media effectiveness - Marketing IQ

Brand fame; what it is, what it delivers and how to build it.

Simon Foster — Tue, 19 Mar 2024 17:41:23 +0000

Let’s take a closer look at why brand fame is such an important part of building brands

In this POV originally written for mSix & Partners, I explore brand fame; what it is, what it delivers and how to build it. We are assisted by insight from some of advertising’s leading thinkers; Paul Feldwick, Sir John Hegarty, Byron Sharp and Orlando Wood. Simon looks at how fame contributes to brand success and identifies the media channels that are best positioned to build it.

Think of a brand and write its name here…………………………………………………………

Chances are you wrote one of the following brand names: Apple, Microsoft, Amazon, Google, Samsung, Toyota, Coca-Cola, Mercedes, Disney, Nike, McDonald’s, Tesla, or BMW.

These are some of the world’s most famous brands – let’s unpack the meaning of fame in a bit more detail. The word fame is rooted in the Latin ‘fama’ meaning fame, hearsay, kudos, renown, repute, and rumour. You will see ‘fama’ present in modern words like “familiar,” “familiarise” and “famous.” In short, having fame means being in ‘the state of being known or talked about by many people, especially on account of notable achievements [1].’ You will see that all the brands above, tick all the boxes in the Latin phrase book.

Within the market context, fame is slightly more nuanced. Brands are famous because they have intrinsic appeal, they communicate with mass audiences, they demonstrate distinctiveness, and benefit from wide social diffusion. These are, according to account planning’s elder statesman Paul Feldwick, the key components of brand fame [2].

The Marketing Society and ITV [3] described the main elements of brand fame as connection, standout, talkability, familiarity and universal meaning, with universal meaning and familiarity being the most important components of the set. Connected to these elements is consumers’ need to be seen to be making endorsed choices; both during and especially after purchase. Mass appeal equates to mass endorsement.

In this POV we will think more about brand fame; why it’s important and how it is achieved, and how, over the last decade we may have lost sight of the best ways to build brand fame.

Why is fame important?

Fame is important for three reasons. First, high fame means high mental availability and we know from the work of Byron Sharp [4] that high mental availability confers commercial benefits. Feldwick summarises this as being “thought of more often, by more people, and therefore chosen more often by more people.” Second, fame can disturb consumers’ economic rationality. This is one of the main, and sometimes overlooked, functions of ‘brand’ advertising. If consumers are prepared to pay a 10% price premium for a brand because, in Feldwick’s words, reasons of intrinsic appeal, mass audience communication, distinctiveness and social diffusion, then the brand will generate more scale and revenue. And thirdly, if the brand can use its fame to sell more volume through higher purchase frequency at that slight price premium, then the brand’s revenue will be even greater.

How do we build brand fame?

Now we know the value of fame, we can explore how it is built. Because the ‘whole’ of brand fame is composed of intrinsic appeal, mass audience usage, distinctiveness, and social diffusion ‘parts,’ it follows that our strategies to build brand fame should be strategies to build those component parts.

Whilst intrinsic appeal is driven by product utility, mass audience appeal and usage, distinctiveness and social diffusion can be assisted by marketing communications like advertising, media, and PR activity.

And here’s where the quest for fame becomes more challenging. If we want brand fame, we need to seek connections that are not necessarily logical but which appeal to right brain. These connections are not driven by rationality, but by emotional appeal. System 1’s Orlando Wood summarises this in his book “Lemon” he says the right brain is guided by implicit connections, empathy, novelty and metaphor. Contrast this with the left brain, dominated by explicit facts and logic, “cause and effect, literal, factual” [5].

How do we apply this to media strategy?

Rather ironically, we have media insight from one of the industry’s best known creative practitioners, As recently as March this year, Sir John Hegarty made an impassioned plea in the BBC’s CEO Secrets series, arguing for more use of broadcast advertising, “if you are constantly wanting to expand your brand, make it more famous and add value to it – only broadcast does that” [6] . In addition to Hegarty’s comments, we have strong clues from more of advertising’s most respected and prolific thinkers. Paul Feldwick talks about mass audiences whilst Byron Sharp extolls us to maximise mental availability.

Most importantly, Orlando Wood requires that we stimulate emotions – which points again towards the channels that can do that, TV, AV, VOD, Cinema, and online video. The fact that the moving image elicits an emotional response is long-proven by both academics and empirical experience. This response has been researched extensively by Uri Hasson and his team at NYU who have coined the phrase “Neurocinematics”. The NYU team found that film can elicit a powerful neuro response, provided that the film itself is structured in certain ways [7]. Although the work was originally based on movie responses, the learnings on how to tell a story to maximise emotional ‘System1 response’ are clear throughout the paper.

These findings about using film to build emotional connections are also corroborated when we build models to analyse marketing and media effectiveness. When we analyse different forms of media activity, we find that these moving image channels are often delivering some of the strongest results.

