4 Steps to Reach Your Next Customer with Big Data

March 29, 2016

8:45 pm

Growing your business can be an extremely difficult task. There are billions of people who could be your next customers and thousands of channels to reach them. So it’s no surprise that understanding where to allocate your marketing dollars has become an increasingly complex challenge.

Modern technology and the explosion of data have taken the guess work out of marketing. Marketers now have the ability to predict potential future customers, eliminating ineffective and budget busting campaigns— thanks to the help of Big Data and predictive marketing. Predictive marketing replaces the campaign targeting guesswork with Big Data and machine learning, in order to predict who your next customers are going to be and identify the optimal ways to reach them.

Here are the 4 steps to reach and convert your next customer, using the predictive marketing methodology:

Collect Data

Without knowing what characterizes your existing customers or which marketing campaigns previously worked, your chances of successfully identifying your optimal target audience are extremely low.

Start compiling data as early as possible. Collect customer data like names, emails, addresses, and phone numbers. Collect behavioral data: visits, clicks, and conversions for web, and installs, in-app engagement and in-app conversions for mobile. Use analytics systems to track the performance of all marketing campaigns, including cross-device attribution.

Enhance the Data

There is always a trade-off between the amount of information that you ask customers to give you and the likelihood of them finishing the conversion process. Imagine how you would feel if every time you were at the checkout of your local grocery store you had to provide your name, address, email address, phone number, social security number, marital status, and top five favorite books of all time. Chances are you would just go to some other store.

Luckily in the online world you can do a lot with very little data. There are hordes of publicly available data and 3rd party data providers that can take an email address and find public information about the person with that email.

You should leverage these tools to take whatever first party data you have about your customers, and turn it into a rich data set with large amounts of information about each customer.

Understand Your Customer DNA

After making the most of your data, you must understand the DNA of your customer. Most brands believe they have a solid understanding of their target market, until they dive into the data. Truth be told, most brands are way off base and target a fabricated demographic based on a desired audience, in lieu of customer’s who actually purchase their products. When analyzing customer’s DNA, you must look at two components: demographics and psychographic information to avoid this pitfall.

Identify Potential Future Customers

Now that you know what characterizes your existing customers, it’s time to stop looking at the past and start focusing on the future.

Your next customers are going to be similar in some ways to your existing ones. So you might think that you should simply target your top customer segment across different channels. While that might work, in many cases it is not the optimal customer acquisition technique.

Often times, a company’s top customers will be more expensive to target on popular platforms like Google or Facebook. Targeting secondary and tertiary customer segments proves to be just as beneficial, but is usually less expensive.

After launching your campaign, keep track of the methods that work. Do certain images attract customers more than others? Is a certain demographic converting better on a particular channel? The more data you collect, the better your next campaign will be.

The predictive marketing methodology is a proven way to increase the performance of your campaigns. The analysis of massive amounts of data usually require a predictive marketing platform, however the basic concepts can be learned and applied to improve campaign performance.

Tags: ,

Did you like this article?

Get more delivered to your inbox just like it!

Sorry about that. Try these articles instead!

Yuval Baror is Co-Founder and Data Scientist at MentAd. He has developed and managed development teams with extensive experience in data mining, machine learning, and social network analysis. http://www.MentAd.com

Leave a Reply

  • (will not be published)