The analytics era seems to finally have arrived in marketing. 

The question is, “Are marketers actually ready?”

For example, three-quarters of brands say they are spending more on marketing technology this year, with 24% claiming to be spending significantly more, according to a recent Forrester report.

It looks like marketers are finally taking this seriously.

At the same time, just 10% of brands in that report say they have a clear picture of who their customers actually are. Not just the data, but who they are as people, and how that affects their consumption habits.

Something’s not right here. I suspect we know why.

We’ve heard more than a few stories like this. A major marketer is fixated on using advertising to drive acquisitions and wants to better manage its return on investment. So, it turns to one of the many technology partners in the market for help in building a multi-touch attribution (MTA) models.

The marketer is excited about the promise of better analytics, so they invest a significant amount of time and money along the way. Then they start getting insights back, and realize they have loads of questions they can’t answer, such as:

“Are these numbers good or bad?” 

“What is success?” 

“What should we do with this information?”

“How do we take this insights and move our budget to places that will drive more acquisitions?” 

Faced with all these fundamental questions, instead of taking the bold action they dreamed about, the marketer freezes. Eventually the company abandons the MTA tool with little to show for their investment.

Like I said, we’ve seen and heard lots of stories like this. We can help make sure this doesn’t happen to you.

Making effective marketing decisions is complicated. 

There are many considerations to determine which strategies are working and which ones aren’t, but that’s only the first piece of the puzzle. Once you figure this out, the next question is always “Why?” and then “What can we do about it?” 

That’s why so many marketers are investing in advanced analytics tools like Multi Touch Attribution and Market Mix Modelling. These kinds of products theoretically answer those questions, helping you guide effective decision making and uncover optimization opportunities. 

Currently, marketers spend 5% to 7% of their overall budgets on data analytics. According to the CMO Survey, that number is expected to jump to 11.3% in the next three years.

Which is great, but only if these brands get a return on their analytics investment.

This is easier said than done. I’ve seen many organizations adopt advanced analytics approaches but fail to use the insight gained in an effective way. 

In almost every case, these brands want to improve ROMI (return on marketing investment). Yet as they proceed with their analysis, they often realize that different pockets of the organization have very different definitions of ROMI.

If everybody has a different measuring stick for success, as a marketer you will not be able to make many informed decisions. You probably suffer a perpetual state of indecision.

The good news is, we’ve seen this play out before, and we can help you through it. Here are five ways you can avoid letting your analytics investment languish – and turn your insights into powerful, profitable action for your organization.

Determine clear KPIs for the business and have the entire company agree upon them. This can be done by having open honest conversations about what the drivers of your business are. 

Hold everyone accountable to those KPIs. If there is no accountability, people will not take action upon results, leading to a lot of wasted resources. The Harvard Business Review has outlined five ways to hold people accountable: 

Clear expectations

Clear capability

Clear measurement

Clear feedback 

Clear consequences 

Train teams to accurately interpret the results. It’s crucial that your people must understand what the outputs are telling them in order to implement effective actions to drive business growth. For example, ROI is a simple metric that many marketers understand, but what they may not know is what is considered good/bad for an ROI. 

Bring together additional considerations to tell the full story. There is more to performance than just what the model tells us. A marketer should ask themselves why something is performing well or badly. For example, who was the target audience? Is there an opportunity to invest more into those tactics? What additional media was in market? Were there additional factors at play that could have influenced results?

Partner closely with your analytics teams. Unfortunately, I’ve seen many organizations in which these two groups work in silos, which results in marketers not always being clear on what they need, and analytics teams providing less than actionable insights. On the flip side, when the two groups come together, I’ve seen them come up with those Ah-ha! moments marketers dream about when starting their analytics journeys.

Only when all these factors come together can marketers extract the true value out of their models, as they will be empowered to make smarter business decisions. 

For example, a client of ours previously had a seasonal campaign running. Before it started, everyone, including the senior leadership team, came together to determine and agree upon the main KPIs.

