Digital Analytics: Big Data; Big Mess, Part One

Digital analytics, seen by some as a panacea for optimization leading to enhanced performance, is full of problems that are often ignored. This is part of the inherent bias toward numbers and the belief that therein lies the truth. Ushered in by the expansion of big data analytics tools, many managers simply allow the numbers to guide their actions without thought or understanding that those numbers might be lying to them.

In today’s post, we’ll explore how to use data effectively, considering that this data might not reflect the truth. We’ll discuss ways to question your data and derive more nuanced insights from it. So, let’s dig into this very weighty and uncomfortable topic. Buckle up!

Bad press for big data

I’m not alone in questioning the rationale of putting absolute faith in numbers. Recently, a rash of articles has blamed such a reliance on numbers for everything that’s bad with business. In some ways, the backlash against overreliance on data is natural—people resist anything new. But some of the bigger names in marketing perpetuate this type of thinking, so we must consider their arguments.

Here are just a few of the top blogs bashing data with respect to what it’s done for marketing:

• squeezes all the creativity out of marketing [Convince and Convert]
• leads to inaccurate marketing strategies [Marketing Tech]
• doesn’t offer trustworthy insights for marketers [MarketingLand]
• data and predictive analytics are getting in the way of doing basic marketing [Forbes]
• lied about election results, so why should we trust it to help with marketing [Huffington Post]
• doesn’t belong in marketing’s future [Marketing Tech News]

And I could go on and on with the number of people who now question whether marketing should rely so heavily on data, regardless of its size.

Is data killing marketing?

I think several issues underpin objections to using data in marketing — big or otherwise. Here they are:

• Expecting data to provide answers
• Not building insights or building superficial insights
• Using bad data and expecting good answers
• No connection between data and marketing concepts
• Don’t understand how to use data to drive actions

Let’s deal with these one at a time. Today, we’ll discuss the first two issues — data providing answers and gleaning insights. In subsequent posts, we’ll discuss each of the other issues in order.

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 dosage of a drug to prescribe or even whether your patient’s data justifies the prescription.
• If you’re in production management, data determines how much raw material you must order to fulfill demand.

In each of these cases, practitioners have years of education and experience, allowing them to USE the data to answer their operational questions. Plus, there’s a straightforward relationship between the data and what’s really happening that doesn’t rely on context or guessing.

Do marketers have the same training in data analytics? After teaching at universities for over 30 years, I can tell you the answer is a resounding NO. Marketing curricula at most universities in both undergraduate and MBA programs do nothing to prepare marketers to analyze the vast amount of data generated in the digital world, much less how to use that data to build insights.

A bigger problem is that some folks, like the ones in the cartoon above, try to use marketing data the same way you might predict raw material needs—if it takes five pounds of material 1 to build one widget, then I need 1000 pounds to make 200 widgets. That’s very straightforward.

However, marketing data isn’t determinant. For example, if spending $10 on advertising allows you to increase sales by 10 units, spending $1000 should increase sales by 1000, but that’s not how it works because there isn’t a one-to-one correspondence between inputs and outputs when it comes to marketing. I might find my sales increase by much more than 1000 or much less.

I won’t go into why that’s the case, but you can read more on this topic by searching the blog for topics like customer journey, decision-making process, behavioral aspects of marketing, personas, etc. The short answer is that marketing doesn’t work like that because consumer decision-making is a function of a variety of factors (including mood, emotions, social influence, disposable income, and much more). What we’re left with is the ability to calculate probabilities of what, on average, consumers will do in response to our marketing actions.

Does that mean marketing is broken or that using data won’t help with marketing?

Absolutely not. You have to use marketing data in a different way than you do accounting data or medical data.

What data do you have and where it’s from

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, such as how consumers responded to changes in marketing strategy, and data from existing marketing efforts, like email and digital marketing campaigns. That’s pretty accurate stuff. Despite the accuracy of this type of data, you can’t say with certainty that a price change that generated X increase in revenue will produce the same change in revenue if attempted again. In fact, using the strategy too frequently trains consumers to wait for a discount, so they put off shopping when prices are not discounted. That’s something major department stores discovered was the reaction when consumers faced weekly sales. Consumers stopped buying anything unless it was on sale.

Another big batch of data you might use to inform marketing strategy comes through social media. That stuff is a little wonky. First, it doesn’t consist of numbers — 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, and so many companies just don’t. That means you’re losing 80% of your data (IBM estimates that 80% of data is unstructured). Generative AI offers new hope for analyzing this data, but that’s likely in the future.

So, a big part of the marketing problem with 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 discuss this more in the next section.

Data aren’t insights

61% of companies state that Big Data is driving revenue because it is able to deliver deep insights into customer behavior. For most businesses, this means gaining 360° insights of their customers by analyzing and integrating existing data.

Some metrics provide insights. For instance, if sales of lawnmowers starts to pick up in April, you have information to guide you on ordering sufficient inventory. However, a metric related to the number of visits to your website yesterday implies no valuable insights that can help guide strategy. If you express the number of visits over time as a line graph instead (such as the one shown in the upper left in the Google Analytics report below), you can now track the cyclical nature of visits.

