Why Buzzwords Suck
In my previous column, "Don’t Hire Data Posers", I wrote that one of the first signs of a poser is excessive use of buzzwords. This month, let’s talk about why such buzzwords are bad for the data and analytics business — besides the obvious annoyance of overuse.
I don’t deny that there are some benefits of buzzwords. Sometimes buzzwords summarize a long list of complex concepts in one easy-to-understand phrase. Big data, CRM (in the past), customer 360, personalization, customer experience, real-time modeling or in-database scoring are some examples.
For instance, the term big data acts as an umbrella for many different ideas that not-so-technical people may not be familiar with. But by saying that magic term, we can cut to the chase much faster. Marketers and decision-makers often interpret the term as “all data and analytics activities that enable data-based decision-making processes,” regardless of the actual data sets and processes in question. So data players like me no longer have to take 15 minutes to explain what we do for a living, and data geeks have more succint voices in executive meetings nowadays.
Similarly, creation of a single customer view or a 360-degree customer view may include many intricate steps, but who has time to list them all in a planning meeting? Just drop the term customer 360, and people will get the general idea.
But there are definite downsides to these over-simplifications. So, let’s list the harmful effects of abusing buzzwords:
- Over-simplification in itself is bad already, as it undervalues the efforts. Just because it takes less than a second to say it, doesn't mean the actual steps are just as quick and easy. Executors still have to sift through painstaking details to get anything done. I’ve seen marketers who actually thought that properly executing personalization would be simple and easy, when the reality of it is that even the very definition of the word deserves a lengthy consideration. Is it about content, delivery, data or analytics? The answer is all of the above, and one must plan for every aspect separately. Calling personalization simple is like saying, “Why don’t we make more movies like ‘Star Wars’ and make tons of money?” Well, can you make that lightsaber look real in someone’s hand?
Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is president and chief consultant at Willow Data Strategy. Previously, he was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, Yu was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at firstname.lastname@example.org.