If you are fearful of data and machine learning, you are not alone. You may have a job in marketing because you love creating amazing experiences for customers and consider yourself a creative person. Perhaps you were never good at math, and all this talk about data and machine learning makes you feel uncomfortable. You would be surprised how little knowledge you need about predictive analytics to make you an expert! The definition of an expert is somebody who knows more about a subject than 95 percent of the population. Just reading this book will probably place you in that top 5 percent. Plus, while predictive marketing uses predictive analytics under the hood, you don’t need to know predictive analytics at all to be a predictive marketing expert.
There is a huge career opportunity that comes from being an early adopter of new technologies, like predictive analytics, and new business practices, like predictive marketing. There are very few marketers out there who have direct experience using predictive analytics, or practicing predictive marketing. This means that even a little bit of experience can greatly differentiate you in the job market and place you ahead of more tenured marketers. Plus, the demand for data-driven marketers will only continue to increase.
Here are some career tips for aspiring predictive marketers.
Business Understanding Trumps Math
Hundreds of programs around the world are popping up at universities with degrees in data science or data analysis. Don’t sign up just yet! We believe it is a real misunderstanding that in order to be data driven and practice predictive marketing you need to be able to crunch numbers.
Numbers and stats are useless without people who can draw meaning from the data and turn it into strategies, products, and campaigns. This process requires a unique combination of the creative, analytical, and interpersonal skills so often siloed into different departments and job roles. As big data rises, the need increases for big data marketers who can draw insight and inspiration from the stats and target consumers accordingly.
It turns out that finding people who know the business, target market and customer needs well enough to interpret data is much harder than finding data scientists to crunch the numbers. Plus, new technologies are becoming available that hide the complicated math under the hood and present data in a way that is easy for marketers to understand and use.
Although you don’t need to learn to crunch numbers, you still need to feel comfortable using and interpreting them. That means you need to overcome any fear of numbers as quickly as possible. Start by using and learning simple analytics tools such as Google Analytics or even by understanding and reading financial statements of companies you know from your everyday life. Popular books like Freakonomics, NurtureShock, or Moneyball might also help you hone your data-driven way of thinking by applying an analytics approach to economics, education, and baseball, respectively.
Ask the Right Questions
So if predictive marketing is about interpreting and using data, how might a marketer get started with that? The most important thing is to demonstrate curiosity and ask the right questions about your business and customers. Start with a hypothesis. For example, you might hypothesize that you are losing customers because a new competitor is stealing market share or because customers are dissatisfied with your latest product lineup. Once you have a hypothesis it is much easier to go look for the data to support or deny this thesis. Any analytical approach is a tool to solve a problem and not a solution on its own. This is very important to internalize. Many failed projects around analytics are due to this search for the magic bullet that never yields results.
Specifically, ask creative, deep questions about your customers. Increasingly, it is the marketing organization that owns customer data and customer insights. A recent survey of 132 marketing executives found that the marketing department is responsible for customer data in 75 percent of companies. Management is starting to look to marketing to inform major strategic decisions for the company. This type of visibility in the company can be great for your career.
Recently, the director of customer relationship management at a large discount retailer discovered that a larger than average percentage of customers bought from it once but never came back. In other words, a large number of their customers were “one and done,” which is a common problem in retail. Increasing repeat buyers became a huge growth opportunity for the company. The board of directors of this publicly traded company discussed these reports. Ultimately the director received a promotion and was asked to lead a worldwide team tasked to increase customer engagement and customer lifetime value.
Do not just look at the data—mix it with real-life customer experiences. Often the best questions come from real interactions with customers. Do not just stay in your cubicle; get out in the field and interact with real customers. There is no substitute for customer face time.
Dominique once worked in Japan for Nippon Telegraph and Telephone (NTT) when it had half a million employees. Every employee in the company was asked to spend a couple of weekends working in the company store to make sure each employee was tuned in to the customer needs. Similarly, Disney asks all new employees— including executives—to work in theme parks in character costume to understand the customer experience up close. If your company doesn’t have an initiative like this, you might start one. It will surely differentiate you and make you, and your colleagues, better marketers.
Blend the Art and Science of Marketing
In an episode of the television series The Crazy Ones with Robin Williams, a New York advertising agency hires a data analyst against the wishes of Williams’s character, Simon. The company has a new client that sells cat food, and the data-driven marketing campaign designed by the young data analyst outperforms the marketing idea of veteran Simon. Initially Simon and the data analyst clash, but eventually they come to a happy place of blending the art and science of marketing. In this case, prime time television is not far off the mark. Successful marketers learn to combine the science of numbers with the art of creativity. Remember: Your job is to differentiate, delight, and disrupt.
Probably the most important thing to realize is that data science will not replace the need for creative thinkers. Dan Pingree, CMO at Moosejaw, described data-driven marketing as a way to inspire and validate the creative process. Using data, you can discover new customer personas and marketing strategies and test that your creative ideas are working.
Netflix and its chief content officer, Ted Sarandos, have been outspoken proponents of data-driven programming, which they say was behind the company’s biggest successes in in-house programming, such as House of Cards and Orange Is the New Black. However, at a Sundance panel called “How I Learned to Stop Worrying and Trust the Algorithm,” Sarandos conceded: “It is important to know which data to ignore. In practice, it’s probably a 70–30 mix. Seventy is the data, and 30 is judgment—But the 30 needs to be on top.”
Learn from Others
There is still a lot you can learn from traditional marketers. Traditional database marketers, who were focused on direct mail campaigns, are the most experienced marketers when it comes to predictive analytics. Because it is expensive to send a postcard or catalog, database marketers have long used likelihood to buy models and clusters to focus their mailers on the highest response segments. Digital marketers and database marketers don’t typically spend much time together, but they should! The principles used for many years in database marketing directly apply to modern data-driven marketing. If you have a current or former database marketer on your team, take them out for lunch and learn about advanced segmentation from them. If you don’t have a database marketer on your team, perhaps find somebody in your LinkedIn network and make contact.
You are not alone in your desire to learn about data-driven marketing and predictive marketing. There is no need for you to reinvent the wheel. You could look up companies in your industry that you admire and contact peers through LinkedIn. Most will be just as keen as you are to get together to compare notes. Start a formal or informal meet-up group with other people interested in the field and get together on a regular basis to compare notes. You could bring in outside speakers to educate you and your friends. You can even go a step further and make this a larger gathering in the form of a formal meet-up. Leading a meet-up can be a great way to increase your visibility in the industry and to add relevant leadership experience to your resume.
A great source of learning is also technology vendors selling predictive marketing software. These vendors are working with many companies in your industry and can educate you on best practices and benchmarks which otherwise might be hard to get a hold of. Software sales have changed a lot in recent years. Most vendors invest heavily in educational content, training, conferences, and even industry dinners and give you access to all these free resources long before they will ever try to sell you something. You should definitely take advantage of this opportunity and do not feel shy to reach out to relevant technology companies—our company AgilOne included. We would love to talk to you and help you further your career!
This article is an excerpt from Predictive Marketing: Easy Ways Every Marketer Can Use Customer Analytics and Big Data by Omer Artun and Dominique Levin; ISBN: 978-1-119-03736-1
Predictive Marketing is a predictive analytics primer for organizations large and small, offering practical tips and actionable strategies for implementing more personalized marketing immediately. The marketing paradigm is changing, and this book provides a blueprint for navigating the transition from creative- to data-driven marketing, from one-size-fits-all to one-on-one, and from marketing campaigns to real-time customer experiences.