We built a multi-attribution model to measure against the KPIs and trained the client and media agency on how to accurately interpret the results. Multiple stakeholders with different types of data and insights came together to discuss the results and it was determined that some creative messages weren’t resonating with their customers. 

Therefore, the company adjusted their creative in their media during their campaign. This resulted in a multi-million dollar increase in revenue, which more than paid for the models. 

In the example above, all the pieces of the puzzle came together to empower marketers to make smarter business decisions which drove more value for their organization.

It can be done – with the right help.

Originally published on The Drum

Lindsay Egan is a partner at Gain Theory


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The decline in creative effectiveness through the 2010s has been much discussed, with plenty of potential reasons cited. Marketers have been accused of a short-termist myopia, of being too rational, left-brained, and of focusing on awards and not business impacts. As well as this, it has been argued that measurement is to blame: by concentrating on data and not on gut, have marketers have lost the essence of what constitutes great advertising?

As part of Thinkbox’s Demand Generation event in November 2019 I put forward a case that measurement as a practice is not to blame, but that bad measurement practice is, in that it causes marketers to make misguided decisions.

To put measurement back on the right track requires a rule book that all of the industry can follow. This rule book needs to be simple, based on experience, and well-practiced. Therefore, the nine golden rules that follow are nothing new. You may think that they’re too damn obvious. But how many of us can realistically say we follow all of them, all the time?

The rules split into A) Data, B) Methodologies, and C) Further Considerations. Please read them carefully and encourage your teams to do the same. Only by following them all, as laid out here, can we return to proper marketing measurement.

This is a preview of a WARC article published in January 2020. You can read the full article here (available to WARC subscribers only)

Alternatively read our blog from Thinkbox’s Demand Generation event including a link to video summarising the research here

You can also download the 9 Golden Rules booklet here

WPP data science supergroup Gain Theory, MediaCom and Wavemaker commissioned by Thinkbox, have revealed new research giving advertisers practical guidance to get maximized media-driven returns.

The aim of the ‘Demand Generation’ research is to guide advertisers on how to have the best of both worlds: substantial short-term sales and sustained base sales growth. 

This study, based on extensive client databanks and expertise, offers the most comprehensive understanding to date of the planning and measurement decisions that need to be made, alongside the resulting trade-offs in terms of speed of return, base sales growth and risk.

Putting Measurement Right: 9 Golden Rules
Within the research, Matt Chappell, Senior Partner at Gain Theory, leads the ‘9 Golden Rules of Measurement’ – a practical best practice campaign measurement guide that will enable businesses to effectively evaluate, learn and grow their media returns. You can get this guide by downloading it on this page.

Watch the Video

You can watch ‘Demand Generation’ video here which summarizes the research from the Thinkbox launch event covering:

  • The databank and findings
  • How to balance trade off decisions
  • 9 golden rules of measurement
  • How Kellogg’s are accelerating analysis.

Book Now

If you are a client side marketer and want to book a complimentary knowledge share session for you and your team around the 9 Golden Rules of Measurement, please contact

Most online ad experiences still feel like a re-run of last week’s searches and purchases.

We’ve been talking about how advances in digital media and data will result in every person on the web seeing a deeply personalized ad for every occasion since the days of AOL dial-up.

Why, then, do most of our digital ad experiences still feel like a “groundhog day” of what we searched and bought in the previous week? (If you don’t agree, just take a spin around the web and let me know how customized and rewarding your ad experience feels.)

Given the immense amount of personal data at our fingertips, you’d think that by now we’d be communicating elegantly with each customer—precisely when, where, and how they would like to engage with our brands. You’d think, with programmatic buying, that we’d be able to do this and still achieve huge reach.

And you’d think, with AI, powerful ‘identity graphs’ and multi touch attribution (MTA), that it would be easy to execute and measure these sophisticated conversations.

Yet personalization remains mostly a fantasy and right now it’s just not happening between brands and people. It’s time to examine why and to ask ourselves: Is personalization even a good idea in the first place?