For instance, because my website’s target audience is primarily small and midsized business executives (SMEs for short), my website visits are concentrated Monday through Friday (with fewer visits on Fridays). My visits also slow down during holidays, like Christmas (when even businesses are focused on other things) and during the summer months, as executives take vacations.

If I see a spike that doesn’t fit this pattern in either the positive or negative direction, I can interpret this as something worth investigating. A post that resonated particularly well with my target market might produce more visits to my website. I can explore this metric more deeply to see which page visitors visited. I can create more content similar to this post in hopes of a repeat.

Possibly, an influencer mentioned one of my posts, which drove an unexpected amount of traffic to my site. This happened recently when Google Analytics retweeted one of my posts to its followers. This generated retweets from its followers, which likely sent more traffic to my website. Armed with this insight, I can reach out to strengthen this relationship in hopes of a repeat or consider investing more effort to build other relationships like this.

Visualization

Data visualizations are more than pretty pictures, although a nice graph or chart makes the data more appealing. Data visualizations, like this interactive view of the US labor market, should support decision-making by drawing attention to relationships among data and relationships between your data and influential factors that may not be captured in your data.

Take a look at the graphic above. It contains a vast amount of data regarding employment trends across genders, across different job types, and across industries.

Each block represents a particular job classification, and the size of the block indicates the relative number of jobs in that classification. Supra-blocks indicate the same type of information for various industries. In a larger format of the graph, each block would be labeled for the specific type of job, but for simplicity, we’ve only labeled jobs with significant levels of employment.

Colors are used to identify when a particular job class shows increases in male or female employment.

At a glance, you can clearly identify which job classifications are becoming more ‘male’ or more ‘female.’ That’s valuable information if you’re working for a group promoting increased participation for women in certain job classifications where they’ve been underrepresented. Or, if you work for the EEO in your company, you can demonstrate that your levels of female participation are comparable to the industry.

And all that comes without having to pore over vast tables of data, hoping to discover something.

Crafting the RIGHT data visualizations

Just putting your data in a visual format isn’t enough. And, putting your data into a poor format is worse as it is misleading and leads to poor decisions.

Different types of data call for different data visualizations.

• Percentage data is often clearest when represented by a pie chart.
• Trend data is often best displayed using a line graph
• Comparing things against other things is often best done with a histogram
• When you want to display trends across a number of things, stacked histograms likely do the job for you
• Complex data is often easiest to evaluate when you have a heat map, like the one above, or an association tree like the one below

Having the wrong data visualizations can make it impossible to derive insights that aid decision-making. Take a look at this hot mess:

There are too many colors and the slices of pie don’t really help you understand the data. If, instead of using a pie chart, you clean this up with fewer colors and histograms representing the size of the populations, you get a much clearer picture of what the demographics look like relative to each other, which aids decision-making.

Breaking down your data

Graphics offer a great tool for generating insights. However, sometimes they’re not enough. Sometimes, it’s not sufficient to understand that your product A appeals more to men than women or that younger consumers like a particular ad campaign better than younger people. The bigger question is, “How does this impact their purchase decisions?” Hence, you need to follow visitors to your website from the first step in their journey through to conversion. In one company, we found that despite more visits to the website from younger users, older users converted at higher rates and offered a larger AOV (average order value). Thus, our recommendation was to focus more attention on driving this part of the company’s target market to the website.

Another consideration involves knowledge about the customer journey, which looks something like this:

Consumers don’t start by making a purchase. Instead, it happens over time once they become aware of the product (although most consumers drop out of the journey along the way). Thus, you can’t simply focus on conversion, with all your marketing efforts focused on driving that final step. You must spread your efforts along the journey, seeking to drive consumers more effectively through it. Using the example from earlier, young users visit your website in larger numbers but fail to convert. You might create a marketing strategy designed to appeal to these users and convince them to complete a purchase.

Digital analytics don’t speak

Your digital data doesn’t speak—you have to construct queries to answer questions. Construct the wrong query or misinterpret what the results mean, and you’ll make bad decisions.

Here’s what Scott Liewhehr told TechCrunch:

Everybody can use data to tell whatever story you want to tell and it’s a big challenge for marketers. If they don’t know how to run studies, they can make a lot of bad decisions.

Ask the wrong question, get the wrong answer, and make the wrong decision.

This means data scientists need an understanding of the firm — its business model, customers, strategies, etc., which means pairing up data scientists with marketers within the firm or, better yet, training marketers to be data scientists.

The same goes for tools. Tools don’t provide answers; they give a means to ask questions. Buying another tool isn’t going to solve your digital analytics problems magically.

Reaching the top

Even when digital analytics are working well, it’s tough getting top management to make decisions based on insights.

You make your report. Make recommendations. You move on to the next puzzle.

Management hears your findings. Ohs and ahs over your colorful infographics and visualizations. Nods head appropriately.

Then.

Nothing.

Maybe it’s inertia or perhaps fear of the unknown, or even politically unappealing, but using customer insights to guide plans is challenging for even the most data-driven organizations.

Many advocate for a C-level information officer, such as a Chief Analytics Officer or Chief Data Officer, as a champion for digital analytics in the C-suite and as an advocate for using information as a tool in strategic decision-making.

Conclusion

Today was our first installment on using big data to make better decisions to help your business grow. Stay tuned for the next installment or search for particular topics in big data by using this link.

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