Personalisation is about giving customers tailored, meaningful experiences through the content you create. It’s all about relevant communication based on concrete, statistically-proven data.
And it’s also what customers want.
83% of consumers are happy to share personal data as long as they know what companies will do with it and they remain in control of their data.
On top of this, 91% of consumers prefer to buy from brands that recognise and remember their preferences. And consequently provide consumers with relevant offers and communications.
However, to scale personalisation, you need strategies for collecting and analysing customer data and creating content and experiences to match your findings.
Here’s how.
1. Use an effective organisation model
Personalisation is only effective at scale, meaning personalisation across your whole consumer base and every channel.
To implement this, Accenture researchers found four organisational models that can help you personalise at scale:
1. Emergent optimisation
This model puts decision-makers closest to the customer experience. Furthermore, it gives each optimisation unit maximum flexibility to choose its own strategies, budget and metrics for improving and measuring customer experience.
However, due to emergent optimisation’s autonomous nature, it can lead to inconsistent experiences across channels. On top of this, it can lead to teams conducting similar experiences rather than learning from other units.
2. Controlled optimisation
This model puts decision-makers farthest from actual user experiences as it relies on one department to create and manage experience optimisation.
It’s a good strategy for consistent UX, efficient pivoting and resource management. However, it’s difficult to scale due to one department being in charge—plus, it doesn’t encourage collaboration and innovation because a small unit controls optimisation.
3. Centralised optimisation
This model is the third farthest from the customer experience since a centralised cross-functional team controls the universal framework and processes each unit follows.
The benefit is that the central group is aware of what each unit is doing, meaning it’s easy to optimise resource allocation and provide a consistent user experience. But it requires management support and cross-department buy-in before teams can implement anything.
4. Democratised optimisation
This model is the second closest to the actual user experience since teams are almost autonomous but still work under a unified brand.
The main benefit is that teams have the freedom to implement strategies, helping units iterate experience optimisation faster. However, each department still follows the general overarching company strategy.
If done right, democratised optimisation is a powerful strategy, but it requires constant communication between units to ensure company-wide coordination.
Whichever model you choose, it’s essential to use an overall framework for your experience optimisation—Accenture calls this Problem Solution Mapping.
PSM does away with idea-first optimisation and turns attention to solving UX problems. With this approach, Accenture believes UX experiments and consequent experience optimisations will make a more significant impact than other approaches.
2. Integrate customer data into experience optimisation
Customer data is central to effective experience optimisation, and to integrate this data correctly, you need three data platforms:
A customer-data platform to centralise and connect data from disparate systems. Your CDP should also allow technical employees to implement AI and machine learning models to generate personalisation signals.
A data-management platform to take CDP signals and make them available to use in digital channels. Your DMP can also compare internal data against third-party sources to identify further consumer microsegments.
An identity-resolution platform to help you match internal customer data with outside sources gives you a more holistic customer view.
3. Data-driven experience design principles
The traditional design process of developing bespoke content has a better alternative. It revolves around operating a content factory where you break each piece of content into modular components that you can combine as necessary.
The premise of data-driven design is that it solves customer problems and brings measurable benefits. Modern technology now allows us to track measurable performance through metrics such as:
Profit and loss.
Net promoter scores.
Customer satisfaction and more.
On top of this, it’s easier than ever to gather customer data and create experiments to understand how they interact with your company. Analysing how customers interact with various channels and conducting qualitative interviews to understand user problems are excellent places to start.
In total, Accenture identified ten principles of evidence-based experience design that you can follow:
It solves user problems.
It provides measurable results.
It does no harm.
It’s an experiment.
It’s iterative and always evolving.
It stems from quality customer research.
It listens to users.
It gives control back to the user.
It’s curious and always asks questions.
It’s statistically confident in any conclusions it draws.
4. Real-time decisions and content distribution
Remember the three data platforms from strategy two? They’re the keys to real-time decision-making and content distribution by following the 4D framework:
Data: Here’s where your customer-data platform gives you a clear view of your customers.
Decisioning: Here’s where your DMP and IRP analyse internal and external data to provide actionable, real-time signals for your content design through AI and machine learning.
Design: The signals you generate will influence the design of your content, digital asset management and experience experiments.
Distribution: The final part of the puzzle is delivering your evidence-based content and communications to your customers. You then analyse all interactions and feed the results to your CDP to improve your data pool.
To implement personalisation at scale, you must connect each stage of this framework to ensure smooth insight sharing and implementation. This is better than relying on marketing campaigns that don’t leverage customer data.
Scaling personalisation isn’t just for companies with bottomless budgets
With the four strategies above, personalisation at scale is possible for any company willing to implement them.
While you may be concerned about the budget your company requires for effective personalisation, the rewards are worth the effort:
More customer data to leverage.
More repeat buyers and loyal customers.
More conversions and better ROI due to customer-centric marketing
So choose an organisational principle, create or buy the right data platforms and ensure you follow data-driven experience design principles.
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