Traditional segmentation for marketing purposes will soon be overtaken by predictive analytics — learning from large volumes of data to segment customer databases based on forecasted future trends, estimated future actions, and much more.

One of those buzzwords that’s been floating around for the past few years, predictive analytics are changing the way marketers get in front of consumers. Simply put, predictive analytics can be used to create predictions about unknown or uncertain future events.

You’ve probably seen predictive analytics in use in your daily life. When you see a suggested event come up on your Facebook feed, or when Amazon recommends products you’ll probably like, the company is using data to predict which events or products you’re most likely to go to or want. It’s predictive analytics in action.

Lack of Awareness About Predictive Analytics

The market for predictive analytics software is expected to reach $5.2 billion by 2018 (growing from $1.7 billion in 2013). The market could be even bigger, but experts claim lack of awareness about the power of predictive analytics is holding it back.

Of the many industries set to gain from this technology, marketers must be aware of its power or risk falling behind.

For marketers, predictive analytics forecast what a customer will do in the future using a dynamic combination of data analysis, statistics, machine learning and modeling.

Having all of this information, and in real-time whenever possible, allows marketers to greatly improve their campaigns and create one-to-one relationships with customers, instead of relying on outdated practices to “guess” what their customers will do.

Companies like Salesforce and Deloitte are leading the way in helping business-to-business (B2B) companies refine their reporting and close the gap between marketing and sales teams. But now predictive analytics are also on the roadmap for many business-to-consumer (B2C) marketers as well.

By 2017 we're going to see the shift from predictive analytics primarily being used by B2B marketers to becoming an important tool in the arsenal of every marketer. Here’s a deeper look at why predictive analytics should become the marketing norm within a year.

Helping Marketers Gain Consumer Insights

In order to get the right message to the right consumer, marketers traditionally segment their consumers into groups that have similar qualities. Depending on the product, this typically involves a combination of four segments: geographic, behavioral, demographic, and psychological.

However, this type of targeting is usually flawed and, though widely used, can be an expensive mistake for many companies.

In some instances when customers are asked to self-report, marketers can end up with incorrect information. For example, when asked about how often they exercise, people may report five days per week (because that’s how often they plan to go). But their reality maybe hitting the gym just once per week.

This information also can change quickly; for example, family income can increase or decrease depending on the job market.

Additionally, these sample sizes remain relatively small and may not accurately represent a larger group. This type of data is simply not dynamic. This information might be updated annually or bi-annually, but the reality is that many of these factors change quickly.

Harnessing the plethora of available data points for each individual consumer is the solution to the issues above. To get a real-time, global view of customers, marketers must begin to use data such as transaction, interaction and external data.

And while this may seem overwhelming for marketers, it’s a more accurate way to get an overall picture of the consumer. Here’s a breakdown of how each of these new types of data are helping marketing practices evolve.

Transaction Data

This data gives information on when a person bought an item, how they paid for it, when they purchased it, discounts they may have used, etc.

Learning Opportunities

Example: Sarah shops at ABC Store and last Thursday she bought a blue shirt online for $40 and used a 10 percent off coupon code. The next day Sarah went to an ABC Store location and bought a pair of pants for $50 using a credit card.

How this helps the everyday marketer: This helps blend online and in-store habits to allow marketers to have a better understanding of how the customer behaves, as well as their purchase patterns. Customers are increasingly looking for more reward programs and expect a lot from businesses. Using transaction data, marketers know that Sarah is a “loyal” customer and can target her accordingly online and offline.

Interaction Data

This data measures website interactions, social media interactions, phone, email, texts, and any conversation the customer has with the brand. This puts the customer and brand in a one-to-one relationship.

Example: Sarah loved the blue shirt she bought online, so she wrote a review on ABC Store’s Facebook page. She then opened the email newsletter she received the next day but has yet to use the discount code included in the email.

How this helps the everyday marketer: This type of information allows marketers to better understand how consumers feel about a brand, and address any concerns. This data gives qualitative measurements that are incredibly useful to any marketing campaign.

External Data

This is all data outside of the organization’s system; for example, how does traffic affect a retail store? What social media trends have an impact on purchases?

Example: ABC Store wants to open a new physical location. Using data, they can analyze traffic patterns, average income, and age group of nearby potential customers, etc. to discover the best location.

How this helps the everyday marketer: Marketers now have insight to know if users will respond to tactics such as newsjacking or if they should run a promotion at a certain retail store because of traffic or weather patterns. With the right tools, marketers can get this information in real-time — or better yet use this information to predict future situations - so they know what to promote and when.

These three type of data together can create a global view of the consumer. Knowing this type of information about the consumer allows marketers to create better, more specific campaigns. And, in turn, consumers will have access to promotions they actually want. Data takes the guessing out of the equation.

This year marketers will feel pressure to understand and implement data-driven campaigns. According to LinkedIn’s “25 Skills That Can Get You Hired in 2016”, statistical analysis and data mining ranks number two.

Marketers are also under increasing pressure to produce a high return on investment, and as technology advances, data will certainly be the main driver of success.

Title image by Christian Schnettelker