Data-driven videos: Everything you need to know

Data-driven videos vs personalized videos

While similar, data-driven videos are not exactly the same thing as personalized videos. Data-driven videos have a wider scope and can incorporate various types of data, such as demographics, weather patterns, social media insights, market trends, or even sports results, to create content that is more relevant and engaging for the target audience.

Personalized videos, on the other hand, are a specific type of data-driven videos. Video personalization uses data that is related to individuals or groups of people to show certain things that are pertinent specifically to those people or groups, such as their names, interests, habits, past experiences or purchases, etc.

Personalized videos are a subset of data-driven videos, but not every data-driven video has to be personalized.

Examples of data-driven videos

1. Personalized video campaigns

A great example of personalized video campaigns are year-in-review videos, such as Spotify Wrapped. These types of videos use customer data specific to you, such as top songs and artists you’ve listened to, top genres, top decades, how many minutes of listening time you had, etc.

Personalized video campaigns capture more attention from your viewers and can make them feel more valued and important. They are really effective when you want to create a more meaningful connection between your brand and customers, and are great for increasing engagement and brand loyalty improvement.

Spotify Wrapped was shared on social media by 60 million people in 2021, which is around 50% of people who accessed it, and the campaign has only grown since. The good news is you don't need Spotify's engineering team to pull this off. We put together a step-by-step guide on how to make your own Spotify Wrapped-style video for your users. If you'd rather see it in action first, check out our video guide below.

2. Data-driven video workflows

Sports broadcasters often use automated data-driven workflows to create highlight reels or other sports-related videos. In 2022, a German media company and one of our customers, Focus, used a combination of After Effects and Plainly to automatically generate lineup and player statistics videos for that year’s FIFA World Cup.

By integrating football statistics into their real-time video reporting, Focus enhanced their viewer engagement and provided a much more immersive experience for their audience. This type of workflow allows for a more relevant, and most importantly - timely content. Since football lineups are usually published an hour or less before a match, creating these types of videos manually would’ve been time consuming, and would make it very difficult to publish them before kickoff. This would render them useless to any viewer who wants to know these things before the match starts.

3. Data driven ads

Data-driven ads used to be quite generic. They were broad, aimed at a wide audience, and thrown like a massive net trying to catch as many fish as possible by reaching as many waters as possible. The primary goal of these dynamic video ads was raising brand awareness, which was fine. But today, with the vast amounts of data available and new data that’s being constantly collected, video advertisements have turned into dynamic data-driven monsters, specialized, optimized, and targeted to the exact audience advertisers want to reach.

Data driven video ads can be highly effective. By analyzing data such as demographics, browsing habits, purchase history, location data, etc. advertisers can enhance engagement and effectiveness of their ads for the consumer.

With the better targeting that data-driven advertising has enabled, you can specifically target different audience segments and adjust strategies that can resonate best with exactly those people. On top of that, they are more cost-effective and their effectiveness can be measured more accurately, meaning that it is easier to quickly identify what works and rectify what doesn’t.

Another aspect of data-driven advertising, and possibly the one that is most pertinent to this article, is the ability to use dynamic video content in your advertising. This means that you can leverage real-time data to create timely and hyper-relevant content to personalize the user experience. This might be based on viewer’s location, shopping preferences, a new product that you have in a particular area or price range, or pretty much anything else that you can think of.

Great examples of these data-driven ads are campaigns by Coca Cola and Axe.

• Coca Cola used their data to create a personalized video campaign called “Share a Coke”, that would call the viewers to share a Coke with friends and family - but with a little twist: they would use popular names, words, and nicknames in a specific area to encourage people to buy them and share them. This campaign reversed Coca Cola’s decade-long decline in Australia, resulting in a 7% increase in sales. Globally, it contributed to a 2% increase in Coke sales.
• In their reimagining of the tale of Romeo and Juliet called Romeo Reboot, French men’s grooming company Axe used a programmatically created video for the Brazilian market that could be delivered in an astonishing 100,000 different versions depending on the viewer’s interest. This campaign was so wildly successful, increasing retention by a staggering 170% when compared to Axe’s previous campaigns and reaching 27 million views.

How to make data-driven videos

There are two main ways you can approach making data driven videos: you can do it manually or using automation.

If you are aiming to produce a limited number of data-driven videos, it can be perfectly fine to do it manually. At very low quantities, it may not be worth the effort to set up automation, because it could take just as long as creating a few different variations of your video manually.

