I often get asked for a POV on this, so I guess a lot of people are asking the same question.

Recap: What are Priors?

In a Bayesian media mix model, priors are simply what you believe the results might be before you look at the data or do any modelling. Think of them as expressing real-world knowledge — like “it’s very unlikely that my sales go down when I spend more on advertising” — in mathematical language. [5].

My view, in summary: Priors introduce risks of bias which can undermine model integrity — particularly when the priors are derived from last‑touch, platform‑reported metrics or poorly specified experiments. The core issues are that last touch platform reporting is biased to last touch and therefore inaccurate, and experiments are rarely undertaken with the levels of statistical rigour required to make them reliable.

Potential Advantages of Using Priors

Let’s look at the potential advantages of using priors.

1. The biggest argument for Bayesian Priors is that they reduce uncertainty in an MMM environment. Bayesian MMM doesn’t just give you a single point estimate for the impact of each marketing channel. It provides a probability distribution of possible impacts… This allows you to understand the uncertainty associated with the estimates, leading to more informed decision-making [6].
2. Model outputs (e.g. CPA by channel) look “right” and are relatable in the real world: To a large extent, priors fix the model outcomes into sets of values that seem to match the marketing team’s expectations and experience.
3. Priors can help when the data available for modelling is limited: Sometimes there isn’t sufficient spend data to accurate estimate coefficients for media channel performance. Spend may be too low, or the spend in one channel is drowned out by higher spends in other channels at the same time, or the number of weeks with spend may be very low. In these situations, priors can help by preventing coefficients from collapsing toward zero because the data is weak.
4. Priors help when spend variables are highly correlated: Many media campaigns feature multiple channels running at the same time campaigns often take place over 2–8-week periods with media channels being combined to extend reach or delivery efficiency frequency. This means input variables are highly correlated with each other which is a problem for regression models – they can’t isolate the effects of correlated channels. Using priors in a model helps the model find clarity by providing guides to what channels effects should be.

Key Risks and Limitations

1. Priors are beliefs, not data, so they risk injecting bias into your model: A key issue is that Priors are beliefs about media performance. Because they are beliefs about media performance e.g. “our Last Touch Social CPA is always between £10 and £20, so let’s fix that range into our MMM results” – we risk using those findings to inform the model outcomes – clearly this is a form of confirmation bias [7].
2. Priors often rely on inaccurate last‑touch or platform‑reported ROI Data: In practice, most priors come from platform dashboards rather than controlled experiments. The problem with platforms is that they generally provide last touch attribution (sometimes, first etc, but never full funnel i.e. including trend and seasonality an marketing mix variables for example).  This means that from the “get-go” Last Touch reporting is inaccurate – our industry knows that- so why use these results as priors in MMM?  But obviously using Last Touch platform sourced priors is only going to transfer that uncertainty directly into your marketing mix model. This is ironic because most MMMs are commission to provide an alternative view to Last Touch reporting.
3. Priors can pull MMM results toward last‑touch, and away from more statistically reliable findings: One of the big arguments for using priors is that they keep MMM results grounded in reality. But if you are using priors, you are creating an artificial result. If the prior is biased (e.g. towards last touch reporting), the model output will also be biased. Whilst this can make the MMM appear more aligned with platform reporting, but that alignment is artificial and the results are erroneous.
4. Priors can destabilise other coefficients: Let’s go back to high school maths. We know that equations have to balance on both sides – output e.g. Sales on the left, inputs e.g. media spend on the right. If we fix one or more of the input components of our equation on the right, other parts of it will have to move in order to accommodate that prior fix. This means your model will almost certainly inflate some media channels, suppress other, channels especially upper‑funnel media and produce inaccurate results
5. Priors reduce transparency: Stakeholders often struggle to understand how much of a coefficient is “data‑driven” versus “prior‑driven,” which can undermine trust.
6. Priors can mask genuine performance shifts: If a channel’s true effectiveness changes (e.g., due to creative fatigue, privacy changes, or market dynamics), a strong prior can prevent the model from detecting it. Equally performance might improve – dramatically. Let’s say you launch a new social media campaign with a new offer and new creative. Your MMM Social CPA falls from between £10 and £20 to £5. That means your social campaign has become much more effective. But MMM priors would likely exclude that result – or at least suggest it is unrealistic.
7. Priors Do Not Include Adstock or Diminishing Returns: There is a common belief that priors somehow incorporate adstock or saturation assumptions. They do not. Adstock and diminishing returns are structural modelling choices — they define how media works overtime and at different spend levels. They are often subjectively judged but they can and should be extracted from the MMM dataset itself using a grid search loss minimisation technique [8].   This can’t be circumnavigated by experiments as Adstock and diminishing returns are critical parts of advertising evaluation [9]. They can only be determined from  long time-series datasets, not short‑term experiments.
8. Priors can come from experiments, but these are notoriously difficult to get right: Priors can be defensible when grounded in rigorous, repeatable experiments. However, this is a technically demanding area. Getting marketing experiements right requires:
   1. Clean treatment and control regions
   2. No spillover
   3. Stable delivery
   4. Repeatability
   5. Sufficient spend, time and statistical power

