bridge the gap between isolated data streams where you need to. This might not always give you a perfect view because you've only got, again, certain channels and certain touchpoints that you can bring into that, but it can help to start estimate the impact of each channel. I've got an example here of a simple report. This is just from a quick Google search, so really simple to find and to build, but it can really help to give you insight into which channels are driving towards awareness versus conversions, and it can start to give you a sense of how those channels are interacting.
You also want to think about how you can use data warehousing, ETL, and of course, Supermetrics, I can't not give them a shout-out for all the work we've done with them this year, but it can really help you to provide that single source of truth. And that means that you can bring all of those various data sources into one place, which provides yourself and, more importantly, your stakeholders and all the people who you need to report back to with everything that they need in one place.

Nicola:
Better yet, see if you can convince your clients or convince your FDs to invest in econometrics. While we know that ROI is still a short-term metric, it goes a hell of a long way to show that valuable contribution that media is paying into the overall bottom line. We offer our clients MMN models, but we also like to talk to clients about going that step further and looking at more full funnel modeling so that we can start having a look at the indirect contribution to the business. So we're looking at what contribution the brand and the brand strength is having, as well as the right media channel selection. Also, can I just say, the comments in this chat are just giving me life? Yeah, you're a bunch of funny people.

Sarah:
Another element is making sure that you are telling a story with the data that you have. Although we always have loads of data, it's really important to make sure that you are using that to support the business case or whatever it is that you are trying to get signed off or confirmed. A really powerful way to do this is actually by accepting that you don't always need to show all of the data. We are all here, we all love data, we're happy to dig into numbers, but actually the people who you are presenting that back to or talking to might not be the same. They might be like Nicola with her hate of maths earlier on. So try not to overwhelm with numbers. Instead, focus on presenting the impact and that key thing that you want someone to take away.
That's not to say you don't have the data and you can't back it up if you need to or someone wants to dig into the numbers, but actually just have that in your back pocket so that you've got that to provide if you need to. But what you're focusing on is sharing what is going to contribute to marketing success and that ultimate story that you are trying to tell.

Nicola:
Another big data generator is something that we are very familiar with, as I'm sure that you guys are all familiar with, and that is that experimentation mindset, which is amazing to be thinking of all these different ways that marketing might be having an impact on different various moments of the journey. But with experimentation comes lots more data, so it's about being really single-minded with what it is that you are trying to extract out from that experiment so that we can be always looking at what the valuable contribution of those data points are giving us to the big picture and not just doing experiments for experiment's sake.
Similarly to that, we've had discussions recently about what the difference is between experiments and tests. Experiments is really something where you have a hypothesis that you are looking to prove or disprove. And we see that tests are really something that can be a much more short term and much more of a quick tactic. One of those tests obviously is thinking about what the contribution that different creative can have in a campaign. And time and time again, whether it's micro or macro level data, we are always continuously seeing that the power of creativity has on the bottom line versus the weight of a channel or the level of investment in one channel over the other. So really thinking about how we can prove and leverage the strength of that creativity in your media and marketing plans is really crucial.

Sarah:
I would say don't be afraid of some creative maths. And I don't mean creative in terms of fudging the numbers or making it up. Creative maths is a term that we use internally when essentially we have some of the data that we need but we don't have all of it, right? Because not every client or brand measures everything that you are ever going to possibly need to forward-plan for every scenario, for example. And so when we're in one of those positions, we sit down and we think about, "Okay, what can we do creatively with the data that we have, and how can we answer the question that the client or the brand is looking for us to answer?"
And so, a really good way to do this is to actually take the information that you've got and crowdsource what you don't. So there is plenty of data out there in terms of benchmarks, in terms of average performance and things like that. It means that basically then you can show scenarios and recommendations based on the data. So you're not giving the one answer because there's so many variables that you might not be able to give a definitive answer because you don't have all of the data that you need. But actually, it allows you to have a play around with the numbers to show your clients or your stakeholders internally what the options are, what could happen based on the data that you've got and can really help to support those conversations that you might be having.
And then another element towards this, and I've seen this mentioned in the chat a few times around balancing the long and the short of it. We get it, it's really challenging to tell someone to just trust a TV addiction, for example, even though you might not see any short-term impact of that. This is why we talk about the long and short of it so much in the industry. And what's really important is to actually make sure you are setting up both the short-term and the long-term metrics to get the best out of that.
So, a good way to do this is to think about any kind of short-term measures for those stereotypically long-term performing channels. So it's almost those metrics that can let stakeholders know that you are on the right line. So in TV for example, it might be an uplift in search volume. So that's something that shows that actually there is interest in that you are moving in the right direction, and it can take some of that pressure off from your FD or your CFO who's going to be really focused on getting those results in.
You can ladder all of these short-term measures up to all of your long-term measures so that actually you are looking at it at that micro level with the metrics that you can, but it all tears up to your macro level KPIs so that you can provide insight into performance as and when needed.

Nicola:
And bringing it right the way back to what we talked about at the beginning, which is delivering marketing effectiveness and proving the value, data isn't something that sits and lives at the end of any type of planning or implementation activation process. Yes, in campaign is when a lot of the data is generated, but of course people are always coming in and out of market depending on what sector you are buying into, so we really have to think about how we can use data to fuel all of the thinking process and all of the problem-solving right from the beginning through campaign development and of course what's happening when campaigns are live and how we're optimizing and dealing with more real-time data.
How we go about doing that at McCann is we have what we call an effectiveness workshop with our clients so that we can sit down and really think about what are all the big meaty objectives that we are going to commit to in terms of delivering value, and then really chunking it out and breaking it down so that we are being really explicit and really clear with what datapoints are going to be effectively proving one KPI versus another.
One of the questions that we've had in the Q&A for this session is how do we go about proving sales when they're happening in a physical space, not digitally and how we're proving that those efforts are actually working and having a financial impact? One of the ways that we've done that previously with clients is thinking about how we can track and measure footfall data and finding correlations with different sales metrics so that we can agree on whether or not footfall is a proxy for maybe penetration. But it's all about having the conversation with clients to really understand where is there a comfort zone of being able to take one datapoint and using it to prove out X, Y or Z. For the example that I just spoke about, we could align on that looking at a mix of footfall data with counter data that we would be able to show penetration proxy. So hopefully that goes some way of answering that question. I just wanted to weave that question in before we do the takeaways.

Sarah:
So yeah, conscious that you are going to hear a lot today from a lot of different speakers, so we just wanted to wrap up what some of the key takeaways we think are from the projects that we've worked on and the content that we've taken you through today.
I think the first point is around not being afraid. It's okay if you don't have all of the data. There are ways around it, there are sources that you can find. You just have to get creative using what you have, what you know, and what you can supplement that with to ultimately tell the story that you are trying to tell. I know that it's really easy to get caught in the short term, especially when you are under pressure to deliver results, but try and always remember that bigger picture and those bigger loftier, chunkier goals that you want to achieve. Actually, use those as your unifying KPI and then find those metrics that feed into that both in the short and long term. And make sure you've got that central source of truth with a report or a dashboard. It's so much easier to see and to tell the story when it's all in one place. And because you can build all of those data sources in and external data sources, it can really help to put stakeholders at ease and provide the information that they need to see when they need to see it.
So thank you very much. That wraps up our presentation for today. I know we've run out of time for questions and I know there have been some in the chatbox, so if there is any that you would like to reach out about, then you can find myself and Nicola on LinkedIn. Feel free to share any questions there. But that's all from us.