LT has been called a “dangerous fiction” [1] that “gives 100% of the credit for a conversion to the last touch point before the user made a purchase or completed a desired action (the touchpoint usually being an impression or an ad click). It does not matter what other ads were seen before that one, how the customer moved through the funnel, or which channels were most effective at influencing their journey. If your impression or click wasn’t last, it didn’t count.” [2].

What really happens before the last click?

Think of any purchase you made online that involved a google search before you visited the website you purchased from.

Let’s consider as an example, how a car hire purchase might work. Your Google search will have been triggered by a  need; you needed a hire car,  this may be because it’s now school holidays, you are taking a summer vacation. The weather for the next two weeks is good so you decide to do some travelling around. You may “trust” some brands you know like Hertz, Avis or Sixt. You see Avis is running a promo which makes it more competitive on the same car group you want to hire.  Then you go to Google and click to visit a web site or a car hire aggregator.

Now let’s break this down. To make your decision you were informed by these journey points:

1. It is school holiday time – this is a repeating seasonal pattern; school holidays occur at roughly the same time every year
2. You are taking a vacation – a sub-pattern within the repeating seasonal summer holiday pattern
3. The weather is going to be good – weather is affecting your decision
4. You prefer brands you trust – this is important because without this brand trust you might not purchase from one of these companies
5. You see the price promo from Avis  – price promos can successfully pull buyers from one offer to another
6. Only then do you click on Google and your behaviour becomes a last touch attribution metric.

More and more marketers are recognising that Last Touch attribution does not provide measurement of the full journey:

• Disney’s Dana McGraw thinks advertisers should “look at the whole picture to understand performance, rather than rely on the expedience of last-touch attribution” [3].
• Even Google have recognised  that last touch is not the complete picture when they said “Everyone has been overlooking the mid-funnel. Even us [4].
• Neil Patel, one of the most respected independent voices in search marketing, has said that “Last-click attribution is lying to you” [5].
• The American Marketing Association states that: “Single-touch models, such as last-touch attribution and first-touch attribution, do not account for all events leading to conversion” [6].

How misleading can last touch measurement be?

The short answer is very misleading. Even dangerously misleading. Let’s illustrate how wrong Last Touch reporting can be.

In the chart below we see a comparison of the contributions to campaign performance across a number of drivers and between Last Touch reporting and Full Funnel MMM reporting.  These include media channels, plus non-media drivers like price promotions, seasonality and brand trust scores.

There are some notable differences here:

In Last Touch reporting, all the drivers are media drivers, apart from Promotions (10%) so 90% of sales are driven by media.  But what’s also notable is the fact that all the channels reported are digital channels. This is because these channels are reportable through digital tracking devices.

In Full Funnel MMM reporting, we see the introduction of important non-media drivers that explain a significant share of the sales – Promos are found to drive 20% of sales, Brand Trust drives 10% and Seasonality drives a further 10%. These drivers are not measured by last touch.

We can see that Last Touch reporting is creating an illusion of media performance by misattributing non-media effects like price promos and seasonal demand shifts to media.

In the case above we see that:

1. LT reports only digital channels
2. MMM reports all drivers, including powerful non-media effects
3. Last Touch reporting is overstating the effect of media to the tune of 40% of sales

If your reporting is telling you your digital media channels are delivering 1000 sales, they are actually delivering far less, probably about 60% of the total you see. And of course, if you base your media investment decisions on Last Touch metrics, you are therefore using substandard measurement findings and so you will be making substandard investment decisions.

Let’s compare Last Touch and MMM in detail

This table covers and summarises the main points made so far. Please note that MTA is being largely deprecated across the UK and Europe as a result of privacy regulations.

Why is Last Touch platform reporting still being used?

Major digital platforms can create an illusion of accurate measurement; they offer daily, hourly and even minute by minute reporting of multiple metrics, campaigns, ad groups, ads, keywords, referring websites, countries, cities, and so on. These seemingly rich dashboards create an illusion of accuracy and certainty. When asked “how many sales did X generate last week“, the answer can be provided. So these platforms are convenient and expedient.

But despite their apparent data-driven accuracy and their high ease of use, these platforms are conspicuous for the information they do not contain.  Going back to our car hire example, they do not suggest 40% of sales were not driven by media. They do not measure the impact of seasonality, price promos or brand attitudes. In short, Last Touch platform reporting does not provide what Disney’s Dana McGraw called the “whole picture“.