In practice, achieving this level of scientific discipline in marketing experiments is difficult. Geographical regions can be more fluid than they look on a map – we live in a mobile world when people can move from one region to another in very short periods of time. [10].

Real‑World Example: Identical Creative, Different Results

There have been cases where a team ran an A/B test using the same creative in both treatment and control. Despite being identical, the experiment produced different lift results for each “creative.”  This highlights how algorithmic targeting, user heterogeneity, and data aggregation conspire to confound the magnitude, and even the sign, of ad A/B test result [11]. If identical creatives can produce different results, it shows how fragile and noisy marketing experiments can be — and why they often lack the stability required to serve as robust priors.

Conclusion:

I conclude with these points:

1. Avoid using Bayesian priors if you want a high integrity MMM.
2. Not using priors may produce more challenging results – but isn’t that what you want? You are building your MMM to get a different perspective on your marketing performance; to identify hidden opporutunities and to improve marketing ROIs. Why gloss over that valuable insight?
3. This may result in a more challenging conversation with the C-suite – but it’s safter to report a challenging result from a high integrity model than any result from a flawed model.
4. If you use priors to shape the answer, then the answer will look better. That doesn’t mean it’s the right answer, in as much as any model can produce the right answer.
5. Priors introduce confirmation bias into your model. If you believe the enemy of good modelling is bias, you must question the use of priors in your MMM project. They may be acceptable to some, but not to others.
6. If you are going to use priors, you must be very careful about where you source them. There are three main sources – Experience, Platforms and Experiments, but all have flaws and all run the risk of introducing bias into your model.

Implications for Marketers – checklist

• If you are going to use priors you must be certain that they are accurate.
• Be aware that in most marketing contexts the risks of priors being inaccurate are high.
• Don’t rely on last touch data for priors.
• If you use experiments, ensure they are properly specified, and even then, use them with care.
• In cases where you have too little data for full MMM, be careful about using Bayesian priors to overcome this problem
• In data light situations, consider reviewing your data, accepting lower granularity or looking at a different attribution modelling technique like regularisation.

