Marketing Data Fluency for CMOs

Insights, ideas, and creativity – marketers love to talk about these. The latest generation of CMOs can talk about revenue and business growth, sales and leads and so forth. But bring up “data” and you rarely get past the usual qualifiers like “customer data” or a descriptor like “demographic data.”

Today’s marketing leader needs to be fluent in data concepts and terms to steer their organization to building and maintaining the most differentiating resource beyond talent that they can develop: marketing data.

It’s Not Easy

The bad news about marketing data is that we all feel we are behind where we need to be. The good news is that we are all behind where we need to be. Okay, those both seem bad. Still, we are not alone and the important point is to just get going.

A 2018 Forrester study of B2B marketers found, “Eighty-two percent of respondents cited managing data across the business as a challenge.”

That same data pointed to the upside of data, “The study showed organizations with data activation maturity were more likely to report increases across marketing/sales and customer metrics, with 73 percent reporting more rapid sales cycles, 73 percent reporting a higher marketing ROI and 77 percent reporting increased customer retention.”

Data will grow to be the top competitive advantage in marketing. Let’s get fluent.

What Do Marketers Want from Data

For some of us, data is a bit like math. I am glad I was good in other subjects because I am just not fascinated by math. But like math, I am keenly interested in what I can do with data:

• Combine different data sources to more effectively and efficiently target and reach more specific audiences.
• Analyze data for insights that can change my strategy, content or creative and how I engage with prospects and customers.
• Use all types of data to better know prospects and customers, personalize their experience and drive greater action.
• Analyze demographic, firmagraphic, behavioral and account data to develop new products and services that create more value for the customer and the business.
• Discover predictive data that makes marketing more efficient and measurable against impact

And I expect this deeper data capability to give me a competitive edge in the marketplace. In short, marketing data facility is key to business growth and competing and winning for the foreseeable future.

No pressure. 

A Simple Frameworks to Think and Talk about Marketing Data

I tried to find that magic framework that neatly summarizes what types of data, the sources, how to manage and grow the data resource and apply it marketing seven ways from Sunday. I am a sucker for a good framework. I tried to find it, I really did. I would encourage the consulting community to put a bit more elbow grease into marketing data frameworks and then generously share them online.

Until then, here’s a simple way to think about the problem:

The CMO’s Planning Framework for Data

The CMO’s office needs to drive requirements for the use of data in marketing and customer experience and all of the supporting disciplines (e.g. public relations, research, advertising, and brand). Inside the enterprise, data capabilities is a team sport. Marketing relies on IT, business intelligence, data management and more to develop and maintain marketing data solutions. These cross-disciplinarians all benefit from a simple but actionable framework or shorthand for understanding the data needs.

Here’s a way to look at it:

Application: How do you plan to use data and how will you know if it’s helping? Knowing the application of the actual data source is the most critical step in designing a marketing data system that delivers something concrete. You don't have to know everything it will do but it ought to be designed fit-for-purpose as my UK colleagues might say. 

Customer and market insights: This is the broadest and least defined use of any data resource. Essentially, we all want to accumulate over time and increasingly valuable data resource that will reveal unexpected insights to inform product and service strategy, customer engagement, efficacy, and market opportunities.

Personalization: Increasingly improving relevance for our customers is the key to more effective marketing and revenue generation (and customer satisfaction and loyalty). This is the most complex yet imaginable benefit of data mastery for marketers.

Addressability: Reaching the increasingly “right” people to drive value and valuable actions means better data and analytics to know who and where they are, as well as, when we can reach them.

Marketing effectiveness: We need data tied back to the individual to understand marketing effectiveness. Did we sell one more policy to her? Did our retention emails and messages retain her for additional years? 

Structure: How is the marketing data lake structured such that it is actionable, reliable and credibly managed. What is the relationship of this data resource to master data management (MDM) and other enterprise data lakes.

Data enrichment: We need the ability to grow our 1st party data based upon account and behavioral data from both declared information and inferred information. We also need the ability to combine 1st party data with 3rd party data in a “sandbox” environment that is conducive to quick experiments before we make any longer term bets.

Customer-organized: The marketing data is essentially organized around prospects and customers. That includes connecting accounts (business customers) with individual customers where applicable. If you have a sales force, or in our case, an independent agent channel serviced by a field force, the structure needs to connect all of these relationships (e.g. which field staff serve which agents who serve which customers)

Sources: Where does reliable data originate? Account information may come from business systems but it may also be augmented from sales data. Behavioral information can come from dozens of different interaction sources like your Web sites, social channels, social listening, email interactions, point-of-sale, stated preferences and more. Defining sources is anything but a once-and-done exercise and is more about creating a foundation that is constantly adjusted, added-to and managed as a living, breathing organism.

Cleaning:  Data is messy, incomplete and often redundant. This is a fact of life. Distilling sources into a data resource devoid of the most glaring problems like records so incomplete it inhibits addressability or data being updated via a faulty source is hard work.

Identity-resolution: With many data sources comes data confusion about what to attribute to what customer. Resolving multiple emails, devices and cross channel interactions to a single customer is one of those table stakes capabilities that is maddeningly difficult.

Outputs: How will the data be used by other systems and how will we export reports and meaning from the data, itself.

Marketing systems: What marketing systems will use the marketing data lake and how will they return data to that system. That’s one of the confounding realities of data lakes feeding systems. Those systems will inevitably also be sources of data – either new data or corrected data – that must be returned and resolved inside the data lake, itself. Just think about the data generated from a customer email program which tells us about the interests of a customer (i.e. what did they click on). That inferred interest should be associated with the customer “golden record” to be used by other systems like the Web site personalization engine.

Reports and Insights: These rich customer data lakes contribute to marketing performance measurement and will become the crystal ball massaged by analytics brainiacs to reveal heretofore unknown insights.  

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