Reality check
In trying to achieve one-to-one marketing, we might actually be putting our brands at risk of fragmented messaging, multiple personalities and vapor for brand equity. And even if we actually pull this off, there is a ton of mythology surrounding our ability to successfully execute micro-campaigning at the kind of scale we need to move markets and competitive share.

Often this comes down to simple math. If you target a specific slice of the web population using the powerful segmenting tools at your disposal, you may start with a million prospects. But, for every attribute you apply, you end up slicing that group thinner and thinner. Very quickly, you may find that your sophisticated marketing efforts have led you to a handful of shaving cream enthusiasts in the Pacific Northwest.

And we’re not even touching on the grand delusion that MTA can measure it all.

In our experience, working with even the biggest brands, MTA exercises are extremely complicated, expensive, require a crazy amount of work and rarely pay off.

It’s natural for CMOs to want to figure out exactly how every interaction with their brand leads to a purchase over a day, a week, a month and a year—and how much they should be spending on each media touchpoint to deliver that purchase.

But, in terms of practicality, it’s as big a fantasy today as “Game of Thrones”.

And here’s the friction…
Even if it’s possible to execute messaging with such surgical precision, there’s the small matter of whether consumers actually want to connect one-on-one with brands—even the ones they love.

In a survey from March 2018, more than half of adult respondents reported either neutral or negative receptivity towards personalized ads and the influence of such ads on their buying decisions.

We’re at a point where consumers are more savvy, empowered (hello ad blockers) and informed than ever. They are clicking permission check boxes and they are reading about EU fines for Google/Facebook and massive data breaches around the world. We haven’t even begun to solve the privacy issues associated with General Data Protection Regulation (GDPR) in Europe, while the California Consumer Privacy Act (CCPA) will soon be upon us in the U.S. Will identity graphs even be legal in the future?

The future is uncertain, but it will be bright
What we’re left with is a whole new world of ad targeting. We think consumers will end up in a place where they have far greater control and transparency around how they are being targeted.

They may even—wait for it-—agree to get paid by brands for their attention.
That may seem a little far-fetched now, but it actually represents a huge opportunity. Imagine a world where consumers actively engage in what they see and don’t see and they are actually “bought” into the relationship? Instead of wasting billions of dollars on unwanted ads, we will invest where ads are relevant and welcomed by those willing to be compensated.

It can’t get more personal than that.

Originally published on Ad Age

Originally published on the Marketing Society website.

Shawn O’Neal knew he should speak up but froze.

He was a junior exec and didn’t want to upset the apple cart. Plus, everyone else in the room was suddenly ‘yes-ing’ the CMO to death.

He had just spent months collating the data, analyzing, researching and focus-grouping the new Pepsi Blue can that would be the face of Pepsi for years to come. The team was armed with a strong recommendation and the facts to back it up.

Then in one swoop, no questions asked, the CMO pointed at a can and simply declared ’I like that one’. The version that hadn’t performed well in any of the research. The one that had tested poorly and didn’t represent the agreed upon goals of the project.

Yet there they all were, nodding their heads in agreement, trying to make the CMO feel smart.

O’Neal bit his tongue. He’s regretted that ever since and spent the last 20 years trying to ensure that CMOs incorporate data, facts and solid insights to help inform better decisions rather than solely going with the bias of ‘gut’ decisions.

If that incident taught him anything – it’s that marketing excellence requires conviction and bravery.

These were much discussed topics during the ‘Is Marketing Excellence Enough?’ session at Advertising Week in New York hosted by Gemma Greaves Global CEO of Marketing Society. Conversation guests – Subway CMO Roger Mader, Michelle Froah, SVP of Global Marketing Strategy & Sciences at MetLife and Shawn O’Neal NA CEO Gain Theory – debated the role of data in modern marketing, coupled with the continued need to take bold risks.

