With AI and Data Science, Marketers are Maximizing the Power of Customer Data
From CMOs down to digital marketing associates, operationalizing data with data science, AI (artificial intelligence) or ML (machine learning), is among the greatest marketing challenges. Unfortunately for those on the marketing front lines, technology has enabled data collection much faster than it has enabled the use of that data in real life, practical marketing scenarios.
And yet, there is a growing pressure for marketers to use the data to drive programs and decisions. According to Gartner, 77% of senior executives deem data science as “essential” to organizational success today. But when it comes to the use of data science in marketing, it’s often easier said than done.
There’s good news in the sense that there are opportunities to improve marketing efficiency and effectiveness that will come with data science are significant. And for businesses struggling? Using these tools and techniques are nothing short of critical, for long-term sustainable marketing success, especially at scale. A deep understanding of customer needs, how they engage and the experiences they value, is critical for digital agility.
The promise of data science-driven marketing
The modern business now collects and produces a vast collage of structured and unstructured data. Taken individually, each dataset has some insights to contribute. But combine one or more datasets together, and truly powerful, game-changing insights start to arise — thanks to a combination of statistics, machine learning, and data analysis.
“Most of the time, data science quantifies some sort of intuition we already have,” says Katie Slayden, Adobe data science associate. “You might even be able to guess them yourself, but data science is able to put an actual number to them — which gives us more confidence — and it can do this much more quickly.”
In the realm of marketing, customer experience, by its very nature, demands insights from a combination of different datasets to deliver a targeted, personalized experience to every customer across channels, every type of device, at scale. In order to get a clear picture of what content, channels and timing is most likely to engage the largest number of potential buyers, we must be able to combine and tease out patterns using data from ad networks, site analytics, CRMs, and other systems. Any seasoned marketer does this instinctively, but data science enables marketers to find patterns that they wouldn’t otherwise detect, and do it faster and at scale.
However, knowing that we need to inject data science into marketing is one thing. Knowing how to do this effectively is another thing entirely.
Breaking down data silos
The gap between marketing and data science often starts with the teams, systems, and channels where data is generated. By their nature, they form silos around the data. Data creates both power and vulnerability, so teams may not be used to, or understand the value of sharing data with other teams. It takes a leader (and sometimes a data scientist) with an idea or vision for how combined data can be used to create progress and even transformation.
Don’t get overwhelmed by data silos or the lack of a data scientist — technology has advanced to solve some of these problems for you.
Immediate solutions to data science challenges
Fortunately, you have more control than you think in overcoming the above obstacles. You can start with the following:
Connect your data
As much as possible, you need to break data out of silos and collect it in one place. Start with your marketing automation technology — does it make it easy to gather data from many channels into a single customer profile? Cloudflare is a great example of a company that consolidated multiple marketing automation systems to get a better picture of their customers’ needs and improve the customer experience.
You may also need to break through cultural barriers, concerns, or trust issues among teams that own the data. Like Cloudflare, it’s best to start with a customer-centric view and try to connect systems, or at least two or three critical pieces of data in a small project to build trust. For example, in a SaaS software environment, connecting product usage data with renewal rates may produce surprising insights.
You can do some types of data connection manually, in spreadsheets, if the data is not easily connected across systems. But, for the longer term, you’ll want to consider every addition to your Martech stack (more data) with an eye to how well it integrates with your core systems for program execution, content production, analytics and CRM so you can move towards a more automated, scalable and centralized data model. Don’t let critical customer data become a data orphan!
Find opportunities to automate the collection of data within your marketing organization via integrations to core systems. Your core marketing solutions should include pre-built integrations to the most popular tools and APIs to connect to new tools that come on the market.
Move from insights to action
It’s also important to move from customer insights to action when you discover something important. For example, if you learn that customers that implement feature X renew at twice the rate of all others, can you build a list of customers that aren’t using this feature and send them a message? Can you do it quickly and easily? In multiple languages? Without disrupting your current campaigns? If not, you may need to re-evaluate your marketing technology.
Marketo Engage, our marketing automation technology, for example, has its own database, native CRM integrations and pre-built integrations to hundreds of marketing technologies that collect customer data. It also has native integration to Adobe’s DAM — AEM Assets — so you can quickly move from content design to delivery when you discover a new insight. To help you discover those insights, Marketo Engage and our Advanced Attribution solution (Bizible) can also easily share customer data and segmented audiences with Adobe Analytics — no IT required.
With an integrated martech stack and the many new layers of data it brings, you are creating an environment where powerful new insights can arise.
Track campaigns with data hygiene in mind
The quality of your insights will always depend on the quality — or hygiene — of your data. Help your marketing group set shared standards around the collection of campaign data and then enforce those standards.
For example, be on the lookout for “dirty” data, which may have bad records or duplicates. If issues arise, make sure the data gets cleansed. This is a prerequisite before any aggregation and analysis can generate credible and trustworthy insights. Collaborating teams can, in turn, rely on those insights to make smart decisions as to how to best spend the next marketing dollar and who to target in their upcoming campaigns.
Letting technology handle the data science
Many marketers mistakenly believe that they must have data science resources in house to create algorithms from scratch and so believe that AI is out of reach. While this might have been true five years ago, today AI is built-in to many martech products and designed for the average marketer to use — relieving organizations of the need for internal data science resources.
Built-in AI and machine learning opens a world of possibilities. It enables powerful predictive analytics that allow email marketers to precision-target customers based on their likelihood to not just register for, but actually attend an event, to predict program performance, and course correct well ahead of time. It enables audience profiling that anticipates the needs of a customer — even before they have shared their personal data — to deliver the perfect mix of content to meet and exceed their needs.
This is where technology can take over many of the tasks previously reserved for data scientists, or too time consuming for marketers to do manually — delivering the kind of customer experiences brands need to not just survive but thrive in the digital age.
Making data science in marketing easy
Data science is and will continue to be central to marketing success going forward, and marketers should become well-versed in this new area of expertise. You don’t need to be data scientists to take advantage of it — just keep these thoughts in mind:
- Watch out for data silos and look for ways to eliminate them through increased communication, cross-departmental sharing, and support
- Make sure you know how to move from “aha” to “so what are we going to do” by tapping the right stakeholders from diverse corners of the business
- Look for built-in AI (like Adobe Sensei) that will do the work for you
- Don’t be afraid — in the spirit of breaking down silos, be sure you’re exploring outside your immediate team
Data science is finally coming down from the clouds to where the rest of us can use it to look smarter, work more efficiently and create the exceptional customer experiences that our buyers already expect.
Keep an eye out for more info about Marketo Engage’s newest AI-enabled feature, Predictive Audiences, coming out in June. In the meantime, learn more from these customer use cases — then be sure to check out the Adobe Experience Platform Data Science Workspace to better unleash insights from your data, and unlock incredible experiences for your customers.