We use publicly available AA WARC adspend data as a reliable source for baseline spend levels, and we use our own modelling to identify and extend underlying trends to forecast 2026 spends by channel.

Key UK Adspend forecasts by channel – key take-outs for 2026:

• Cinema will enjoy moderate growth, and we forecast adspend / revenue of £254m in 2026
• DM will continue a significant downward trajectory, and we forecast adspend / revenue of £874m in 2026
• Magazines will continue to decline quite strongly, and we forecast adspend / revenue of £414m in 2026
• Newspapers will also continue to decline strongly, and we forecast adspend / revenue of £656m in 2026
• Online classified has not recovered since 2022 and is likely to continue to decline and we forecast adspend / revenue of £984m in 2026
• Online display including social will continue to grow and on a strong trajectory and we forecast adspend / revenue breaking through £20bn in 2026
• OOH including DOOH will continue to grow but growth rates are reducing slightly year on year, we forecast adspend / revenue of £1.5bn in 2026
• Radio, including online radio will be stable YoY and we forecast adspend / revenue of £752m in 2026
• Regional news will continue a strong decline, and we forecast adspend / revenue of £394m in 2026
• Search will continue strong growth alongside online display and social and we forecast search will also break through the £20bn level in 2026
• TV including BVOD will continue to recover vs 2023, but the overall pattern does suggest continued decline and we forecast adspend / revenue of £5.4bn in 2026

Let’s examine the AA WARC estimates to look at the how media spends have grown across the period 2021 to 2024 as actuals (Chart 1)

We see that total Adspend has grown significantly since 2021. Of course that was a COVID year, but recovery has been strong in terms of total spend.

Totals spend in the channels shown has grown from £33.6bn to £43.8bn. Within this, Search has grown from £11.6bn to £16.9bn and Online Display has grown from £10.8bn to £16.7bn. TV and BVOD has remained relatively flat declining only slightly from £5.5bn to £5.3bn. OOH and DOOH has grown from £901m to £1.4bn.  Radio is also flat at £720m to £738m. Cinema, which was hit heavily by COVID restrictions made a strong recovery in both audiences and revenue and climbed from £103m in 2021 to £212m – more than doubling revenue.  Fallers include Online Classified £1,053m to £1,017m, Direct Mail falling from £1,082m to £964m, Newspapers £844m down to £727m, and magazines down from £556m to £469m.

Percentage YoY changes are shown in chart 2 below, with channels ordered by largest spend on the left and smaller on the right (Chart 2)

We see that as well as dominating levels of adspend, Search and Online Display (including Social) dominate growth trends. Between 2023 and 2024, these channels enjoyed YoY growth of 13% and 15% respectively.  The 2025 estimates suggest this growth might be calming slightly to 8% and 9% respectively.

In Chart 3, we see estimated 2025 (full year) media spend across these 11 channels.

We see Search, Online Display and Social are accounting for more than 70% of spend. TV and BVOD sit at 11% and the remaining 8 channels share the balancing 12.4%, with OOH and DOOH the largest of this group at 3%.

Total spend vs growth in UK media 2025 vs 2024 (Chart 4)

Chart 4 plots 2025 estimates spend and YoY growth from 2024. We see Online Display (including Social) and Search continuing to plough ahead in the top right with high growth and the highest revenues. Unfortunately, in the opposite quadrant, we see a lot of traditional print media in YoY decline; Magazines, Regional Newspaper, Direct Mail and Newspapers.

2025 UK Adspend full year estimates (Chart 5)

In Chart 5 below we see growth rate changes between 2021 and the 2025 estimate from AA WARC.   We see that Online Search, OOH including DOOH, Online Display (including Social), Radio and TV and VOD are in growth over the period whilst there are five consistent decliners: DM, Magazines, Newspapers and regional news all decline consistently. Online classified has declined consistently since 2022.

UK media adspend forecast for 2026

Chart 6 uses a modelling technique that extrapolates the evolving (non-linear) growth curves into 2026.  We see growth in 2026 for Cinema, Online Display and Social, OOH and DOOH, Radio, Search and TV and VOD, although TT/VOD is slightly down from its 2021 position. Downward trends and forecasted to continue for DM, magazines, newspapers and regional news.

Implications for marketing and media planning

The cost of media is generally a reflection of supply and demand – that applies across almost all biddable and non-biddable channels.  If audiences (supply) grow, media owner revenues can grow at a flat CPM. This is good news for marketers and their media agency partners (providing demand) because media owners are less likely to raise unit costs (CPMs).

In our analysis of the AA WARC data, we see that some media channels are in high growth; Cinema continues its post-Covid recovery, Online Display (including Social) and Search are powering ahead with growth of more that 8%, slightly down from last year. OOH and DOOH look positive, as does Radio.   This growth means that advertisers are likely to see more stable costs in terms of audience CPMs overall (some sub channels and audience sub groups might differ) – simply because media owners can maintain growth within significantly increasing unit costs.

