Going beyond the 4 Ds, the 5 Steps to achieving customer-centric personalisation, with actionable tips on implementing personalisation
Need to roll out personalisation at scale?
It’s becoming incredibly important: McKinsey showed in 2021 that 71% of consumers expect personalisation, 76% get frustrated when you don’t personalise and 78% will refer friends and family if you do – Google says it even makes customers 40% more likely to spend more with you.
But where do you start? How do you begin rolling out personalisation that’ll actually have an impact, if you’ve never done it before?
Here’s what you need to know about how to implement personalisation…
When McKinsey conducted their pivotal study on personalisation expectations and outcomes, they identified key focus areas for brands aiming to achieve personalisation at scale through technology. These areas, known as the 4 Ds of personalisation, include:
In essence, when using technology to implement personalisation, these become a roadmap for selecting the appropriate tech and tools needed to scale personalisation efforts.
Personalisation starts with centralising customer data. Implement a robust Customer Data Platform (CDP) that unifies data from various sources, such as online behaviour, purchase history, and social media interactions. This unified view is the foundation of understanding your customers granularly.
Choose a CDP that integrates seamlessly with your existing systems and can scale with your business growth. See how to integrate online and offline channels for seamless omnichannel capability.
Data analytics tools are critical for turning raw data into actionable insights. Use these tools to identify patterns and trends in customer behaviour, allowing you to tailor your personalisation strategies effectively.
Regularly review and update your data analytics processes to ensure they stay aligned with your evolving personalisation goals and user engagement strategy.
AI and machine learning are the new frontier in decision-making capability. But if that feels too complex or overwhelming right now, consider starting with a recommendation engine. Some of these third-party tools are already powered by AI in the backend, which can help deliver personalised product suggestions based on your customer data and behaviour.
Consider experimenting with third-party recommendation engines that leverage industry-wide datasets for more accurate recommendations, along with other omnichannel technologies.
AI and machine learning algorithms can help manage and interpret vast amounts of customer data. These technologies can analyse data in real-time, making near-instant decisioning and offering personalised recommendations and experiences at scale, a reality.
When ready, move on to AI-driven recommendation engines that can suggest products based on individual customer preferences. Also, see how to use powerful differentiation strategies to unlock the future of retail.
Once you have some decisioning in place, remember that personalisation extends beyond product recommendations. The content you use to deliver suggestions, promotions etc. through various channels is vitally important – see the power of emotional UX in retail.
Implement dynamic content with personalised messaging and visuals tailored to each customer's profile to enhance the overall customer experience (measured with customer data analytics) by making it more relevant and engaging – see how to use data for better app engagement.
Extend your personalisation efforts to all customer touchpoints on your customer journey, including email, SMS, and push notifications. Tailor your messaging based on customer behaviour and preferences to drive higher engagement.
Use A/B testing to refine your messaging strategies and identify what resonates best with your audience – measure with customer journey analytics.
To scale your personalisation, you’ll want to automate all your campaigns. Imagine being able to send personalised messaging and content across various channels consistently and at the right time, without overwhelming your team.
A good start is to set up triggered email campaigns or push notifications that automatically send personalised content based on specific customer actions or milestones – use feedback and experience tools to find out which campaigns work best for your users.
Customer Relationship Management (CRM) systems are essential for tracking customer interactions and ensuring a seamless personalised experience. What’s great about CRM is they both give you additional data to make decisions on and allow you to engage with consumers (i.e. set up automation) from inside the platform.
Plug your CRM into your personalisation system, updating it regularly to have it help you gather feedback data for your personalisation efforts. Also, see these retail app features for personalisation.
You want to be sure that your personalisation efforts are actually delivering what you want – more engagement and sales. So be sure to build reporting on your personalisation campaigns, tracking key metrics like customer engagement, conversion rates, average order value, and customer retention, before, during and after deploying new personalisation efforts.
This is where your CRM can be quite useful again, as well as tools like Google Analytics that help you monitor specific metrics and pull reports for your team to scrutinise and make adjustments to your strategies as needed.
See how to use analytics to boost engagement and strategies for increasing app engagement.
Personalisation is not a one-time effort. Things that worked well for a while can suddenly become less effective as customer behaviour shifts or the marketplace or competitors introduce new tech. That’s why you need to employ design thinking and treat personalisation as you would rolling out a new product – by using an approach akin to MVP rollouts and iterating based on user feedback.
You can stay ahead by continuously testing and refining your strategies to maximise ROI. Schedule regular reviews of your personalisation efforts, conducting A/B tests to determine the most effective tactics so that you can always make data-driven decisions as customer preferences and behaviours evolve.
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