What are data-driven campaigns?
Imagine if baseball teams played the game without knowing any stats about their players or the opposing team. They wouldn’t know who hits the ball well, which pitcher is best against certain batters, or even the strengths and weaknesses of the players. It would be like playing blindfolded. Data-driven campaigns in baseball are a bit like having a playbook filled with all the stats and details. Teams collect and analyze data on how each player performs—like who hits the most home runs, who’s great at stealing bases, or which pitcher throws the best curveball. With this information, teams can make smart decisions, like choosing the best lineup or deciding when to change pitchers. It’s all about using the numbers to play the game strategically and increase the chances of winning.
In the world of marketing, data-driven campaigns are a bit like creating a personalized playbook for reaching fans. Instead of guessing what people might like, marketers collect data on customer behavior—like what products they prefer, when they shop, and how they respond to different ads. It’s a bit like knowing which baseball player hits the ball well in certain situations. With this data, marketers can tailor their messages and data-driven advertising to be like a favorite team cap: just the right fit. So, just as baseball teams use statistics to play better, companies use data to make marketing that’s more enjoyable and interesting for everyone.
How do data-driven campaigns work?
Alright, let’s break it down! Data-driven campaigns are like a recipe for making your favorite sandwich. First, you gather ingredients (data) from different places like how many friends want sandwiches, who likes what fillings, and who prefers mustard or mayo. Then, you mix all this info together to understand everyone’s tastes. In the same way, data-driven campaigns gather information about people – like what they click online, what they buy, or what they enjoy. This collection of data helps companies understand their customers better, almost like knowing which sandwich toppings everyone likes.
Now, once you have all the info, it’s time to make the sandwich (or, in this case, the campaign). Just as you’d customize each sandwich for each friend, companies use the gathered data to personalize their messages. They create data-driven advertising or messages that are more like a perfect sandwich for each person—showing products they might like or deals that suit their tastes. It’s like getting a sandwich made just for you, instead of a one-size-fits-all approach. So, data-driven campaigns are a bit like making customized sandwiches for everyone, using the special ingredients of each person’s preferences and behaviors to create a more enjoyable and tailored experience, all based on information you already know about them!
What are data-driven campaign strategies?
A data-driven strategy involves using data analysis to inform decision-making, where companies leverage available datasets to understand customers, enhance products, and improve operational efficiency. This approach offers numerous benefits, including a clearer understanding of the business landscape, leading to 5 to 8 times more return on investment in marketing. It also facilitates faster decision-making, cited as a primary benefit by 57% of companies, and helps eliminate biases, preventing organizational silos or corruption.
Here are some data-driven ad campaign strategies that companies can use to optimize their advertising efforts:
Personalization: Analyze user behavior on websites, apps, and social media to understand preferences and interests. Tailor ad content based on this behavior to increase relevance. For example, if a user frequently searches for travel destinations, show them ads for vacation packages.
Retargeting: Target users who have visited your website but didn’t make a purchase. Display retargeting ads to remind them of products they viewed, encouraging them to return and complete the purchase. This strategy aims to capitalize on existing interest and increase conversion rates.
Dynamic ads: Create dynamic data-driven advertising that automatically adjusts content based on user behavior and preferences. For instance, an ecommerce site can show personalized product recommendations in ads, reflecting items a user has viewed or similar products that might be of interest.
Predictive analytics for ad placement: Utilize predictive analytics to identify the most effective channels and times for ad placements. Analyze historical data to predict where and when your target audience is most likely to engage with ads, optimizing your media buying strategy for maximum impact.
Learn more on how businesses can leverage information about their audience to deliver more personalized and effective advertising message.
Where does the story about data-driven advertising and baseball even start?
Billy Beane became General Manager of the Oakland A’s in 1997. The franchise had a storied history and had won 9 World Series titles; however, it was working on a severely limited budget by the mid-90s and Beane found himself struggling to attract top players to a club with limited finances and reduced ambitions.
