Data personalization that customers actually love

If you were to receive a blank greeting card that simply read “Happy Birthday!” with no indication of who sent it, or even that it was for you, you’d be a little perturbed to say the least. No one likes feeling perturbed, especially on their birthday. 

Of course, that doesn’t happen because everyone knows that you need to add a few details to make birthday cards work. Call me old fashioned but I want my birthday cards to open with “Dear Esat” and, at a bare minimum, I want them to have a “Love from" sign-off. I also want them to turn up on, or close enough to, the right day.

The point is, we can personalize the “birthday card experience” to varying degrees. For example, we can buy cards that start us on the right track, maybe with a “Happy Birthday, Dad!” or “Happy 40th!” message. And then, to create a special experience for the birthday boy or girl, we can go that extra step and use what we know about them personally to craft a message and make the card unique.

That’s (more or less) the same thing that happens when marketing teams use customer data to create personalized digital content experiences. 

Like birthday cards, there are basic ways we can implement digital content and web personalization at a high level, but to deliver outstanding experiences that our customers love, and that keep them coming back, we need to harness data about them. And we need to integrate that data into our customer journeys accurately and efficiently.

Finding and using data for data-driven personalization can be tricky — and in this post, we’re going to explore that process. We’ll look at the benefits and challenges of managing data and personalization, and discuss how a digital experience platform like Contentful can help you optimize your data personalization process to boost customer satisfaction and loyalty.  

Data-driven personalization defined

Simply put, data-driven personalization is about using data to tailor content experiences for customers and boost engagement. But there’s more to it than that.

All personalization strategies involve data on some level. Very fundamental personalization, for example, requires brands to leverage relatively superficial information about customers: their age, gender, location, and so on, in order to establish, and assign those customers to, specific segments. Marketers can then adjust the digital experience accordingly, tailoring content to boost customer engagement and the likelihood of conversion. 

Data-driven personalization takes the personalization process beyond basic audience segmentation. This approach isn't so much about finding data on customers, but finding the correct data and then using it in the correct way to deliver more impactful and engaging content experiences. 

Let’s go back to our birthday metaphor. In a data-driven personalization strategy, the sender wouldn’t stop at finding a “Happy Birthday Dad!” card, but would seek more data to optimize the card as much as possible for the recipient. That might mean acquiring more, or more detailed, data from past birthdays, including, for example, Dad’s preferred card shape, card color, even card artwork. Basically, if a variable has enhanced the birthday experience for Dad in the past, then it can be considered useful data.  

In marketing contexts, data-driven personalization requires brands to build a new level of depth into their data collection, analysis, and activation process, capturing all the information necessary for segmentation along with additional insights, such as how a customer has previously interacted with content or their purchase history at an online store. That might involve the use of new technology, such as artificial intelligence (AI) and machine learning tools, and the implementation of new personalization workflows.

Then, the brand needs to roll that data forward, keeping it up to date, learning from it, and using it to shape future experiences with that customer. 

Scale that process up to as many customers as possible, and you’ll likely have a data-driven personalization process that delivers meaningful results.

What are the key elements of data-driven personalization?

An effective data-driven approach entails collecting enough information to build a holistic picture of individual customers. Here are the key elements of the process:

1. Acquiring behavioral data

You’ll need to establish as detailed a picture as possible of how a customer has engaged with content in the past. This means collecting data on an array of metrics, such as past purchase behavior, browsing history, time on page, and so on. 

2. Creating the right audiences 

You need to be able to target the right customers with the right experiences, which means customer segmentation is the foundation of effective data-driven personalization. By organizing customers into groups based on characteristics like age, gender, location, and so on, you’ll be optimizing the impact of the data-analytics process that you apply as part of the data-driven personalization process. 

3. Ongoing analysis

The personalization process relies on brands learning from customer behavior, and using that insight to inform future personalization. To that end, data analysis should be ongoing: Data from every new instance of personalized engagement should be fed back into the personalization solution. 

4. Privacy and ethical considerations

Data and personalization necessarily goes hand in hand with privacy and ethical concerns. Given the vast amounts of personal data required for effective personalization, brands must ensure that their approach aligns with the relevant data privacy regulations. Ideally, that means leaning into first-party data collection methods — collecting data directly from customers, with consent. 

