balancing on a log
Marketers are asking the right questions of the wrong data, which leads them nowhere fast PHOTO: Joshua Earle

Only a few months ago, businesses were scrambling to finalize their Pokemon Go strategies.

Fast forward to today, and you'd be hard pressed to find anyone playing Pokemon Go, let alone discussing it in a boardroom. 

Digital marketing success often results from a combination of preparedness and opportunity. 

Marketers still dream of having their equivalent of Oreo's "You can still dunk in the dark" 2013 Super Bowl moment, but few opportunities will have the drama of a black-out at the Super Bowl. Nor is it feasible to aggressively staff a social media war room 24x7 to take advantage of such events. 

While it may not be possible to prepare for every fleeting moment, what is possible is to be prepared to hit rapidly changing targets. 

And the way to do that is with high-quality customer data that delivers insights and fuels serendipitous moments. 

Right Questions, Wrong Data

By 2017, nearly nine out of 10 companies plan to compete on the customer experiences they deliver. Chances are you’re one of them. But is your data customer-focused as well? Are you prepared to meet new opportunities with data that is reliable, complete and accessible to you and your team? 

For instance, many teams work toward customer-oriented objectives while relying on product-oriented processes and views of customers. The problem is, product-oriented processes and structures are internally focused and have little alignment with customers. This leads many companies to realize that making a transition to becoming customer centric is critically linked to data centricity. 

Product-centric views of customers can’t tell marketers what they need to know to act on hidden or emerging opportunities. Instead, marketers ask the right questions of the wrong data, and as a result, don't get the customer experience transformation they need. 

The enduring reliance on product data isn’t a simple matter of inertia. Oftentimes, organizations favor product over customer data because the ability to manage the latter lacks the maturity, quality and reliability of the former. 

Customer Data's Underdog Status

Mature technologies such as Product Information Management (PIM) tend to provide product data the rigor, breadth and depth that hasn't yet been fully discovered and applied on the customer data side. Product data is deeply integrated into business processes. It is also easily mapped to suppliers and channels, making it easy to manage, segment and analyze the data in meaningful ways. 

Customer data is seldom in the same league within most companies.

Even our terminology around customer data technology belies its stepchild status: customer data “integration” is commonly used, NOT “management.” 

Whereas the word management implies a strategic objective, integration implies a weaker, tactical approach. Where we do use “management,” it’s in reference to customer relationships whose technologies aren’t really designed to strategically manage data, and not the data we see today.

As a case in point, recent research indicates how executives undervalue the importance of customer data management. In a Gartner survey of Chief Customer Officers, mastering of customer data came in a dismal 19th in a list of 25 technologies to enhance customer experience. 

Chief Customer Officer Survey
Customer data management is far down the list of technologies considered important to customer experience enhancement
 

Yet great data can improve many of the items listed in the survey. When teams responsible for customer experience delivery recognize the significance of customer data management and elevate it as a priority, their attempts to compete on customer experience start to reach their full potential. 

Elevating Customer Data

The lack of rigor with customer data can be attributed, in part, to a lack of clear ownership. 

Many groups, including marketing, sales, finance and customer service, share customer data. In turn, the responsibility to care for the data also ends up being shared across an organization. 

This is a good recipe for inaction. Whether it’s in business or our personal lives, tasks that aren’t explicitly designated tend not to get prioritized. 

The reality is that the primary responsibility for customer experience typically falls within marketing, and so marketing must assume responsibility for the quality of the customer data. A strong, mature set of technologies exist to help marketing strategically manage customer data. They include:

Master Data Management (MDM)

Not to be confused with Mobile Device Management, MDM in this context is foundational to realizing a single view of the customer. MDM actively manages business-critical data across multiple sources to ensure it is free of duplication, conflict and obsolete entries. 

In helping marketers align the right message and offer, it also gives context and visibility around a customer’s relationships to other entities, such as members of a household, products purchased, channels and locations most often visited, and customer preferences. This clean, consistent and connected data then feeds the analytics that identify the next best offer and the business applications familiar to most teams.

Product Information Management (PIM)

An MDM application, PIM increases collaboration and coordination across suppliers and product, digital and marketing teams. The result is product data that is robust and detailed. Using PIM for onboarding, managing and coordinating product data helps marketers get to the top of search results and ensures that product information across channels is consistent and complete, which, in turn, creates a better customer experience. 

Data as a Service (DaaS)

For your messages to actually reach your desired audience and targets, your customer contact data must be valid, verified, complete and comply with standards. 

DaaS does that with contact information both at the time of entry and on an ongoing basis. As contact information changes, your records remain accurate. DaaS can also enrich the customer file with customer details including opt-in/out preferences, mobile designations, and third party information such as income, education, occupation or interests for more granular segmentation by including the distinct characteristics you care about. 

Data Quality

High quality data conforms to rules and standards. For example, where null values are — and aren’t — allowed. Data quality tools enforce these rules in each of your applications. They can also identify data sources you can trust the most — and the least.

These technologies don’t merely prepare you to capture opportunities, they deliver quality in a fleeting second, which is essential to success in a digitally-driven world. 

While many companies tackle customer data issues with one-time, sporadic or annual fixes, every company will have to take action to address it. Technology that automates these processes on an ongoing basis puts trusted data in front of decision makers before it’s too late to act. 

You Won't Get Anywhere with Flawed Data

Marketers have strong incentives to improve customer experience. Consumers have high expectations, are increasingly sensitive to errors and experience little friction in moving from one business to the next. 

Good enough is no longer good enough where customers are concerned. And our digital world requires certainty and speed. 

Unless we raise the bar on customer data quality, our goals will remain challenging to reach. Just as you cannot navigate effectively using a flawed map, you cannot give a quality experience using flawed data. Enforcing strategic rigor on customer data is a required step in the journey to enhanced customer experience.