Every marketer knows that personalization is critical. Once considered a nice touch to spruce up campaigns, today it’s a full-on necessity for digital-first brands. Plus, the bar for personalization is getting higher and higher, to the point that consumers expect real-time, highly-curated interactions on every marketing channel.
The groundwork for personalized marketing begins with knowing your customer. Before you can so much as sketch out a campaign idea, you need to know who you’re marketing to. However, even this seemingly simple question is getting a lot more complicated thanks to the phase-out of the third-party cookie.
Nowadays marketers need to take stock of their cookieless identity resolution options and ensure they have the rich customer profiles they need to achieve true personalization.
Looking for cookieless options for enhanced customer data? Eager to add more sophisticated personalization strategies to your arsenal? Here's what this article covers:
- Customer insights and segmentation
- Personalized advertising
- Personalized product recommendations
- Personalized promotions
Third-party cookies have always gone hand in hand with personalized marketing, since they’ve unlocked access to swaths of data that marketers can use for surgically precise segmentation. But the end of the cookie doesn’t have to sound the death knell for highly personalized ads. If anything, it’s ushering in a new era of more meaningful personalization strategies, where people see content that speaks to them as people, not a click to be retargeted.
Segmentation is still key to achieving human-centric interactions. Without third-party cookies, you can certainly segment your first-party data, but frankly, it won’t offer enough nuance to craft personalized campaigns. Now more than ever, marketers need to prioritize data enrichment from responsible sources. This cookieless consumer data enables brands to build out rich customer profiles based on a wealth of demographic, psychographic, and firmographic data.
Once you’ve enhanced your first-party data, you’ll have the full picture you need to segment your audience in a meaningful way. This might mean segmenting by income level, household size, or even hobbies and interests. The savviest brands go even further and leverage machine learning algorithms to parse through billions of data points, surface the most significant patterns in their audience, and cluster their customers into highly accurate persona groups.
All of this paves the way for true personalization at scale. By segmenting your audience into clusters or personas, you can craft highly personalized campaigns that speak to each unique segment and orchestrate more meaningful interactions across channels.
If that sounds like a plan, read on for the strategies you can use to start bringing those campaigns to life.
Personalized advertising is a fantastic tool for connecting with good fits across the buyer’s journey. Since consumers are bombarded with ads all day long — social media, TV, radio, podcast, flyers, billboards — meaningfully personalized ads help cut through the noise and create an immediate connection.
It’s one thing to segment your existing audience based on rich consumer data, but quite another to personalize ads for potential customers who aren’t yet known to you. Lookalike audience options from platforms like Facebook used to reliably expand brands’ reach to new pools of good fits, but with the advent of iOS 14 and Apple’s privacy updates, these lookalikes have lost a lot of signal and are less cost-effective by the day. You’ll need additional strategies up your sleeve to reach new audiences.
One such strategy involves seeding Facebook lookalikes with AI-driven personas, which gives Facebook a more defined seed audience to work with and enhances its ability to surface consumers that fit your personas. Think of it like giving Facebook’s algorithm a bit of a boost. Then, you can craft highly personalized ad campaigns for individuals you would never have reached otherwise. Brands doing this typically see increased conversion rates and lower CPA.
Another option is to skip Facebook’s native lookalikes and leverage AI to build your own custom lookalike audiences. In this strategy, algorithms can uncover great fits from an identity graph and match them to your brand’s personas, providing you with fresh audiences to upload to Facebook or any other ad platform — greatly expanding your audience beyond what these platforms would normally surface.
With these in place, and your personas already established, you can personalize your ad campaigns with confidence in spite of having never interacted with the audience before.
Once someone has made themselves known to your brand— for instance, subscribing to your email list, creating a free online account, or signing up for a rewards program — personalized product recommendations are another invaluable strategy. You can leverage insights in your CRM to create a truly personalized experience on your website, in-store, via email marketing, and more.
Crucially, consumers have high expectations for these experiences. In such a competitive market, where shoppers can find dozens of similar products and services online with just a quick Google search, it’s the real-time, personalized interactions that help brands stand out from the crowd and earn their trust. Orchestrating this type of personalization requires sophisticated automation and algorithms to make recommendations instantaneously and at scale.
Truly personalized recommendations tend to come back to great life cycle marketing. Once you have an accurate picture of where current and potential customers fall on the buyer’s journey, you can map recommendations to each stage — such as promoting a product quiz or survey to your social followers in order to collect more first-party data, launching email campaigns that are tailored to your unique personas, or nudging customers about new releases.
With most modern ESPs and email marketing platforms, such as Iterable, Klaviyo, and MailChimp, you can also build powerful workflows to trigger personalized product recommendations based on specific actions. For a financial organization, this could look like an email with more information on home loan options after a lead schedules a consultation. For an apparel brand, it could mean triggering an alert that an item has come back in stock for every rewards member who'd added it to their virtual wish list.
By aligning recommendations with an individual’s journey stage and automating them to be delivered at the perfect moment, you create the real-time, personalized interactions today’s consumers crave — and you’ll see the results in your revenue.
For marketing teams that have already dialed up product recommendations and want to offer even more valuable suggestions to potential customers, there’s also the option to take it one step further with predictive journey mapping. Rather than make an assumption about what a person is going to purchase, this entails using AI to actually predict what they’ll get.
These predictions are far more accurate than any other product recommendation you could make, allowing your marketing efforts to become less like a sales pitch and more like the advice of a trusted friend. Just like automated email campaigns, these predictions can be triggered from a platform like Iterable to deliver the perfect recommendation to a given individual at the perfect time.
Sometimes consumers need a little extra encouragement to get them to commit. For these individuals, a well-timed promotion or discount can make an enormous difference. The trick is to tailor the promotion to the consumer in order to protect your bottom line — after all, you don’t want to offer 15% off or a first month free to someone who’s likely to pay full price, but you definitely want that to be an option for valuable lead that’s on the fence.
To be certain that you’re only offering discounts to the customers that matter most, customer lifetime value (LTV) predictions can eliminate an enormous amount of guesswork. LTV is difficult to determine on your own, but a wide variety of AI tools available to marketers make it a piece of cake.
With LTV predictions uploaded to Salesforce, HubSpot, or wherever you track your customer information, it becomes much clearer how to personalize discounts for distinct audiences. You may want to try rewarding your most valuable customers with a discount, as a small thanks for their brand loyalty, or you may want to test out special offers for customers with middling LTV to see if that extra push inspires them to spend even more.
Another proven discount strategy relies on churn prediction. As with all things predictive, AI is the best tool to utilize here, with churn predictions at least five times more accurate than those that traditional methods produce. Once you understand exactly which customers are at the highest risk of falling away, you can intervene proactively, with a personalized touch, rather than reactively.
For instance, you can tailor the discount to the level of churn risk. High-value customers at great risk of churn might deserve a generous discount offer, whereas customers with lower risk can get a smaller one. And just like with product recommendations and LTV predictions, churn predictions can be delivered seamlessly to wherever your team needs them most, whether that’s triggering an email drip or refreshing the customer profile in your CRM — empowering you to provide a seamless customer experience.
Whether you’re just getting started with personalization or are looking to truly master 1:1 communication at scale, you’ve got numerous opportunities available at every stage of the buyer’s journey. Consumers overwhelmingly prefer personalized offers, and an increasingly crowded digital space is making it harder and harder to gain an edge — so it’s more important than ever to double down on personalization before it’s too late.