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


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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.

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.

The WPP Global Retail Forum is a continuing education opportunity designed for marketers in retail. This year’s event featured the likes of Samsung, Dell, and IBM to name a few, speaking about key shifts in consumer buying behavior and ways to better connect with today’s shopper.

Jennifer Hahs – Research Director at Gain Theory – took the ground to download the biggest takeaways to help keep marketers one step ahead, to become faster and smarter in the ever-changing retail landscape.

We have curated this interactive, all device friendly playbook, so you can take away the major themes:

1. Get Back to Basics
Optimizing existing practice.

2. Be Nimble
Changing things that aren’t working.

3. Be Disruptive
Reinventing the norm.

4. Be Kind
Connecting with humans.

5. Future Now
Embracing the next generation.

6. Engaging Today’s Shopper
Seeing through the customer’s eyes.

To see the full download, click on the animated gif below and follow the arrows.

[embedit snippet=”wpp-grf-v2″] 


The raison d’etre for the majority of Market Mix Modelling agencies is to calculate the marketing return on investment, or MROI.  Gain Theory takes a different approach – knowing the ROI is nice, but does little to solve the pain points of our customers –how to optimize marketing investment, the allocation between media channels and to ensure that all touchpoints are working coherently.

As one Marketing VP recently said in an independent CMO survey: “MMMs provide some insight into making better investments, but that is still fairly one dimensional”.

Because we start from a different position – dynamic improvement rather than static reporting – our approach is also different.

The common approach to media impact

By far and away, the most common approach to estimating the impact of media is to use an ad stock.  This may largely be seen as taking the ratings that your target audience had an opportunity to see and then decaying them.  Using a decay allows the media to be tested for an immediate effect, as well as an impact over the medium and longer term.

Now, this approach is fine if you are only concerned with identifying the ROI.

But it says nothing about wastage and it gives little insight into weekly phasing or even where diminishing returns begin to set in.  This is because a rating has little definitive to say about the chances of your target audience hearing or seeing an ad.

Ratings are a trading currency.  Nothing more.

Consider a simple example.  What do 20 TV ratings actually represent from a viewing perspective?  It could mean that 20% of your target audience have had an opportunity to see (OTS) one exposure.  Or it could mean that 10% are at 2 OTS.  Or some other combination.  On its own, it is impossible to say.  As ad stocks are based on this rather ambiguous metric, they have little to say about the level of reach or frequency required to drive improvements in your ROI.

Our tried and tested AdModel approach is much more forward thinking and considers five key parameters that provide key information for all stakeholders.

  1. Effective Frequency    
    How many exposures need to be seen to trigger a response?
  2. Recency Frequency    
    How many exposures need to be close to the purchase?
  3. Recency Window    
    What do we mean by ‘close’ – a week? 2 months?
  4. Memory Decay    
    How long is the exposure remembered?
  5. Habit       
    Is there repeat purchase?

The fifth parameter – Habit – describes the long term impact of converting new users – basically a measure of trial and repeat.  They may be completely new to the brand, or they may be existing users for who marketing has helped them discover new opportunities for use.

Going beyond ROI to help marketers take action

Identifying the first four parameters defines the budget required, the phasing strategy, and optimal investment.  For example, knowing that for your brand consumers need to see 4 exposures, 2 of them in the last 7 days throws up a completely different approach than if consumers just need 2 exposures, just 1 of which can be seen anytime within 2 weeks of the purchase decision.

Planning on norms, benchmarks and experience can lead to some serious inefficiencies.  Let’s look at the following simple example.

base plan to optimised plan

ratings alternative plan

The base plan from the agency seemed OK – continuity was deemed important, and running with a broadly constant level of weight.  However, the AdModel tells us that, to trigger a response from those active in the market, we need:

admodel table

Using this insight we can deliver a 30% increase in the ROI.  Based on science, not hunch.

As you can see, some big improvements in efficiency, just by understanding how media is working for you.  And this is for just one channel – the same efficiencies can be seen across the media and marketing mix.

This approach has been tested across all verticals and across all continents.  If you want to move away from reporting just an ROI to dynamically improving your marketing investments, it’s time to rethink your approach.

With so many different marketing channels out there today, it’s difficult to identify what drives sales.

In this paper, we present a new solution to an age-old problem – how to effectively measure the long-term effects of advertising.

Some people view this as being impossible, others do it inaccurately but we’ve figured out how to get it right. Here we discuss the issues involved, dismantle existing systems and present our own solution.

