Of course, there is a clearer dividing line between marketing tech and ad tech: personally identifiable information, or PII. Marketers today have two different types of data, from different places, with different rules dictating how it can be used.
In some ways, it has been natural for these two marketing disciplines to be separated, and some vendors have made a solid business from the work necessary to bridge PII data with web identifiers so people can be “onboarded” into cookies.
After all, marketers are interested in people, from the very top of the funnel when they visit a website as an anonymous visitor, all the way down the bottom of the funnel, after they are registered as a customer and we want to make them a brand advocate.
It would be great — magic even — if we could accurately understand our customers all the way through their various journeys (the fabled “360-degree view” of the customer) and give them the right message, at the right place and time. The combination of a strong CRM system and an enterprise data management platform (DMP) brings these two worlds together.
Much of this work is happening today, but it’s challenging with lots of ID matching, onboarding, and trying to connect systems that don’t ordinarily talk to one another. However, when CRM and DMP truly come together, it works.
What are some use cases?
Targeting people who haven’t opened an email
You might be one of those people who don’t open or engage with every promotional email in your inbox, or uses a smart filter to capture all of the marketing messages you receive every month.
To an email marketer, these people represent a big chunk of their database. Email is without a doubt the one of the most effective digital marketing channels, even though as few as 5% of people who engage are active buyers. It’s also relatively fairly straightforward way to predict return on advertising spend, based on historical open and conversion rates.
The connection between CRM and DMP enables the marketer to reach the 95% of their database everywhere else on the web, by connecting that (anonymized) email ID to the larger digital ecosystem: places like Facebook, Google, Twitter, advertising exchanges, and even premium publishers.
Facebook’s custom audiences uses email addresses to target ads
Understanding where the non-engaged email users are spending their time on the web, what they like, their behavior, income and buying habits is all now possible. The marketer has the “known” view of this customer from their CRM, but can also utilise vast sets of data to enrich their profile, and better engage them across the web.
Combining commerce and service data for journeys and sequencing
When we think of the customer journey, it gets complicated quickly. A typical ad campaign may feature thousands of websites, multiple creatives, different channels, a variety of different ad sizes and placements, delivery at different times of day and more.
When you map these variables against a few dozen audience segments, the combinatorial values get into numbers with a lot of zeros on the end. In other words, the typical campaign may have hundreds of millions of activities — and tens of millions of different ways a customer goes from an initial brand exposure all the way through to a purchase and the becoming a brand advocate.
How can you automatically discover the top 10 performing journeys?
Understanding which channels go together, and which sequences work best, can add up to tremendous lift for marketers.
For example, a media and entertainment company promoting a new show recently discovered that doing display advertising all week and then targeting the same people with a mobile “watch it tonight” message on the night of it aired produced a 20% lift in tune-in compared to display alone. Channel mix and sequencing work.
And that’s just the tip of the iceberg — we are only talking about web data.
What if you could look at a customer journey and find out that the call-to-action message resonated 20% higher one week after a purchase?
A pizza chain that tracks orders in its CRM system can start to understand the cadence of delivery (e.g. Thursday night is “pizza night” for the Johnson family) and map its display efforts to the right delivery frequency, ensuring the Johnsons receive targeted ads during the week, and a mobile coupon offer on Thursday afternoon, when it’s time to order.
How about a customer that has called and complained about a missed delivery, or a bad product experience? It’s probably a terrible idea to try and deliver a new product message when they have an outstanding customer ticket open. Those people can be suppressed from active campaigns, freeing up funds for attracting net new customers.
There are a lot of obvious use cases that come to mind when CRM data and web behavioral data is aligned at the people level. It’s simple stuff, but it works.
As marketers, we find ourselves seeking more and more precise targeting but, half the time, knowing when not to send a message is the more effective action.
As we start to see more seamless connections between CRM (existing customers) and DMPs (potential new customers), we imagine a world in which artificial intelligence can manage the cadence and sequence of messages based on all of the data — not just a subset of cookies, or email open rate.
As the organizational and technological barriers between CRM and DMP break down, we are seeing the next phase of what Gartner says is the “marketing hub” of interconnected systems or “stacks” where all of the different signals from current and potential customers come together to provide that 360-degree customer view.
It’s a great time to be a data-driven marketer!
Chris O’Hara is the head of global marketing for Krux, the Salesforce data management platform.
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