What are the benefits of data-driven personalization?

Data optimizes the potential of personalization strategies and, by extension, content strategies. Let’s examine the benefits in more detail. 

Engagement

Personalized experiences enable you to connect with customers meaningfully, with content that reflects their individual preferences and behaviors more accurately. That connection typically encourages customers to engage positively with your brand’s output, and convert at higher rates. 

Customer loyalty

Data-driven personalization optimizes the digital experience with greater depth and nuance than segmentation. That typically increases the appeal of the marketing message to customers, improving the customer journey, and increasing customer lifetime value by strengthening the loyalty that individuals feel to a brand. 

Increased revenue

Increased engagement and conversion rates typically lead to higher sales — which means increased revenue for the company, and better prospects for growth. 

Compounding data

The compound benefits of data-driven personalization create downstream commercial benefits, not least for ongoing personalization. Essentially, the more customers you engage through effective personalization, the more data you can collect to feed back into the system to inform future personalization efforts. 

Scalability

If trying to grow your customer base, an effective personalization solution can make the scaling process easier. You’ll be able to use your customer data to personalize new sites and new pages across multiple channels, in different regions and for different brands — and help your company shape its path to growth. 

Barriers to personalization

Numerous obstacles prevent brands from activating data efficiently for the creation of personalized experiences.

Data silos

In complex digital ecosystems, critical customer data may become siloed — and end up fragmented across different platforms and drives. Siloing hinders data-driven personalization significantly, making it impossible to create holistic, unified customer profiles. Siloed data also makes it more likely that customer experiences will become inconsistent, and that customers will be shown irrelevant content. 

Data quality

The saying “garbage in, garbage out” applies to content marketing, where the quality of data that you recruit for personalized experiences significantly affects chances of engagement. But finding and maintaining high-quality data isn’t easy: you’ll need to perform regular reviews to ensure that the information you've collected is accurate and up to date. 

Tech stack

Complex content tech stacks can make it difficult to share and access data — which, in turn, makes it more difficult to execute effective personalization. Personalization strategies that require users to jump between multiple tools, for example, risk fragmentation — it’s much easier to centralize and unify all these moving parts under a digital experience platform like Contentful, and make it available as a single source of truth for all front ends.  

Analytics

Brands that lack the capability to analyze customer data accurately and efficiently struggle to implement data-driven personalization. To that end, it’s necessary to integrate testing and experimentation tools within the tech stack in order to refine content experiences and test hypotheses, and to create a closed or infinite loop of continuous digital experience optimization. 

How does Contentful help brands personalize content?

Contentful’s digital experience platform makes data-driven personalization effortless — removing barriers to data activation, and enabling marketers to generate more ROI from their existing data and content investments. 

Increasing data value

The innate extensibility of Contentful means that you can add a range of powerful customer data connections to activate the data sitting in your MarTech stack and boost ROI. You could, for example, connect Contentful Personalization to Shopify, Klaviyo, or Salesforce CRM to help activate data for personalization and experimentation at scale — turning the otherwise idle information held in your system into actionable, revenue-driving insight. 

Marketer autonomy 

Contentful Personalization is built right into the platform, and available at the click of a button. The platform is designed to be completely accessible for nontechnical users, which means marketers can apply personalization without any need for developer support.  

Testing and experimentation 

Contentful Personalization also provides native testing and experimentation tools, including no-code A/B testing support to help brands refine their personalization strategies with built-in customer data analytics. Our testing tools will even suggest A/B test variations to help you get the most out of the data you hold. 

Analytics and insight 

Contentful makes it easy to track the performance of personalized content closely, feeding real-time data back into your personalization solution continually in order to enhance customer experiences in the future. 

Wrapping up

It might not be as special as a birthday, but the time your customers spend with you online can still be memorable. 

With that in mind, don’t underestimate the value of data personalization, or the power of a platform like Contentful to help your brand reach its content goals and deliver experiences that not only delight customers, but keep them coming back for more. 

Explore our dedicated product, Contentful Personalization, to learn more about our platform’s personalization capabilities, or drop a line to the sales team to discuss your next step.

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