Anatomy of an Election, The 2024 Labour landslide Part 1: Digital Marketing Strategy

Tom Ewing
Station10
Published in
5 min readJul 8, 2024
UK General Election Results 2024

Analysing the story of the 2024 General Election through data.

As expected, The Labour Party won the 2024 UK General Election by a landslide and opinions on why and how this has happened are flying around social media without much in the way of data to back them up.

This is the first in a series of posts that aim to bridge that gap and start to tell the story of how and why Labour won the 2024 Election through data and analysis.

Overall Digital Marketing Strategy

Meta introduced Ad Library in 2019 as a direct result of the Cambridge Analytica scandal in a bid to make digital advertising more transparent. Covering both Facebook and Instagram ads it gives us a great insight into the digital marketing strategies of the various parties.

Meta Ads spend by party in the lead up to the 2024 UK General Election

As expected, both Labour and the Conservatives spent more than their rivals, however they adopted very different strategies in doing so.

Labour’s Digital Marketing Strategy

Labour used Meta’s inbuilt targeting tools to target their ads most notably through Age and Location.

Labour’s Age-based targeting in Meta Ads: 4th June 2024–3 July 2024
  • Labour spent less on the 18–29 demographic. This demographic is traditionally 50–55% Labour voters anyway so has less yield than other demographics.
  • The bulk of their spend was on the 30–64 demographic. The demographic is more contested and offers the most ‘bang for their buck’
  • They spent much less on the 65+ demographic. These people traditionally skew 60–70% Conservative, are entrenched in their views and unlikely to defect to Labour.

Labour’s Digital Marketing Strategy: Geotargeting

Labour also heavily geotargeted their Ads, targeting individual Postcode sectors and towns. The detail of this will be analysed in Part Two of this series, however there are still some interesting high level trends:

  • The biggest targets for spend were key ‘battleground’ constituencies which had a history of voting Tory, rather than recently lost constituencies such as the “Red Wall”.
  • London and other traditional Labour strongholds were either not targeted or not prioritised.
  • Labour aimed to “fight battles they can win”. Constituencies that ended up voting Liberal Democrat or Reform UK weren’t high on their agenda.

Labour clearly prioritised constituencies they felt they could win including Aldershot, Kettering, Southend East and Rochford alongside ‘blue wall’ constituencies such as Rochester and Strood and Canterbury.

The Conservatives Digital Marketing Strategy: Audiences

The Convervatives opted for a different strategy using Audiences and Lookalikes. Geotargeting through Meta Ads only accounted for a small fraction of their spend.

Conservative Party Custom Audience and Lookalike Audiences Spend

Through using a Customer List (details of those who have previously shared their data) they created lookalikes; people who aren’t necessarily on the customer list but share characteristics with those who are.

Evidence from the Financial Times indicates that in the final days of the campaign the Conservatives “appear to target prospective Lib Dem and Reform voters in an attempt to minimise the party bleeding votes to smaller rivals” and the content of their ads backs this up:

Conservative Ad for the 2024 UK General Election

Similar to how Labour focused on key demographics, the Conservatives did so too, albeit through a Custom Audience:

Age and Gender breakdown for the “Make a Plan to Stop the Supermajority” Convervative Ad.

Lastly, throughout the Campaign they also used Meta Ads detailed targeting feature to target people with interests including:

  • Monarchy
  • Vehicles and Vehicle communities (e.g. Top Gear)
  • The Armed Forces
  • Football Fans

Whilst the amount of money spent on this form of targeting was relatively small, it still provides some insight into the people the Conservatives were trying to appeal to and why.

Overall, we can surmise the following from their strategy:

  • The Conservatives place most emphasis in England, likely because they were unlikely to win seats in Scotland, Wales or Northern Ireland.
  • The 55–64 and 65+ demographics were key. In line with their literature, they were likely trying to limit their losses to the Liberal Democrats and Reform.
  • They didn’t appear to Geo-target their approach in the latter stages of the campaign, opting instead to appeal to key demographics in a bid to limit their losses to the Lib Dems and Reform.

Conclusions

There has been a lot of focus on Labour’s vote share under the UK’s First Past the Post (FPTP) system with commentators pointing out that they received higher percentages of the vote in previous elections (which they lost). Labour’s strategy has contributed to this

  • Labour “played the game” very well. They knew the constituencies they were either going to win anyway or would have no hope of winning and placed little emphasis on these.
  • This allowed them to commit more resources to key battleground constituencies, thus maximising their ROI in terms of seats won.
  • The British political landscape has become more fragmented with the Reform and Green parties in particular making large gains in terms of the popular vote. Many of these gains were in Labour seats, perhaps best illustrated by Kier Starmer receiving around half the votes in his Holborn and St Pancras constituency as he did in 2019 with Reform and the Greens being key beneficiaries.

Next Up…

There’s a wealth of data and questions around the UK 2024 election. In future Blog posts, we’ll be using Station10’s Scientia data product, a large collection of UK data and statistics to analyse some of these and provide some analysis, answers and, no doubt, more questions.

About

Tom Ewing is Head of AI and Data Engineering at Station10 and likes making maps more than he cares to admit so loves a good election.

Check out Station10's Awesome-Marketing-Machine-Learning list repository on Github which contains a list of open-source AI tools with applications in marketing.

Station10 believe in open source and sharing knowledge and our Medium content is never paywalled, so please leave a clap if you’ve found this useful. This helps motivate us to write more content and gives us a better idea of what to write more about.

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Tom Ewing
Station10

Head of AI & Data Engineering @ Station 10. Writes about non-LLM AI in marketing (yes that still exists).