In conclusion:

Fame builds brands and emotional connection builds fame, and the moving image builds emotional connection. So, if you want to generate powerful emotional engagement with your brand, use moving image media channels. If you want to read more about the connections between emotional engagement, cinema, brand development and the impact on short-term performance, in an applied media planning context, then the DCM Cinema Effectiveness Roadmap [8] is a good place to start.

References

Oxford Languages / Dictionary definition of Fame
Why does the pedlar sing? Paul Feldwick, 2021
How much is fame worth to the bottom line? Market Leader, 2005
How Brands Grow Byron Sharp, 2010#
Lemon, Orlando Wood, System1, 2019
Has Social Media killed the famous ad?” BBC News 14 March 2023
Neurocinematics: The Neuroscience of Film Uri Hasson, Ohad Landesman, Barbara Knappmeyer, Ignacio Vallines, Nava Rubin, and David J. Heeger, NYU, 2008
Cinema Effectiveness Roadmap, Digital Cinema Media, 2023

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Why digital media attribution could be compromising your media ROI

Simon Foster — Tue, 17 Oct 2023 08:25:09 +0000

You’ve probably heard the expression ‘The devil is in the detail‘. It tells us that focusing on detail is the way to solve problems. In many ways, this expression is true, but in this post I’d like to argue that placing too much focus on the digital detail can mean marketers and their agencies miss the bigger picture and it is, in fact, the big picture that drives your commercial sales and success.

How marketing and media metrics have changed

Prior to around 2005, the main metrics marketers used were of three types:

The media metrics that monitored the delivery of their campaigns – GRPs, reach, frequency etc.
The attitudinal metrics that measured how these campaigns had changed attitudes towards their brands – e.g. brand consideration, preference and purchase intent.
And of course, commercial metrics that captured the impact of marketing investments: unit sales, value, volume, purchase frequency and market share.

Since 2005, the digital media industry and particularly its giants, Google, Facebook Microsoft have produced huge amounts of microscopic detail covering almost every digital movement made by millions of online consumers. Through the cookie, we are able to see exactly where consumers have been, what they’ve looked at, what they’re interested in, where they have engaged, what they have registered for and what they have bought. And modern marketers inhabit this world of tracking, measuring, analysing and reporting the microscopic detail produced by digital media owners and their platforms.

Real time micro-measurement has become the main source of campaign performance insight for a generation of marketers. It is relied up by marketers and their agency partners across the industry and across the globe. Micro-performance data is used to set budget and optimise campaign on the presumption that it is accurate and correct. But what if it isn’t accurate and it’s not correct?

Some senior marketers in leading brands have questioned real-time digital measurement data. Here are two examples:

‘This real-time ROI can mean brands get tempted into ploughing investment heavily into digital – but, actually, he noted, that can result in short-termism that doesn’t ultimately grow the brand or sales, and can give “misleading” results – Simon Peel, Global Media Director, Adidas.

‘Digital attribution doesn’t take into account the [full consumer journey], [like] the fact [that consumers have] been influenced by a TV ad, or that their mum recommended this product to them. While it’s brilliant that we’re getting more accurate with digital measurement, there are so many more factors that influence why and what the customer does’- Rosie Hanley, Head of Marketing, eBay

This problem may be even worse that it looks when we consider the opportunity cost of doing the wrong thing

There is good evidence that managing and optimising this digital performance detail compromises your overall media ROI and even worse, too much focus on this detail can harm a brand’s commercial health and have a major opportunity cost. Here are four very strong large-scale case study examples that have provided support for this point:

Case study 1 – Airbnb

In 2020 AirBnB cut $50 million of performance media investment. The result: it made no difference to their overall business performance.
During an earnings call in February 2023, Airbnb CEO Brian Chesky said that AirBnB now sees the role of marketing as evolving from buying customers to educating markets and has shifted its marketing priorities accordingly.
Airbnb CFO, Dave Stevenson added that this strategic change in marketing had proven to be incredibly effective during the period 2020 to 2022. He added “Our brand marketing is delivering excellent results overall with a strong rate of return, and it’s been so successful that we’re actually expanding it to more countries”.
Great news. But consider for a moment the resource costs required to deliver the digital planning, activation, tracking, measurement and reporting that $50 million of performance marketing spend would require.

Case study 2 – Adidas

AirBnB are not alone. Around the same time, Adidas undertook a similar shift. The result: they concluded that they had too much focus on short term ROI and this had led them to over invest in performance marketing at the expense of brand building.
What’s interesting about the Adidas case is that they had previously assumed only performance activity drove e-commerce sales (ie total reliance on the digital ecosystem), but further analysis showed the brand development activity was actually driving 65% of sales across wholesale retail and e-commerce.
At that time Adidas’ marketing investment was split 77% into performance and only 23% into brand. The reason for this misalignment was an overfocus on short term digital performance metrics. Simon Peel, the global head of media at Adidas, called out some specific metrics as being responsible: Google last click, Google custom, Adobe and Facebook, and within these platforms, too much of an emphasis on short term, real time measurement.
This cycle was only broken when Google AdWords went down in Latin America and search was halted. During this time, Adidas did not see a dip in traffic or revenue from search marketing activity.

