5 Reasons Multi-Channel Data Is Unique and Difficult to Measure

Those of us who work with data tend to think in very structured, linear terms. We like B to follow A and C to follow B, not just some of the time, but all the time. Multi-Channel data isn’t that way. It’s both diverse and complex making traditional linear analysis useless.

There are several characteristics of multi-channel data that make it unique.

1. Much of the multi-channel data is in multiple, non-connected places.

Location of multi-channel data tends to reside in multiple places. From different media buying systems, CRM, sales DW’s, to digital, finance and customer DW’s. The data comes from all over the organization and from many vendors /agencies. Aggregating this data into a single, central system, such as an enterprise data warehouse (EDW), makes this data accessible and actionable.

Multi-channel data also occurs in different formats (e.g., text, numeric, streaming digital data, pictures, videos, multimedia, etc.). Sometimes the same data exists in different systems and in different formats. Such is the case with media buying and campaign performance data. A search term performance is recorded via Google tool, but appears as a unique tracking code in the digital DW’s.

And the future holds even more sources of data, like customer-generated social content, data from devices, etc.

2. Multi-channel data is structured and unstructured.

Marketing performance DW’s has provided a foundation for a consistent data capture, but the reality is campaign data capture is anything but consistent. For years, documenting marketing campaigns and findings on excel worksheets has trained marketers and agencies to capture data in whatever way is most convenient for them with little regard for how this data could eventually be aggregated and analyzed. Analytics teams attempt to standardize the data capture process, but marketers and vendors are reluctant to adopt a one-size-fits-all approach to data documentation. As a result, much of the multi-channel data captured in this manner is difficult to aggregate and analyze in any consistent manner. As Multi-Channel analytics technology improves, as marketers become trained to standard workflows, and as vendors and agencies become more accustomed to entering data in structured fields as designed, we will have more and better data for multi-channel analytics.

3. Inconsistent definitions: new multi-channel digital solutions are coming out every day.

Oftentimes, multi-channel data can have inconsistent or variable definitions. For example, one group of marketers may define “unique customers” differently than another group of marketers. Ask two marketers what criteria are necessary to identify someone as a “unique customer” and you may get three different answers. There may just not be a level of consensus about a term or a definition.

Also, even when there is consensus, the consenting experts are constantly discovering newly agreed-upon knowledge. As we learn more about how the multi-channel customer engagement works, our understanding continues to change of what is important, what to measure, how and when to measure it, and the goals to target.

There are best practices established in the digital analytics industry, but there’s always ongoing discussion in the way those things are defined. Which means you’re trying to create an order out of chaos and hit a target that’s not only moving, but seems to be moving in a way you can’t predict.

4. The multi-channel data is complex.

Customer and Sales Data have been around for years and thus it has been standardized and scrubbed. In multi-channel world, this type of data is incomplete. Digital data from sources like websites, webinars, emails, online chats, e-commerce, social give a more complete picture of the customer’s story.

While developing standard processes that improve data quality is one of the goals in digital multi-channel analytics, the number of data variables involved makes it far more challenging. You’re not working with a finite number of identical parts to create identical outcomes. Instead, you’re looking at an amalgam of individual customer experiences that are so complex we don’t even begin to profess we understand how they work together. Managing the data related to each of those experiences (which is often being captured in disparate systems and applications), and turning it into something usable across a customer population, requires a far more sophisticated set of tools than is needed for other type of analytics like standard BI solutions or relational DW’s.

5. Constantly changing data privacy requirements.

Digital privacy regulations and requirements also continue to increase and evolve. The advent of digital marketing has been accompanied by a far higher level of scrutiny of privacy issues than was ever the case with traditional media. Some of that is to be expected. Data are, after all, at the center of the digital world and it’s critical that consumers’ personally-identifiable (PI) data be protected. Much of the privacy concern that digital marketing has encountered relates to the observation of consumer behavior and the targeting of ads based on this behavior. Ironically, the myriad new digital ad targeting approaches that dramatically increase the relevance and effectiveness of the advertising being delivered -- all while simultaneously reducing waste -- require no PI data about the consumer. Anonymous cookies are the basis of this matching up of advertising with behavior.

Conclusions:

• Multi-Channel Data will only get more complex.
• Multi-Channel Analytics faces unique challenges and with that comes unique data challenges.
• Because multi-channel data is so uniquely complex, it’s clear that traditional approaches to managing data will not work. A different IT approach is needed that can handle the multiple sources, the structured and unstructured data, the inconsistency, the variability, and the complexity within an ever-changing data privacy environment.
• The solution for this unpredictable change and complexity is an agile approach. If I start out from point A in direct route to point B and the location of point B suddenly changes or an obstacle arises, I certainly wouldn’t want to have to retrace my steps back to point A, redefine my coordinates, and set off on the new course. Rather, I need to take one step at a time, reevaluate, and pivot inflight when necessary.
• The generally accepted method of aggregating data from disparate source systems so it can be analyzed is to create an enterprise data warehouse (EDW). It is a common method across many industries. However, how you aggregate that data can have a huge impact on your ability to gain maximum value from it.
• Traditionally we looked ahead at what business questions we want to answer. We knew exactly what information we need. Our data warehouses, then, stored everything we need. Multi-Channel analytics is not like those old EDW’s where business rules and definitions are fixed for long periods of time. The volatility of multi-channel data means a rule set today may not be a best practice tomorrow. We see many EDW projects that never deliver results or even come close to completion because the rules and definitions keep changing.
• A better approach is to bring data into the EDW from the source applications as-is, and place into a source data mart. When you need to turn it into information, it is then transformed into exactly what the analysis requires. If there is a change to the business rules or definitions, that change can be applied within the application data mart rather than having to transform and reload all the data from the source.

How have you addressed the complications of multi-channel data? What do you think is the biggest obstacle to good multi-channel data analysis?