In some ways, the backlash against big data is natural — people resist anything that’s new. But, bad press for big data is perpetuated by some of the bigger names in marketing.
So, today, I thoughts I’d explore whether big data leads to bad marketing.
Bad press for big data
Here are just a few of the top blogs bashing big data with respect to what it’s done for marketing:
Big data squeezes all the creativity out of marketing [source]
Big data leads to inaccurate marketing strategies [source]
Big data doesn’t offer trustworthy insights for marketers [source]
Big data and predictive analytics are getting in the way of doing basic marketing [source]
Big data lied about election results, so why should we trust it to help with marketing [source]
Big data doesn’t belong in marketing’s future [source]
And, I could go on and on with the number of people who now question whether marketing should use big data.
Is big data killing marketing?
That’s a question I raised in a recent blog post refuting another blogger’s contention that Google Analytics is ruining marketing (You can read what I had to say here).
I think several issues underpinning objections to using data in marketing — big or otherwise. Here they are:
- Expecting data to provide answers, not insights
- Using bad data and expecting good answers
- No connection between data to marketing concepts
- Don’t understand how to use big data
Let’s deal with these one at a time.
Expecting data to provide answers
In some areas, data provides answers.
If you’re an accountant, data generates an income statement and figures your taxes.
A financial planner uses data to determine how much money you need to invest to retire comfortably.
If you’re a doctor, data helps you calculate the proper dose of a drug.
If you’re in production management, data determines how much raw material you must order to fulfill demand.
You just have to use marketing data in a different way than you do accounting data or medical data.
First, take a look at where marketing data comes from (according to IBM — and they should know). Some of it is concrete data, including transactional data and data from existing marketing efforts, like email and digital marketing campaigns. That’s pretty accurate stuff.
Another big batch of data comes through social media. That stuff is a little wonky. First, it doesn’t consist of number — unless you include vanity metrics like shares and likes (which most data analysts don’t). Most of your data is unstructured, which is IBM’s way of saying it consists of messy words that are hard to interpret — so many companies just don’t. That means you’re losing 80% of your data (IBM estimates that 80% of data is unstructured).
So a big part of the marketing problem with big data is that you’re ignoring 80% of it.
You also need to recognize that consumers aren’t robots. Sometimes they say one thing and do another. Or, they do something today and don’t do it tomorrow. They say variety is the spice of life and consumers try to prove that every day.
Data can only provide insights into consumer behavior. It can’t provide ANSWERS. We’ll talk about this more in a later section.
Using bad data and expecting good answers
Some data is just dirty and needs cleaning. Maybe some missing values shifted all your columns off — just like when you were in school and messed up the responses on your scantron. Sometimes weird values got inserted. Maybe your key was off when you merged two databases. The bigger the data, the more like you have some dirt in there.
Whatever the reason, you need to clean your data periodically and there are good tools out there to help with cleaning.
A bigger, more subtle problem might exist in your data — it’s not representative. Yet, you make broad assumptions based on skewed data. For instance, Facebook comments over-represent young, affluent, outgoing folks, which don’t represent the total population and might not represent your target audience. The discrepancy between predictions and results from the US presidential election are a good example of how your predictions are wrong when you assume a biased sample represents the whole.
No connection between data and marketing concepts
For me, this is the most serious problem I see with big data. The people running the analysis don’t understand marketing concepts (at all). Often, these are engineers or data scientists who are great with numbers and stats, they just don’t know what they’re looking for.
I’m working with a mentee now who’s trying to bring more analytics to her marketing role. I think she expected some magic bullet — like go learn R or SQL (yes, I recognize these are challenging software programs, but they’re concrete). Instead, I told her to go back to the organization and determine what marketing goals should drive decision-making. Without this information, I have no clue what data is important.
The same is true for marketing concepts. For instance, I need to understand adoption/diffusion to understand which data help me speed the process. I need to know, for example, that observability speeds adoption, so I know to look for images containing someone using my product to see how well I’m doing visually.
Don’t understand how to use big data
Just as there are too many data analysts who don’t understand marketing, there are too many marketers who don’t understand how to analyze data.
In businesses, firms dealt with increased needs for data by either hiring people who could analyze data (but weren’t marketers) or hiring analysts to provide nice, neat visualizations so marketers could make decisions without having to deal with messy numbers.
As a marketing professor for the past 15 years, I understand how this happened. Students chose marketing simply because it didn’t involve numbers. They liked the touchy-feely nature of marketing that made it a practical application of psychology (which is a very popular major). As marketing became more data-oriented, we failed to incorporate data into our classes.
Now, it’s time to bite the bullet, for marketers to retool with better analytics skills and for marketing programs to become more analytical.
A variety of online school offer classes in data analytics, such as EdX, where instructors from top schools offer classes similar to those offered on campus for a fraction of the cost. So, there’s no excuse to stay ignorant of analytics.
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