My machines know my name.
Whether it’s part of some slow-boil eventual robot takeover or a simple byproduct of a personalization strategy that’s meant to improve my life, it feels strange to see my name pop up all over my email inbox, get populated in display ads, and hear it spoken by my Alexa from one room and Google Home in the other. But at the same time, I’ve quickly come to accept this along with the conveniences it affords. My online shopping is faster with better recommendations. My digital bank feels slightly less sterile when it greets me upon login. The alarm on my phone even knows my REM cycles at this point.
In a chicken-or-egg type of scenario, personalized recommendations and interactions have become the norm in today’s digital society as a welcoming side effect of big data. While brand’s develop more capacity to reliably learn and store information about their visitors, users, and customers, marketers continue to come up with creative ways of using this information to improve their work.
And it is in this new, data-saturated, personalized world that content marketers are given the essential foundation to create one-to-one experiences.
Before your brand makes a big, costly, time-consuming decision to build out a personalization scheme, it makes sense to first consider whether this is what your audience actually wants. I might be comfortable with every device in my home knowing a bit (or a lot) about me, but does that actually translate to audiences at large?
According to Salesforce, providing data in return for personalization (particularly discounts or buying recommendations) is accepted by more than half of Generation X and millennials, with baby boomers coming in at around 40 percent acceptance. Meanwhile, Monetate’s 2017 Personalization Study found that, of companies that were exceeding their revenue goals, around 72 percent had a documented personalization strategy. This was closely followed by brands who met their revenue goals, with around 65 percent having a documented strategy in place, before tumbling to 40 percent for brands who missed revenue expectations.
But just because consumers are looking for personalized experiences doesn’t mean that they’re readily finding them. The Boston Consulting Group has found that only about 15 percent of companies “can be considered true personalization leaders,” while only another 20 percent are experimenting with (but not necessarily deploying) one-to-one personalized marketing tactics.
This leaves two questions for a savvy content marketer to answer: What’s stopping the other 65 percent of brands, and am I working for one of them?
Image attribution: Mike Wilson
One of the biggest obstacles to a hearty personalized recommendations system is the vast data collection infrastructure that your brand has to have in place.
Most personalization systems are impacted by three levels of data collection and attribution. These are:
Session-based personalization is confined to information and actions shared by a visitor within one site session—for instance, a site may change its configuration and information based on an interest you express upon entry without storing this preference for later visits.
Users have their preferences and data stored on their device (usually as a cookie), which is referenced by various marketing tools and activities when they interact with your brand. While more individualized and persistent than session-based tracking, there is room for breaks in the personalization experience if a user wipes their device data or accesses your content on more than one device.
User preferences are stored in a centralized place that is constantly updated and referenced by marketing activities. This type of personalization requires a CRM of some kind, and is often found with brands that require users to log into an account or have implemented sophisticated multitouch attribution systems.
The higher the level of your organization’s ability to gather and store your visitor data, the more options your brand has to personalize. But this is a double-edged sword: Many brands hit a kind of paralysis with personalization, where it becomes difficult to know what data should be used to improve experiences, because there’s just far too much to choose from.
Between too little data to use or too much data to choose from, many brands stagnate. From a content marketing perspective, however, there is a lot of room to both improve your audience’s experience and your brand’s ability to better utilize your data.
Take for instance some simple session-level implementations. Health insurance giant Anthem uses geography as a way to filter their site content to better match the needs of their visitors. This is automatically populated when a user visits the page, but can also be manually adjusted by a user at will. In this way, Anthem is able to improve their site experience and create a possible avenue to analyze changing audience interest by looking for trends or spikes in manual changes to a particular area.
Doubling up session-based personalizations with some cookie tracking can lead to even more in-depth results. Skyword’s Personalized Recommendation tool is a great example of this. Functionally, it appears to a user to operate in a similar way as other content recommendation engines, serving content based on previously read material. But additionally, this recommendation data can be analyzed by the Skyword team to identify topic areas that are of particular interest to their audience, which in turn informs their content strategy. In this way, personalization serves to inform new applications for gathered user data, while also reducing friction between visitors and the stories that matter to them most.
However, the crème de la crème of personalization happens at the account level. This is where huge brands like Amazon, Facebook, and Google tend to take the spotlight, due to their place at crucial wheel hubs of our digital lifestyles. But the best example of account level personalization that gets it too right is Target—a brand that is so sensitive and reactive to the buying habits of its customers that it figured out a teen girl was pregnant before her father knew. While this particular case is a bit extreme, massive data lakes give brands the ability to run analyses of large-scale patterns, and then respond with a slew of convenience marketing recommendations across channels that ideally come across as seamless to the recipient.
The trick to personalization success is to start small. Be honest about what data your team actionably has access to and try to see what you can personalize from this to improve the audience’s expereince of your brand. The faster you can bring visitors to content that engages them with the part of your brand story that matters to them most, the more likely they are to tell you more about themselves and return to your brand again and again.
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Featured image attribution: Simon Migaj