In today's digital world, collecting data is an important aspect of marketing. Advertisers use this data to gain better insight into their customers and optimize their marketing strategies. There are different types of data that can be collected, such as contextual data, behavioural data, and relationship data. For each type of data, it is important to consider the difference in quality, how the data is collected, and how long it can be used. In this blog, we will delve deeper into the differences between these types of data.
Contextual data refers to information about the environment in which an ad is displayed. It may include the content of a website or app on which the ad is shown, the location of the user, or the time of day. Contextual data can be useful for advertisers as it can help them tailor their ads to the interests and needs of the target audience. For example, advertising on a website related to the product or service being offered increases the likelihood of the ad being seen by people interested in it. The value of this data is high because it directly targets someone's interests. However, this value disappears quickly as it often relates to what the user is doing at that moment. Additionally, we see that contextual data can be used on almost every platform. Some examples include:
- Leveraging channels such as Digital Out of Home, Social, and Display through trigger-based marketing (e.g. weather-based triggers);
- Playing on the emotions of a user through the context of a podcast, program, or article;
- Utilizing a user's location or time through digital channels.
- Retargeting certain pages or products;
- Reactivating recent customers.
Relationship data (first-party data)
Relationship data is information that is directly collected by a company about its customers. This can include information about the purchases a customer has made, their contact information, or preferences. Relationship data is the most valuable data for businesses because it allows them to directly connect with their customers and tailor marketing strategies accordingly. The advantage of relationship data is that it does not disappear and can be supplemented based on new contacts with the customer. For example, someone who became a customer of a company a long time ago and later buys a product again. Because a user makes a purchase with PII, such as an email address, it is possible to match these two purchases and learn from them. It is therefore essential for companies to have a login strategy so that relationship data can be stored and then activated. By analysing this data correctly, it is possible to gain insight into which customer journeys can achieve high conversion rates. By setting up these customer journeys through always-on campaigns, it is possible to have an automated conversation with the consumer. This usually happens through owned channels such as email and phone marketing, but by using customer match and data bunkers, it is also possible to link paid channels such as Social and Display. Examples of relationship data include:
- Encouraging upselling by selling additional products. For example, selling pillows after a customer has bought a bed;
- Use of personal data for new conversions. For example, birthday campaigns;
- Communication to provide better service.
The main difference between contextual data, behavioural data, and first-party (relationship) data is the source of the information. Contextual data is collected based on the environment in which the ad is displayed, while behavioural data and first-party data are collected based on the user's behaviour and interactions.
Another important difference is the accuracy of the data. Contextual data can sometimes be less accurate because it is only based on the environment in which the ad is displayed. Behavioural and first-party data are usually more accurate because they are based on the actual interactions of the user.
As previously mentioned, third-party cookies will be phased out completely by 2024. This will make it increasingly difficult to use behavioural data in marketing campaigns. Advertisers will become increasingly dependent on contextual and first-party data to run campaigns. Fortunately, more and more tools are able to link first-party customer data with external networks, making it possible to target on paid platforms in a personalized manner. It is also possible to use this data for lookalike targeting to reach and retain new consumers.
Additionally, it is good to see that contextual targeting capabilities are becoming increasingly advanced, making this data more usable. By being contextually relevant, it is easier to stand out in a highly fragmented landscape. A combination of contextual and relationship data can be the key to success. Contextual data can help the customer stand out, while relationship data helps deliver the right message at the right time.
The downside of relationship data is that it is primarily focused on existing customers and may not be able to attract new customers. Having a login strategy can be of great value in this regard. If leads can also be recognized in the advertiser's database, it means that these individuals can also be approached at the right time.
To better tailor advertisements to the interests of the target audience, contextual data can be useful for advertisers. Behavioural data provides insights into the behaviour and preferences of the audience and can be used for targeted advertising. First-party data is the most valuable data for companies, as it enables them to directly connect with their customers and tailor their marketing strategies accordingly. However, behavioural data will become increasingly difficult to obtain in the future due to the phasing out of the third-party cookie.
Companies must therefore improve their login strategies to collect more relationship data that can be used to set up automated customer journeys. By analysing the data effectively, companies can gain insights into customer journeys with conversions and structure them through an always-on approach.
In short, companies need to have a strategy for collecting, analysing, and utilizing data to optimize their marketing objectives and better understand their customers. The use of relationship data is the most valuable for companies, but it is important to link the different types of data where possible to create targeted and effective advertisements.