How Big Data Can Make You a Better Marketer

Big data is everywhere these days. Among other things, it's created some big expectations for marketing — especially when it comes to mining information. And while it may have the potential to change the game when it comes to data driven marketing, the reality is that it has yet to fully deliver due to a myriad of marketing methodologies clogging the funnel.

What does this mean?

Let’s back up for a minute. Before we can tap the results of big data, we need to examine the perspectives that are used to fill the funnel — growth and sales — and think about some of the fundamental shifts that are taking place. Then we’ll more clearly understand how big data fits in.

More for Less

One question that I always hear is: “How do I reach my sales goals and how do I grow the business with minimum investment?” Mathematically answered, growth typically equals more revenue (aka sales goals achieved). So we’ll call this the Value of Goals and look to find the value by dropping it into the formula below:

Total Value of Goals = (Number of Potentials who Engaged) X (Average Conversion Rate) X (Average Value of Conversion)

Still with me? Great. So let’s now say that we want to increase sales for an e-commerce store. In that case, the total revenue will be:

(Number of Visitors) (X) (Conversation Rate from Visitor to Purchase Times of the Average Sale Price)

Pretty simple. So how do we really grow sales?

The traditional perspective is the marketing funnel

(Spoiler alert: It doesn’t work). Improving business outcomes using the traditional funnel approach only considers three variables: Volume, velocity and value.

Greater Volume

Double the volume means double the revenue, right? Well, assuming the volume is of the same quality.

Even if that assumption is correct — and it rarely is —you need to weigh in the cost of acquiring the new customer. If your conversion rate is 1 percent, then your generated customer lifetime value needs to be more than 100 times the cost to acquire the customer for the math to work. With decreasing effectiveness and increasing costs to get more leads, this becomes tricky.

More to the point — your target market segment is not infinite, so this is not a sustainable approach.

Better Conversion Rates


A typical objective for any marketer is customer journey optimization. You pick an experience — say the home page or a critical landing page — then you optimize it.

Starting with the most frequent experiences generally makes sense. Traditionally, this is where digital marketing professionals have focused. This works, but it is a heuristic — an experience-based decision making technique that is not guaranteed to be optimal ... just good enough, for now.

One problem is the scale. Do a quick report on just the number of web pages and campaigns you have and you’ll realize you cannot optimize everything you create. This becomes a prioritization problem. You need better techniques to pick the exact conversion rates you want to influence.

Another problem is value. Currently the decision on what to optimize is still opinion-based, and yet data-driven approaches can detect the low-hanging fruit that will yield the highest return.

Higher Averages Per Goal

This metric is very tricky to influence and doesn’t have a clear answer in traditional marketing. But the fact is that raising prices just doesn’t work.

A more common, traditional marketing methodology often promotes the opposite — create discounts and promotions to increase volume. Very few companies, even Apple or Tesla Motors, specifically target customers who want to pay more and don’t value the word discount.

In reality, influencing this metric usually entails a change of customer type, quality of product, product strategy, perceived value, etc. Here, the data-driven answer is a bit simpler: more of the right buyers and the right products — if you know your customers and serve them well they are always willing to pay more.

So volume, velocity, value. Which one of these should you focus on? They are directly proportionate, so in theory it doesn’t matter. However, in practice, conversion rates typically are smaller numbers and seem likely to be the easiest to change. Changing a conversion rate from 1 percent to 1.3 percent is much more doable than adding 30 percent more qualified leads.

But remember this: Not all customers and experiences are the same. Increasing quantity and velocity through the funnel is not an answer to sustainable growth. The role of data in this case is to help you influence all three, but through a completely different paradigm, so in this case big data is not used.

A business naturally wants to grow faster than the market. The only way to achieve that growth is to cause negative growth in some of your competitors, otherwise you will eventually run out of marketplace buyers. This is why the funnel model is reaching its limit, leaving businesses to pursue more customer-centric models.

The Data-Driven Perspective


This model, the so called customer journey, approaches growth with a different philosophy.

It’s not a simple flow measurement, measuring people in and out of the buying cycle and seeking to increase them. Instead, it captures every step of every customer, searching for patterns.

With patterns, you can resurface priorities such as most important customers, biggest customer potential, which customer engagements are working and which can be improved. Essentially, instead of looking at volume and velocity as factors to influence, you prioritize the customers that make up these factors.

The customer journey fixes some of the biggest flaws in the funnel, namely, its inability to prioritize customers and actions. Creating a model of sustainable, sometimes exponential growth, amid a continuous learning system improves customer acquisitions and produces happy customers.

Phrase the original question differently: “What is the optimal growth in key metrics that influences my goals and is achievable over a period of time?” If you receive information on 50 campaigns that present the biggest opportunity for your conversion optimization, can you execute on all 50 in a week, a month, a year?

This is why it’s important to consider a strategic and tactical perspective. Draw models and processes based on a long-term strategy and automation, but consider optimizations and tactics based on data driven priorities.

Here is a model you can consider and where big data fits in:

  • Start with your existing and ideal customers. In big data perspective, this means getting all data together in a single place
  • Learn why you have become successful in acquiring and converting them and where you have failed the rest – something a learning algorithm can do on top of that data
  • This becomes an insight to acquire more of your best customers — an actionable insight you can apply within a demand generation or advertising strategy — therefore creating “more of the right buyer"
  • Based on those insights, you can also optimize some of the key routes they go through and become more successful in acquiring new customers, which, in turn, resurfaces new insights on new routes to optimize
  • Analyze results based on business goals, not clicks

What Do You Want to Achieve?

Simply put, in a data-driven marketing world, we first answer the question, “What do we want to achieve: sales, donations, sales conversation?”

With data we can start answering questions such as: “Which customers deliver this result most often?,” “What’s the profile?," “Which are the key steps?” and more.

Consider the 80/20 rule of productivity — 20 percent of your efforts bring 80 percent of the results. A more likely rule for your business is the following: 2 percent of your visitors bring in 80 percent of the revenue. This is an essential component to consider when starting examing the customer journey. Influence the customer experience for those folks first, so you can attract more of them. When a similar customer comes in, you can usher them toward a faster path to conversion.

Repeat and refine the process for each successive group until the level of granularity is so acute it approximates a personal conversation.

My advice? Examine your approach and think about the part that big data can play in that approach. All of the above number crunching is technically “big data” once it’s crunched. It is high time that we use said big data to deliver upon the expectations that it has set for marketing, and transition the industry toward proactivity instead of reactivity. 

Title image by Michael Heiss  (Flickr) via a CC BY-NC-SA 2.0 license.