Inside this paper we cover:

  • Why is this important to me?
  • We need a floating base
  • What are the existing measurement methods and why don’t they work over long periods?
  • What attempts have been made to tackle this?
  • What is the solution?

To download the article, click the link on the right.



Digital marketing channels today are divergent – search, video, social, display, email, mobile – the list goes on. Marketers have a myriad of options to choose from to reach consumers to hit KPIs. But the biggest challenge is understanding which digital media work best…and how to optimize those.

The holy grail of many brands today is establishing which digital investment gets the credit for delivering a conversion event. Where should we spend our money? What can we cut from the budget? How? When?

As more media is bought digitally, more data is produced and with that comes an ability to measure effectiveness at a granular level.

The future is increasingly connected and for some big digital advertisers, requires the right measurement solution.

Digital Attribution can provide the right measurement and optimization solution. But you need the right tools and conditions to do so and what’s more – it’s not for everyone.

We have compiled an 8 Step Guide to Digital Attribution to help navigate marketers through the subject and understand whether it’s a journey they want to embark on.

The 8 steps are:

  1. Significant Digital Media Spend
  2. A Clear Online KPI
  3. A Skilled Team of Data Scientists
  4. The Right Purpose
  5. The Ability to Optimize Quickly
  6. The Right Data Set-Up
  7. Unified Digital Tracking
  8. The Right Methodology

You can read the in depth article published in AdMap, by downloading the article on the top right hand corner of this page.


Visit our website to view the video.


Matthew Chappell, Partner at Gain Theory, attended the IAB Video Conference: Thinking Differently in London on Wednesday 16th November 2016. Here are his thoughts from the event:

Halfway through AOL’s session, Mark Milling asked the audience to put up their hands if they’d seen the new John Lewis advert. And then hold them up if they’d seen it online first. And then keep them held up if they’d seen it on TV at any point in the last week.

The only people who didn’t have their hands up to start with were probably asleep following lunch. Almost all of those who had seen it had seen it online first. Only seven people in an audience of 600 had seen it on TV at all. Seven.

Online video is no longer the future – it’s here. But how do we make the most of it? And, as the question posed by the conference title asked, how do we think differently?

First, a look back. In the early noughties, when digital was emerging, brands talked about how much money to invest in digital. Then, they talked about how much to invest in specific channels. Now, with the biggest digital channels we need to be asking how we allocate spend within them and how we create content that fits – what is the right message at the right time to the right people?

This is especially pertinent in video. In the past, brands put their TV ad online and that was that. Now the opportunities are limitless. Indeed, according to Millward Brown the average consumer spends 65mins a day watching video on mobile or tablet (vs 61mins on TV). There is vast potential attention but brands need to capture it quickly.

Two of the best sessions, from Facebook and Google DoubleClick, focused right in on this.

The Google DoubleClick session, run by Atossa Vaziri, discussed three different types of video content: meal, snack, bite. Sometimes you want a three course meal, or a three minute brand video; sometimes you want a snack. Sometimes you don’t even have time for a snack, just a bite of your friend’s cake. At different times you have different needs, and video advertising can and must reflect this. For example, on Facebook mobile, 98% of videos are watched in portrait (according to Jonathon Milne, CrO Celtra), most with the sound off. Advertising in this circumstance should probably be bite or snack sized, not a four course French dinner.

Ian Crocombe, from Facebook, also discussed three different ways of telling stories and capturing attention. He started with the old fashioned TV ad: a 30s build to a climax, with the brand displayed at the end. In the online video world this is flipped on its head, you show the brand and the punchline first, before exploring further in the next however many seconds. There is a third way, by which a brand pulses impact, humour, and emotion throughout the video, such as in the McDonalds example from Brazil, below. This is a fascinating way to grab attention and hold it for a long period, in this case 1min 45s.

Both told the same story in slightly different ways, but the takeout is the same. As demonstrated by Lucy McHenry from Twitter, Nielsen have shown that there is little correlation between video length and engagement. However, 81% of videos which drove high engagement had a hook in the first three seconds. And most good videos were optimised for sound off.

The main goal for advertising is still to capture attention and engage with consumers. There are many ways to do this, and for some brands this might not be video. The great thing about any digital media is its agility – the ability to test and learn quickly, fail fast and move on.

What we learnt today is that more brands than ever are willing to do this; to think differently on how to use video to improve their marketing effectiveness.


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