References

1. J Martin and P Perez, Frequentists vs Bayesians and Marketing Science, Quantified Nation, July 2024 – https://open.substack.com/pub/quantifiednation/p/qn9-frequentists-vs-bayesians-and
2. Hits and Misses of Meridian – A Thorough Deep Dive, Aryma Labs Feb 2025 https://arymalabs.substack.com/p/hits-and-misses-of-meridian-a-thorough
3. Duncan Stoddard, Is Bayesian MMM worth the faff? DS Analytics Blog, February 2024, https://dsanalytics.co.uk/thoughts/is-bayesian-mmm-worth-the-faff
4. Two key problems that ail Bayesian MMM – Aryma Labs April 2024 https://arymalabs.substack.com/p/two-key-problems-that-ails-bayesian
5. Marty Sanchez, What Are Priors in MMM – And Why They’re Difficult to Get Right (But You Need To) Get Recast Blog June 2025 – https://getrecast.com/what-are-priors-in-mmm-and-why-theyre-difficult-to-get-right-but-you-need-to/
6. Rohit Nair, What is Bayesian MMM & why use it? Medium April 2025 – https://medium.com/@rohitnair.inft/what-is-bayesian-mmm-why-use-it-942d0193e7eb
7. Cognitive bias and data: how human psychology impacts data interpretation – Penn LLPS Features October 2025 https://lpsonline.sas.upenn.edu/features/cognitive-bias-and-data-how-human-psychology-impacts-data-interpretation
8. Nephade, D., A Predictive Modeling Approach to Multi Objective Marketing Mix Optimization: Balancing Performance, Acquisition, and Efficiency – in International Journal on Science and Technology (IJSAT) Volume 16, Issue 1, January-March 2025.
9. Gijsenberg et al., Understanding the Role of Adstock in Advertising Decisions, SSRN, 2011.
10. Tyler Buffington, Eppo, The Bet test – Spotting Problems in Bayesian A/B Test Analysis Dec 2024, https://www.geteppo.com/blog/the-bet-test-problems-in-bayesian-ab-test-analysis
11. Braun and Schwartz, Where A/B Testing Goes Wrong: How Divergent Delivery Affects What Online Experiments Cannot (and Can) Tell You About How Customers Respond to Advertising, Journal of Marketing, American Marketing Association, August 2024. https://journals.sagepub.com/doi/abs/10.1177/00222429241275886

The post Using Bayesian Priors in MMM: Do Better‑Looking Results Mean You Have a Better Model? first appeared on Marketing IQ.

Let’s assume you run a test TV / VOD campaign over 4 weeks in March 2025. How can you measure the sales revenue uplift it created? In this post, we’ll look at three ways to evaluate your marketing experiments:

1. Year on Year measurement
2. Causal impact studies
3. Difference in Difference (DiD) regression

Option 1 – Year on Year uplift measurement

• Year on year measurement is a very simple and relative clean way to make empirical judgments about marketing and media campaign performance.
• We compare the sales pattern over time this year to the sales pattern over time last year.
• We chunk the data over time into three phases, pre-campaign (4-6 weeks before the campaign), in-campaign (4 weeks) and post-campaign (4-6 weeks after the campaign) –  this latter stage is important as it captures post campaign effects.
• Using an analysis tool like R or Python we can produce the following Year on year uplift outputs:

YoY Uplift Test by week showing change by week and campaign in grey

YoY Uplift result in pre-campaign, in-campaign and post-campaign periods

YoY Uplift Test table showing results detail

Option 2 – Causal Impact uplift measurement

• The principle behind this technique is the measurement of an intervention, where the intervention could be our new TV / VOD campaign.
• This technique estimates the levels of sales that would have been generated without the intervention and then estimates the weekly (pointwise) and cumulative (build) of sales after the intervention.
• Casual Impact is very useful as it doesn’t need YoY measurement, so it’s especially useful in launch situations where historical data is limited.
• Using an analysis tool like R or Python we can produce the following post intervention sales uplift outputs:

Causal Impact Test Example showing estimate of underlying sales without intervention and observed sales (top facet), with observed weekly sales in the middle facet and the cumulative incremental sales build over time in the bottom facet.

Option 3 – Difference Regression (DiD)

• Difference in Difference uses a regression approach to measure the difference in the changes between pre- and post-campaign periods during each year for 2024 and 2025.
• The DiD estimate subtracts the changes observed in 2024 from those observed in 2025 to calculate the campaign uplift.
• DiD automatically controls for underlying seasonality and year-on-year trends in the data because it is comparing changes within two different years.

