7 Ways to improve experiences, where to get better data and how to implement it across multiple channels in your business – this is using customer data analytics for hyper-personalised experiences
Want to deliver hyper-personalised customer experiences?
Salesforce data recently showed that 84% of customers say they want to be treated like a person – not a number.
But how do you create personalised experiences when you have a broad and segmented customer base across a vast geographical region, demographics and preferred channels and platforms?
The answer is better customer data analytics to inform more advanced interactions, seamlessly across multiple channels. Here’s what you need to know…
Personalised customer experiences are about seeing, recognising and treating every customer as an individual. Seemingly impossible 10-20 years ago, it is now fundamental to business success – seeing as we can use technology to make it so.
It’s about creating customised journeys for each customer by getting to know them on a deeper level, including their preferences, behaviours, and past interactions with your brand. This is best done by collecting data and then using it to inform customised content for them, delivering unique product recommendations, sending personalised messaging and even providing tailored support.
For example: Online retailers use personalisation at scale to recommend products, offer discounts, and create tailored shopping experiences for each customer. This can include dynamically altering website content to reflect individual interests or sending targeted emails with product suggestions based on past purchases, Enabling remarkable customer experience management.
First and foremost, personalised interactions boost customer satisfaction by enhancing their engagements with your brand – in a way that makes them feel understood and valued.
When content and offers are relevant to individual customer preferences, they are more likely to capture customer attention and drive engagement, which drives better ROI on promos and campaigns.
Most importantly, personalisation at scale has been shown to create a deeper connection with your brand, making them more likely to remain loyal and keep shopping with you, while the quality and performance of your personalisation strategy simultaneously help set you apart from your competitors.
Customer data analytics involves collecting, processing, and analysing data to gain insights into customer behaviour and customer preferences. Apart from the normal demographic data (age, location etc.), you might also collect the following:
And you can usually collect this either as:
See how to use big data to understand customer needs and the keys to using data to boost customer loyalty.
Dividing your customer base into distinct segments based on shared characteristics or behaviours allows for more targeted and relevant marketing efforts.
For example, laser-focusing on a cohort such as “women aged 24–28 living in urban areas” allows you to analyse and test different actions or campaigns to try and appeal to, attract or re-engage young urban women. You can run tests on tailored messaging, promotions and product recommendations until you find what works to boost engagement and conversion rates, and then rinse and repeat the process with every other customer segment you have.
See how to use analytics to drive engagement and advanced customer experience management.
Using historical data to predict future behaviours and trends can make your marketing more proactive. Predictive analytics involves analysing past customer behaviours to forecast future actions, such as purchase likelihood, churn risk, and product preferences.
By anticipating customer needs, retailers can send timely offers, restock popular items, and optimise inventory management. For example, a retailer might use predictive analytics to identify which customers might be interested in a new product launch and target them with personalised marketing campaigns.
Learn how to use machine learning for personalisation.
Implementing recommendation engines can significantly enhance the shopping experience. These systems analyse customer data, such as purchase history and browsing patterns, to make tailored product suggestions, which is shown to increase the likelihood of purchase And help customers discover new products they might not have considered.
Personalised recommendations can be displayed on product pages, in shopping carts, or through email marketing, creating a seamless and intuitive shopping journey.
See the crucial app features for personalised user journeys.
Customising website content, email marketing, and other communications in real-time based on customer data ensures each visitor has a relevant and engaging experience.
Dynamic content personalisation uses algorithms to adjust the content shown in an app or website according to each user, ensuring you deliver the right message to the right person at the right time, and driving engagement and conversion rates.
Discover our approach to web app development and see how to implement personalisation.
Analysing the customer journey to identify key touchpoints and opportunities is crucial for enhancing the experiences. By logically breaking down all the interactions a customer can/should have with your brand and then using customer journey analytics to measure what happens at each touch point.
This helps you identify pain points and opportunities to provide personalised experiences at each stage. For instance, sending a personalised follow-up email after every purchase or offering targeted promotions during key decision-making moments can enhance customer satisfaction and loyalty.
See the guide to customer journey mapping and how to set up customer journey analytics.
Conducting experiments to test different personalised messages and offers, and using the results to refine your approach, is essential for optimising personalisation strategies.
A/B testing involves comparing two versions of a marketing element (such as an email subject line or homepage banner) to see which performs better. By systematically testing and analysing the results, you can identify the most effective message and tactics, and continuously improve your marketing efforts.
See the 10 validation experiments to test your product idea and learn to use A/B testing in digital banking.
Designing loyalty programs that reward customers based on their preferences and behaviours fosters deeper engagement and long-term loyalty. Not to mention they help you collect and centralise customer data across various platforms for better omnichannel capability.
What’s more, loyalty programs can be tailored to reflect customer segments’ interests and shopping habits. For example, a retailer might offer exclusive discounts, early access to new products, or personalised rewards based on past purchases.
By recognising and rewarding loyal customers in a personalised way, retailers can strengthen customer relationships, encourage repeat business, and increase lifetime value.
See the omnichannel technologies to keep you ahead of the game.
To achieve a truly omnichannel personalised experience, retailers should implement personalisation at scale and strategies across various channels:
See how to implement personalisation.
Need help optimising your customer experience?
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