Sponsorship deals can be a great way to boost brand awareness, by piggy-backing onto the public profile of a popular event or personality. Even better, by visibly aligning your brand with something like a cultural or sporting event, a team, or even an individual athlete, you benefit from a kind of halo-effect whereby the characteristics and value of the thing you’re sponsoring rub off on your company a little.
The perfect example of this is Red Bull. Just thinking about that brand conjures up images of extreme sports, high-octane motor-racing, and mind-blowing stunts, which is quite an accomplishment for a company which sells canned drinks.
Sponsorship deals can deliver great results for brands, so it's no surprise that they can be quite expensive arrangements and marketers understandably want to measure the ROI of these activities as forensically as possible. Conventional metrics used for measuring the performance of sponsorship programs include:
- Media Coverage
- Social Media Mentions
- Web Traffic
But all of these come with certain limitations, and it can be tricky to conclusively link the performance of these KPIs with sponsorship activities. That’s not to say they’re not useful indicators, just that they might not give marketers the full, detailed picture that they’re looking for when they need to assess the value of a particular activity.
The good news is that advances in AI make it possible to get a more complete view of how a sponsorship deal is delivering for you by tracking what we call “Earned Brand Exposure”.
In short this means easily tracking exposure of your brand in social and online media, not just in written text but also visually in images and videos, specifically within the context of the sponsorship. And by measuring this against the cost of the sponsorship activity you’ll be able to understand metrics such as:
- Cost per post
- Cost per impression
- Cost of estimated reach
- Cost per engagement
Using AI for Sponsorship Measurement
AI can help us with sponsorship ROI measurement in a couple of ways.
Firstly, it’s good at making sense of large data-sets and organizing them in useful ways, which makes it easier for us to get to the answers we’re looking for. Let’s say we begin with a mountain of big data comprising a significant sample of the world’s social and online media content from a set period of time, such as the past month. AI can be used to quickly, and accurately whittle that down to only the data concerning the event or individual we have sponsored, as well as containing a direct or proximate reference to our brand.
So now we have a much more manageable dataset which we know, with a high degree of confidence, is specifically focused on conversations about our brand that have been driven by our sponsorship deal. By analyzing the trends and patterns in that data, we get a much clearer idea of ROI that we could from raw, unstructured data.
The second major way in which AI can help us is through understanding the content of images and videos. AI can easily spot logos in visual content, as well as identifying environments, objects, or even individuals, such as an athlete or other celebrity we might sponsor.
This means it’s possible to track how often a company’s logo is seen in images or videos that are specifically connected to any kind of sponsorship. Much the same as the structured textual data, we can focus on content that is both relevant to the sponsorship and features the brand, so we have a clear way of measuring how much visual exposure the sponsorship is generating in online media.
Why AI Makes a Difference
In both of the scenarios outlined above, it would be practically impossible to achieve the same results with conventional technology and with human analysis. The volumes of data are simply too great, and the cost of applying enough man-hours to the task of analyzing it all with precision would far outweigh the benefits.
But AI is capable of intelligently structuring large volumes of raw data quickly and efficiently, and it’s also better than ever at interpreting the content of video and still images.
At Meltwater, we’ve harnessed the capabilities of AI to build a solution that’s uniquely tailored to the needs of marketers and rights-holders who want a deeper understanding of the ROI of sponsorship deals. You can read about it in more detail by downloading our eBook “Measuring Sponsorship ROI with AI in 2023”.