Marketers are often wrestling with when to take ‘job risking’ gambles, when to lean on data, or when to play it safe. To Mader, the idea of being brave in pursuit of excellence requires “confronting sacred cows treated as religion in the organization.” One of those ‘sacred cows’ is that marketing is limited to just marketing. Today, it’s crucial for executives to push “beyond the boundaries of marketing” – such as speaking up about whether their company is selling the right products, has the right pricing or the appropriate strategy.

“You need to be brave enough to say this is not just about marketing. This is about our customers’ experience and marketing is just a window onto that.”

Mader said he’s hopeful that in the coming years, as more marketers are able to get comfortable with and unlock the benefits of data and artificial intelligence, the more time and ammunition they’ll possess to take on some of their bolder challenges.

Of course, there’s always the worry that too much reliance on numbers can hold a major brand back from chasing big – sometimes unproven ideas.

“I think that there are organizations that set some stretching ambitions,” said Froah. “Then when it comes down to setting the goal, or the target to meet, no one wants to fall short of that.”

Froah urged brands and their agency partners to make sure they are clear on what success looks like, before simply trying to ‘chase excellence’ without a clear plan. That requires getting everyone signed off on quantitative and qualitative measures. 

“I think data is critical to making the right decisions,” she said.

“But you have to use data for good and that means using it to really listen to customers and really understand what issues you can fix. Also, you can galvanize the whole organization to set the right marketing requirements, to reset customer experience, create new solutions. There is still very much an art and a science to it. The biggest watch out is remembering that data represents people, but people aren’t data.”

True. But the panelists also noted that marketing is a long way from being as ‘scientific’ as it can be. O’Neal quoted a recent study which found most Marketers are using data to inform only 40% of their decisions. Which left him to wonder, what happens with the remaining 60% of decisions?

“We’re still a long way from call it, the perfect balance between art and science,” he said “40%! Come on! Are you telling me that 60% is guessing?! I mean what are we doing here?”

“There is still a ton of room for science to be part of this equation,” he added.

The more science backing up your ideas you have, the braver you can be.

Especially if you think your CMO is picking the wrong soda can.

When it comes to gaming and gambling, user behaviours and player loyalty can be tricky to pin-down.

So in a business defined by keeping the players playing, it’s important to understand how advertising affects different users, new and returning, as well as what effect it has on the product mix in general.

Gain Theory brings its experience in working with a range of gambling and gaming brands to explore player behaviour and define what’s important when advertising to users.

In this report we answer:

  • What makes a customer continue or stop playing?
  • What increases loyalty and engagement?
  • How should you balance investment between new and existing customers?
  • How can we get existing customers to trial other products and games?

To download the article, click the Free Download button.

It’s a long journey from the classroom to the C suite, and managing a global organization isn’t a job many people can relate to. So, what is that journey like? What are the lessons learned? What are the leaders really like behind closed doors?

In this exclusive and candid WPP Stella podcast we hear from Manjiry Tamhane, Global CEO at Gain Theory in conversation with Jay Kandola, Commercial Director at Mediacom talk about her journey to the C suite, perspective on life and leadership.

Highlights include:

  • Manjiry’s journey from university graduate to Gain Theory CEO
  • Why being brave is vital to success
  • What makes Gain Theory special
  • The book that had the biggest impact on her
  • On building data informed cultures
  • How to deliver marketing excellence
  • …and why shy bears get no honey!

Listen to the episode below, if you enjoy it, share it!

Stella Conversations is a series of informative and inspiring conversations between members of the WPP Stella women’s leadership group and a cohort of future leaders.

Gain Theory has a global partnership in place with EffWorks an initiative led by the IPA that champions marketing effectiveness measurement, culture & best practice.

Client side brands involved include Diageo, Jaguar Land Rover, Telfonica, Mondelez, Lego, Kraft Heinz, Unilever, Samsung + many more; alongside industry associations such as 4A’s, IAB and CAA ; and media giants such as Google and facebook.