Case study 3 – ASOS

The third case study is ASOS, who also made a similar set of discoveries. Across the 2020-22 period more than 80% of the ASOS marketing investment had been put into performance marketing.
According to ASOS new CEO, Jose Antonio Ramos Calamonte, insufficient levels of brand investment was a contributory factor to a slowdown in customer acquisitions. Calamonte observed that historically ASOS had under invested in marketing relative to its peers (aka Share of Voice), and that marketing spend had not been “effectively prioritised”, or “managed effectively” to ensure a return on investment.
As in the case of Adidas, it was a halting of spend, in this case brand spend, that led to the change in marketing investment thinking; after pausing a broad reach [brand] campaign in the US, ASOS saw customer acquisition and visits growth slow.

Case study 4 – eBay

In 2015 eBay was spending 90% of its budget on performance using hyper-targeted product to audience techniques. By 2017 revenues had fallen to pre-2010 levels at $7.4bn.
By 2022 it had switched back to full funnel marketing and a focus on the experience of using the eBay brand. Revenues grew to $9.8bn.
In a 2022 earnings call CEO Jamie Iannone said the shift away from “just lower funnel optimisation has worked out really well for us”.
These four brand case studies are further supported by multiple additional studies. In March 2022, Kantar chimed into the debate saying, “There is inalienable evidence that unbalanced brands won’t win in the long term. Multiple Kantar studies reveal that if marketing mix allocation consistently favours performance marketing, baseline sales will steadily weaken”.

Case Study 5 – Uber

In 2018, Sundar Swaminathan, an analyst at Uber was reviewing data and suspecting that Meta was not driving incremental returns in Uber new driver acqusition.
As a result of his recommendations, Uber ran a dark test turning off Meta acquisition activity for new riders in a test region.
The test ran for three months.
The results of the test showed that there was no incremental gain from Facebook activity.
Uber turned off this activity permanently across the US and Canada and saved $35m.

Is there any robust experimental research evidence to further support this view?

Yes. A brilliant and comprehensive large scale, field experiment designed to measure the true effectiveness of brand and generic keyword search terms was undertaken by eBay and the university of Chicago in the US in 2013.

These were not small scale tests but large scale experiments. One stopped bidding on a 30% sample of eBay’s US traffic across a 60 day period.

This study sought to understand whether search marketing really has any genuine incremental uplift effect on consumer purchase behaviour. Here is what the eBay experiments found:

The brand, keyword, advertising experiments found that halting brand terms resulted in no detectable drop in traffic and sales.

Search engine marketing did have a significant effect on new registrations and those consumers with a low purchase frequency <2, but this was not sufficient to offset inefficient results across higher frequency eBay users.

The generic keyword experiments showed that search engine marketing had a very small and insignificant effect on sales.

Conclusion and actionable insight

These case studies make clear that an overemphasis on the detail of performance marketing does not add value to the business and risks a significant opportunity cost through misplaced marketing budget investment.

This is not just about the unhelpful “brand” and “performance” categories and nor is it about digital versus traditional mainstream high reach media. The problem is around why and how much marketing budget we deploy across all channels. It’s about the objectives we set, the strategies we develop, the plans we implement, and the way we measure and optimise.

In terms of actionable insight, simple Occam’s Razor maths tells us that in the case of Adidas, if 23% of budget was driving 65% of sales then 35% of budget could deliver 100% of sales. And, more importantly, shifting more budget into brand would grow sales substantially. In this case, 50% of budget could potentially grow sales by 150%. That’s a 50% increase in sales for 50% of the current budget.

More broadly, we must ask, how much more shareholder value would have been created if the $50 million spent by Airbnb would have been generated if this money had been focussed on growing market penetration, purchase, frequency, and overall market share?

If you are working in a category where the majority of spend is over committed to performance marketing, you have a significant opportunity to build share whilst your competitors over optimise activity that is probably not contributing to business growth.

And meanwhile, over at Google

The company posted annual revenues of $182bn in 2020, $257bn in 2021 and $280bn in 2022.

Just imagine the increases in market penetration, purchase frequency and market share that marketers would have generated if just a fraction of that revenue had been invested in building and strengthening in high reach media.

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Why we must learn to build brands in digital media

Simon Foster — Sat, 15 Apr 2023 08:44:47 +0000

In this article, written for the mSix’s website, I highlight how digital media has passed the tipping point and why marketers must evolve beyond the simplistic ‘long and short duopoly’ and use digital media to build brand fame as well as driving performance.

