We’re back with some more tips and tricks to help your brand optimize your declared data strategy.
This week we’re talking about all things personalization, since it’s no secret that personalization has been a priority for brands across all industries especially in recent years.
Yet the days of personalizing a consumer’s name within an email, or sending emails with relevant subject lines based on a few discrete data points will no longer cut it.
We’ve now entered an entirely new era that’s redefining what personalization actually looks like. As technology improves and digital selling soars, these simple ‘personalization’ strategies are now a thing of the past.
Consumers want more and expect more from brands at every stage of the customer journey. And for good reason.
Since the first HTTP cookie was invented in the early 1990’s, consumer data and tracking has become increasingly widespread (and not to mention, personal) over the last few decades.
Many brands typically rely on behavioral data (ie. how long a consumer viewed a specific product) and transactional data (ie. what products the consumer purchased, if any) to power future personalization.
The drawback of simply relying on behavioral and transactional data is that often the results are based on inferences and assumptions. There’s no direct conversation between the brand and the end consumer, which means the likelihood of a consumer receiving irrelevant advertisements is nearly inevitable.
We’ve now entered the age where personalization is being much more humanized through conversation. Consumers want brands to get to know them and use the information they willingly share to provide relevant experiences, products, and content. a consumer expecting the brand to know exactly what they want and provide a highly personal and relevant experience across all touch points.
Standard recommendation engines take many of the data points described above into account. Third-party cookies, plus behavioral and transactional data, enable recommendation engines to splice a brand’s customer base into audience segments and make recommendations based on trends.
For example, if shopping history shows that a female consumer between the ages of 24-36 purchased baby clothes, then a recommendation engine might start populating any and all products related to babies, newborns, toys etc.
While this may seem relevant, this is based on assumptions. The consumer could have purchased the clothes as a gift, and even though she herself does not have children, she will begin to see advertisements that are simply not relevant.
Standard recommendation engines may also drive recommendations that are simply meant to drive a purchase. Yet, the consumer might be looking for something more out of their experience.
Sure they might be looking for a product that will work well for them, but they also might be looking to learn something new or receive another form of genuine value as well.
The bottom line is a standard recommendation can’t possibly take individual consumer preferences into account.
So what are brands to do to power personalized experiences? Especially in light of the recent alarms surrounding a cookie-less future, many brands are left scrambling to power greater relevance in every interaction.
Enter declared data.
Declared data, zero-party data, whatever you want to call it is data collected from the most accurate and reliable source – your consumers.
There’s no guesswork or generalizations with a declared data strategy. Individualized recommendations and experiences are powered by actionable and accurate data that is explicitly shared by consumers.
Now, you may be wondering “how am I supposed to connect with every single one of my consumers to ensure they receive a truly personalized experience?”
Keep reading for a guide to understanding what makes a product recommendation truly personal and how you can adopt a first-party data strategy and begin personalizing the consumer journey at scale.
A personalized product recommendation is one based on consumer preferences. In order to understand consumer preferences at scale, many leading brands have adopted quiz commerce in order to engage with consumers via interactive experiences.
NARS has implemented numerous quizzes aimed at better understanding each of their consumers. Let’s take a foundation match quiz, for example. If a consumer indicates that they have medium, combination skin, then NARS’s personalized product recommendation would only recommend products that fit those preferences.
Another example comes from our friends at the NFL. Many of their interactive experiences explicitly ask fans which team is their favorite. If a consumer indicates that the New England Patriots are their favorite sports team, then the NFL knows not to send any irrelevant advertisements from other teams across the league. In doing so, the NFL can drive greater fan engagement at scale.
Bottom line: personalized product recommendations exceed the capabilities of generalized targeting and audience profiling.
Increasing engagement, conversion, and loyalty are some of the top priorities of brands across all industries.
If your brand can engage your consumers for more than a few seconds, drive them to convert, AND keep them coming back for more in the future, then you have the perfect recipe for eCommerce success.
Achieving all three of these goals in tandem may sound daunting, but each of these are a guarantee when you deliver personalized recommendations to every consumer.
A personalized product recommendation says a lot more to your consumers than you may think. In fact, 80% of consumers are more likely to make a purchase when brands offer personalized experiences.
Yet on the flip side, an estimated 78% of brands say they struggle with “data debt” or not having enough quick data about their customers to not launch relevant personalization tactics.
If consumers want personalization, but many brands struggle to collect relevant data in order to implement personalization at scale, then where do we go from here?
Lucky for you, Jebbit can help your brand collect relevant first-party data at scale in no time at all!
Jebbit’s platform is simple.
You can give consumers the experiences they want, while simultaneously collecting the first-party data you need.
How does it work?
Create a new experience within the platform and start asking the questions you’ve always wanted to ask each of your consumers. With dozens of templates to choose from, it’s easy to jumpstart an experience and make iterations as you go.
The best part is you can infuse your brand voice, tone, and style into every experience.
Now, you may be wondering “where do personalized product recommendations come into play?”
Well, we’re excited to announce a new product feature that will take personalized product recommendations to the next level.
Jebbit’s Dynamic Product Feed enables brands to upload their merchandise files and automate the results of their product quizzes.
Dynamic Product Feeds replicate the guided selling process that takes place in-store by providing a seamless, personalized shopping experience and increasing purchase confidence across any digital channel.
The Jebbit platform has always allowed brands to create logic-based Outcomes for personalized recommendations, but these can get unwieldy when dealing with more than a handful of possible results.
Now, brands with dozens, hundreds, and yes, even thousands of products in their catalog can use Jebbit experiences to recommend any of their products based on an individual’s specific needs.
Not only do customers get what they want faster, but brands secure higher average order values and lower return and cart abandonment rates. The new feature also expands the number of product recommendations that can be given in a consumer’s result.
Stay tuned for our official product launch next week, where we’ll dive into all of the specifics and share a quick tutorial for how your brand can implement Dynamic Product Feeds and deliver personalized recommendations to every one of your consumers.
Looking for more tips and tricks to navigate the cookieless future? Check out this discussion on all things first-party data with our friends at Salesforce.