Written by: Shantha
In today’s fast-paced world, we’re doing a lot online—ordering food, buying electronics, clothes, footwear, reading the news, watching movies and shows; you get the drift.
So, it should be no surprise that after you’ve recently spent time browsing a few different websites to buy new headphones, you’re seeing advertisements for headphones and related products everywhere. Not only that, your favourite news sites are also throwing up articles about the pros and cons of noise cancelling headphones, the damage listening to music on high volume can cause; so on and so forth.
On the one hand, you’re nervous that your searches are being tracked and on the other, you’re impressed. All your favourite websites know what you’re interested in; so you’re being shown and advertised content and products that you actually like and want to purchase or know more about.
This paradox is pretty much everyone’s life today. Do we want to be known so well by hundreds and thousands of brands and publishers? Don’t we also want any ads or content we’re shown to be personalised, making it less of a hassle to dig through data?
The reason websites and brands are able to show users relevant, useful information and products is because someone out there took basic information about X individual and augmented it: deriving nuanced, rich insights about everything from gender, age and geography, to lifestyle, purchase-intent, customer preferences and so much more. Enriched data essentially helps brands fill in missing pieces of the puzzle so you can develop content that appeals to your audience, and help you find new, similar customers. Typically data enrichment could involve either or both, first-party data—customer information that you collect directly—and third-party data—aggregated first-party data collected and compiled from a variety of different sources.
According to the Statista Research Department, programmatic ad spending in the USA is expected to reach roughly $95 billion USD in 2022! Now if that’s anything to go by, publishers everywhere are likely to be coming to the same (or similar) realisation—more and more people worldwide are spending time online forcing businesses to restructure and adjust to changing circumstances.
Taking this into consideration, the potential for personalised and customised advertising experiences that build solid relationships between brand and customers, is immense. That there is no additional human resources required, is attractive for advertisers and publishers alike.
Publishers are constantly gaining new and anonymous users resulting in vast amounts of data trends and data types that can aid them in decision making. As digital media grows, it’s now possible for publishers to both understand and track user interests, preferences and behaviour. Skilled tools dive into individual user profiles, usage patterns, search histories, etc.
Achieve a level of granularity in customer data that will propel the depth of observation for your client
Due to the sheer scale of lookalike modelling you can provide brands with a larger audience pool, allowing them to untap new audience segments
Help brands segment data in ways you—and them—never thought possible, combing parameters like demographics, consumer patterns, and preferences
Offer insights to personalise content delivery so it not only addresses a customer by name but also put across unique, relatable experiences for them
Provide recommendations on what promotions and loyalty programs to run
Each of these will help your clients enhance their tactics, presence and behaviour, ultimately resulting in customer satisfaction and loyalty. In turn, this of course will boost your ad revenue figures. Win-win-win. Remember, just collecting and cleansing data isn’t enough. Enrichment is the trick to staying ahead of the game.