The time is right to challenge and develop the original works of Binet and Field. ‘Marketing in the Era of Accountability’ [1] and ‘The Long and the Short of It’ [2] are now more than a decade old. This work reflected the mesia landscape as it as then and put TV at the heart of brand building, but since then the media consumption landscape has changed – and it has changed dramatically.

In 2013, and taking the US as an example, TV, radio, and print dominated with 53% of media consumption minutes. Within this, TV delivered 4:30 minutes per day (38%). At the same time, digital channels accounted for the 4:50 mins (40%). But by 2022, US TV, radio, and print consumption had fallen to 34% and US mobile and desktop had grown to around 62% of consumption (although total media consumption minutes have increased by around 15%, likely due to mobile ubiquity) [3]. Most significantly, US mobile consumption has almost doubled from around two hours per day to over four. Patterns for the UK are broadly similar, with 2:26 mins on TV in 2012 failing to 1:42 in 2022 and 24:00 mins per day in Social growing to 1:17 mins by 2022 [4].

Against these tectonic shifts, Binet and Field’s 2013 argument that TV is the prerequisite brand-building channel must be challenged and, as a consequence, we need to ask, how do we build brands in digital media?

Traditionally, TV has been the place where ‘System 1’ messaging has been delivered through emotional connections, fame and reach. System 1 messaging is a core ingredient of fame building. This type of communication aims for high mental availability and fast effortless decision making through ubiquity – which is essentially, fame. At the core of this thinking is reach – reaching as many of your potential audience as possible. Put in a slightly different way by the late Jeremy Bullmore, “if you want to be as famous as BMW, it’s no use being known only by the tiny percentage of the population who can afford to buy your car today”.

Until recently, digital channels have been used primarily to target these “tiny percentages” with System 2 “buy now” messaging. But this approach severely undervalues the reach potential of digital channels. According to IPA Touchpoints the post lockdown high reach digital media channels are social media and functional internet (commercially funded websites which are not for media, social media or communication e.g. search, shopping, researching). These channels can deliver 70% to 80% weekly all-adult reach, putting them on a par with commercial TV and way ahead of commercial radio, magazines, newsbrands and cinema.

Of course all this means moving your guardrail KPIs from performance to brand metrics like attitudinal shifts, recall, preference, purchase intent, and incremental brand growth. Measurement and monitoring techniques need to shift uplift experiments, MMM and tracking studies.

The shifts in focus that are required to build brands in digital are summarised in the checklist below:

Targeting: Move from tight signals targeting to brand audience reach
Mental message processing: Move from System 2 (aim to close the sale) to System 1 (aim to change instincts)
Messaging: Move from rational to emotional engagement
Mental availability: Move from Low to High
Optimisation: Move from short term CPA to longer term attitudinal metrics like consideration and purchase intent
Evaluation metrics: Move from performance metrics to a set of agreed attitudinal metrics
Evaluation cadence: Move from short term to medium term

By changing the way we plan, activate, measure and benchmark digital channels we unlock their ever-expanding potential to build and reinforce brand attributes and, in doing so, prove it’s now time to move beyond the long and short of it.

References

Binet and Field, ‘Marketing in the Era of Accountability’ IPA 2007
Binet and Field, ‘The Long and the Short of it: Balancing Short and Long-Term Marketing Strategies’, IPA 2013
‘Average time spend with media in the US’, eMarketer April 2016 and April 2022
IPA Touchpoints 2023
IPA ‘Making Sense: the Commercial Media Landscape’ 2022

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Maximising Media Effectiveness and Efficiency

Simon Foster — Tue, 01 Mar 2022 20:19:29 +0000

This is a piece I wrote for an m/SIX newsletter in January 2022.

Effectiveness and efficiency are not the same but they are both critical in media strategy, planning and activation

Intro

Marketing effectiveness and campaign efficiency are intertwined terms across the open plan workspaces of both advertisers and agencies. But they mean very different things. Now is a good time to remind ourselves what these terms mean and to explore the differences between effectiveness and efficiency and how they apply in media investment.

Effectiveness is about doing the right thing. It is sometimes referred to as ‘goal orientation’- are we doing the right things to reach our goal? At m/SIX we refer to these effectiveness options as “levers”.

Let’s look at some examples of effectiveness; if we want to build sales revenue by growing the market share of a brand we need to increase its market penetration. To increase penetration, we need to move our brand into the consideration and preference sets of more consumers and in order to achieve this goal we need to deliver increased reach. In this case the goal of increasing reach is our route to effectiveness. Actual effectiveness is the degree to which our approach delivers proximity to the selected goals – increased market penetration through increased reach.

In another effectiveness example we may wish to increase revenues by repositioning our brand versus competitors. For example we may wish to position our brand as more environmentally friendly than other brands in the category. To do this we may need to change the way consumers view our brand and ask them to associate new meanings with it. In order to do this we may need to change the memory structures associated with our brand which in turn may require the use of media channels capable of delivering that “change in memory structure” goal.

