The Rise of Technology in the Growth Marketer’s World
The world of technology has changed dramatically in the last decade. As a result, the world of marketing has changed dramatically. These days the marketing world is on fire about “growth hacking.” Growth has become as much about retention as attraction. The idea is to not only attract customers but also provide an experience that keeps customers around for years to come.
Ten years ago, marketing for growth looked like firing a shotgun: blast as much media out there as possible and see where it hits. Mailers, sales, and bulk emails were all sent out into the world on a wing and prayer. Marketing looked a lot like a “squishy” process, one that relied more on intuition and creativity than hard data. However, the rise of new technologies has flipped the script.
Marketing has transformed from a “soft” science, driven by intuition and luck, into its very own form of data science. The amount of data available about modern consumers is effectively infinite. Therefore, the idea that these data can be used for real experiments and analyses has firmly taken hold. The modern marketer looks a lot less like a graphic designer and a lot more like an engineer.
The bottom line is this: the data science approach to marketing is changing what marketing means. Growth marketing encompasses concepts such as:
Targeted ads that are personalized for customers.
Experiential marketing that connects products and customers emotionally.
Customer ecosystems that help retain customers.
Organic customer growth through peer-to-peer marketing.
All this is not to say, however, that the jump from traditional marketing to growth marketing isn’t without significant risk. Many new marketing technologies are unproven, and finding the diamonds-in-the-rough is not easy. Not to fret, though. This article is here to help you untangle the world of data science as marketing.
The New World of Data Science
Massive amounts of data bring the potential for massive amounts of confusion. This is especially true for new growth marketers that may not have “grown up” in the hard sciences. Ultimately, the challenge growth marketers face is this: how to make sense of a billion points of data? Sounds like a job for the newest data science buzzwords: Big Data and Machine Learning.
First, let’s look at Big Data. Sure, we’ve all heard the phrase “Big Data.” Furthermore, many of us have heard the famous Dan Ariely quote: “Big data is like teenage sex: everyone talks about it, no one really knows how to do it.” Given this skepticism, how do marketers learn to use big data for growth?
The problem is that there is not just a lot of data, there is a lot of different data. Customer tweets are data. Web traffic is data. Browsing time is data. Half the world is on the internet, which means half the world is constantly creating data. One of the best ways to turn these data into growth is through deep data analysis and segmentation.
Calling data segmentation the most important new marketing tool is an understatement. Think of data as a puzzle. Just like a puzzle, the best place to start building the picture is organizing the pieces. New data segmentation tools let growth marketers do just that. This technology turns a big mess into a clear picture of the major customers.
The latest data science technologies put together a picture of each customer as an individual using cutting-edge behavior analysis techniques. Today’s data segmentation technologies are driven by demographic, historical behavior, and personality traits collected over time. These new tools come from the hard sciences and show the true power of data segmentation. They combine data points into a concise summary of not just that customer’s purchasing habits but also their personality. Which is how growth marketers can create ads that truly “speak” to and engage a customer emotionally.
Once you know the personality traits of your main customer demographics, it’s a simple step to ads that speak to each group individually. Gone is a one-size-fits-all set of marketing materials meant to attract as many new customers as possible. Custom ads can be created for each group – ads that don’t just entice customers but keep customers. Furthermore, the better you understand a customer, the better you can predict their behavior. Data segmentation and behavior analysis enable not just more effective ads but also feeds directly into machine learning tools to optimize future ads.
Now that the data is segmented, big data’s twin brother, machine learning (ML) takes over. ML tools can turn current trends into future predictions. They learn about the data while the data is collected and segmented. After a customer views an ad, ML tools can study what the customer does next. Did potential customers from this group prefer ad A or ad B? Why did they prefer it? How do I make a better ad for their world and not the world?
Better yet, machine learning can do this with concrete facts and numbers. ML provides methods for content scoring – measuring what exactly about an ad spoke to each customer. ML can tell you what part of the ad caused positives outcome. And then, because both the machine learning tools and the growth marketer know what made an ad successful, they can work together to quickly generate new ads. Ads that play to the strengths ML showed them. Growth marketers can improve upon ads and generate new ads essentially in real-time.
After looking at enough customers, data segmentation and ML will figure out why the potential customer did what they did. Giving you the knowledge of what is was, exactly, about your ad created a new customer. Knowing this gives one of the most powerful insights in marketing - how ads affect potential customers on an emotional level. Knowing this means knowing exactly how to create ads that lead to more favorable outcomes, more often.
The positive outcomes the new technologies create are many, and because they are built on data science, they are measurable. Suddenly, growth marketers have metrics they can test against. Numbers they can watch improve constantly, that tell them what is working and what isn’t. These new tools aren’t just about better ads, they’re about better ads created much faster.
There are a wealth of metrics for assessing ads. Some of the specific metrics a growth marketer can track using ML include:
Click-through Rate (CTR) – You’ll be able to measure whether or not your ads are drawing in the right customers.
Life-time Value (LTV) – You’ve got the right customers; now that you’re connecting with them emotionally and providing them an ecosystem in which to interact with each other, the new customers are much more likely to stay.
Average Order Value (AOV) – You’ve got your customers, they’re here to say, and now you can measure how much each customer is spending as a function of your new data-driven ads.
The Bottom Line
The world of marketing has changed at a fundamental level. Traditional marketing has given way to growth marketing. Companies don’t need to attract as many customers as possible at the “top of the funnel.” At the end of the day, many of those customers don’t stick around for the long haul. The marketing of today has replaced shotgun marketing that appeals to everyone with laser-focused marketing that appeals to every one.
The way to go from shotgun to laser lies in the new technologies available in data science. Growth marketers are discovering that data science tools can be used to realize true growth marketing. Data science, in particular big data and machine learning, give marketers previously unheard of insights into consumers. Marketers can quickly segment the data, learn from the data, and turn what they learned into impactful advertising; advertising that doesn’t just get new customers, but gets the right customers and keeps them for years to come.