But we also see that some channels are facing revenue declines; Online classified, DM, Newspapers, Magazines and Regional News. Clearly, this is a major concern for both the media owners and advertisers. If the businesses in these channels have high fixed costs, then they have to increase unit costs to increase maintain revenue. This has both short and long term implications; in the short term, media becomes more expensive and potentially performs less well. This in turn makes these channels less likely to be used in the medium term which further exacerbates the revenue decline problem.

Please note this caution: The 2025 and 2026 data are forecasts which identify and extrapolate the underlying growth trends for each channel. They do not account for other important factors such as the economy, the international situation, changes in consumer demand, the health of firms using marketing spends and the budget shifts they may apply in response to changing market scenarios. 

The post UK Media Adspend and Revenue Forecasts 2026 first appeared on Marketing IQ.

Second party data is data you share with a known and named partner. For example, if you are a hotel group you might exchange data with an airline to improve your targeting models;  might add data (sometimes called appending or augmenting) from its airline partner to improve its targeting model. The appended airline data might reveal that a customer always travels business class by air but always books an economy room thus presenting an opportunity for cross-sell. The data added by this cross-party transfer improves the level of insight that can be generated about a given customer and presents commercial opportunities on both sides. This data sharing is enables by the consumer if they tick a data sharing box in a permission request.

Third party data is data that does not belong to you but can be bought or used by you to improve insight or targeting. This is usually sold by third party data suppliers such as Acxiom or Experian. This data has been sourced directly from the consumer and permissioned through an opt-in. Third party data is often used in “matching” projects where a first party database is matched to a third-party database (like the way second party data is used in the scenario above) which can add incremental information to that already held by the first party data owner. So, for example, if you are a retailer of clothing you might want to match your database to a database of clothing purchasing habits to target consumers with products which appear to be relevant to the third consumer base.

Impact of GDPR on first, second and third party data

The General Data Protection Regulations (GDPR) will affect all three types of data and all the companies who are storing and managing that data. Companies holding first party data will need to make sure that their data is properly stored, consented, encrypted and secured in order to meet the regulations. And whilst there is a strong onus on first party data holders to comply with GDPR they, as the data owners, are in a strong position to comply because they are in control of their own data, storage environments and protocols.

The real complications arise when we look at second and third-party data. As first party data is shared with second and third parties, the responsibilities of the first party data owner “stretch” as far as the data goes.  So, if a second or third party commits a breach involving your data, you may still be responsible, at least at joint level with the party you have shared to.

This means you will need to ensure that the way your data is used after being passed to a second or third party remains compliant all down the line. It may not suffice to have your partners sign an agreement saying they will manage the data in line with the requirements of GDPR. If they do not, you may still be liable.

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Unfortunately, many marketing and media channels are planned, negotiated, delivered and evaluated in silos. This means it can be difficult to get a set of comparative response rates which allow you to forecast how well any one channel may work for your business or brand. If you can’t compere them side by side it’s difficult to optimise budget distribution – particularly for customer acquisition activity.

Guide to response rates by media communication channels

With over twenty years’ experience of planning, managing and evaluating campaigns across practically all mainstream media channels, I thought it would be useful to share the metrics that I use as standard response metrics. These are given as percentage response rates of the audience seeing the ad.

Note: These are the response rates I would expect to see based on my experience. They should be used  as a guide and are not a guarantee. They are subject to the caveats listed below.

The caveats

1. Response rates are driven by a number of factors including the product, offer, the creative treatment and the audience selection (media). Ideally, you should work to the highest possible standard in each of these four areas. Compromise on any of these factors will reduce response rates.
2. Most channels have sub-sets of response rates depending on how the channel is being used. For example, TV ads can be “brand awareness” ads, “brand response” ads or “direct response ads”. Each of these have different levels of responsiveness. Brand awareness ads which are designed to change attitudes rather than short term behaviour will not deliver a high response rate.
3. You must factor in the cost of media on a per audience basis. A favourite mistake of response rate observers is to look at response rates without factoring in channel costs. Here’s an example; the response rate from DRTV is about 100 times lower than the response rate from DM, but remember, DM costs around 100 times more per person than TV. In reality, both channels may produce a similar cost per response. That’s why it’s important to look at both factors when analysing and forecasting responses.
4. Response rates aren’t everything; what generates revenue is sales so you need to factor in a conversion rate from response to sale.  As a general rule, personal channels like DM tend to convert at a higher rate than broadcast or online display. You can have a channel with a low response rate and high conversion rate performing as well in cost per sale terms as a channel with a high response rate and a low conversion rate.
5. Marketing activity is subject to diminishing returns; response rates will fall as budgets increase.