Implications for media planning and business growth

All businesses need to grow. But to grow you need to find the channels that bring new audiences, new buyers and new sales revenue to your company, brand and products. Last Touch reporting does not help to identify and isolate incremental growth.

• At the recent Business of Apps London (2026), Aidan Rouse, Head of Growth Advertisers for UK & Nordics at Snapchat, made the case that most growth teams are optimizing for where credit lands, not where growth actually comes from [7]. This is critical distinction.
• Byron Sharp of the Ehrenberg Bass Institute at the University of Adelaide studied the buyer behaviour and growth economics of many categories and brands [8]. Sharp found that incremental business growth is delivered by finding new audiences of new buyers.  By definition this means you need reach.

Last Touch reporting will be giving you a false sense of security about your digital channels performance, you may find that your reporting metrics look great, but it will be diverting you from the main task; finding new audiences and new buyers.

If you want incremental business growth you must reduce your reliance on Last Touch reporting.

Can you measure media and non-media effects on sales simultaneously?

Yes, this can be done and Marketing and Media Mix Modelling or MMM is the best way to measure media and non-media effects simultaneously.

MMM is different because it doesn’t rely on digital tracking mechanisms and so that permits the measurement of many non-digital drivers. Instead MMM looks at patterns in changes in business outcomes vs changes in media and non-media inputs. For each input variable MMM estimates the degree of change in the output variable, e.g. sales for every 1 unit change in the each of the input variables; that could be for every £1 change in social media spend, every £1 change in OLV spend or every £1 change in TV/AV spend. Plus MMM can estimate the sales change for every 1 degree Celsius change in temperature, or every 1mm of additional rainfall or every £1 price discount vs the list price if a sales promotion is running. With this unit coefficients in place, a Marketing Mix Model can show you the pattern of how each of the drivers contributes to the creation of sales, especially sales that are incremental to underlying seasonal patterns.

Summary and actionable insights

Last Touch reporting is a broken measurement system. It does not provide the full picture of what drives sales.  Digital channel performance will be being overstated. I don’t mean might be being overstated, I mean it will be being overstated. Relying on last touch attribution might be the path of least resistance, but in today’s world, it is also the path of least return on investment [9].

If you are relying on last touch attribution you are optimising your brand and products into decline and out of successful growth.

Marketing and Media Mix modelling offer a compelling solution to the problem of measuring media and non media drivers together. MMM measures non-media drivers like seasonality, weather and economic context. MMM measures the effect of non-digital media channels, the so called audience builders: TV/AV, OOH, Cinema and Radio. And of course it measures digital media channels-  set in the correct context.

Find out more about our full funnel MMM services here:

Quick References

1. Why Last Touch is a Trojan Horse for Walled Gardens, Mattia Fosci, Advertising Week, https://advertisingweek.com/why-last-touch-attribution-is-a-trojan-horse-for-the-walled-gardens/
2. Mattia Fosci, Advertising Week, as above
3. Disney’s Dana McGraw says last touch attribution missed the full picture, The Current: https://www.thecurrent.com/video/disney-dana-mcgraw-last-touch-attribution-misses-full-picture
4. Think with Google: https://business.google.com/en-all/think/consumer-insights/mid-funnel-marketing-brand-performance/
5. Your best ads are failing because you’re measuring them wrong, Neil Patel: https://www.linkedin.com/posts/neilkpatel_paidads-datatruth-marketingmetrics-activity-7398344791094116353-_6zj
6. Multitouch Attribution in the Customer Purchase Journey, Kannan, P.K., and Hongshuang (Alice) Li (2021), Impact at JMR, (April), Available at: https://www.ama.org/multitouch-attribution-in-the-customer-purchase-journey/
7. Why last-touch attribution is costing you growth – Event spotlight – Business of Apps London Where apps grow June 26, 2026 https://www.businessofapps.com/insights/why-last-touch-attribution-is-costing-you-growth/
8. How Brands Grow, Byron Sharp, Oxford University Press, 2010.
9. Why Marketers Need To Stop Focusing On Last-Touch Attribution, Jeremy Fain, Former Forbes Councils Member, Forbes June 28, 2018

The post Media Attribution – The Risks of Last Touch Media Measurement 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.

Before we look at the 4Ps in detail let’s summarise the difference between strategy and tactics:

Strategy: Sets out which direction you have selected to achieve the macro marketing objectives your organisation has set. In marketing terms this might be to increase share by depositioning weaker competitors or to increase sales by increasing market penetration into new audiences. Think of strategy as the journey you need to make to get to your destination relative to everything else that is going on in the economy and in your category. Strategy is about the management of your resources in your business environment. Strategy is delivered over the medium to long term – it usually takes time to deliver, months and sometimes years. Strategy is what you’re going to do to achieve your objectives.

