Better Beats Bigger Data, Every Time.
"Better Data" vs. "Bigger Data" ― hopefully, this doesn’t even sound like much of a debate. Yet in many cases, the distinction isn’t so subtle. These two broad descriptions are often easily, and innocently, confused. Let’s try and clear that up a bit.
Oftentimes, marketers we help with marketing database development find themselves in a rush to “build their database.” Virtually all retail organizations today are tasked with “building the database.” CMOs know intuitively that amassing data about customers and prospects has value. One CMO recently gushed, “that data is gold.”
Not surprisingly, given the data was valued like gold ― a precious commodity ― then more must be better. And what came next was somewhat predictable after working with dozens of brands on leveraging their data to achieve scale and performance.
Customer, marketing, operations and finance all rolled into a singular database being constructed by a modest and already overwhelmed IT staff. Years went by before the project was killed ― and creating value with the data was postponed indefinitely.
Here are just a few of the key issues that develop when the focus meanders to “bigger” rather than “better”:
1. The Goal Isn’t Entirely Clear. If you begin with the goal of capturing and storing data, that’s what you're going to do. Conversely, if you begin with the goal of growing customer value, a discrete set of data points come into focus. How they are captured and organized is clearly informed, and how they will be utilized gets clearer from the start.
Similarly, marketing data can fail to meet the expectation when those expectations were only loosely formed shortly after the result was delivered!
2. The Culture Doesn’t Embrace Making Decisions With Data. Let’s face it. Historically, many organizations embrace decisions from the gut. Intuition and opinions rule, even as talk of using data to inform decisions is the norm. These organizations can only shift from valuing data by the terabyte to valuing data by its financial performance after the top-level decision-makers in the organization embrace data-driven decision-making.
3. Skepticism vs. Materiality. Building on the above, there will be skeptics. Skeptics will challenge if data can or does create business value for the organization. A healthy degree of skepticism is helpful. A "Data Athlete" does bring a healthy curiosity about what data suggests, and how it is captured, transformed and considered. There’s unhealthy skepticism, even if it is innocent in its nature.
A common example of where skeptics combat a data-driven culture is finding examples of incomplete records, or inaccuracies. Another example is illustrating the gap between two systems. These all, at face-value, seem terribly problematic. However, those “gotcha” moments need to be considered in terms of context ― a 2 percent discrepancy between the core financial system and the marketing database is extremely important in the financial database ― and most likely immaterial in the marketing database.
4. Complexity Can Undermine Results. In the first example, an ambition to squash “silos” of disconnected systems was the justification for “biggering” a new database approach. To be certain, there were prior experiences where “siloed” data created frustrations in the CIO’s capabilities he/she extended inside the organization. However, none of these were directly aligned with the CMO’s objective of improving messaging, response and sales.
Bigger data doesn’t just mean more of the same data types ― it often means adding more types of data. The complexity and design of the underlying schema or data model is directly correlated with the sheer number of data fields being captured. So capturing more and more adds to the challenge of making it economically viable to create value with it.
The same can be said for the complexity of the data fields themselves. While leveraging transaction data can be done through reasonably well-understood statistical methods and models, incorporating social signals is typically more challenging. Marketers frequently cite correlation of certain observable social behaviors, (a common example being “likes”) with buying behaviors when causation is what’s needed to discern the economic value and business impact of these types of data.
Given the relationships between disparate data types aren’t always clear (much less actionable), the underlying data models grow murkier as more “fuzzy” data is added to the database. In this context, “fuzzy” refers to the implicit value it has in a structured or statistical application to target messages.
The reports they generate may be highly engaging and interesting (see "Analytics Isn’t Reporting"), even as they reduce the probability of successful outcomes of database marketing.
5. Bigger Isn’t a Silver Bullet: Specialization vs. Generalization. With the advent of a Big Data industry, pundits, generalists and traditional agencies, have all volunteered opinions. This cacophony leads to adding more and more to the mix ― compounding all the issues we’ve already covered herein. This underscores one of the top challenges in utilizing data ― it’s not just the tech, it’s people. There is an overwhelming shortage of talented and experienced individuals who have the marketing, database, technology and analytics experience to convert data to insights and those insights to profits. True Data Athletes are in high demand. Put another way, generalists and opinions don’t cut it. Utilize specialists and pare the scope and expectations of your database marketing to the level of talent you have or can realistically budget to engage.
While there are many challenges to utilizing data to create leverage in your business, the opportunity is clear and expansive.
Here’s the simplified checklist for marketing executives on how to overcome the common challenges in leveraging data to create marketing and business value:
• Begin with a pure business outcome as the first thing you decide. What will success look like after you’ve implemented your database marketing solution?
• Align your data collection methodically with your objectives. If it’s not clearly necessary, throw it away ― or store it somewhere else.
• Be patient and thoughtful upfront ― and capture the data in a schema or data model that supports the kinds of questions and queries you realistically expect to ask and answer of your database.
• Think Small to think Big ― what’s the most important question you can answer? Focus on that first and foremost. Getting more of the most valuable outcome is a big win ― even if you have to tighten or shrink your focus at first.
• Avoid “Boiling the Ocean” ― your likelihood of successful marketing outcomes is highly dependent on the complexity you create along the way, and the quality not quantity of resources you bring to solve the data challenge.
If you’re like most marketers, you’re probably under significant pressure to do more with less, achieve greater scale and drive greater profitability.
Data is not a “tool,” per se, to help you achieve those goals, however. It’s a strategic data asset. When you think “quality” first and “quantity” second, “bigger” takes a back seat to “better” data ― and bigger performance.