1st Party Data
1st Party Data is information collected from a business (and its activities), for that business, by that business. This commonly refers to details about audiences or customers, but also covers performance of marketing strategies and advertising campaigns. Some common sources include CRM, website, mobile app, customer feedback, e-commerce platform etc. 1st Party Data is owned by a brand, that has complete control over how it is collected, processed and used. Information is unique to that business and generally the most accurate type of data.
2nd Party Data
2nd Party Data is simply somebody else’s 1st Party Data. It is procured through a direct transaction and is normally the result of a reciprocal partnership, though not always. In theory, it enables a data exchange benefitting both parties and involves a pre-determined and defined agreement. An example could include a hotel chain and airline sharing information to target audiences with relevant offers.
3rd Party Data
3rd Party Data is information compiled from a variety of sources by an unrelated company, which is then anonymised and packaged into off-the-shelf segments. A 3rd Party Data provider might have relationships with multiple publishers and companies to build a scalable audience of ‘in-market automotive shoppers’. This can then be purchased and plugged into either data management platforms or demand-side platforms for use in targeting ads and marketing messages.
Artificial Intelligence (AI)
Artificial intelligence (AI) interprets vast reams of data to enable decisions to be made at a scale and pace not possible by humans. It helps fill the gap between the huge amount of information marketers have and the ability to comprehend it. AI is different to traditional computing in that it can not only interpret data, but act on it – by deploying algorithms that learn over time. This can be applied to the world of search engine marketing, digital advertising, e-commerce, marketing forecasting, and other initiatives that need analysis of large volumes of data.
An algorithm is a computing procedure or software code that performs calculations to solve a problem
or deliver a business outcome. They are not static, but continually learn over time using live data to
Algorithms exist within demand-side platforms to plan and buy ad spots, where they take into account campaign parameters and bid information, then purchase the most cost efficient and best performing media. They also power things like dynamic creative and landing page customisation tools.
Application Program Interface (API)
An Application Program Interface (API) can be viewed as the pipes that connect web programs and software systems for the purpose of sharing data. APIs enable digital products and services to send and receive information in a way that makes sense to both platforms. While there are a number of ways to use an API, a common example would be when a marketer wants to have both web and mobile analytics pushed into a data visualisation platform for graphical representation. This can be plugged in using an API.
Attribution Tools refer to technology which helps marketers assign value to each of the touchpoints a brand has with a customer in the lead up to an online purchase or conversion. Attribution models vary in complexity from those that simply look at last click (what was the last thing a consumer engaged with) to more sophisticated multi-touch systems. A good attribution tool will consider exposure to advertising (on the AdTech side), as well as engagement with email marketing and website offers (on the MarTech side), then assign the right amount of influence that each has had on the consumer’s purchase decision.
Cloud-based technology are products and services hosted on the internet. Most AdTech and MarTech platforms are cloud-based solutions that users log into and execute via web dashboards. This concept has been extended further to represent an aggregate of technology services that enable businesses to outsource digital processes to a single vendor (such as Adobe’s Marketing Cloud or Advertising Cloud).
Data Management Platform (DMP)
A Data Management Platform is a central system that houses and manages both audience and campaign data. For marketers it can provide a single source of truth that informs both AdTech and MarTech platforms and a unified view of their audience. A good DMP will enable the creation of custom audience segments and facilitate look-a-like modelling, where users with similar attributes are grouped together to increase the scale of a segment. These can be used to target relevant advertising creative to specific audiences and/or customise an offer on the website. It is one of the few components in the stack that truly bridges both marketing and advertising functions.
Match Rate refers to the percentage of unique users who can be matched between two different systems. For example, the number of users in an audience segment housed in a DMP, that can be identified when pushed into a DSP. The industry average for match rate between two different platforms sits at around 30-40 per cent. This is where the value of a combined stack, built on the same technology and powered by a single source of data, becomes apparent as match rates are significantly higher.
Multi-channel / Omni-channel
Multi-channel or omni-channel are used interchangeably in both advertising and marketing. They refer to any campaign or strategy spanning more that one medium, device or element of the marketing mix. This kind of approach is aimed at maximising reach into audiences and creating a fluid customer experience across all touchpoints, while still taking advantage of the unique benefits of the individual channels. For example, adding interactive elements to a video ad served on a mobile device, compared to linear streaming of a TVC on television. The key is to understand the opportunity and nuances of each channel and measure them effectively (usually using an attribution tool of some form) to determine the impact they have on their own and in tandem.
Optimisation in the sense of AdTech and MarTech, refers to the refining of strategies to drive better results or business outcomes. It can include both manual changes – made by someone using the technology platforms – and automated updates through the use of algorithms (see earlier in this section).
Return On Investment (ROI)
Return on Investment can be calculated and applied differently by different businesses. At a high level, it refers to how much of the intended outcome was generated, in relation to the amount of money spent on the activity to achieve it. It is a business metric that ultimately measures how advertising and marketing strategies contribute to an organisation’s bottom line. The increasing pressure on marketers to do more with less and greater accountability driven by advanced attribution tools, has enabled a shift away from softer digital metrics to quantifiable evidence of how strategies are contributing to real business outcomes.
Source: Adobe guide – Bridging the AdTech and MarTech Divide