Tactics: Sets out the individual actions you will undertake in order to deliver the strategy. In marketing this might mean increasing revenue by increasing prices and using advertising to drive preference and reduce sensitivity to price.  Think of tactics as the individual decisions you have to take to complete your journey. Tactics can happen quickly – days, hours or even minutes.

Against this background the 4Ps are not exclusive to strategy or tactics, they can contribute to both. Let’s examine how each of the 4Ps works in a bit more detail.

P1 – Product

• What do we mean? Products have attributes which can confer advantage, or mean that the product lags behind market trends. If the product is ahead of demand trends, it should perform well in market. If it’s behind, it will do less well. The development of attributes is referred to as NPD – New Product Development – the more NPD generally means better, more competitive product and vice versa.
• Examples –
  • Apple used product technology to revolutionise the mobile phone market. Apple’s iPhone set totally new standards in mobile technology by combining a phone, a music player and a web browser, not to mention developing its associated app marketplace. Today Apple still retains around 24% of the mobile market.
  • Toyota led the way in hybrid auto technology development and retains the dominant share in this category.
• Timescale – All products (and most services) have to be researched, designed and tested before they can be launched. Product development generally takes time, it can be months and, in some cases, it can be years.
• Strategic or tactical? Such are the costs, resources and timescales required product development it has to be regarded as a strategic issue.

P2 – Price

• What do we mean? Price is what we pay for goods and services. There is no question that price can change consumer behaviour. As a general rule the lower the price of a good, the more units it will sell and vice versa. However, a high price can also be used to assert and reinforce superiority in a category. Discounts and sales promotions fall under the Price element of the 4 Ps.  These can be used tactically to change price for short time periods and increase sales for price sensitive goods and services.
• Examples –
  • Aldi commits to no frills value and prices itself as being seen as the lowest price supermarket.
  • John Lewis used to guarantee that they were “Never knowingly undersold”. Recently, this mantra was dropped. Since then, the company’s fortunes have changed suggesting this price promise had a positive impact on consumer behaviour.
  • Stella Artois is positioned as reassuringly expensive.
• Timescale – Price changes and promotions can be activated quickly, by day in retail and in real time in online / e-commerce environments. however, there can be longer term commitments to price vs category average. The Stella example shows a long-term commitment to upholding a price premium to position a brand. We could say that supporting a premium price is a longer-term initiative, reducing price is a short-term initiative.
• Strategic or tactical? Price reduction and discounting can be tactical in the short -erm but maintaining a long-term low or premium price relative to a category average usually requires a longer-term strategic commitment. In the case of Aldi, the whole business – from supply chain to checkout is structured around delivering a low-price, this is a long-term strategic initiative to secure a market specific position.

P3 – Place

• What does this mean? Place means Distribution. It’s where and how consumers are able to buy your product. For many years, distribution was simply about retail, but since commerce has migrated to online, distribution has now had an online manifestation. This could be the more generic impact of e-commerce such as wider access to product through much reduced impact of distance, but it’s also about how consumers assess distribution quality. Quality can be measured through speed of delivery, ability to try and buy and the returns policy.
• Examples –
  • Traditionally, retailers would sell more products if they increase their number of stores and vice versa.
  • Banks continued to close branches as more and more of their customers transition their banking activities from the counter to online.
  • Amazon revolutionised distribution by creating a massive and accessible e-commerce platform.
  • Apple revolutionised how music is distributed and bought.
  • ASOS revolutionised the distribution of multi brand clothing and fashion items.
  • Netflix has revolutionised how we consume movies – and had effectively killed off other physical formats such as DVD.
  • In the e-commerce world, delivery times, costs and returns policy all form part of the distribution characteristics of a company or brand.
• Strategic or Tactical? Traditional retail distribution networks are a strategic asset but they can be leveraged in a tactical way. They are strategic because they involve the use of a lot of capital and are slow moving. They can be leveraged tactically through localised incentives. Digital channels e.g. ecommerce are distribution channels but they are much more flexible and can therefore be used both strategically and tactically.
• Timescale – changes in traditional retail distribution are generally slow moving although the opening and closing of retail stores can have a significant impact on short term revenue. Changes in e-commerce distribution policy can have a quick effect. Increasing delivery costs or free delivery thresholds can have an immediate effect on consumer behaviour.

