Tools and strategies, how to implement and how to ensure you get max ROI for your efforts – this is the guide to personalisation at scale
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Creating super personalised experiences is no longer an exception but the norm – McKinsey’s now-famous study showed that 71% of consumers expect personalisation, 76% get frustrated when you don’t personalise and 78% will refer friends and family if you do.
If you get the personalisation game right, Google’s consumer insights show that customers become 40% more likely to spend more than they would have.
But we know that: Local niche businesses flourish at a small scale because of their individualised service.
The question is, in a larger retail, e-commerce or commercial space, how do you achieve the same level of personalisation at scale?
In a hyper-connected world, your ability to deliver highly customised experiences, mimicking that of a small, local store to many customers across various touchpoints, channels and, quite often, across vast geographical regions is what differentiates you and drives loyalty and ROI.
Unlike basic personalisation, which might involve simple tactics like using a customer's name in an email, personalisation at scale integrates sophisticated data analytics, automation and sometimes even artificial intelligence to provide relevant content, offers, and interactions to each individual based on their unique behaviours and preferences.
See how to implement personalisation. Also learn to reduce churn by using segmentation tools to identify and retain your high-value customers.
As we’ve seen, customers expect and appreciate feeling like they’re more than a number, which is crucial for enhancing their experience, improving satisfaction and ultimately driving loyalty.
Being able to deliver more relevant content, information or offers at the right time and place really resonates with your consumers, leading to higher engagement rates, which in turn can drive conversions and boost revenue – in e-commerce for example, personalised experiences have been shown to raise average order values.
Personalisation also builds brand loyalty – customers are more inclined to stick with brands that understand and cater to their needs, fostering long-term relationships and creating brand advocates.
See how to use data to help boost customer loyalty.
When McKinsey and company did their landmark study into the expectations and effects of personalisation, they found specific areas of action that brands can use to create personalisation at scale using technology, called the 4 Ds of personalisation, namely:
So when one talks about using tech to create personalisation, those vectors create something of a roadmap for what tech and tools you need to create personalisation at scale.
At the heart of personalisation lies robust data collection and management. Customer Data Platforms (CDPs) play a pivotal role by unifying customer data from various sources – such as online behaviour, purchase history, and social media interactions –to create a comprehensive and cohesive view of each customer. This unified data allows businesses to understand their customers on a deeper level.
Data analytics further enhance this process by helping you analyse collected data to identify patterns, trends, and preferences, helping you make informed decisions and tailor your strategies effectively.
Discover how to use big data to understand customer needs better and hyper-personalise with customer data analytics.
There’s a vast amount of data in personalising at scale, and getting truly granular is hard even for large teams. Using solutions with immense computing power such as AI is fast becoming a necessity.
Instead of slowing things down or risking inefficiencies in reaching cohorts, you can use these near-tireless technologies to delve into the nitty gritty and make decisions based on parameters you set, customer data and predictive analytics with fairly high accuracy.
If you’re reluctant to release AI on your data, another option is employing a recommendation engine. Though probably also largely powered by AI these days, the benefit is you can use a third-party provider’s tools, and those normally have the benefit of being built off larger, industry-wide datasets.
See how to use AI and machine learning for personalisation.
Engaging customers with personalised content is possible with the right design principles. Dynamic content systems can help generate a visually appealing and personalised presentation that’s tailored to each customer profile. By ensuring that the messaging and aesthetics resonate with the individual’s interests and needs, you can enhance the overall customer experience.
Email marketing platforms are some of the best-known for personalisation. Taking a page from the same book, you can get great results from applying similar approaches to notifications, SMS and more.
Learn to use A/B testing in digital banking.
At Specno, using advanced design techniques for personalisation has historically helped brands ensure they deliver relevant content that’s also visually compelling, significantly enhancing customer engagement and satisfaction.
See the power of empathy at scale with design thinking for great experiences.
By automating your marketing campaigns, you get both timely and consistent messaging across email, social media, and mobile apps, but it also allows you to bring in a high level of personalisation without overburdening your marketing teams.
CRMs were built for individualised engagement with customers. These systems ensure that all customer interactions are tracked and the data used to inform you so you can leverage it to provide an even more seamless and cohesive personalised experience.
Discover more powerful omnichannel technologies for advanced retail.
Start with a strong data foundation by investing in robust data collection and management, then leverage advanced analytics, using AI and machine learning to gain deep insights into customer behaviours and preferences.
With these insights, you can start developing the content you need to deliver and build in the dynamic content management elements, to tailor interactions to customer profiles – always aim for relevance, first. See how to implement personalisation.
Finally, you can automate the content delivery and then continuously measure, test and refine your approach to optimise effectiveness and maximise your return on investment.
Learn the value of testing new ideas faster (and safely) with the retail MVP method and learn to use A/B testing in digital banking. Also learn to reduce churn by using segmentation tools to identify and retain your high-value customers.
Now all of this only makes sense if it actually helps you boost engagement and drive up basket amounts. So it’s important to start measuring the impact your personalisation efforts are having from day one – i.e. ensuring a strong return on investment (ROI).
Some key metrics to track are:
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