The solution? Implementation of an AI-based Audience Engine to exploit first-party data to its full potential, in full compliance with regulations. This solution allows audiences to be segmented in an automated way for activation across all digital marketing platforms.
Constraints and regulations = opportunities for marketers
New issues surround the collection and processing of personal data. Some are technical (imminent disappearance of third-party cookies, toughening of consent collection); others are regulatory and behavioural (consumers are paying more attention to the protection of their data). These changes are profoundly impacting marketers’ digital strategies.
The disappearance of third-party cookies and the decrease in the volume of first-party data make targeting and retargeting more complex. The technologies that will replace third-party cookies won’t achieve the same degree of relevance and volume. Measurement capabilities will be considerably reduced: it will no longer be possible to track and reconcile all digital touchpoints. And complying with regulations will make collecting and processing personal data more complex.
The ability to deal with these constraints is an opportunity to become more competitive. The key is to build a three-pronged consumer data strategy:
Exploit first-party data (CRM, website and app browsing, media, etc.) to their full potential.
- Build an ecosystem – sustainable and compliant – of interconnected tools to activate this data and measure marketing action performance. A Customer Data Platform, or CDP, which we discuss later in this article.
- Enrich first-party data with external data, third- or second-party data.
Audience engines to optimise and personalise your campaigns
To make the most of first-party data, i.e., to personalise and optimise digital marketing campaigns, you need to be able to build relevant and easily activatable segments. This is the role of Audience Engines.
Audience engines are built in cloud solutions, such as GCP, AWS or Microsoft Azure, and centralise site-centric and app data, always while respecting the user’s data privacy. Second- and third-party data are added to enrich proprietary data.
These data can then be aggregated using algorithms that can have different functions, including:
- Scoring: identification of population segments based on expected common preferences. Scores are assigned according to browsing data, purchase history, socio-demo, etc. After distinguishing behavioural types using these scores, the advertising content (messages, creatives, offers…) can be customised for each population. This can also help optimise media investments through biddings adjustments based on the value attributed to an audience segment.
- Clustering or look-alike: assembling audience communities and connecting them to marketing activation platforms to enable audience extension.
- A/B testing and Insights: isolation of part of the audience to verify the relevance of digital strategies and created audiences. In this way, segmentation capabilities can be continuously improved.
The resulting audience segments are then fed into the advertiser’s ecosystem of activation platforms – Illustration below
Implementing an Audience Engine – Reckitt Case & results
As with most players in the FMCG sector, Reckitt owns relatively little consumer data. The group wanted to enrich its first-party data with external data, build audience segments and, ultimately, optimise its audiences. Reckitt called on Artefact to create an Audience Engine to meet these objectives.
As soon as the pilot campaigns in the United States & United Kingdom were deployed, the personalised audiences from the Engine proved to achieve better results than the traditional socio-demographic targeting previously used. In less than a year, the Audience Engine has been rolled out in over 20 countries and is fully operational for all Reckitt’s Brands that meet the minimum consumer data threshold to get the most out of the tool.
After a year of activity, the Engine results exceeded team expectations, both in terms of cost efficiency (average of -20% in CPM versus traditional targeting), as well as ROI generated (average of +5% in offline sales uplift directly attributed to the campaigns).
What are the key takeaways to successfully engage in your 1P-driven digital transformation journey?
Artefact top #3 recommendations:
- Define a strategy to own and expand your first-party data (digital assets, data quality, data governance…)
- Remember that Tech is key but it won’t work without other enablers (organisation, talent, measurement, intelligence layers…)
- Define a learning agenda driven by business and continually test & learn. The insights might be as useful as the results themselves
Reckitt top #3 learnings:
- Start by defining your strategy, sponsored at the highest level in your organisation
Reach out to the right external partners and experts to help you gradually expand your in-house capabilities (See, Do, Teach)
- Use your Test & Learn insights to continuously optimise your “Business as usual” business decisions (eg. budget splits).