P4 – Promotion

• What do we mean? Promotion means marketing and advertising communications. In the marketing mix, promotion does not mean price promotion. Price promotion sits under the price element of the marketing mix. Long term commitment to advertising spend can confer competitive advantage and a long-term commitment to investing on a share of category spend (Share of Voice or SOV) that is greater than your market share (Share of Market or SOM) has been shown to drive growth.  This is called excess share of voice or eSOV. See a post on this topic here. Commitment to advertising consistently and at scale is a core component of consumer goods marketing where prices are generally low, decision making is as much emotional as it is rational and consumer purchase decisions are made quickly on System 1 ‘autopilot’ decision making. To enable this high mental availability is required, and that in turn requires always on advertising which is efficient at reaching mass or large segment markets.
• Examples –
  • Examples of large scale “always on” advertisers include Unilever, P&G, Sky, McDonalds and Tesco – these brands represent over £500m in adspend – seems a lot, but for these mass market brands, they are investing less than £10 per person per year to maintain high mental availability and high brand preference.
  • Of course, these brands are not representative and there is a long tail of advertisers who use much lower spends to deliver targeted communications to build online traffic, clicks, leads and sales.
• Strategic or Tactical? Clearly promotional communication activity can be both strategic and tactical. We talk about ‘brand building’ and we talk about ‘performance’ media. There is little doubt that strategic activity is about

The post What are the 4Ps of the Marketing Mix 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.

With MMM models built, we are able to run scenarios and forecast outcomes from different types of media activity. This enables advertisers to optimise their media investments to maximise sales returns from media or marketing budgets invested.

Why is it called Marketing Mix Modelling?

MMM derives its name from the traditional ‘marketing mix’. The name recognises that all the variables in the marketing mix have a role in generating sales. So, let’s remind ourselves of the traditional marketing mix – the so called 4 Ps – Product, Price, Place and Promotion.

Product: In MMM, we might attach some attributes to the product – fast, smooth, light, powerful, low CO2 etc. These might be significant drivers within the category.

Price: Price is always a key determinant in consumer behaviour. As a general rule, products priced competitively sell more, and products priced less competitively relative to a category or competitors sell less. If we are to avoid confusing price effects with media effects, we naturally have to isolate the effect of price and take it out of the effectiveness equation.

Place: i.e. Distribution variables, whether they’re online (like delivery times) or offline (like store opening times) distribution variables usually have an effect on sales. If more stores are open, more product tends to be sold. If delivery times are long, versus competitors, less product tends to be sold. If we are to avoid confusing distribution effects with media effects, we also have to isolate the effect of distribution and take it out of the effectiveness equation.

Promotion: This variable is the promotional or advertising media spend variable. It can include advertising in media channels like TV, online display, search, social, OOH, cinema and print media.

What does an MMM actually look like?

This is a question many clients want to ask. MMM is often presented as a complex black box, when in fact it is simply an equation that captures the impact of the different elements of the marketing mix outlined above.  A Marketing Mix Model equation looks like this:

Sales = base (the levels of sales with no marketing) + (product attribute * a coefficient) + (price * a coefficient) + (distribution* a coefficient) + (promotion * a coefficient) + an error term.

Think of the coefficient is a statistically determined response rate which captures the rate at which sales are generated from changes in investments or activities in each of the Ps.

In statistics, the above might be written like this:

y = B0 + B1xX1 + B2xX2 + B3xX3 + error

How is MMM used to optimise paid media investments?

When we know the rate at which each “P” in our model generates sales, we can set values for each of the Ps excluding media advertising and then adjust the media advertising investment levels to see how those changes impact sales. This is a powerful tool in the marketer’s portfolio because it permits strong cases for media investment to be made. And that’s essential if to you want to maintain or secure increased budget from your CFO.

WE OFFER MARKETING AND MEDIA MIX MODELLING (MMM) TO OUR CLIENTS: FIND OUT MORE HERE

The post What is Marketing Mix Modelling (MMM)? first appeared on Marketing IQ.

ESOV was originally identified by John Philip Jones a hybrid marketing practitioner-academic who looked at a number of relationships between marketing and media investment and sales responses.

Here’s how eSOV works. If you have a 10% share of market (SOM=market share) and a 12% share of voice (SOV=share of category adspend) then your eSOV is +2. Jones was able to estimate the statistical relationship between eSOV and sales using panel data.