Difference in Difference Uplift Measurement outputs showing campaign uplift as 224 sales per week at 5% sig.

Which option should we use?

Any of these options will give you a good measure of your campaign uplift, but once you have set up your tests, run them and collected and formatted your data, all three are relatively straightforward to run in a code environment. My recommendation  – do all three.

The post Techniques to evaluate marketing uplift experiments first appeared on Marketing IQ.

Establishing incrementality is critical if you want genuine brand growth. Why? Because many performance platforms collect, report and even double-count sales from multiple sources, including those which might happen even if you didn’t run any activity. This means you are attributing to media spend sales that would have happened without media spend. This type of misattribution will mean you are using flawed data for budget optimisation and this in turn will lead to sub-optimal media performance. Misattribution makes your budget less efficient and less effective.

In order to detect incrementality we need to establish what would happen if your product or service didn’t have any marketing activity. There are three things – sometimes called “components” to look at here:

1. Trend – what is the underlying trend in your category an din your sales – are sales they in growth, decline or stable?
2. Seasonal cycles – What are the repeating patterns in the data – do sales increase or decrease in certain months, certain weeks on a regular predictable pattern?
3. Base brand equity – how many sales would you expect to see if you paused your marketing activity

These components can often account for more than 75% of your sales revenue. If your performance platforms are reporting 100 sales, it could be the case that 75 of these sales would have happened without any marketing activity. For many advertisers this is an “OMG” moment.

Imagine if you could identify the sales that would have happened without marketing or media support and then focus your marketing budget on activities that deliver genuine incremental growth rather than paying a platform “tax” for sales that were going to progress through your sales pipeline without any short-term marketing spend.

Let’s take a closer look at trend and seasonality and why it’s important. We’re going to use the “Bike Sales” dataset from Kaggle.

First let’s look at the sales data itself:

Here we can see bike sales from July 2017 to July 2022 over a total of 260 weeks.  We can make some initial observations. There is an underlying growth trend. We can also see that there are a number of peaks and troughs in the data. We see that the highest sales weeks are around 110k and the lowest sales weeks are around -30k so the weekly sales have a range of c. 140k.

Now let’s extract the trend component from the dataset:

We can see the underlying trend in the data, quantified using a moving average. We can see there is a strong upward trend from 50k sales to almost 85k sales.

Next, let’s extract the seasonality component from the data set:

It’s important to note here that “seasonality” doesn’t mean “seasons” as in Spring, Summer, Autumn and Winter. Here seasonality refers to any repeating cycles in the data. We can see there is  clear pattern of repeating cycles. These repeating cycles range from +10k to -20k.

And finally we are left with the Random component:

The Random component represents sales that are not explained by trend and seasonality. You can see that these random sales i.e. not explained by trend or seasonality, range from about +35k to -30k.

This random data is the data we test for contributions from media spend.  More on that model and its outputs in the next post.

The post What is incrementality in marketing – extracting trend, seasonality and brand equity first appeared on Marketing IQ.

How marketing and media metrics have changed

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

1. The media metrics that monitored the delivery of their campaigns – GRPs, reach, frequency etc.
2. The attitudinal metrics that measured how these campaigns had changed attitudes towards their brands – e.g. brand consideration, preference and purchase intent.
3. 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.

The post Why digital media attribution could be compromising your media ROI first appeared on Marketing IQ.

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.

WE OFFER MEDIA ATTRIBUTION MODELLING TO OUR CLIENTS: FIND OUT MORE HERE

The post Maximising Media Effectiveness and Efficiency first appeared on Marketing IQ.

