Let’s start with the obvious, we live in a digital era marked with data abundance and analytics, enabled by technologies that allow companies to gather massive datasets from plenty of sources. Gathering data has become a commodity for the large majority of the organisations. However, being able to refine this new oil into value is the new challenge. Valuing it and doing something actionable with that data has become the modern organisation’s biggest challenge.
The current trap: data-as-a-product
Not all companies are equal when it comes to valuing the data they collect. From our perspective, we differentiate two types of organisations. The first type is actively trying to make meaningful sense of the data overload they are facing. Some succeed, many struggle. The most important though is their ability to try, fail and start again. There is no magic solution, it is all about people, culture and processes.
We could call the second type of organisations the: “data rich, insight poor”. These organisations consider themselves data-driven simply because they are collecting data. These organisations consider data as the final product of the value chain. They are running what we call data-as-a-product. In the best case this data is available to the different stakeholders within the organisation. The data-as-a-product concept also implies that the stakeholders still need to turn the data into actual value. Delivering the data at the doorstep of the stakeholder represents the last task in a data-as-a-product culture. Depending on your role and technical skills this might be either a walk in the park or a complete nightmare.
Data-as-a-product doesn’t work for marketers
Imagine you’re a marketer and you’re spending most of your time planning campaigns, optimising them and reporting on the results. On top, you need to report to top management on how marketing is contributing to performance all while managing the agency that’s working for you. You’re obviously interested in the value the data could hold. Yes, you would like to know who your top customers are so you could turn them into a lookalike audience. Yes, you’d like to know which customers are about to churn so you can reach out before they do. All of that info is hidden inside that heap of data. The only problem? You, dear marketer, lack the time and skills to mine the insights yourself. As a result, having the data delivered at your doorstep doesn’t actually solve your problem.
From data-as-a-product to audiences-as-a-service. Or how to create value from data for marketers.
This is where audiences-as-a-service comes into play. We define it as a process in which meaningful insights are generated around individual customers, these customers are then bundled in audiences and the audiences are packaged and delivered to the marketers.
Audiences-as-a-service addresses two main challenges: (1) data literacy and (2) data actionability. Accessing data often requires advanced technical skills (SQL querying, etc.). This context makes it difficult for marketers to define an audience strategy at macro-level and force them to work in their tool-based silos (Media vs CRM vs etc.).
The above diagram visualises the audiences-as-a-service idea. Where data-as-a-product only delivers the raw data (with or without the churn risk or customer lifetime value at customer level), audiences-as-a-service creates an audience and integrates this with the marketer’s platforms. Next to the one-off delivery, audiences-as-a-service also ensures that the audience is refreshed without manual intervention. This means that marketers can leverage the audiences in always-on campaigns.
As a result, audiences-as-a-service is the key to running 1st party data fuelled always-on campaigns at scale.
How do you get started with implementing Audiences-as-a-service?
Starting with audiences-as-a-service is something that happens gradually. A successful deployment of the concept requires some preparation, a global team commitment and a continuous approach. We recommend a number of steps:
The first step is to build with all marketers involved a company wide audience blueprint. This blueprint serves a double purpose.
It aims to ensure all communications initiatives are centralised both from a strategic and operational point of view. This blueprint also serves to identify which data should be used to build the required audiences. It’s your “world map” of audiences.
It creates a common understanding about which audiences are in play and what exactly is referred to when someone refers to “audience A”.
Data quality is a key element when talking about customer data. Especially in the context of an automated approach. Even more important is the process and data quality safeguards that are being put in place in order to ensure data quality in the long run.
Do not try to automate all audiences for all platforms. Start with a small scale proof-of-concept to prove the value and understand the challenges and pitfalls. Once the PoC is deemed successful the rest of the blueprint can be rolled out.
How we recommend to proceed
We recommend taking a progressive approach here to ensure the team has the time to train and the project remains manageable without everyone having to change its habits from one day to another. As you would have understood it, audiences-as-a-service requires an important organisational change. It needs to be part of the company culture driven by the company management.
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