This relationship has been used by marketing planners for around twenty-five years. It’s a relatively easy concept to grasp, communicate and evidence with data. Most importantly, it’s a marketing argument that many boards are prepared to give a hearing and accept.

In recent years the marketing effectiveness specialists Les Binet and Peter Field have re-examined this relationship and added some interesting findings about how the eSOV concept is impacted by creativity.

But, as with many marketing concepts, there is some devil in the detail. There are five big points that are often overlooked but which should still be considered as part of the marketing planning and budgeting processes.

• First – not all categories and advertisers behave in the same way when it comes to eSOV.
• Second – SOV and SOM calculations often exclude companies than don’t advertise – think Google, Facebook or Tesla – brands that grew with little advertising support in their early years. They gained share of market through product advantage.
• Third – eSOV tends to work much better when the excess share of voice is carrying award-winning creative work and vice versa.
• Fourth – you need to think about the relationship between the required SOV and the impact on profits.
• Fifth – Jones recommends using econometrics to fully understand how eSOV works as a component driver in your marketing mix. SOV or eSOV may not be the only explanatory variables in your mix. You will need to understand the contribution of all drivers to make valid statements about eSOV.

The post Does excess share of voice (eSOV) guarantee brand sales growth? 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.

Background

Although TV attribution is growing in popularity, it is not new. Direct response advertisers have been using simple ‘spot matching’ routines for around 20 years. These spot matching routines typically used a 5-10 minute window or ‘response curve’ which followed a TV spot transmission to match the spike of phone traffic that followed a spot transmission back to that spot. As phone response was usually received on one unique number used for each TV campaign it was a relatively simple task to match that single file of time stamped response data to a spot transmission schedule.

Using attribution to understand how TV drives web traffic

Today, as more and more brands invest in TV advertising to drive web traffic, the focus is on using attribution models to explain how TV spots drive web traffic. However, this is a much more complex area than analysing phone response.

The main challenge is that all brands receive web traffic from a wide variety of sources, 24 hours a day, seven days a week and when paid media advertising is either running or not running. Just a quick look at a Google Analytics report will show you how many sources drive your web traffic:

• Organic search
• Paid search
• Direct visits
• Referrals
• Affiliates
• Display campaigns
• Paid media campaigns
• Revisits

Which traffic do we analyse?

Let’s look at what TV viewers do when they see an ad. They are likely to do one of three things:

1. Enter the brand address directly into their browsers or
2. Click on a paid search (PPC) link or
3. Click on the top organic link.

Reflecting these behaviours, most brands look at:

1. New user traffic through direct browser entries
2. New user traffic through paid search
3. New user traffic through organic search

You will notice that there is a focus on new users. Clearly, new users are of more  interest to brands targeting new customers.

Identifying the baseline web traffic

Identifying the base is complex. This is because a campaign can have a number of baselines depending on the time sample you are looking at. Each hour of the day may have a given level of “natural” traffic. Each day of the week may also have a given level of traffic (this is often the case) and weeks and months may have repeating patterns. Over and above this the brand may have a long-term upward trend in web traffic where each week increases slightly on the previous week. All this means that applying the same baseline to all your analyses will make your results flawed.

The answer to this problem is to use a model which incorporates different baselines based on different times of day, days of week etc.

How does the TV spot matching algorithm work?

Algorithms are mathematical equations that allow a number of variables to be considered simultaneously. So, for TV attribution we need an algorithm that considers the following:

1. The seasonal base
2. The trend
3. The weekly base
4. The day of week base
5. The hour of day base
6. The time of the spot transmission
7. The volume of audience delivered in the spot transmission
8. The time the response is received
9. The way the response distributes over the time period following the spot transmission (the curve)

With this algorithm in place it is possible to calculate a probability that a new web visit that occurred within say 7 minutes of spot transmission was caused by that spot transmission. This process is then repeated across all the spots in the campaign until a probability for all new traffic response to be driven by the TV activity has been calculated.

What type of reporting is available through TV attribution?

Because we have the attributes of the spot (TV station, date, day of week, time of day, type of break, type of creative etc) we can report all these metrics on an aggregated basis. So, for example, we can say Fridays are the most responsive on a % response rate basis, or the most efficient on a £ CPA basis. We can also say which channels and time of day or most responsive.  With this insight we are able to optimise the TV buy to focus budget into the station, days of week and times of day that will deliver the highest ROI.

You can read more about optimising DRTV campaigns here

The post What is TV attribution modelling? first appeared on Marketing IQ.