As an unbiased direct-to-client consultancy that champions marketing effectiveness, Gain Theory’s involvement with EffWorks is to drive high value content and conversations that shape thinking around client challenges, measurement strategies and actionable insights that have a positive impact on the bottom line.

In line with this, Gain Theory has led the creation of a green paper entitled Marketing Measurement in the Digital Era. The paper was created in collaboration with Facebook, Google, Thinkbox, Royal Mail, Mondelez and folded in interviews with Dixons, Samsung, Direct Line and many more client side brands.

The green paper intends to provoke debate and act as the foundation to further research and best practice in 2018.

Key themes:

  • Marketing – definitions
  • Effectiveness
  • Metrics that Matter
  • Measurement Strategies: what good looks like
  • The Future
  • 6 Step Marketing Effectiveness Plan

Despite all the hype, for MTA to be useful its limitations must be clear and clearly communicated.

For many, Multi-Touch Attribution is both exciting and intimidating. The ability to track a sale back to the marketing initiatives that produced it has rightfully garnered a lot of fevered interest. If executed properly, MTA can enable prescriptive and granular media optimizations in near real-time that create significant ROI improvements.

The phrase is thrown about emphatically with terms like Big Data, User Level Analytics, and Real-Time Learnings. All that hype can create confusion, and ultimately, an expectation that can’t be met. Precious little airtime is given to the nuances and pitfalls of implementing MTA. That’s not to say it shouldn’t be activated, but that for it to be its most useful, the limitations and nuances of MTA must be clear and clearly communicated.

Understanding the desktop/mobile divide.
Industry reports and surveys suggest the average US consumer uses 2.3 internet-enabled devices. But MTA datasets usually show fewer than 1.6 devices per user. That’s a significant gap, and savvy clients will be expecting 2.3 because they’ve seen the industry reports and think their cross-device ID is weak.

To be sure, cross-service solutions are not perfect. They are a rapidly-improving component of the ad tech landscape. But in reality, the bigger culprit is that your desktop and mobile ad placements may not be as tightly aligned as you think. They target two similar (based on demographics and psychographics) yet different sets of consumers.

A simple way to dissect this is to create two user groups—Multi-Device users and Single Device users—then profile the media activity of these groups against each other. This will reveal sites that over index for Multi-Device users. These sites may represent a small percentage of the media budget that is causing the gap.

Integrating offline media is not as simple as advertised.
All MTA sales pitch decks have a slide about the integration of offline media, with a chart showing significant web traffic spikes attributed to TV’s impact. This approach is quite often naïve and breaks down in a few ways.

For brands with massive website traffic, TV airtimes do not consistently create obvious web traffic spikes; without the obvious traffic spikes TV’s impact can’t be seen. In addition, TV cannot drive immediate in-store purchases the same way it drives immediate .com visits. This is critically important for brands less concerned with online metrics and more concerned with in-store sales.

And the approach does not easily scale to other offline channels beyond TV. The other offline channels are not designed to elicit immediate response. As a result, alternative approaches for integrating offline media are often required.

Deploying MTA results via a true DSP integration is easier said than done.
Your DSP has a highly tuned and specific algorithm that adjusts bids on an ongoing basis. While MTA providers will trumpet “Partner Integrations” and delivering MTA results directly to DSPs, it is an entirely different challenge for that DSP to then incorporate those MTA results into the bidding process. If not engaged early in the process, DSPs may crudely apply up-weighting or down-weighting bids based on features of the Attribution analysis, which may not really result in optimal bids.

In general, as analytical outputs become increasingly granular, it is becoming more of challenge for downstream partners to implement them. Any ad tech partner that you’d expect to leverage the MTA outputs may require a change to their scope of work in order to undertake it.

