Data-driven advertising is a huge opportunity. But it’s also complex. This list of some of the most common questions we get asked is here to help cut through that complexity. Hopefully you’ll find the answers you need below – but if not, then get in touch and we’ll be more than happy to talk you through any other questions you have.
- What are the different types of consumer insights?
- How does contextual advertising work?
- What is a cookieless future?
- What value does contextual advertising bring to marketers?
- What does a customer data platform do?
- How does a data clean room work?
- How does a demand-side platform (DSP) work?
- How does digital fingerprinting work?
- Why is first-party data important?
- How to collect first party data
- How does identity resolution work?
- What is the difference between first and third party data?
- What are the benefits of retail media networks?
- What is online to offline attribution?
- What is the difference between deterministic matching vs probabilistic?
What are the different types of consumer insights?
Consumer insights reveal information about different consumer aspects. Types of consumer insights can include consumer behaviour, consumer preferences, consumer motivations and more. Companies that gather, evaluate and apply consumer insights can make better business decisions.
How does contextual advertising work?
Contextual advertising involves the use of different signals to determine how relevant a piece of content is for a prospective user. If the content is deemed relevant enough, it will appear in front of the user. Say someone is researching an article about shoes, they might then see an ad for shoes on the webpage they are on. First-party data complements this strategy. By using information gathered directly from a user’s interactions and preferences, advertisers can enhance the accuracy of contextual targeting.
What value does contextual advertising bring to marketers?
Contextual advertising brings huge value to marketers. Whereas advertisers may once have relied on the more traditional behavioural targeting (leveraging a customer’s browsing data, say), evolving privacy regulations make this approach less appealing. Contextual advertising represents a more sustainable alternative. Instead of leaning on cookies to serve relevant ads, insights based on the context of an ad are just as effective for creating content that resonates.
What does a customer data platform do?
A customer data platform aggregates and unifies first-party customer data from disparate sources, and uses that data to generate a singular view of each customer.
How does a data clean room work?
Data clean rooms work by anonymising the data submitted by a content provider, and aggregating it into different demographic groups. Partner companies of that content provider can then use the same data clean room to access that anonymised data, ensuring proprietary data remains secure.
How does a demand-side platform (DSP) work?
A demand-side platform (DSP) works by replacing manual ad purchasing with real-time bidding. Take the following example:
- An advertiser chooses a target audience and uploads the ads they want published.
- Publishers (websites) make their ad inventories (i.e. their advertising real estate) available on the DSP by using either ad exchanges or supply-side platforms (SSPs).
- The ad exchange or SSP make the ad impression available to the DSP.
- The DSP then decides whether or not to make a bid for that ad impression, according to how relevant the ad’s targeting criteria is.
- The advertiser from Step 1 competes with others for that same ad impression by placing bids in real-time, using the DSP.
- The DSP then buys the impression: and the ad displays on the publisher (website).
How does digital fingerprinting work?
Digital fingerprinting works by identifying an individual according to their device’s properties and browser settings. The individual’s online activities are matched with a set of reference points aka ‘fingerprints’. Learn about the problems associated with digital fingerprinting here.
Why is first-party data important?
First-party data is important because it is based on real interactions with a brand, spread across multiple consumer touchpoints (instead of audience ‘lookalike’ behaviour). As a result, first-party data represents the cornerstone for understanding customers.
How to collect first party data
First-party data – the likes of purchase history, onsite behaviours and demographic information – is best collected via interactive content. Unlike blogs or case studies, which are consumed passively, interactive content like surveys and quizzes offer the best chance of capturing valuable information.
How does identity resolution work?
Identity resolution works by taking the following steps:
- Creating identity graphs – lists of recognised customer identifiers, essentially forming a ‘guide’ to help organisations weave together multiple customer interactions.
- Integrating customer data – Identity graphs allows organisations to integrate (or deduplicate) customer actions.
- Attaching customer data to different entities – Organisations stich entities to individual customer profiles.
What is the difference between first and third party data?
The difference between first and third-party data boils down to their respective sources. First-party data is data that has been collected directly from your own audience. And while second-party data involves a direct exchange of data between two trusted parties, third-party data is data that has been collected or bought from another company. Learn more via our article on first, second and third-party data.
What are the benefits of retail media networks?
- RMNs provide additional revenue streams for retailers and brands, who use them to monetise website traffic.
- RMNs provide more targeted advertising: Brands like to advertise on RMNs because they can display their ads directly to an audience of consumers who are actively looking for products.
- RMNs deliver greater brand awareness: When brands advertise their ads on well-known retail sites, they boost their own products’ visibility. This leads to enhanced brand recognition and higher customer loyalty.
- RMNs provide significant ROI: When you target shoppers who are more likely to buy, you realise higher revenues for fewer costs.
- RMNs come with granular analytics: Advertisers who use retail media networks can rely on detailed reporting and analytics. This makes it easier to evaluate their advertising efforts, and adjust their campaigns when necessary.
- RMNs offer different ad formats: Choices include sponsored product ads, display ads and more, depending on their target audience and goals.
What is online to offline attribution?
Online to offline attribution is the practice of measuring digital ad exposure and engagement, with a clear link to resulting sales instore. This practice involves the use of a robust identity infrastructure to measure exposure and interaction with ad touchpoints, and attribute to offline sales.
What is the difference between deterministic matching vs probabilistic?
Deterministic and probabilistic matching are the two primary methodologies used for resolving devices to customers. Deterministic matching works by creating device relationships i.e. using personally identifiable information (PII) like phone numbers or email address. Devices are only linked when they are directly observed using the PII tied to a consumer, prioritising accuracy and limiting false positives. Probablistic matching works by creating device relationships by using a knowledge base of linkage data and predictive algorithms as the foundation for an identity graph.
Devices are also grouped together implicitly (via device fingerprinting, IP matching, screen resolution, operating system, location, Wi-Fi network and behavioural and browsing data), using statistical modelling at a given confidence level. These groups can be linked to IDs based on predictive algorithms. Choosing one or the other depends on one’s marketing objectives. Deterministic matching makes sense if your goal is to target only actual buyers of a specific product. Probabilistic matching makes sense if your goal is to target people who might buy or be interest in a specific product (because probabilistic data will give you greater reach).