It’s not new news that big data is on every marketer’s mind. Marketing departments are focused on analyzing customer data “in order to uncover insights about how marketing activity affects buyer purchase decisions and drives loyalty.”
What’s missing in the current marketer’s mindset towards marketing analytics is that customer data “is suitable for some applications and analyses but unsuitable for others.”
Below we’ll explore what areas contain restrictions for customer data and how that is significant for marketing analytics.
Marketers have access only to the customer data “of individuals who have visited your owned digital properties or viewed your online ads,” which does not provide a holistic view of who are your customers.
Because the buyer’s journey occurs across multiple marketing channels and devices, it’s become “impossible to tell for any given touchpoint sequence how fragmented the path actually is.” This means, in many cases, the buyer’s journey is unclear.
Customer data “is far from being accurate or complete, which means that there is inherent danger in assuming that insights from this data applies to your consumer base at large.”
Customer data is fantastic knowledge when you want to personalize your website or improve email campaigns. But there are many marketing channels where “it is difficult or impossible to apply customer 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.”
“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.”
“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.”
Analysis can inform which of your marketing campaigns is performing the best, justifying a move of resources to focus on Campaign A instead of Campaign B. But algorithmic analyses can’t answer why you should move marketing dollars “in a manner comprehensible to the average marketer.”
You’re probably thinking something along the lines of, “Wait a minute, big data means big insights which equal big learnings!” Unfortunately, that’s a wrong notion.
Let’s look at website personalization as an example. You take your customer data and reflect customization on your website to meet your buyer demands and interest. This causes your conversion rate to increase, but the only thing this has taught you is the importance of personalizing your website. “This result certainly raises the bar on marketing, but it does nothing to raise the bar for marketers.”
“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… small data remains far more efficient at producing practical real-world learnings that you can apply to execution today.”
“In analyzing touchpoint data, you will run into situations where, for example, a particular cookie received – for whatever reason – a hundred display impressions in a row from the same website within an hour… Should this be treated as a hundred impressions or just one, and how will it affect your analysis results?”
Customer data is typically filled with misleading data sets that take extensive clean-up time to reveal accurate results.
“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.
All of these factors limit agility of analysis and ability to collaborate.”
Source: Marketing Land
Image: Courtesy of ©iStockphoto.com/ugurhan
Glenn Gow is an expert in marketing performance, Coach, Board Advisor, Author, Speaker, Podcast Host and Founder & Advisor of Crimson Marketing. Follow me on Twitter, LinkedIn, Google+. To get a free copy of Crimson’s One-Page Marketing Metrics Funnel, visit here.