Foundations of personalisation

At Luminary, we understand the significance of tailoring experiences to individual preferences and behaviours. In this blog post, we'll explore personalisation and in a follow-up piece, we’ll extend this to experimentation, unveiling key insights and a forward-looking strategy.

What is personalisation?

Personalisation is about adapting an experience to the behaviours (and ideally the needs) of a visitor. Almost anything is possible. So, it’s about choosing how far to go.

Reactive: Changing content based on low-level segment data to fix broken interactions for the masses.

Tailored: Unifying data sources and adding localisation, device detection and on-site behaviour to tailor content to an individual’s preferences.

Predictive: Using the data of many with AI and machine learning to make predictions about the potential needs of the individual.

Understanding personalisation levels

Personalisation works best with a multi-faceted approach, ranging from reactive adaptations to predictive hyper-personalisation. By understanding the nuances of each level, experiences can be tailored to match evolving customer expectations.

Explicit personalisation: Based on user-provided data like preferences and contact details, offering targeted experiences within known parameters.

This form of personalisation is good, so long as users’ preferences or circumstances don't change. This type of personalisation relies on cookies or CRM data that has been collected about a user or has been provided intentionally through surveys and registration forms.

Implicit personalisation: Leveraging user interactions and behavioural patterns to infer preferences and deliver relevant content.

This type of personalisation is based on relationships between users, your services and your content. It relies on a large amount of data to create associations and build an accurate picture of your segments. Aggregation of usage, location and device data form a complete picture of users’ interactions with a brand.

Contextual personalisation: Incorporating external data sources to enhance personalisation, such as weather or location data, for more nuanced experiences.

This form of personalisation is very rich, but relies on accurate external data sources. This could include purchased consumption data from LinkedIn, weather data or live traffic from Google Maps.

Data types

We must consider the quality of data as well as its volume. Combining them is what creates a rich personalised experience. The data ranges from high-volume/low accuracy (3rd party cookies and on-site behaviour) to low-volume/high accuracy (Explicit/CRM data).

Image reference: Dynamic Yield

Building a successful personalisation strategy requires a solid foundation. From understanding your audience demographics and behaviours to defining clear goals and tactics across various channels, it's about aligning every effort towards delivering exceptional experiences. Below is a summary of some such examples following the what, who, where and how framework.

What (goals)

Conversion

Efficiency (MROI)

Relevancy

Engagement

Support

Who

Demographic: role, location, age, industry

Behavioural: their goals, buying and usage behaviours

Where (channel)

Web (public)

Web (logged in)

Web app

Native app

Marcomms (SMS and email)

Lead management

Advertising

How (tactics)

Change/swap

Dynamic content

Abbreviations

Copy/messaging

Upselling/cross selling

Inbound marketing

Outbound marketing

Typical goals

With a myriad of tactics available, from dynamic content replacement to tailored messaging and recommendations, there are near-endless ways to meet your goals.

Conversion: Simplify key journeys and target actions by creating behavioural benefits and optimising content, navigation, search and filtering.

Efficiency: Hyper-targeted marketing efforts aimed at maximum ROI by only creating email campaigns and personalised content for high-value segments.

Relevancy: Provide product recommendations and secondary calls to action to similar or related content.

Engagement: Tailor messaging and CTAs to site visitors based on their customer and visitor status. Or, create offers that are unique and personal.

Support: Chat services and wayfinding support to make returns and customer care frictionless.

Key takeaways

Start small, learn, and grow: Scaling personalisation isn't an overnight endeavour. Clients typically require 12-24 months to develop and expand their personalisation strategies. It's about incremental progress, learning from each step, and evolving accordingly.

Strategy first, technology second: While there's a plethora of technology options available, the foundation lies in crafting a robust strategy centred around the customer. Implement only what aligns with your business objectives and resonates with your audience.

How to get started

1. Analyse: Identifying pain points in the analytics, by looking for high traffic correlated with poor performance. Use the review to find data to define your segments OR define your segments upfront and see if you have the data to back it up.

2. Align: Run internal sessions to seek alignment on the organisational goals of personalisation. And, prioritise the channels, audiences and journeys that ladder up to those goals.

3. Define: Create draft use cases, scoring them for each segment. Then, prototype the journeys so that you may identify the technical and content requirements to enable them. A helpful tool for deciding on what to do is Scoring Calculator, by Dynamic Yield.

4. Review: Review your technical infrastructure, content and data readiness. Identify if existing systems are suitable and document the requirements to get from where you are today to where you need to be.

5. Design + build: Undertake a program of work to design your solution architecture, resourcing and budgeting needs. This should include roles, tech stack and licensing costs to support your desired level of personalisation.

6. Test + learn: Start small with simple (explicit data-led) personalisation. Then measure your results and ROI to create a business case for growth.

Personalisation hero image showing customers from different countries

Technology foundations

Having a mature technology stack is crucial, because personalisation requires that you be able to gather, analyse, and use customer data in a primarily automated manner. Without it, you will be limited to generic and resource intensive manual processes. There are a myriad of solutions available, and each will depend on your requirements, capabilities and operational budget. Defining your solution should be the result of a dedicated discovery effort.

Typical solution design

Picture of solution design for personalisation

Image reference: McKinsey

Looking to the future

The rise of specialised platforms: Rather than relying solely on monolithic DXPs (Digital Experience Platforms), the future of personalisation might gravitate towards specialised, cost-effective solutions, particularly in the realm of headless architectures.

AI and machine learning integration: The advent of AI and machine learning brings unprecedented opportunities for scaling personalisation. By harnessing data-driven insights, businesses can anticipate and meet individual needs more effectively.

Luminary projects showcasing personalisation

Heart Foundation

The Heart Foundation project team designed a personalisation strategy, with infinite content personalisation driving the selection of content tiles based on the user’s self-identification of their role and reason for visiting, and then their on-site behaviours. As the site learns about the user, increasingly tailored content is presented in real-time.

UNICEF

The UNICEF project team utilised audience and behaviour-driven A/B testing and personalisation. Taking a behavioural science approach to the experience, we implemented personalisation and experimentation options around default values, and information nudges. We setup the personalisation and experimentation options for UNICEF editors to configure going forward.

Pet Culture

From a content perspective, one of the key features of the site was personalisation. Pet Culture customers provide information such as their pet’s name, breed, weight, favourite foods and age, so that relevant content and product recommendations can be customised throughout the site. The information from a pet’s profile can be shared by their owner much like ‘Facebook for pets’.

William Angliss Institute

Students select their status (local or international) as well as where they want to study, and then their entire William Angliss Institute experience of the site is personalised to the selected parameters. This includes everything from the content on the home page, right through to the available courses displayed. This high level of personalisation enables very specific messaging that ultimately optimises conversion.

Summary

Personalisation comes in all shapes and sizes, each requiring different data, platforms and demands on your organisation. But, the choice to do it in the first place, should be a considered one, with clearly defined goals and a plan to get you there. If you have any further questions or are looking for a personalisation partner, get in touch with us here at Luminary.

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Foundations of personalisation

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Understanding personalisation levels

Data types