Are you struggling to make sense of all the data you come across? Collecting is easier than analyzing and organizing it to meet your online marketing objectives. Understanding how data is classified is the first step towards reaping its immense benefits.
1. Data Categorization
Customers inform you about their preferences every time they interact with your brand. Data is generated in an intricate ecosystem composed of:
Every consumer gadget generates data. The most ubiquitous is the smartphone. They’re carried everywhere and provide important data such as location, shopping preferences, and call lengths. The Internet of Things is turning more gadgets into smart devices capable of relaying data. Examples are smart TVs, set-top-boxes, thermostats, wearables, kitchen appliances, and security systems.
- Delivery process
It involves systems that relay data to and from the consumer. Content delivery is done through the web, ad servers, search engines, social media sites, email among many other channels.
- Collection and Storage
You can collect and store user activity through surveys, POS systems, web analytics, operating systems, browsers, and digital beacons. Customer Relationship Management (CRM) and Data Management Platform (DMP) systems also offer unified solutions for managing data.
By analyzing collected data, you can figure out how to use it to power your online advertising strategy.
2. Common Types of Data
Data is generally classified under the following common types:
- Declared vs Inferred Data
Declared data is submitted through direct interaction with your brand. This type of undiluted, first party data is valuable because it’s offered explicitly by the consumer. It also contains information that’s useful for targeted advertising, such as age, gender, product preferences, contact details, and hobbies. These come in handy for long term engagements with the consumer.
Inferred data, on the other hand, is gleaned from user behavior when interacting with your product. Examples are the pages their social media profiles like and comment on, videos they watch online, and locations they check into. Although this information is also useful, it’s not completely accurate. For example, you may know a user’s interests without being sure about their gender.
It’s best to combine the two if you want maximum accuracy. Declared data can be cross-checked with inferred data to verify that the information given aligns with the user’s online behavioral patterns.
- Observed Data
This data is collected by observing a consumer’s association with a specific product or section. Even without making a purchase, the visitor will offer enough information to allow retargeting.
- Intent Data
It shows a user’s intention to purchase your product in the near future. This could be via the keywords used in search engines, the kinds of products, images and videos they view and doing product comparisons. Intent data is also perfect for retargeting purposes.
- Interest Data
Based on a consumer’s interest in certain products, you can tweak your advertising campaign to suggest related products to them. For example, a consumer who’s interested in tech-related blogs will be more likely to purchase the latest electronic gadgets.
3. Types of Targeting
To succeed in online advertising, you should be conversant with the 6 main types of targeting:
- Data Targeting
Data collection methods have a huge influence on targeting. The three main types of data are 1st party, 2nd party and 3rd party data. 1st party data comes directly from the consumer. Other than being submitted through surveys, registrations and social media interactions, it’s also collected from observing behavioral patterns.
As an online marketer, this is the kind of data you should aim for. Not only is it accurate, it also costs less to collect compared to other data types. 2nd party data is purchased directly from a company that owns it, without involving a middleman. 3rd party data is acquired from data aggregators, who in turn buy it from the original owners. 1st and 3rd parties are the most popular forms of data used in targeting.
- IP Targeting
It uses a device’s IP address to determine the type of ads the user might be interested in. Data privacy laws are increasingly restricting this practice, especially in Europe.
- Contextual Targeting
This method shows ads that are relevant to a particular website’s category. For example, apparel websites only show fashion-related advertising.
- Contextual Keyword Targeting
This is similar to contextual targeting but more specific. Ads are only served to web pages that have certain specified keywords.
Also known as cookie targeting, it’s an extremely effective marketing method due to its high conversion rates. When consumers visit your website, a small piece of code known as a cookie is dropped in their browser.
This code will follow and track their behavior across the internet, long after they’ve left your site. Showing them your ads even when on competitor websites makes it more likely to buy from you.
Now that you have a better grasp of how online data works, you can fine-tune your digital marketing solutions accordingly. Professional data collection, organization and analysis not only saves costs but also increases sales in the long term.