Boosting Customer Satisfaction: How Data And Analytics Drive Personalised Customer Experiences

Are people becoming harder to satisfy? Yes. The likes of Amazon and Apple have set new standards in customer experiences that we expect other businesses to match. But there are other factors, too. The cost-of-living crisis has made all of us a lot more critical. According to the Institute of Customer Service, customer satisfaction in the UK has fallen to its lowest level since 2010 .

In this article, we’ll explain why personalising customer experiences can boost customer satisfaction, and why we believe leveraging data, and applying analytical techniques, is the mechanism for achieving this. We’ll show how you can kick-start your personalisation strategy today using the data already at your disposal and build from there.

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Why Customer Satisfaction And Personalised Experiences Are Critical

Customer satisfaction is critical for long-term business success. It often correlates with higher revenues and market share. There are many ways to increase satisfaction levels, but one of the most effective is improving the customer experience.

A better experience drives customer loyalty and retention, reducing churn and increasing customer lifetime value. It directly impacts perception and encourages repeat business. And it converts customers into brand advocates, driving sales through positive word of mouth.

Particularly in saturated markets, where competition is high and growth increasingly difficult, customer experience can make all the difference. It sets companies apart, meeting a need that many businesses don’t seem to be fulfilling. A survey in the US found that 81% of customers prefer companies that offer a personalised experience. Plus, 70% said it was important for personnel to know their past purchases and interactions .

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How Data Fuels Customer Personalisation

Personalisation starts with data. It helps businesses understand customer preferences and behaviours. Data can show everything from gender to preferred touchpoints, purchase histories, and brand perceptions. With these insights, companies can tailor their interactions and offerings to customers’ requirements.

Multiple types of data are available. All of them are useful in personalising customer experiences; however, it doesn’t matter if you don’t have access to all of them. The thing to focus on is using all the available information and then devising a plan to collect the data you don’t have and then integrating that with your existing data.

Here’s a list of the kinds of data you want to collect:

• Primary data – You collect this from your website and apps. Primary data covers click patterns, browsing history, search records, reviews, and user preferences. It will help you understand individual user behaviours and preferences.
• Survey data – To provide a more nuanced understanding of your customer base, you should conduct customer feedback surveys, preferences surveys, net promoter score surveys, and customer satisfaction surveys.
• Third-party data – This covers areas like web scraping, which we discussed in a previous blog . It also includes macro data to better understand the world and insights from data brokers or aggregators, which sell data to help you better understand your target audience. We can also add data clean rooms, which are secure environments where brands, publishers, and advertisers can share and analyse their first-party data. Plus, there are walled gardens, such as Google’s Ads Data Hub, which provide detailed insights into user behaviour, preferences, and demographics.
• Historical sales data – Using sales data to support personalisation is self-explanatory. It’s an excellent source for understanding a customer’s preferences and buying patterns, and it can help identify upsell and cross-sell opportunities.
• CRM data – These systems are designed to support personalisation and are a great tool for tailoring customer interactions. CRMs record and manage all customer-interaction data, creating a unified customer profile. You can then interrogate that profile to help personalise customer communications.
• Social media engagement data – Using social media data, you can learn about customers’ preferences and interests. Many companies use it to segment their audiences based on commonalities such as demographics and behaviours.

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Turning Data Into Insights To Guide Your Customer Experience Strategy

Get to work with the primary data at your disposal. Start measuring and tracking how customers are interacting with your website and apps. Conduct customer journey analysis to expose the cause-and-effect relationship between touchpoints and marketing channels. It’ll give you an immediate understanding of how different channels work together to create customer behaviour.

Here are some of the many techniques for extracting the goodness from your data for personalisation. Such as:

1. Heatmapping – These visually represent website behaviour through colour-coded overlays. You’ll see, for example, the areas of your website that draw your customers’ attention and where they may be searching for information, giving you insights to improve signposting.
2. Behavioural analysis – It enables you to examine how, when, and why customers engage your company through purchasing habits, brand interactions and product usage. It’s a great aid for customer profiling and segmentation.
3. Conversion rate optimisation (CRO) & funnel analysis – CRO is the systematic process of increasing the number of website visitors that perform a desired action. Testing and optimising webpages allows you to find the right level of personalisation to boost conversion rates. Likewise, funnel analysis, which identifies critical events along the customer journey, will help identify those points where personalisation will have the greater impact.
4. A/B and multi-variant testing – Using A/B and multi-variant testing, you can spot the best-performing personalisation messaging, visuals, and signposting strategies. You’ll find CRO and A/B testing are an effective combination for creating experiences that resonate better with consumers and increasing website performance.
5. Predictive analytics – For this technique, you can use propensity modelling to predict the likelihood of future action based on historical user activity. As such, you can pre-empt customer needs, delivering the right message to the right person at the right time to provoke a specific event.
6. Churn analysis – Here, you’re studying historical churn data to make churn prediction possible. By analysing churn data, you can identify the moments when a personalised customer experience could make the difference between a customer remaining loyal or going to a competitor.

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