In a third example we might want to deliver revenue growth by increasing purchase frequency. To do this we might need to give consumers reasons to purchase more often by reframing the way they use the product. This would typically increase the number of usage occasions that the product can contribute to. The decision to reframe the way the product is used, and our success in doing that is the measure of the campaign’s effectiveness.

Efficiency is about doing things right. Efficiency tends to be process or ways of working orientated. At m/SIX we refer to these efficiency options as “switches”.

Now let’s look at how the three examples above might benefit from increased efficiency.

In the case of increasing market penetration, we would need to examine which channels are able to deliver reach most efficiently – typically, we might ask which channels can do this quickly, or which channels can do this in the most cost-efficient way – how much reach and attitudinal shift can be generated per pound or dollar invested. Another aspect of efficiency might be which creative assets we use, exactly when we use them, where we use them and the time and cost involved in producing them.

In the case of repositioning a brand, efficiency might be measured as the number of points of attitudinal shift per £pound or dollar invested. We know that some channels are more efficient at achieving this goal than others. We also know that certain ways of using those channels are more efficient – a moving image may be more efficient than a static image, a larger format ad may be more efficient than a smaller format ad. Higher frequency over a short time period may be more efficient than lower frequency – or vice versa.

In the case of increased purchase frequency, the most efficient route might be how an agency and marketing team can remind consumers with prompts or triggers to change their behaviour – this is usually signals-based targeting; it could also be a carefully planned search campaign to target recipe searches for example. Or it may be a signals-based media and creative optimisation to target active meal planners; if we know that a consumer is going to shop online, we need to deliver our prompts and triggers in the right way and at exactly the right planning moments.

Efficiency and effectiveness is not a binary choice between one approach or the other – we have to deliver both, but in the right measures

Now we have explored these two concepts, we need to emphasise that one without the other amounts to suboptimal marketing and media investment. Making effective strategic decisions without efficient delivery is likely to be slower and more expensive than it needs to be. Delivering campaigns efficiently, does not necessarily deliver the best goal delivery – ie effectiveness outcomes.

At m/SIX we manage both effectiveness and efficiency; the levers and the switches. We have teams of strategic planners who are able to focus on making the right goal choices to maximise marketing and media effectiveness. We have teams of audience planners who look for the audiences most likely to deliver our goal and the channels and targeting criteria that will deliver those audiences in the most efficient way. And we have teams of display, search, social and CRO ad CX specialists who help us ensure that the strategy is delivered efficiently.

But whilst the choice is not binary, the balance between maximizing effectiveness and efficiency has to be carefully considered – our strategists and analysts work on optimsing this balance so you can be assured that your budgets are being invested in ways that will maximize your overall business outcomes.

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Best practice in marketing and media attribution to increase ROMI

Simon Foster — Tue, 17 Sep 2019 07:38:09 +0000

Introduction

This paper defines the marketing attribution problem and looks at the ways marketers can generate an accurate view of their return on marketing and media investment.

There are lots of views, approaches and tools available to assist marketers with marketing and media attribution but unfortunately, most are limited in one way or another and some are actually dangerously misleading.

This paper aims to sort the wheat from the chaff when it comes to attribution – especially in paid media environments.

First, let’s define the problem attribution modelling seeks to solve.

Marketers are investing large sums of money for their companies and and for many larger brands, that money is often shareholder funds. Marketers therefore have a responsibility to ensure that their budgets are being invested optimally.

The marketing ecosystem is much more complex than it appears; different channels work in different ways across the marketing funnel. And, in addition, the channels that present sales are not necessarily the channels that drive sales. Moreover, what appears to be a cause often isn’t; before marketers can make judgements about their own investments they must strip out the effect of extraneous factors like competitors, pricing, climate, the economy, seasonally and any other variables that may have an impact on how their own sales are, or are not generated. Failure to consider all these effects will ultimately lead to flawed analysis.

Inaccurate analysis will lead to poorly informed media investment decisions at best and ineffective – potentially loss making – media investment decisions at worst. With media budgets running into millions, those mistakes can be expensive.

So it’s critical that the best approaches are used. Anything less carries risk. Let’s take a look at some of the main forms of attribution here:

1 – Last touch media attribution

Last touch attribution is the most readily available but potentially most misleading form of marketing attribution. Last touch measures the last journey point that consumers touched before they reached your business or brand. Usually this is a google search, but it could also be a banner view or click or a visit to an aggregator. Either way, it’s the last action the performed before they visited your site visit or call centre. All these touch points can be measured – Google produces a full suite of last touch metrics via Google Analytics and GA360. Direct site visits can be measured by your own web stats package or GA. Phone calls can be measured and attributed to a source through the use of unique phone numbers.

