3rd-Party Data Falls & Marketers Focus on Internal Customer Data


Written by ActionIQ CEO, Tasso Argyros

Is third-party customer data going from the next big thing to a thing of the past? If so, what can and should marketers do to respond?

Restrictions on third-party data are coming fast and furious. Due to revelations in March of its questionable use of Facebook data, Cambridge Analytica has already declared bankruptcy. And Facebook has begun shuttering its Partner Categories Program, which enabled advertisers to target ads based on third-party data.

At the same time, the European Union’s General Data Protection Regulation (GDPR) goes into effect this month, bringing much stricter rules for the storing, sharing and use of personal data of EU customers—forcing changes far beyond European borders.

For years, third-party customer data looked like marketing’s magic bullet. By acquiring data from other companies, they could understand the behavior of individuals and use that knowledge to target them via social media and other media platforms. However, the current upheavals are forcing marketers to refocus their resources on the data they already do possess—their own (i.e. first-party) customer data. The good news is, marketers don’t have to pay for that data. And most companies have the opportunity to leverage it far, far more than their current capabilities allow.  

Mining Your Own Gold

The digital economy has already made the effective use of first-party customer data a competitive mandate. That’s because consumers have far more information and power, and far less brand loyalty than they did just a few years ago. Marketers can’t expect to acquire consumers once and be done with it. They must keep acquiring them, over and over again. That is why there has already been a massive shift in investment from customer acquisition to retention, loyalty and lifetime value. And of course first-party customer data is crucial to these efforts.

Obviously, enterprises haven’t ignored first-party data. Most have made enormous investments to organize it and make it actionable. The problem is, the data they’re wrangling—and the ways to make it actionable—keep growing, changing, and getting more complex. As soon as all the data is gathered from multiple marketing systems into a data lake or other traditional solution, changing data sources or data types start breaking down the connections needed to make it useful.

Meanwhile, marketing must wait on overburdened  IT resources to provide access to that data in an actionable form. Connecting actionable data to new and/or evolving marketing execution systems only make the process that much more more complex.  

Here are some questions marketers should ask themselves to uncover where they are falling short in using their customer’s data—and identify opportunities they may have never even considered possible.  

  1. How much of your first-party customer data are you actually using?

Even first-person customer data comes from disparate systems, with data types that don’t “talk” well with each other. And even the way you want to define and use that data is constantly changing—for example, the definition of an “active” subscription customer—is it someone who has made a purchase in the last month, the last six months, or the last year?

The workaround? Expensive (and limited) IT to make that data talk. Given these challenges, it is not uncommon for large marketing organizations to make use of only one percent of even first-party data. The good news is, this means that, if they can find ways to speed and automate data unification, they can grow their base of actionable data by up to 100X.

  1. How long does it take you to gain insights on your consumers to create targeted audiences and build campaigns? Hours and days or months?

It is not enough to have all your customer data in one place, of course. Marketers should have the ability to create customer attributes and build campaigns, without having to source lists and attribute changes from IT. Unfortunately, traditional solutions can’t support this fast, self-service model. They require complex coding to create or change customer attributes, which may in turn require even more work to prepare data of individual customers with those attributes.

However, that changes when an intuitive interface gives marketers direct access to all their customer data, and the ability to use, and even create or change, the attributes they want to use to build audiences.  Suddenly they can use their expertise and creativity to mine the gold in all that data and quickly build more effective campaigns.  

  1. How easily are you able to integrate AI and data science models on top of your customer data to inform marketing strategies and customer experience? Are you simply limited to out-of-the-box capabilities for predictive analytics?

With billions, even trillions, of first-party customer events and transactions, enterprises are sitting on top of a goldmine of insights. Out-of-the-box predictive analytics, which have not been built for, or informed by, your unique data, invariably provide mediocre, even disappointing, results. Alternately, you have to make huges investment of scarce data science talent to start truly learning from your own data. And even then, insights are often never put into action, because there is no easy, scalable way to to do so.  

Instead, you need a framework that enables your data scientists to work directly on your data in an efficient way, instead of in siloed solutions—and then make those insights available to the business automatically. With the ability to seamlessly integrate AI and data science, from data science modeling through to marketing orchestration and execution, you can drive a much more enhanced customer experience.

  1. Are you able to orchestrate all your marketing activities across all your campaigns and channels— a fundamental requirement to drive personalization across the whole customer journey?

Like data unification, businesses have invested heavily in orchestration tools that tend only to work within a single channel like email, or across one marketing cloud solution like Adobe. This requires multiple teams to manage multiple channels, resulting in disconnected processes and operational inefficiencies. For example, teams for each channel must manage their own segment creation, and it is complicated to craft truly personalized conversations and speak with with “one voice” across all channels.

But what if you had a single orchestration platform that draws on a single set of data yet connects to all your channels? Then you could, for example, segment customers once and execute across all channels at scale—and craft a single conversation with customers, no matter how many channels you are using to communicate with them.   

  1. Can you perform testing and demonstrate conversion and lift across all campaigns and channels?

Testing and measurement capabilities are fundamental for you to demonstrate marketing effectiveness—and constantly refine marketing activities in today’s ever-shifting competitive landscape, with fickle customers, marketing noise and small brands able to make big splashes. However, testing and measuring tends to be as siloed as the marketing execution systems you use. The result: over-reliance on last-touch attribution, with all its limitations, as well as channel-specific performance metrics (opens, click, etc) versus business-value metrics.

But what if you could test and measure in a highly coordinated way across every channel in a campaign? Then you could automate multi-level holdouts, suppression  A/B/n testing— and measure lift across an entire campaign far more accurately. And if behavior across channels is always connected to individual customer, you can see how each channel does (or does not) contribute to total purchase value or other key performance indicators of your design.   

Next Steps

As access to third-party customer data wanes, I hope that these questions demonstrate how much untapped opportunities are still remain within reach.

Tasso Argyros
Tasso Argyros
Founder & CEO
Tasso left the Stanford PhD program to start Aster Data, which was acquired by Teradata for $325M. At Teradata, he continued to solve big data problems for Fortune 500 enterprises. Tasso’s passion for empowering business users led him to start ActionIQ, with the mission of bridging the gap between data & action.
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