For more information on our High Performance Media Planning course please follow this link

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The Australian Defence Force wanted to hire engineers. So George Patterson Y&R Melbourne developed this DIY radio kit to engage engineering students as potential recruits without instructions.  It won a D&AD Gold in 2014.

Here’s the story behind the campaign

The post Examples of brilliant Direct Mail first appeared on Marketing IQ.

Warm Direct Mail – mailings to your active customer file: In our experience, warm direct mail, i.e. DM sent to your customer file should deliver a response rate of between 1% and 5%. The average figure is around 3.5%.

Cold Direct Mail – DM send to prospects via a “cold” list: Response rates here are lower as the consumers you are mailing are less familiar with you and your brand. Typically 0.5% to 1.5%.

The DMA in the UK cites a response rate of 4% and claims that overall 7% of recipients will take some kind of action as a result of receiving direct mail.

The DMA in the US has produced a lot of information in its 2015 Response Rate Report and cites response rates of 3.7% for a house list and 1% for a cold prospect list.

The post Direct Mail Response Rates first appeared on Marketing IQ.

Here’s an example: Let say you sell 10 products. It may be the case that all purchasers of product 8 are: 1) in a certain geodemographic group, 2) married with more than one child and 3) own more than one car. All these factors can be analysed and combined to predict the likelihood of any consumer in your database buying product 8. Usually this combined measure is referred to as a ’score’ i.e. a figure which represents the presence or combination of certain variables in the consumer record. Once you have developed your scoring model you can rank all customers by their score. When you’ve stripped out those who have already bought product 8, you are left with a set of high potential prospects.

Predictive modelling can also be undertaken based on transactional information about past purchases. Going back to the 10 products, it may be the case that 80% of people who buy product 7 have previously bought products 2, 5 and 6 and in that order. So we can say that people who have bought products 2, 5 and 6 (in that order) but who have not yet purchased product 7, are much more likely to buy product 7 than everyone in your database. Again a score is attached to these behaviours and that score can be used to rank your prospects in terms of untapped sales potential.

Of course as well as predicting purchase behaviour, these techniques can be used to predict risk. In credit assessment for example, it may be the case that those customers who have certain demographic characteristics combined with a certain type of past purchase behaviour are highly likely to default on a credit agreement. This is sometimes referred to as credit scoring. If you are rejected for credit at a bank or in a shop it will be because your data has been analysed and your credit risk score is deemed too high or low to meet the criteria of the lender.

These predictions can help you target your communications very efficiently and also help you control commercial risk in customer behaviour. What’s interesting about these techniques is that they help both the marketing department and the finance department. Marketing delivers customers who are both highly likely to convert to sales or high lifetime value whilst at the same time, producing customers who are less likely to cause problems for the finance department. Overall, this means that the resources of the business are being better utilised.

The post What is predictive modelling in marketing? first appeared on Marketing IQ.

At the most basic level your database should contain a name and address for each record. The name and address can give you valuable information. The postcode in the address opens up the potential for geodemographic analysis using tools like ACORN or MOSAIC. These tools work by grouping consumers into clusters of similar people based on the types of neighbourhoods they live in. The principle behind these systems is simple; birds of a feather flock together. The owners of these segmentation systems undertake research into the clusters they have developed. For example, Cluster 1 may contain people who are known to be affluent pre-retirement couples with children who have left home. Research may show that these people are three times more likely to drive a certain car, purchase certain electrical products or take holidays to certain destinations. So from just the address record you can build a much wider picture of the record in question.

But the full name and address have even more potential. They can be used to match your customer file with an external data file containing more information about the same person. This data can come from many sources, but more often it comes from lifestyle surveys. If a customer in your database has completed a lifestyle survey then you can buy supplementary information to significantly expand what you know about that person.Here’s an example. You may only know the name, address and age of a customer. But if that record can be matched with a respondent to a lifestyle survey then you can see the answers to tens or even hundreds of other purchase preference questions that person has shared. For example, you may be able to see what type of car they own, when it was bought, when they intend to replace it. They may even tell you what type of car they are considering next.

If you have transactional data then you are able to undertake an analysis of the types of products and services bought by the customer. From this data you would be able to say that a customer owns products X, Y and Z and you will probably know when they bought those products. You will be able to see how the often products are purchased and the preferred means of payment. If there is cyclical behaviour in the purchase pattern you may be able to predict when this customer is likely to purchase those products again.

With these high levels of customer understanding you are able to take a lot of the guesswork out of marketing. You can be much more focussed in terms of selling specific products to specific individuals. As a result you response, conversion and customer value rates are likely to improve significantly.

The post What can a database record tell us about customers? first appeared on Marketing IQ.