Data unification is the first step towards personalized content

Content personalization

Personalizing content based on how customers interact with a brand online can improve the overall brand experience. But it can also add volume to an organization’s content inventory, which may not be ideal. An organization can still do this, of course, but building assets that adapt to brand visitors under varying contexts is a better way to approach it.

Creating adaptive, intelligent content takes work, no doubt about it. It also requires a steady stream of user data — and the interpretation of that data — to give us the insights we need for success.

Imagine Jen and Grace, two hypothetical visitors to an e-commerce site. Both women make a purchase. Jen spent more time browsing and bought one thing, while Grace found many items she liked and purchased several.

If we know this information about buyer behaviour, wouldn’t we encourage Grace to buy more with an incentive?

Wouldn’t we want to assist a casual browser like Jen, by recommending products she might like, thereby helping her find what she needs?

Both women identify as new customers, but their behaviours show they are different types of users. If they can get different, effective user experiences, their time with the brand will benefit everyone.

Two people may interact with your brand and buy something but be completely different customers.

Interpreting their behaviour requires an emphasis on using analytics to monitor content and supporting assets, like paid media and social. We see this happening more in a marketing and awareness sense. Think of the last retargeting ad you saw as you browsed around the internet and you’ll know what I mean.

Not everyone wants to purchase or submit information immediately. Perhaps they just want to research and know more before buying. If we have the means of interpreting behaviour and segmentation in relation to different user archetypes, we can serve users content that positively augments their experiences with a brand, which is better than aggressively pushing content based on assumptions alone.

There’s more content to plan and create than ever

We’re starting to see practitioners of intelligent content use customer data in increasingly strategic ways to inform how to create custom experiences.

From an outreach and marketing perspective, Kraft creates a lot of content and uses third-party data to serve this content to the right customers, at the right place and time.

According to Digiday, this third-party data yields about a 50% accuracy rate for Kraft. But what would that accuracy be if they had combined third-party data with their own intel on user behaviour captured from across their brand presence?

From a product and service onboarding perspective, think about the last time that you were pleasantly welcomed (in context of your arrival) to a new website, product, or service. It takes a lot of challenging work to evolve onboarding beyond a simple “Welcome” email.

I recently started a new account at a financial firm. I received the prerequisite robotic welcome emails. Yet my needs for information about how I wanted to use this account were unmet. A UI walkthrough, investment tips, and even appropriate contact information so I could ask questions was lacking.

The prospects of using custom, intelligent content for deeper brand personalization and welcoming new users is exciting. Imagine if that financial firm could use my behaviours on their website and app to inform what content I need and deliver an experience optimized for my time with their brand.

Contextualized user welcoming is an important part of the first-impression experiences. Every little nuance toward making things easy, pleasant, and successful counts.

Looking at custom content from a support angle, many organizations are realizing there’s a lot of value in data-driven support. LivePerson, for example, aggregates all the user data it can to empower customer support reps so they can serve and upsell to users as best they can.

To mitigate support costs even further, content strategists can pay closer attention to customer success factors. Customer success is a great opportunity for meaningful, intelligent content to repair relationships with customers and regain their faith in a brand’s products and services.

Things break and user errors occur all the time. While a great customer support representative can help assuage things, empowering customers with problem-solving information achieves the same thing, while reducing user frustration, and maybe lowers customer support overhead in the process. A three-way win.

Unify your user data to reveal personalization opportunities

There are many benefits for an organization that builds a personalized content strategy to improve its customer service. But before creating content, user data must be collected and unified to draw behavioural insights.

We content strategists know that great content comes by aligning an organization’s needs with those of its users. We model content to render properly in various devices and formats, and we target personas that represent the people who matter most. We also understand that as businesses evolve and our target audiences change, content must evolve and change as well.

A person in context of data-collection devices.
Collecting customer behavior and unifying that data yields new behavior-based insights.

Collect behavioural data from every touchpoint tool you have. This user data becomes the bedrock for the personalized content a brand might create for any kind of outreach (web, paid media, social, email), product or service onboarding (email, product UI), and support (email, support tools, documentation).

Don’t choose arbitrary metrics. In his book, Enterprise Content Strategy: A Project Guide, Kevin P. Nichols reminds us to tie the data we collect to particular organizational objectives. We need to keep an eye on what’s going to push the organization forward in terms of performance, service, and relevance.

Apply this same intensity to tracking the behaviours of users. Advanced analytical tactics and tracking can give a better sense of how users behave as they experience content.

Once unified, hone in on behaviour and intent

Collect the right kind of data to stay a step ahead of these changes. Once a sustainable means of collection is underway, a customer data platform like Lytics, for example, can unify and interpret the data. The right behaviour-analytics tools can yield new user profiles and behavioural segments to inform and influence content creation and maintenance.

I’m no data scientist, but even I can see a feat like this requires tracking behaviour wherever you can. It means ditching old formats — PDFs, for instance — that yield little data. Instead, plan for structured, standards-based assets that make it easy to see where users drop off, where they linger, and what content appeals to them most.

The same principles apply to paid media, social campaigns, and ads spending, and even to customer support libraries. The questions are: How are users consuming the content? and with that information, How can the first line of defense against angry customers be improved?

Personalized content requires more than a volume of clicks and a duration of views. We need to find where users stop consuming content, which kinds of concepts and components appeal to them, and how they interact with other elements of a brand’s experience.

Once we have this kind of data, we can interpret even a small sense of user behaviour and build content strategies around these insights. We can tap into the collective pool of user data to glean insights on demographic, and use machine-learning tools to discover user behaviours.

Content governance powers personalization

A strong governance policy can help build the foundation for personalization. Adjustments to content governance policies allow practitioners to implement data unification and collection around user behaviors. It imbues content maintenance plans, upkeep, and future creation with a behaviour-first approach.

A great governance plan already includes content evaluation and scoring elements. Now is the time to add more user-centric metrics and analyses to evaluate content success.

Content strategy and governance is increasingly challenging, and fun, as we consider how user behaviours influence the content experiences we offer. Do we continue to build one-off assets as our user base changes before us, or do we adapt our content to the specific contexts of each visitor and loyal customer?

Yes, it does require work, but cohesive, consistent experiences are what keep users coming back. I know I’d like to keep planning, building, and delivering meaningful content. If it means learning more about measuring user behaviours, interpreting that data, and adjusting my strategy, governance, and content modeling framework in relation, then it’s worth it.