When it comes to marketing these days, everything is all about the “big data,” as companies attempt to come up with new ways to collect and analyze data in order to find trends and insights regarding how marketing activity has an effect on consumer purchase decisions and increasing loyalty.
Columnist Kohki Yamaguchi recently broke down the seven big points to look at in a new article for Marketing Land, which are as follows:
First off, user data can easily be fundamentally biased. Yamaguchi explains, “The user-level data that marketers have access to is only of individuals who have visited your owned digital properties or viewed your online ads, which is typically not representative of the total target consumer base.
Even within the pool of trackable cookies, the accuracy of the customer journey is dubious: many consumers now operate across devices, and it is impossible to tell for any given touchpoint sequence how fragmented the path actually is.
“Furthermore, those that operate across multiple devices are likely to be from a different demographic compared to those who only use a single device, and so on.”
Certain execution can exist with select channels, according to Yamaguchi. “Certain marketing channels are well suited for applying user-level data: website personalization, email automation, dynamic creatives, and RTB spring to mind. In many channels however, it is difficult or impossible to apply user data directly to execution except via segment-level aggregation and whatever other targeting information is provided by the platform or publisher. Social channels, paid search, and even most programmatic display is based on segment-level or attribute-level targeting at best. For offline channels and premium display, user-level data cannot be applied to execution at all.”
When user results do come up, they can’t always be presented directly. “More accurately, it can be presented via a few visualizations such as a flow diagram, but these tend to be incomprehensible to all but domain experts. This means that user-level data needs to be aggregated up to a daily segment-level or property-level at the very least in order for the results to be consumable at large,” explained Yamaguchi.
And certain algorithms don’t always explain “why,” either. “Largely speaking, there are only two ways to analyze user-level data: one is to aggregate it into a “smaller” data set in some way and then apply statistical or heuristic analysis; the other is to analyze the data set directly using algorithmic methods. Both can result in predictions and recommendations (e.g. move spend from campaign A to B), but algorithmic analyses tend to have difficulty answering “why” questions (e.g. why should we move spend) in a manner comprehensible to the average marketer. Certain types of algorithms such as neural networks are black boxes even to the data scientists who designed it. Which leads to the next limitation…”
That led to the next point, how user data isn’t always suited for product learnings. “Let’s say you apply big data to personalize your website, increasing overall conversion rates by 20 percent. While certainly a fantastic result, the only learning you get from the exercise is that you should indeed personalize your website. While this result certainly raises the bar on marketing, but it does nothing to raise the bar for marketers,” explained Yamaguchi. “Actionable learnings that require user-level data – for instance, applying a look-alike model to discover previously untapped customer segments – are relatively few and far in between, and require tons of effort to uncover. Boring, ol’ small data remains far more efficient at producing practical real-world learnings that you can apply to execution today.”
The following two points were just as crucial, discussing how user-level data is subject to certain interference, and how some of the data can’t be so easily transferred. “Because of security concerns, user data cannot be made accessible to just anyone, and requires care in transferring from machine to machine, server to server.
“Because of scale concerns, not everyone has the technical know-how to query big data in an efficient manner, which causes database admins to limit the number of people who has access in the first place.
“Because of the high amount of effort required, whatever insights that are mined from big data tend to remain a one-off exercise, making it difficult for team members to conduct follow-up analyses and validation,” concluded Yamaguchi.
“All of these factors limit agility of analysis and ability to collaborate.”
More information on this study, including the part that big data plays, can be found here.