Offline purchases may require special considerations.
E-commerce revenue, digital media ad spend and the overall percent of transactions completed via credit card are all going up over time. These shifts are major components of what makes user level MTA possible as they all generate user level data. But what about cash purchases? These are inherently anonymous and, for some verticals like QSRs, may represent 50 percent of sales. Cash purchasers may have far different media consumption habits than credit card purchasers. Tuning media via MTA outputs could be inadvertently tuning your marketing efforts towards credit card purchasers and away from cash purchasers, which could create longer term issues.

Every aspiring brand will be forced to encounter at least a couple of these issues as they tackle MTA. If you’re not prepared for them, your MTA implementation could stall and ultimately lose traction. Your best bet for success is to go in with your eyes wide open and expect that your MTA provider fully understand your business, how you sell your product and to whom and how you plan and execute your media and marketing activities.

Russell Nuzzo is Global Head of Attribution and Marketing Technologies at Gain Theory.

Market Mix Models, where one focuses on a single KPI like sales volume, are an extremely powerful technique and especially useful at disentangling the key elements of all the marketing levers that are available – price, promotion, advertising and so on.  Add in some enhanced granularity – modelling CPG by SKU and retail account, for example – and the technique becomes even more powerful.

But even the best models report back that an impact on your single KPI could not be found for some parts of the marketing mix.

This is not because these initiatives don’t have any impact.  It is more to do with the naïve measurement approach that is adopted by some marketing mix professionals.

Now, I’ve long been a believer that any marketing activity should ultimately be assessed on its ability to drive sales and profitability.  Else, what’s the point in it?  But the way it does this and the time period over which it works does not have to be as simple as “engage customer base with social media in week 1 and see sales go up in week 2”.

We’ve had the path to purchase  more than a century.  Let’s be smarter about using it.

In the modern era of digital media, instant response and online purchases, we are the first to admit that the path to purchase is changing.  But it is still key in building our understanding of how brands interact with the ecosystem which they inhabit.  Marketers use path to purchase to plan their campaigns. Modelers, who are  responsible for assessing the effectiveness of campaigns, should start using it too.

And when we talk about the path to purchase , we’re not talking about simple linear stages  that a customer goes through; becoming aware, interested, then desiring your brand and finally taking action and purchasing.  We doubt this was ever really the case.  It is hard to see how it’s the case now.  Stages are blurred and marketing can affect none, one or even all of the stages.  What’s more important is that some marketing channels won’t have a direct effect on sales at all, but will help to build the effectiveness of other levers.

Our solution is Integrated Marketing Response. There is no preconceived shape to the path to purchase  – it is an atheoretical approach that allows the data to tell her own story.  And instead of focusing on a single KPI like sales, it examines as many KPIs as required to derive a holistic understanding of the marketing ecosystem.

The strength of this approach is that you  no longer need to make excuses as far as measurement is concerned – blaming the inability to measure something on esoteric technical or data concerns.  There may not have been a direct effect on the end goal from some marketing stimuli, but then it probably wasn’t designed with this in mind.  Does anyone seriously believe that a company that succeeds in boosting its re-tweets by 10% will see a corresponding increase in sales at the till?  Well, perhaps –  if it is communicating a huge  discount. But we don’t need to rehash arguments about what short termism  will do to your brand health and profitability.


Instead it is quite likely that these tweets will feed into some intermediate stage that may in turn feed into sales.  Let’s look at a simple example:

Diagram 1 shows a stylized example of an IMR ecosystem.  Caveat that while Gain Theory’s experience suggests that there are similarities across brands, each IMR is bespoke to a particular client and brand.  We can see from the schematic that some marketing initiatives have a direct effect on the end KPI (increased sales) while others are purely enablers, working with other stimuli to make them more effective.

To summarize, if you run an MMM analysis, the marketing ROIs will be based completely on the direct effect.  If you run an IMR the marketing ROIs will be based on both the direct and indirect effects.  This holistic view of marketing performance will lead to greater understanding of the impact of all touchpoints, as well as enhanced efficiencies within budget setting and allocation.

This approach is not theoretical: it is a proven success across retail, CPG, pharma and tech brands.

© Gain Theory 2021. All rights reserved.