SIX pioneers of marketing effectiveness past and present

1. Claude Hopkins, the copywriter who earned $2.7m per year selling Bissell vacuum cleaners

We all talk about market effectiveness and marketing science, but these topics are not new. In fact, one of the first proponents of marketing effectiveness was a copywriter called Claude Hopkins. Hopkins was paid by his agency Lord & Thomas to write copy to sell Bissell vacuum cleaners in the US. Here’s the remarkable bit; Hopkins was paid on results and he was paid more than $200k in the 1920’s. That’s the same as being paid $2.7m in today’s money.  How many copywriters today are paid on payment by results? And I wonder how many could earn $2.7m if they were?  Hopkins was so obsessed with trying to understand how advertising worked that he wrote a book called “Scientific Advertising” to share his knowledge – published after his retirement.  Many effectiveness practitioners will tell you this is the first book on the subject of increasing marketing effectiveness.

You can read about Claude Hopkins here:  https://en.wikipedia.org/wiki/Claude_C._Hopkins

You can also buy Hopkins’ book ‘Scientific Advertising’ here

2. Simon Broadbent, quantifying the memory effects of advertising

Around the time that Hopkins retired, another pioneer of marketing effectiveness was born. Simon Broadbent was born in 1928. As a Cambridge mathematician he was the first person to quantify how advertising diffuses through populations (interestingly his original work was on pandemics of disease in orchards). Within this broad framework, Broadbent identified that the memory effects of advertising can be quantified. This idea morphed into the concept of AdStock.  AdStock now sits at the heart of the current debate around short- and long-term advertising effectiveness.

You can read Broadbent’s books about optimising media budget setting here:

https://www.amazon.co.uk/When-Advertise-Simon-Broadbent/dp/1841160482

3. Andrew Ehrenberg, explaining consumer behaviour with statistics

At about the same time as Broadbent was born, another pioneer of effectiveness might have been taking his first steps to marketing greatness.  Andrew Ehrenberg was born in 1926 and initially trained in statistics and psychiatry. He moved into market research in 1955 and his mission shifted to identifying scientific laws that might underpin consumer behaviour. The most famous of these was his application of the ‘Double Jeopardy’ law to marketing. Ehrenberg found that larger brands have more buyers and better frequency characteristics so if you want to grow sales you must grow market penetration. Ehrenberg proved this theory many times over, across multiple categories, and observed the pattern to be so reliable that it could be called a marketing law.

Ehrenberg died in 2010 and you can read his obituary here: https://www.warc.com/newsandopinion/news/obit—andrew-ehrenberg-marketing-pioneer/27183

4. Judie Lannon, one of the pioneers of identifying the emotional sell, and the first woman to sit on the board of JWT.

Judie Lannon was the first woman to be appointed to the board of ad agency J Walter Thompson (now Wunderman Thompson) in 1976. After graduating in psychology at the University of Michigan, Lannon began her career working in research at Leo Burnet in Chicago but moved to JWT and stayed there for the majority of her career. She was one of the first researchers to identify that emotional arguments were as important as rational arguments in selling consumer products.

You can read more about Judie Lannon here: https://www.marketingsociety.com/news/rip-founding-editor-market-leader-judie-lannon

5. Gerard Tellis, 29,000 citations on Google scholar and an expert on advertising in recessions.

Speaking of measurement, imagine having 29,000 citations on Google Scholar. Gerard Tellis is Director of the Institute for Outlier Research in Business & Professor of Marketing at USCMarshall. With 29,000 citations, it’s clear that Tellis has covered many marketing topics, but one of these is a must read for every marketing specialist and that’s his work on how advertising effectiveness changes during a recession. Tellis undertook extensive work into the fortunes of brands that either cut or grew advertising spend during recessions.  I wonder how many global marketers were aware of his finding that, “When the economy expands, all firms tend to increase advertising. At that point, no single firm gains much by that increase. The gains of the firms that maintained or increased advertising during a recession, however, persist.”

