All of us today are inundated by digital marketing content—just think of all the promotional emails in your inbox, the sponsored posts in your social media feeds, and the ads in front of your YouTube videos. Over the past five years, digital ad spend has doubled and the cost of acquiring customers has steadily increased.
Marketers are well aware that only highly relevant campaigns serving the right message to the right audience at the right time will stand out from the noise. But doing that is exceptionally hard, and the traditional methods of audience targeting and personalization just aren’t cutting it anymore.
To engage today’s users, marketers need to serve them with timely experiences in the full context of how they behave in-product.
First-party data gives marketers context about users
Most marketers make decisions about who to target and what content to serve based on demographic and engagement indicators like installs, signups, and pageviews. While this does provide some basic information to tailor campaigns, it falls short of the deep personalization needed to break through extremely noisy channels like email and push.
Luckily, users are constantly sending signals about what they like, their intent to purchase or convert, and where they are in their user lifecycle. These signals come in the form of in-product actions. For example:
- The last time a user made a purchase on your ecommerce site
- What genre of music they frequently listen to
- Whether they were invited via a referral
- Whether they have watched all seasons of TV show but haven’t started the latest season
- How engaged a user on a trial plan is
Imagine the level of personalization you could achieve if you knew your users with this level of granularity.
Somewhat personalized marketing looks like this: re-engaging shoppers between ages 20-30 with a deal on summer clothing.
Hyper-personalized marketing looks like this: re-engaging shoppers between ages 20-30 who purchased swimwear within the last month with an exclusive deal on sunglasses.
First-party behavioral data helps marketers create highly specific segments that take into context how users engage with the product. The more specific you can make your user segments, the more personal your messaging can be. And the more likely you are to capture your users’ attention.
Marketers can’t access the first-party data they need
Unfortunately, it’s often not easy to access or read user signals like these. Behavioral data is often elusive, especially to marketers.
Even if a business has analytics in place, in-product behavioral data is often inaccessible to marketing teams. The data itself isn’t self-serve, often requiring SQL knowledge to analyze it.
Exploring user journeys is difficult. You may have some general ideas about what signals a user’s intent or their preferences, but you don’t have a way to validate those ideas or uncover new opportunities for marketing engagement.
When you need to pull the user segments you need, you may have to talk to an analyst and then wait days or weeks for a response. By the time you get those segments, they’re already outdated and you’ve missed out on timing your campaign right.
Without access to first-party data, marketing ROI is limited
Without the ability to do behavioral targeting, marketers simply become “campaign factories”: they regularly blast large portions of their database with campaigns targeted to users based on demographic inferences, and hope that something sticks. While some portion of your users might connect with your message, a large portion might not.
This means marketers are not only squandering their time and budget, they’re also not getting the full value out of the rest of their marketing stack. Marketers already heavily invest in automation tools and engagement platforms—but using these tools to blast users with generic messaging won’t give you the return on investment that you could be getting with deeper behavioral targeting.
This brings us to ROI. Teams today often measure the success of marketing campaigns by looking at engagement metrics (e.g. impressions, click-through-rate, bounce rate, etc.). But how do you know whether your campaign influences metrics like conversion and retention? It’s often very difficult or impossible for marketers to connect campaign engagement to business’s North Star metric and then use their learnings to build even better campaigns.
A vision of the future
So that’s the status quo for marketers today.
But imagine a future where you could have first-party behavioral data at your fingertips and where you can easily build out contextualized segments. Not only that, imagine a future where you’re able to do this with precise timing and at scale for all of your campaigns, and then you’re able to see how those campaigns impact your business’s bottom line.
At Amplitude, our mission is to help companies build better products. We’re now evolving our thinking of the “product” to be not just the in-product experience, but everything a user sees. We’re thinking about how we can help teams talk to their users outside of the product in a meaningful way, so that the next time they see an email, they don’t immediately hit unsubscribe.
In the next couple weeks, we’ll be revealing something we have been working on here at Amplitude that will help marketers use product data to deliver better campaigns experiences, faster and more easily than ever before. Stay tuned.
