One of the most powerful things about digital media is its ability to reach specific groups of people with great accuracy. That said, digital is not a magic wand that can summon anything or anyone we think of. There are pre-defined classifications of audiences and targeting filters that are available to us. Here, we will go into detail on the various targeting options we can leverage.
Audience Targeting Functions
Geo Targeting
Geo targeting, as the name implies, is simply location targeting. Depending on the publisher's ability to access the location settings on your mobile phone, wifi network of your devices, IP address and your declared location, you could get as granular as a 1km radius, a postal code, a state or a country with your targeting.
Dayparting
Dayparting refers to the selection of time that you would like your ads to show to specific audiences. You can schedule your ads to run at certain times of the day or certain days of the week. This is often used for ‘limited time only’ promotions, or to target specific periods where your audience is most likely to engage with your ads based on research.
Demographic Targeting
Demographic refers to superficial audience profile information such as their age bracket and gender. This is a combination of probabilistic data and deterministic data. Probabilistic data refers to the high chance of you being of a certain age or gender based on your internet behaviour and content consumption. Deterministic data on the other hand is your declared information such as you selecting ‘Female’ as your gender and offering your birthday in an account profile setup.
Contextual Targeting
Contextual targeting gives us the ability to select media placements based on the topic of content on the site or page. This could be topical websites such as ESPN, which is purely sports, or portals that house various sections with their own topics such as News portals with sections for Current Affairs, Sports, Finance, Entertainment etc.
Interest Targeting
Interest targeting allows us to selectively show our ads to people who have expressed an affinity towards a particular subject based on the content they consume or pages, posts and articles they may have ‘liked’. Most publishers profile their visitors based on their consumption habits to ensure that the advertisements they see are relevant to them. This in turn helps advertisers select the most appropriate audience for their advertising campaigns. For example, if I am looking up articles about home renovation and gardening, I might be categorised as being interested in DIY projects. This would help an advertiser like Bunnings reach me with an appropriate ad.
Behavioural Targeting
Behavioural targeting allows us to reach people who have exhibited certain behavioural patterns based on actions and inactions they have taken online. Beyond just consuming specific content, it is also about the range of websites visited, how they engage with websites and apps, what kind of purchases they’ve made, what they might be searching for etc. The amalgamation of this information gives us a sense of who this person is based on what they’re doing. For example, someone who constantly reads the news, subscribes to the Wall Street Journal, and searches single seat flight tickets twice a month might be categorised as a business traveller.
In-Market Targeting
In-Market targeting reaches people who are actively researching and comparing particular goods or services online. While it is a behavioural signal, this differs from behavioural targeting in that the actions of In-Market audiences are specific to purchase decisioning while the actions of behavioural targeting assists to profile the user. For example, someone may be looking to purchase a flight ticket (in-market), but are not business travellers (behavioural).
Retargeting
This is a targeting method that allows you to reach people who have visited your website and, in some cases, engaged with your ad. Given that these people have already shown an interest in your ad or product, they would be considered ‘hot prospects’. Retargeting would allow you to follow up with these hot prospects with a specific message to convince them to return to your website to complete an intended action. For example, if you are an online fashion retailer, you would be able to reach someone who has been browsing a dress and even added it to cart but did not complete the purchase. Often advertisers use small discounts or item specific messaging to convince the user to complete the purchase.
Understanding the Data behind Audiences
It is no secret that data is collected from all kinds of interaction we may have online with apps, websites and devices etc. Audience segments are built by organising and grouping the data that is collected. With the amount of ad dollars being pumped into accessing these audience segments, it is no wonder data is seen as the new ‘gold’. Most of the data that is used for advertising is anonymised so no Personally Identifiable Information (PII) is used to protect users’ privacy.
Data is usually grouped as 1st, 2nd and 3rd party. This allows us to verify the legitimacy of data by identifying the source of the data. Below, we go into detail on each of these data sets and how they differ from one another.
1st Party Data
1st party data refers to audience data collected from their direct interaction with your owned properties such as your website, lead forms, customer registration etc. They are considered ‘1st party’ data because you collect and own this data without going through anyone else.
The data is often split into customer data and non-customer data. Customer data refers to actual customer information that you have saved as part of Customer Relationship Management (CRM). This often includes their email address, phone number, and the transactions they have made with you. Non-customer data refers to data you have collected from leads and prospects purely from their interaction with your owned properties. This can include pages they have visited or actions they have taken on your site, as well as lead information they have volunteered which often includes email address and phone number.
Using a Data Management Platform (DMP) such as Adobe Audience Manager or Salesforce DMP, you can group audiences together based on various defining criteria to build specific audience segments for your marketing activity. For example, a bank might group customers aged 28-40 who have a significant amount in their bank account as potential first home buyers.
2nd Party Data
2nd party data refers to data directly collected and owned by another company. Quite often businesses monetise the data that they have on their customers as an added revenue source. This is done by selling audience segments directly to other advertisers looking to reach a specific group of people. The data is of course anonymised, so no Personally Identifiable Information (PII) is shared. An example of this would be a luxury brand leveraging Qantas Airline’s data of people who are frequent business class and first class passengers for their marketing activity.
3rd Party Data
3rd party data refers to data sourced and pulled together by data aggregators. The data aggregators do not own the data, but have access to multiple sources of data. They then aggregate and segment the audience into various profile groups. An example of this is a data aggregator sourcing data from Forbes, Skyscanner and Qantas to piece together an audience segment of people who are luxury travellers. This is an audience segment that a 5 star resort or hotel might use. Some of the more common 3rd party data providers are Lotame, Eyeota, Acxiom, and Oracle.
Planning with the audience in mind
Using the understanding of how we can identify audiences online, we can begin putting together an audience profile for each objective. The general approach is to loosely identify audiences at the top of the funnel (awareness), and become more specific with your audience as you move down the funnel (DR).
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