You can read more about Gerard Tellis’ 29,000 citations here https://scholar.google.co.uk/citations?user=MhV-CrYAAAAJ&hl=en

6. Byron Sharp, picking up the baton of marketing science from Andrew Ehrenberg.

Some readers might connect the name ‘Ehrenberg’ with the Ehrenberg-Bass Institute in Australia, the academic base for one of marketing’s current high-profile pioneers. Byron Sharp became Professor of Marketing at the Ehrenberg-Bass at the University of South Australia in 1995 picking up the baton from Andrew Ehrenberg. Sharps work is widely publicised and he works to maintain the same standard of understanding marketing and media effectiveness as Ehrenberg-Bass’ founder.  He now leads a team of sixty specialists – all working to put science at the heart of marketing understanding. Sharp’s insistence on brand maximising reach is directly linked to Ehrenberg’s view that brands can only grow by increasing penetration i.e. reaching new customers.

You can read more about Byron Sharp here https://www.marketingscience.info/staff/byronsharp/

The post Six pioneers of marketing effectiveness past and present first appeared on Marketing IQ.

Over the last decade, as an industry, we have become brilliant at harvesting the lower funnel. Every prospect who is showing “signals” of making a purchase can be digitally tracked and retargeted from the first flicker of interest to the point of purchase. This tracking has evolved to increase any one brand’s chances of closing the online sale.

But, and it’s a very big but, harvesting doesn’t grow brands. Brands that want to grow must grow their presence in their chosen category and they must find ways to convert increased presence into increased demand.

The agricultural analogy is useful here. A farmer may harvest in August but he or she has had to tend the land and the crop over the previous year to enable that harvest to happen.

To ensure the best crop, the farmer will have selected the right seeds for the soil and climate, ensured that the soil remains irrigated, applied fertiliser to assist plant nutrition, controlled pests, worried about the number of sunny days or frosts and, for more sensitive crops like grapes, tended to each vine manually as the growing season progresses. And, if all these things are done properly, then the farmer should be able to expect a good harvest.

Good brand-building marketing is no different. Brands must be nurtured, positioned, distributed and priced correctly in order to become more demanded by consumers. Only when all these components are aligned can demand be harvested.

This point was recently emphasised by Under Armour who are shifting a greater proportion of its marketing budget on brand and top of funnel activity in order to ‘spend money in the right way’ according to CEO Patrik Frisk (Marketing Week Feb 20).

This echoes a similar sentiment from Adidas who in October 2019 admitted that a focus on efficiency rather than effectiveness led it to over-invest in performance marketing at the expense of brand building (Marketing Week Oct 19).

So, growing the crop is different to harvesting it, and growing the brand is different to collecting the sale. So it’s probably not a coincidence that Byron Sharp chose “How brands grow” as the title for his literary masterclass in marketing. And, if you’ve got this far in this post, it will be clear why he didn’t call it “How to harvest brands more efficiently”. Food for thought wouldn’t you say?

The post Brands must grow before they can be harvested first appeared on Marketing IQ.

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

The post How is brand advertising different to direct response advertising? first appeared on Marketing IQ.

This hierarchical path is sometimes called the sales funnel. Funnel metrics allow marketers to measure and control brand awareness, consideration and preference. These metrics allow marketers to say things like “We have 75% awareness and of those 35% would consider purchasing from us and 75% of the consider group place our brand in their preferred set.” Most marketers spend most of their time working to raise brand awareness, consideration and preference. Let’s explore brand awareness, consideration and preference one by one:

Brand awareness

Awareness is the number of people or percentage of a group that are aware of a brand. Awareness is measured in two ways, either as prompted or unprompted (spontaneous) awareness. Prompted awareness is measured by asking people if they are aware of the mentioned brand. It could be the brand name itself, a logo or the brand as part of a list of other brands. Unprompted or spontaneous awareness questions do not mention a brand name but asks consumers to name brands they are aware of in a given category. Examples would be “could you tell name ten airlines that you are aware of” or “can you name five soft drinks brands”.

Brand consideration

Consumers do not purchase all brands they are aware of. They purchase some but may actively avoid others. So, consideration examines whether consumers would consider purchasing a brand they are aware of.  Those who would consider purchasing a brand are measured as a subset of those aware of a brand. Here we see the hierarchical nature of the funnel in action; it is not possible to consider a brand you are not aware of. Consideration questions might ask “of the ten airline brands you are aware of, which would you consider using?”