Because these last touch channels are so readily measurable and can be linked so closely with sales it’s tempting to make them the sole focus of your attribution analysis. But there’s a problem here – focusing your attribution measurement on last touches doesn’t give any insight into the previous interactions in the customer journey i.e. what consumers were doing just before that last touch – in other words, what really drove them to you. This could mean you fail to spot the important stages in the customer journey before you see that last touch.

Let’s look at an example: a consumer follows a typical need-research-shortlisting-choice-purchase customer journey. A number of influences will be at play in the early stages of this process – a display, AV or outdoor ad may encourage our consumer to say “I must do something about that . The prospect may wait a few days and then another AV ad re-prompts a visit to solving the problem. The consumer then moves to the “information search” stage of the buying process – they may use Google, they may visit a retailer, they may visit a brand directly – in the case of financial services, they may visit an aggregator. Still no final decision though. And then they receive a bill from their current supplier. At that point they perform a brand search and review competitors. From this brand search our prospect shortlists three potential suppliers and then another AV ad prompts them to undertake another brand search and make a final purchase.

We can see this is a complex journey with many touch points. The last touch is a tiny part of the journey. It’s the last one you see, but it is by no means the only one.

This means that rather than focusing on the last touch you have to find out how where credit is due in the customer journey.

2 – ‘Artificial’ attribution models

One of the most popular ways of attributing across the journey is to use a predefined attribution model to break prospect journeys into a number of stages. The best example of these is six variants often used in digital marketing – last touch, first touch, even weight across all touch points etc. These models aim to split the responsibility (and revenue) for driving the prospect to your business across several pre-last touch points.

Unfortunately, there is often very little factual basis in these models. The postulated pre-last touch pattern is little more than an unproven hypothesis of what might have happened in the journey. As such, artificial models offer little or no scientific value to discerning marketers.

3 – Direct URL or phone number tracking

This approach – sometimes called “linear” tracking – was originally developed by traditional direct marketers. They used coupon codes and later unique phone numbers to track the origin of sales back to different ads and media channels. As the internet grew, this approach was trialled by digital marketers using unique page URLs. However, whilst unique phone numbers were a reliable way of capturing the last touch, the web URL approach was not. Consumers tend to search brands and products they’ve seen advertised, or distinctive phrases that have lodged in their memory – “compare the meerkat / market” for example. But consumers don’t search www.abtaholidays.com / tube. And, in any event, direct tracking does not move us beyond the last touch attribution problem.

4 – Universal Journey Tracking

Universal tracking can only be implemented in digital environments. It involves setting a tracking tag or cookie at each touch point – a banner ad view, an email opening or a site visit. If each tag is time-stamped it is possible to plot the chronology of the different times and channels in which views or visits were made and the tag was activated. This allows analysts to plot the journey before, up to and including the last click. Setting up this type of campaign can enable a reasonable amount of journey reporting through DV360, GA360 or simple GA. but the problem here is that this type of tracking is restricted to digital only environments. It can’t be deployed in offline media – and it’s often offline media that drives the upper funnel by providing high reach and scale-up opportunities. So, for many brands the challenge is to understand the relationships between upper, mid and low funnel activities.

5 – Attribution through patterns, trends and correlation

What are the options if you want to move beyond last touch or linear tracking to get an understanding of how your marketing and media drives clicks, leads, calls, sales and market share?

There are two options here – econometrics or simple trend and correlation analysis. We will cover econometrics in point 6 – first, let’s look at more simple correlation analysis.

When marketers run a campaign they usually have an objective to change a variable. Let’s call it sales. In the statistical space, the sale becomes the “dependent variable” – that means it’s the variable we are trying to change by applying different “forces” to it. We might apply TV, OOH, press, radio or digital advertising to drive reach, brand uplift and visits.

By collecting daily or weekly levels of investment over time in each media channel, alongside the dependent variable, it will be possible to observe whether any one or combination of media channels are correlated with changes in our dependent variable.

So we might observe for example, that direct web traffic increases are highly correlated with a new TV campaign.

Some TV “spot matching” tools like Adalyser and TVSquared fall into this category. These tools match two trend datasets – TV impacts over time and web or call traffic over the same period – often on a minute by minute basis.

These trend approaches based on time-series data give us a much clearer picture than “last touch” metrics. “Last touch” would simply observe an increase, but it wouldn’t necessarily explain why it was happening – especially if the driver variable/s are outside the digital ecosystem. But trend analysis has an Achilles heel – any changes observed must be separated from what might have happened if no advertising had run at all. This “baseline” question is a key issue in trend analysis and only econometrics can solve it correctly.

6 – Econometrics / Market and Media Mix Modelling

The trend analysis outlined in point 5 above takes us to a more sophisticated level of multi-channel attribution. But it doesn’t consider the external factors that shape the effectiveness of our own advertising – things like underlying seasonality, competitor spend, price promotions, incentives, consumer confidence, weather and even house prices and other wider economic measures like RPI and unemployment. It’s only when you consider your own marketing investments within the context of all the factors that you have the full and clear picture of your marketing effectiveness.