However, if you are looking to create a larger number of data-driven videos, you can greatly benefit from using some type of automation.

1. Using automation for data-driven ads

Creating content for data-driven advertising at larger scales can really chew up resources, and that’s why automation is key in increasing efficiency. There are many tools that can help you with this, but possibly the most well-known one is Celtra.  

Tools like Celtra can become essential when your advertising uses a lot of frequently changing variables. This occurs often in the travel industry advertising, where there is a plethora of varying factors such as availability, weather, destination options, dynamic pricing, discounts, etc. Another frequent use of these kinds of automation tools is for businesses that have a large inventory of lots of different items (such as in e-commerce), where creating ads for each item or category individually would be an unfeasible task if done manually.

Creating data-driven ads with Celtra is pretty straightforward. All you need to do is sign up for Celtra, create a campaign, and design your ad using Celtra’s user-friendly interface. Next, you can customize the video with your branding and other elements such as images and text. Finally, connect your data source to Celtra and use their dynamic creative optimization tools to tailor the ads to your target audience, and take a look at your ad and test it to make sure it's working properly.

However, while tools like Celtra can be very useful for dynamic ads automation, they do have their limitations when you need to produce more elaborate data-driven video content, as they’re not optimized for higher levels of customization. So, let’s look at how you can do that next.

2. Using automation for data-driven videos and personalized campaigns

If you want something more complex than a simple slideshow, Plainly is the way to go.

With Plainly, you can create pretty much any video template you want in After Effects, making customization options essentially endless. This is not only great for personalized marketing campaigns, but also for other uses where data-driven video material can come in handy, such as automated customer onboarding, customized tutorials, product showcasing, weather updates or forecasts, and (as we already explored) sports - and much, much more.

Essentially, because you can easily create bespoke After Effects templates for your specific needs, if you have pretty much any type of data and you think automated videos might help your cause, Plainly can do it for you.

The setup is quite simple and consists of 3 steps: add your After Effects template into Plainly, mark the dynamic elements (these could be text, images, videos, audio and colors), and connect your data source. Here's what you can connect:

• CSV and Google Sheets: each row becomes one video and each column maps to a dynamic layer, which makes videos from CSV the easiest way to render big batches like product catalogs or event recaps.
• RSS feeds: point Plainly at a feed and turn new articles into videos, a natural fit for publishers and newsrooms.
• API: the Plainly API lets your own system send the data and get finished videos back, with full control over how and when they render (see the API documentation).

CSV and Google Sheets are the simplest way to get started, while the API opens the door to pretty much any data source or custom integration you can think of.

Data driven videos in the age of AI

Artificial intelligence has transformed the technological landscape in the past few years, and it has consequently affected data-driven video production and video creation automation as well.

AI’s impact on data-driven video technology is significant in two main ways. First, there is better availability of high-quality data with the proliferation of digital platforms and devices, and AI can use this vast data for hyper-targeting to tailor the ad content to individuals’ or audiences’ preferences. Secondly, AI can efficiently parse this data, meaning that it has now become easier to find insights that previously took a very long time to find or were very difficult to obtain. According to Forbes, 64% of business marketers expect AI to increase their productivity.

These advancements have significantly improved content relevance, meaning that AI can now understand what type of content resonates with different audiences, and help create videos that are more likely to engage those viewers.

Apart from this, generative AI can produce and optimize video content dynamically, adjusting images and text based on how audiences respond, so every new round of ads gets more relevant than the last. It also makes personalization easier and workflows faster, both in advertising and beyond. Teams are already putting this into practice. In Vidyard's Future of Revenue report, a 2025 survey of 656 sales and marketing leaders, 46% of teams using AI with video said they automate video creation and delivery based on buyer behavior.

What are the next steps you can take?

Whether it's for marketing campaigns, sports highlights, or dynamic advertising, the ability to automate video production process allows you to quickly create different variations of your videos. When paired with your own data, you can engage viewers even more effectively, reach larger audiences, and spend your advertising budget more efficiently.

Now that you know what data-driven videos are, how they are similar yet different to personalized videos, and most importantly – how to make them – there is only one thing left: go out there and harness the power of data and video creation automation software to increase your efficiency and reach more people in your marketing and advertising campaigns.

Try Plainly out with a 14-day free trial or book a demo to get started!