Navigating the AdTech landscape with big data analytics
The tech that’s transforming the AdTech sector
The AdTech sector is constantly evolving, mostly driven by faster and faster advancements in technology, the technology available as well as changes in regulations and, most importantly perhaps, customer behavior.
One of the biggest developments in the last decade or so has to be the explosion of big data, with more and more organizations being able to utilize it (rather than just those with huge budgets). Advertisers now have access to an insane amount of data about their customers, and can use it more easily than ever to improve the targeting and optimization of their ad campaigns on an extremely personalized level.
However, this wealth of data also presents a challenge: how can advertisers make sense of it all and best use it to their advantage?
That’s where big data analytics comes in.
Big data analytics is what it’s called when people are using advanced analytical techniques to extract in-depth insights from large and complex data sets.
In the AdTech sector, this can include data on consumer demographics, behavior and/or reactions to advertising and specific campaigns.
By using big data analytics, advertisers are able to gain a much deeper understanding of their target market and make informed decisions about ad targeting, optimization, and based on hard data measurement.
It can also help identify patterns and trends that may not have been immediately obvious patterns and trends that might better guide ongoing strategies and campaigns.
Understanding and utilizing big data analytics is crucial for any advertiser looking to stay relevant in the AdTech landscape.
An overview of the AdTech landscape
The AdTech sector covers an enormous amount of technologies, tools and platforms… far too many to all cover here.
Suffice to say, they’re all used, in one form or another, to deliver or measure the effectiveness of an organization’s advertising efforts. Some of the terms you may be used to include:
-
Programmatic advertising refers to software used to automate buying and selling of ad space.
-
Real-time bidding is a subset of programmatic advertising where you can bid in real-time (so pretty much as it sounds).
-
Ad exchanges are platforms that facilitate real-time bidding through more efficiently connecting advertisers and publishers.
-
Demand-side platforms (DSPs) is a type of software that allows advertisers to buy ad space programmatically, through multiple ad exchanges.
-
Supply-side platforms (SSPs) is a type of software that empowers publishers to sell ad space programmatically, through multiple ad exchanges.
In the modern world, AdTech is a shifting, constantly evolving landscape with new technologies being released all the time.
Some of the trends we’ve seen grow in popularity are a huge shift to mobile advertising, the rise of ad-blocking (and how to combat it), the increasing importance of data privacy and better data management and storage to empower big data analytics.
Big data analytics in AdTech
Big data analytics plays a critical role for the modern AdTech sector, helping advertisers make sense of the vast amounts of data that are generated by customer interactions.
By using big data analytics, they can gain a much deeper, more nuanced understanding of their target market and can make better decisions about their ad targeting, optimization, and measurement.
One of the key ways that big data analytics can be used in AdTech is to improve ad targeting. By analyzing the data, advertisers can identify patterns and segments of their target audience.
These segments and cohorts allow them to create more effective ad campaigns by tailoring messages and offers to specific groups of consumers.
Advertisers can also use the data to identify the best times, platforms, and formats to deliver their ads to reach the right, target audience.
Another way that big data analytics is being used in AdTech is by optimizing ad campaigns.
By analyzing data on consumer responses to advertising, advertisers can measure the effectiveness of their campaigns in real-time and make adjustments on the fly. By analyzing data on consumer behavior, advertisers can also identify patterns and segments of their audience more likely to convert allowing them to more quickly shift budget to where it will do the most good.
Implementing big data analytics
Incorporating big data analytics into an AdTech strategy requires a structured and methodical approach if you’re looking to follow best practice.
The first step is to clearly define your business objectives and the key metrics that will be used to measure the performance of campaigns.
Once you’ve defined your objectives and key metrics, you can begin to collect and organize the data that you will need to analyze. This can include data on consumer demographics, behavior, and responses to advertising.
Additionally, it can be useful to integrate data from external sources such as social media and web analytics, to gain a more comprehensive view of your target audience.
That will all need to be stored with the correct data architecture, in the most efficient way possible, in a data warehouse (or data lake depending on the type of data).
After the data’s collected, it needs to be cleaned and preprocessed, ensuring it’s as accurate as can be. This step typically includes removing duplicates, filling missing values, and transforming the data into a format that’s suitable for analysis.
Next, it’s important to choose the right big data analytics tools that will allow you to extract insights from your data.
This will include data visualization and possibly machine learning and statistical modeling tools.
With the insights extracted, it’s time to start making data-driven decisions, by implementing the insights into your AdTech strategy.
Regularly monitoring and evaluating the performance of your campaigns using the key metrics, as well as the health of your data, gives advertisers the power they need to stay ahead of the competition.
The future of big data analytics in the AdTech sector looks promising, with more and more data being generated every day, and new technologies emerging all the time to allow for more advanced analytics.
In this article, we’ll talk about:
Start your trial today
