DATA, DATA, DATA—and more DATA! Being a marketer for over a decade I rely on good, solid data; it’s an invaluable resource that allows me to discern a client’s problems and issues, and good data assists me in making well-informed decisions to strategize and execute a plan for success. In our overly-connected world, there’s data everywhere. We’re constantly inundated with poll after poll, stats stacked on stats, and more and more sources make their data public on every topic you can possibly think of—with or without bias.
And then there’s the data about our own lives. In a single day, per our connected device of choice, we know the number of hours spent on our phones, which apps we’ve used, the mood and tone of our texts and e-mails, the number of miles we drive, where we’ve been and when, the level of volume we've exposed to our ears, and even the biometrics of our health. Besides data being collected about our personal lives, we dole out personal data with every profile created, every customer rewards program we signed up for, every credit card application, and every network we engaged on the net.
Yet whether this is good or bad, right or wrong, too much or not enough—all that data is a valuable when it comes to DDDM in marketing. What’s DDMM? Defined as using facts, metrics, and data to guide solutions, Data-Driven Decision Making allows us to make decisions to achieve our client’s goals and objectives. So, no gut feelings, no going on a whim, and no costly second guessing. But I’ve seen colleagues mistakenly dive head-first into data mapping programs, thinking that numbers alone are a simple hack or the magical panacea to solve problems and produce success. Any data on a subject is an important but relying on just one type of data—such as data that’s quantitative—is merely giving you a number and is only revealing half the story. Whatever the approach to analyzing a client’s problem, I find that quantitative data needs to be paired qualitative data, or else you’re bound for a misstep.
Qualitative vs. quantitative—what’s the difference?
Data that is qualitative is descriptive and conceptual, it is categorized based on traits and characteristics. The data from qualitative research is used for theorizing, interpreting, and developing initial understandings. Investigative and open-ended, data of this nature can be used to pose the question “why?” to a client’s problem.
Quantitative data is purely statistical and structured—it is often more rigid, more defined. Measured using numbers and values, this type of data is very concise and perfect for analysis. With quantitative data, we can observe the “how much” and “how many” to make a more exact assessment for the client.
In terms of DDDM, my experience is this; while numerical data will lend you figures that are more definitive, it doesn’t reveal varying factors like what piqued a consumer’s interest to create a profile for a particular app or service, or why you were going to where you were going on that random Tuesday night, or why someone’s heart was racing per their biometric record at 10:37pm. Essentially, a number is just a number.
Case Study: "My Leads Are Down!"
Recently, a client revealed that sales on his website were down and assumed that he needed more leads. So, I dove into the data; his site generated the same number of leads as the previous year within the same time frame, there were no drops in the leads count. Although sales were down, the data exemplified was contrary to his assumptions because there was an actual increase in leads being driven from listing sites. I decided to perform my own investigation to gather qualitative data. After an in-depth discussion, my client revealed that he had lost two internet sales managers, and that a senior member of the team was on maternity leave. The problem was clear to me; the team’s bandwidth was crippled with the loss of 3 team members. Therefore, my client couldn’t service the overload of leads. With the qualitative data I gathered, I referred to my client’s CRM to get the quantitative data on lead follow-ups per team member. My recommendation was to assess the lead quality by source, lower budgets where leads are not converting, and free up team members’ bandwidth for them to follow up their book of business. The problem wasn’t low sales, it was following up with the leads. It’s akin to going into a Starbucks during the morning rush and finding only one barista manning the entire operation—you’d elect to go somewhere else. So, If I went strictly by the numbers and didn’t delve deeper for qualitative insights, I would’ve been hard pressed to remedy the problem.
The best qualitative research methods for success:
Direct feedback is an excellent method for obtaining qualitative data. Draw up a list of unbiased questions and conduct in-person interviews or arrange a focus group. Direct feedback allows you to observe and take note of a participant’s verbal and physical reactions—this method can be done easily with virtual videos and chats. I find that direct feedback lends you solid competitive insights.
For example, I A/B tested my client’s ad on Facebook for his sunglasses company, using different images of the same model—one ad was zoomed in on the face of the featured model, the other a full body shot. The zoomed-out photo garnered the same number of clicks and conversions as the zoomed-in photo. Instead of assuming the results, I requested customer feedback. What I discovered was interesting; users clicked the ad to buy the dress worn by the featured model but were completely unaware that the product being advertised was for sunglasses. As a result, a few users were retained and explored the site, but weren’t inclined to make a purchase.
Using both quantitative and qualitative data, I recommended that the imagery of featured models should always focus on their eyewear moving forward.
Shadow shopping and selling
Being a shadow shopper and seller is another great method of gathering solid qualitative data. With this method, you can observe how customers interact with services, products, or ads through eyes and ears of your sales or customer support team. Whether it be in-person or virtually, you can better pinpoint challenges, and document customer sentiment based on verbal and visual cues.
Finally, one of the most invaluable methods to collect qualitative data is by holding an immersive event. I know, immersive events can be a bit daunting and overwhelming to create and plan, maybe even killing your creativity and motivation in the process, but don’t let that stop you—and don’t just do the bare minimum. Holding immersive events are an awesome opportunity for you to engage and maximize time with your customers, leveraging their participation to impart valuable feedback about your business.
Immersive events are excellent as they are tactile and visceral in terms of participation. They allow the customer to interact directly with a product or service, while you maximize your time and leverage their participation, gaining insight from raw reactions, unique perspectives, and no-filter feedback. An immersive event allows you to engage, observe, and gather qualitative data on how a consumer reacts—or doesn’t react—to a product and service.
Conclusion—numbers are good but dig deeper.
With Data Driven Decision Making, quantitative data will always be the go-to, but relying solely on the numbers is risky. Numbers are rigid and finite, and they often don’t allow us to see the bigger picture to remedy a problem and find solutions. To generate better results, we need to dig deeper, be more creative, and explore investigative methods that provide us with data that is qualitative. We need to investigate data that is derived from unique consumer experiences through direct feedback, interactions from shadow selling and shopping, and by holding immersive events. I found that using both qualitative and quantitative data, we can make better, more informed decisions for our clients, and move forward with a more thorough and well-executed plan of action. Because behind every number, there’s a story.