And that’s where econometrics comes in. Econometrics considers all these factors and attaches a value to them – positive, negative or neutral. It will also help you understand what the unique impact of these factors is and it will also allow to identify factors that work together.

But most importantly, econometrics allows you to understand how your brand, promotions, media and pricing sit in the context of the way your category works and in particular the impact of competitors.

Econometrics also gets marketers and media planners over the “last touch” problem because it allows you to quantify the effects of all channel metrics on sales. It will reveal the effect of all upper funnel channels sales channels – so for example – econometrics will tell you how many sales TV generates but it will also tell you how the sales generated by TV distribute across all lower funnel channels like search, direct traffic, phone and retail.

So in any time period econometrics can attribute your total sales back from the channel where they appeared to the channel that generated them – whether online or offline – with consideration for all factors driving sales and therefore with a high degree of accuracy.

Conclusion

Because of the sums being invested and the value of the results at stake, marketing and media attribution are core responsibility areas for marketing directors, CMOs and agency media practitioners. But it’s a field where last touch linear metrics offer a tempting but misleading view of how marketing and media investments really work. More sophisticated approaches require a wide number of datasets and take time to enable, but they give you a much more accurate view of how your investments are working. The time and patience required to deploy advanced techniques will pay off and enable you to fully understand how these investments work and optimise them to produce the best possible results.

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How is brand advertising different to direct response advertising?

Simon Foster — Tue, 19 Jun 2018 20:42:26 +0000

Brand advertising techniques are very different to direct response advertising techniques. Even when you are running an integrated multi-channel campaign it is important to understand the key differences between the two approaches so that you can orchestrate your overall campaign plan and budget to deliver maximum ROI.

To illustrate some of the key differences here is a paid media summary in the context of TV:

Objectives:

Brand advertising tends to seek a change in attitudes towards a brand and deliver uplifts in “lower funnel” sales channels such a display, search and social media
Direct response advertising tends to seek an immediate behavioural response – the generation of immediate clicks, leads, sales or donations.

Creative strategy:

Brand advertising tends to position products and services relative to each other in their category and differentiate them using emotional involvement and engagement.
Direct response tends to persuade consumers to buy immediately using rational messaging.

Here’s a brand advertising TV creative example: Brand advertising building emotional connections – Moneysupermarket

You can see how the essence of the Moneysupermarket ad is entertainment – it uses striking imagery to make an impression on you, build an emotional connection and increase brand trust. The aim is to increase your emotional preference for the brand and reduce your reliance on the functional benefits of the product. That way, when it comes to conversion you will opt to buy from a brand you’ve heard of, feel connected to and trust – even if the pricing or functional benefits are not necessarily the best in the market. In the case of Moneysupemarket, the “do you feel epic?” line invites consumers to be part of a movement.

Here’s a direct response TV (DRTV) advertising example: Direct response advertising is looking for an immediate behavioural response – clicks, quotes, calls, leads or sales

Here you can see how DRTV aims to deliver short-term behavioural change – i.e. web visit response – by covering a lot of selling points in a very short period of time. There is no attempt to gain an emotional connection through entertainment. Quite the opposite – here the intention is to persuade consumers using rational argument.

Ad Timelengths:

Brand advertising can work on lower timelength edits – typically these are 30 seconds or less – 20s or 10s.
Direct response advertising tends to require longer timelengths to allow the persuasive arguments to be built and the call to action delivered.

Media Frequency:

Brand advertising requires both reach and controlled repetition to drive memory. Typically this might be 80% reach at 5-8 OTS – that requires between 400 and 640 TVRs.
Direct response advertising aims to maximise reach at lower levels of frequency so TVR weights can be mush lighter. Given that in the UK, 10 adult TVRs equates to 5m impacts, this weight is adequate to test the responsiveness of an ad.

Media Dayparts and Programme Type:

Brand advertising requires access to working target audiences which means advertising when they are available to view – typically this is when they get home from work post 5.30pm – otherwise known as peak. Tends to require high quality programme content environments to maximise chances of engagement with advertising.
Direct response advertising tends to work best in low interest programme environments and in dayparts where airtime is less demanded and therefore less expensive – this tends to push DRTV advertising into off peak airtime.

Media Weight:

Brand advertising tends to require heavier campaign weights. This is because of the requirement to build reach and frequency. There is also strong evidence that share of voice can correlate positively with share of market outcomes
Direct response aims to maximise reach on the basis that consumers who do not respond on the first or second exposure are unlikely to respond to subsequent exposures in the short-term.