How do you compete with and even get ahead of competitors without having to spend more money? It’s a question that is just as relevant in retail and online ad campaigns as it is in Major League Baseball.
Understanding that the old method is not going to bring new results and that innovation is crucial, Billy Beane followed on from what he had been taught by his predecessor, Sandy Alderson, and threw himself into sabermetrics—otherwise known as the art of using statistics in the game of baseball. He utilized hard data to identify seemingly undervalued players (diamonds in the rough or, perhaps, roughs in the diamond) and built a team that became the first in the long history of baseball to hit a 20-game winning streak all through the means of data-driven decision-making.
How are analytics in baseball and data-driven ad campaigns similar?
Although Billy Beane didn’t invent sabermetrics, his data-driven approach did redefine what it means and the value it can bring. There are a number of similarities between how he found success and the approach we take at RTB House to data-driven retargeting.
Go deep. Other teams were using data driven by analytics but were looking at a smaller number of parameters and were drawing more superficial conclusions. Essentially, Billy Beane was making more calculations, which is exactly what our Deep Learning-powered algorithms do—completing up to 2500 times more calculations than competing Machine Learning systems
Trust the data. Other teams were using metrics as a secondary cross-check. Their initial interest in players was based on their instincts. Beane discounted his own pre-held assumptions about players and put the data first to drive forward the decision-making process. Our Deep Learning and Context AI features do the same in online marketing campaigns. An online user is not deemed to be a strong prospect based on demographics. Online behavior and contextual analysis are the only truly relevant factors. The end results is more effective targeting and less wasted budget through displaying ads to wider demographic groups without proven product interest.
Go all in. Billy Beane didn’t just play with sabermetrics, he lived by the principle of this data-driven strategy. Even when others found it too innovative, he held that his approach to data-driven decision making was right and has been consistently proven right. For example, the A’s remain one of the teams with the lowest budget in the league but have made the play-offs six times in the last decade. In 2017, RTB House took a similar tack when we became the first MarTech company to use Deep Learning in 100% of our campaigns. This data-driven marketing approach seemed a bold move for many, but we immediately saw up to 29% increase in our overall campaign performance and have continued to push on from there.
Good to know: The Market Is Changing And Advertisers Must Adapt Novel Technologies To Thrive
What is the key to a data-driven approach?
Successfully using data-driven analytics for baseball recruitment or online ad campaigns boils down to the same two principles. Firstly, you need to put your KPIs first, not your data. We do not collect all the data and then look for patterns. Instead, we set the target and then build the correct type of statistical machine to reach that goal. For Billy Beane, the main KPI in his data-driven strategy was finding players with high on-base average and a low market valuation. For a data-driven marketing campaign, we may start with any number of KPIs—such as cost per video view, conversion rate, click-through rate—and then ask the algorithm to achieve those aims. The data is not the start point; it is the fuel for the algorithm as it strives to complete a task.
How does the use of Deep Learning play into the future?
It’s hard to say what happens next in the world of sport. It’s not predictable, which is what makes it so fascinating. However, we can be a little more certain about the MarTech domain and data-driven analytics—even though people feel like the end of cookies in 2023 raises many unknowns.
Realistically, we feel that change will be difficult, but it will be readily accepted if what comes next is as good or even better than before. And this will be the case with the new, cookieless targeting methods.
Currently, most of the industry cannot imagine personalized advertising without the use of cookies, which allow for individual-level tracking and personalization. However, we strongly believe that it is possible to deliver outstanding advertising using our data-driven strategies in a privacy-friendly way, as you can see in some of our other cookieless-related articles. Using Deep Learning and contextual analysis put us ahead of the curve here, as we are using more powerful technology and are also already not entirely reliant upon third-party data at any point of the ad serving process.
If you have any questions, comments or issues, or you’re interested in meeting with us, please get in touch.