Brand preference

Consumers tend to have a “preferred set” of brands – these are the set of brands within a category that they prefer to use. Going back to our hierarchical model, the preferred set can only come from within the considered set. Getting into the preferred set is the Holy Grail for many marketers but it’s not easy. Finding a place in the preferred set requires a contribution from all elements of the marketing process. A place in the preferred set is the result of many factors that can drive brand preference; a strong product developed through strong NPD, a product made available through good distribution,  a product pitched at the right price and a product back by good service.  You can see why advertising in itself cannot guarantee brand preference, but advertising can communicate a brand’s attributes which in turn can help to secure preference. Brand preference is measured by asking consumers which brands they prefer to purchase and use within a particular category.

How are these metrics measured?

Brand awareness, consideration and preference are attitudinal and exist within consumers’ minds. The only way they can be measured is through surveys which ask consumers about their relationship with brands.  These surveys can be collected though online panels, face to face or via phone research.

Proxy measures can also be used to build understating but theses are not a substitute for brand awareness research. Digital proxies include search traffic metrics which can be correlated to awareness and consideration. The important point with digital metrics is that they are behavioural, not attitudinal; they tell you that something is happening, but don’t explain why.

The post Understanding brand awareness, consideration and preference first appeared on Marketing IQ.

How TVRs build campaign reach

Let’s assume you buy 100 TVRs in a given region. We know from our last post on TVRs that 100 TVRs is an amount of audience that is the equivalent of 100% of our target audience base.  But here’s the first important lesson in how TVRs build reach and frequency. 100 TVRs will not deliver 100% reach of that base.  In fact 100 TVRs will probably build around 50-60% reach depending on how those TVRs are distributed in the plan. So what is delivered by the TVRs that don’t deliver reach? Well, they deliver frequency.

How TVRs build campaign frequency

In the early stages of campaign, most people will see the ad only once. But some will see it twice and some may see it three times. Let’s say, for example, that 50% see it once, 20% see it twice and 15% see it three times 10% four times and 5% five times. These percentage total 100 and this is effectively how your 100 TVRs are distributed. This is called frequency distribution.

How to estimate frequency from TVRs and reach

There is a simple formula for estimating how TVRs deliver both reach and frequency.  Let’s continue to assume you have 100 TVRs. Frequency (sometimes called average opportunity to see or OTS) is calculated by dividing your campaign reach into your campaign TVRs. So, if you have 100 TVRs and your campaign delivers 50% reach then your average OTS is 100/50 = 2.

How many TVRs does my campaign need to be effective?

This depends upon whether or not you adopt the view that reach is more important than frequency.  Modern “recency” planning advocates (John Philip Jones, Erwin Ephron, Byron Sharp) argue that each point of reach will deliver more sales response than additional points of frequency (i.e. the percentage of people seeing the ad twice, three times etc). So they advocate building maximum reach on a weekly or a monthly level, but not building frequency. To achieve this objective media planners will seek between 100 and 150 TVRs per week and often plan the delivery of these TVRs in a week on, week off “drip” pattern. This type of campaign plan tends to suit campaigns that are designed to regularly remind consumers about a product they are already aware of.

More traditional media planning approaches (Krugman for example) suggest a minimum frequency of 5 OTS before a message begins to resonate with a prospect.  Our calculation tells us that if we want to achieve 80% reach at 5 OTS we will need 80×5 = 400 TVRs. Targeting an average of 7 OTS would require 560 TVRs. You can see why a launch campaign would typically be around 600 TVRs.

More advanced forms of planning use statistical modelling to estimate the sales response curve to advertising. These models show how budget and TVRs drive sales response (could be retail or online sales) on a weekly basis and forecast when spend levels will hit diminishing returns. For more on this please see these pages

Thanks to Ivan Clark for comments.

The post How do TVRs build media reach and frequency? first appeared on Marketing IQ.