A data-driven marketing strategy is at the core of marketing. However, many marketers consider themselves as creatives rather than statisticians. But if you want to be successful today, you must know how to use data effectively.
In my B2B Digital Marketer podcast, I interviewed Ruben Ugarte on Data-Driven Marketing: How to Use Data In Your B2B Marketing Strategy Effectively.
A data-driven marketing strategy is based on facts rather than biases. It’s more accurate and leads to better outcomes. But you don’t have to be driven solely on data.
What Do B2B Digital Marketers Need To Be Thinking Right Now?
One is personalization, the idea of communicating with customers directly in a way that makes sense to them. I do think data, in general, is vital. But there is a balance to be struck.
Retailers are switching to a more direct-to-consumer model, and now what big role data might play there. Lastly, I think it’s also about understanding the limitations of data.
Impact Of The Pandemic On Data-Driven Marketing Strategy
The IMF estimates that the global economy shrunk by 4.4% in 2020. The organization described the decline as the worst since the Great Depression of the 1930s.
A lot of companies in the pandemic were unable to make decisions. There was so much uncertainty, so much lack of data, so much lack of understanding, and they got stuck.
A lot of companies found themselves in no man’s land. Never before had I witnessed such a drop in numbers like this. I think all companies faced questions on what actually to do to survive.
Data-Driven Marketing Strategy: When To Use Data On Decisions
59% of marketers say that faster decisions are one of the benefits of using data. (SurveyAnyplace)
However, you also need to be able to make decisions without data. Because one day you won’t have it. So if you’re paralyzed without it, then you’re really going to have issues with taking action.
In my view, most organizations end up using data more as a backup than as a driver in their decision-making process.
It should actually come at the very beginning when you’re trying to decide what to do or where to go. For example, use data to segment your audience.
From there, start to figure out the different demographics, what they care about, what you need to say to those people, and where to find them.
Like both sides of a spectrum, companies need to properly balance their insights, gut feelings, biases, and data.
Fears In Using Data-Driven Marketing Strategy
It’s like people who don’t want to go to the doctor because they’re afraid of what the doctor may find. When you think you’re sick, you should definitely go to a doctor. You might be even sicker, Without knowing.
The same idea can happen with your company when you think you’re doing well.
But if we start diving into the numbers, we may really discover that we’re not doing that well, that there are serious holes. So you have to do it.
In the words of Dan Zarrella, “Marketing without market research is like driving with your eyes closed.”
What Being Data-Driven Really Means
Data-driven has been the driving force, a mantra for maybe 20 years now. However, it’s a little misguided.
It forced some companies to think that every single decision has to be made with data. Companies should really think of establishing a data supportive culture—a culture where data supports you and your decisions.
It gives you insights and guidance. You don’t have to rearrange a company around data completely. You don’t have to let data fully decide for you.
According to Forbes Insights, 64% of marketing executives “strongly agree” that data-driven marketing is crucial in the economy.
Data-Driven vs. Numbers-Driven
I hear a lot about numbers-driven from the financial side of the company. Marketing teams might talk more about data-driven versus numbers-driven.
The distinction is that financial numbers are straight-up numbers. i.e., revenue, margin, and costs, and you are driving a company and making decisions based on that.
When I say a company or team is data-driven, they are talking much more than just numbers. Here you have numbers like landing page conversions and clicks on ads and things like that.
Those might be quantitative, but then you also have qualitative, NPS, or what customers say on surveys or some other qualitative measure.
Basically, you’re being driven, guided, or supported by this quantitative and qualitative combination versus just the pure numbers. Stephen Levin is of the view that “Data-driven decisions: respect the numbers, then make your choice.”
Data-Driven Marketing Strategy: Where Data Is Being Used Now
It was all about your data and your customer data, emails, names, and phone numbers for a long time. Now you see much more enrichment of that data to break demographics, gender, and lifestyle numbers.
There’s now a much more holistic, more human picture of who your actual customers are, what they care about, and how they use your product in their everyday lives.
GlobalDMA found that 49% of marketers use data to enhance customer experience.
Quantitative has been the major thing companies want financially. They look at the numbers, and if it’s not profitable, it’s not profitable.
But it also misses a few things like customer support, customer happiness, or customer satisfaction; it’s not always fully captured by several metrics.
How Much AI Has Affected Data?
AI is already here. Everyone has likely experienced that in some shape or form.
Larger organizations primarily use it. I think individuals and small companies should be generally aware of what’s possible and what’s hype, and what’s not.
It doesn’t mean it might be applicable either to yourself or to your clients, but at least, you know, what the landscape is.
AI would be useful, especially on anything to do with pattern recognition. According to Business Daily, “Fast processes and lots of clean data are key to the success of AI.”
We see it when Facebook recognizes faces and photos or Google photos do the same thing. I love payment processors. Companies like Stripe and PayPal use AI to detect fraud before it happens.
Hence, they minimize some of the costs of those chargebacks you might see.
In my Fast Leader Show podcast, we have covered AI in several episodes. I’d recommend listening to my expert guests if you’d like to understand the impacts of AI on society, customer experience, leadership, and building strong organizations. AI will impact all, and we need to understand this to be better marketers.
Dealing With Bias
It is vital to put ideas to the table, defend and debate these ideas. I find this to be a great way to deal with bias.
Sometimes it’s just about playing the devil’s advocate expressing why something won’t work and the reasons. This can help you surface things that might actually derail your plans.
Everybody has to deal with bias – even frontline operational leaders. I cover it in several areas in my Call Center Coach Academy. There are all types of biases influencing our decisions every day. To lead better, we need to be aware of bias and not allow it to contribute to making bad choices.
Experimentation culture is at the heart of a lot of what really great companies do well. Often, when debate arises whether or not to do something, you can actually run a test.
Do it in a way that’s not intrusive to customers. You don’t have to show it to all your customers, your entire website traffic.
You can start to get a little more insights. What you need is to figure out what the hypothesis is. You don’t necessarily have to answer the major questions. Learn to test your way to it.
Data provides valuable information for business decisions and has become one of a company’s greatest assets. However, despite its value and importance, most companies still fail to make a data-driven marketing strategy.
If you’re serious about generating more revenue for your company, you have to know how to properly and effectively leverage data.
For further insights on this subject, I recommend that you read The Data Mirage: Why Companies Actually Fail To Use It by Ruben Urgarte. The book helps readers formulate an analytics strategy that works in the real world.
Watch My Interview with Ruben Ugarte
- How much has AI affected data?
- Where should the data come from when formulating decisions?
- From your experience, how can bias be dealt with?