Campaign Evaluation:

Brand evaluation is based on its objectives – typically these are awareness and consideration shifts and uplift effects on other media channels such as display, search and social.
Direct response advertising tends to be evaluated based upon immediate response metrics,. clicks, calls, leads, sales, subscriptions and donations
You can read more about evaluation here

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Media ROI Evaluation Techniques

Simon Foster — Fri, 30 May 2014 11:23:16 +0000

Techniques for tracking advertising and media ROI are often discussed by both advertisers and agencies as they seek to identify and maximise the ROI effect of media budgets. Deciding on which techniques to use can raise a number of issues depending on the data and budgets available for advertising evaluation.

Before we get into the techniques themselves you will see that one recurring theme is the impact of ‘extraneous variables’ – that is the impact of factors beyond or outside the media campaign itself. The six main extraneous variables are:

Competitor spend – almost all companies and brands have competitors. Competitor spends can have an impact on your campaign, usually by taking business away from you. You will need to quantify this effect before you can make statements about your campaign’s effectiveness.
Distribution – if you have uneven distribution of a product or service or if you have a shortage of a given product, this will impact on the results you report.
Economy – two economic variables are known to have an effect on marketing and media performance are interest rates and consumer confidence – both of which can have a positive or negative effect on consumer spending. When confidence is high consumers spend more and campaigns perform better. You will need to understand how this affects your own campaign performance.
Seasonality – most markets have inherent seasonality which can have a powerful effect on sales patterns. If you are evaluating activity in seasonal peaks or troughs, you must account for the seasonality effect.
Pricing – price remains an important influence on consumer behaviour. If you have a 50% off sale, you will generate higher traffic and sales response than in a period of normal pricing. If your competitors are using pricing aggressively, this will also have an impact on your media ROI. You will need to take account of this.
Weather – many product sales are influenced by weather. Good weather can increase sales, bad weather can decrease ales, or vice versa. Again you will need to take account of this in any meaningful analysis of media ROI.

Why are extraneous variables important?

Extraneous variables can have a major influence on the results your campaigns generate. At best they can render any top-line results erroneous. At worse, the impact of extraneous variables could lead you down the route of making suboptimal media investment decisions which could damage your ROI.

Here is a short summary of the main media advertising evaluation techniques available and a note on whether or not extraneous variables are considered:

Linear data reporting
- Involves the use of response codes, phone number tracking, SMS number tracking, drop downs or tick box menus to ask consumers how they found you
- Tends to report only the last touch or last click i.e. the last thing the consumer remembers seeing before they connected with you
- This tends to favour lower funnel channels like PPC and DM
- Findings tend to reflect what the consumer thinks motivated them to interact
- Works very well if you are only running one channel and one campaign, but can produce dangerously misleading results in a multi-channel environment
- Limited to observed data only
- Does not account for extraneous variables eg competitor spend, economy, pricing, seasonality or weather
Descriptive data reporting
- Basic counting of descriptive results e.g. web traffic during a TV campaign
- Plots traffic before, during and after campaigns
- Should be possible to measure and plot uplifts in campaign period (e.g. YoY)
- Incremental traffic can be plotted against TV spend to calculate incremental CPC
- Limited to observed data only
- Does not account for extraneous variables eg competitor spend, economy, pricing, seasonality or weather
Uplift Analysis
- Similar to 2 above but looks at the effects of media spend within a sales funnel or database
- So, did sales conversion rates increase or bounce rates fall?
- Did enquiries from current customers increase?
- Did churn rates fall?
- Does not account for extraneous variables eg competitor spend, economy, pricing, seasonality or weather
Correlation and Regression
- Looks for basic statistical relationships in-campaign between media spend and a response variable eg web traffic
- Allows advertisers to measure relationship between spend and response e.g. for every £1000 spend 2,000 clicks appear to be delivered
- Limited to observed data only
- Does not account for extraneous variables eg competitor spend, economy, pricing, seasonality or weather
Multiple Regression
- Uses statistical modelling to estimate (I use the word “estimate” in a statistical sense) the effect of multiple independent variables (e.g. adspend by channel) on a dependent or target variable e.g. web traffic new users
- Can be structured to account for extraneous variables eg competitor spend, economy, pricing, seasonality or weather
Non-Linear multiple regression (AKA econometrics or Media Mix Models)
- More advanced version of 5 above which incorporates the estimation of non-linear effects
- Examples of non-linear effects include AdStock (the rate at which advertising spend effect decays over time) and diminishing returns (the rate at which adspend becomes less efficient as spend is increased).
- Requires time series data covering multiple years to incorporate seasonal patterns in data
- Requires at least 100 observations of data (e.g. weeks)
- Will account for extraneous factors such as seasonality, competition, pricing or weather

All approaches are data dependent which means that if you are not collecting response or sales data you will need to.

Costs for implementation can vary but should always be viewed in the context of the potential savings that can be made from subsequent optimisation. For example, if an econometric media mix model costs £25k, but can optimise a £2.5m budget to save £500k, then the £25k is money very well spent.

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