What Does Predictive Analytics Mean For My Business?

Kerrie Davis (Wuenschel), Director of Analytics

Predictive analytics has quickly become one of the most attractive trends that marketers are integrating into their business’ digital strategy.

As CMOs and CIOs lean into the business opportunities that predictive analytics offer, analytics vendors across the martech rel="noopener noreferrer" landscape have responded by evolving their platforms to include predictive analytics as a must-have feature. The outcome has resulted in lower operational costs for businesses, better customer service for consumers, and more thoughtful and data-driven marketing programs.

In this article, we give B2B and B2C marketers alike a fundamental understanding of what predictive analytics is, what it’s not, and its value as it relates to their business.

What is Predictive Analytics?

Predictive analytics can be defined as combining patterns discovered through machine learning with human knowledge to identify strategic adjustments that extract hidden value from individual users. In essence, it’s using data mining and statistical modeling techniques (machine learning) to apply marketing philosophy to realistic responses and value tradeoffs (human knowledge) to therefore increase returns based on previously unidentified patterns (extract human value).

Another helpful way to grasp predictive analytics is to understand what it’s not.

Predictive Analytics is Not:

  • Segmentation/Clustering - Dividing a population into groups that have similar characteristics and appealing to that group as a specific audience (i.e. targeting on Facebook)
  • Forecasting - Using overall historical data to determine the direction of general future trends (i.e. revenue growth based on annual earnings from the last 5 years)
  • Diagnostic – Examining data to identify causation and provide an answer as to why it happened (i.e. a retweet leading to a spike in web traffic)
  • Trend Analysis - The process of comparing data over time to identify any consistent results or trends (i.e. seasonality)
  • Inferential Analysis - Interpreting a characteristic about a population based on a sample of data from that population (if 80 out of 100 Snapchat users are < 18 years old, than high schoolers use Snapchat)
  • Correlation Analysis – Measuring the strength of a relationship between two variables (i.e. Using household income to measure average order value)
  • Exploratory – Analyzing the data to form possible hypotheses that will be tested in the future (I.e. Using gender and geolocation to predict car purchases)
  • Prescriptive – Simulating multiple outcomes to choose the “best” one
  • Data Profiling – Assessment of values in a data set for consistency, urgency, and logic in preparation for analysis

What is The Value of Predictive Analytics to My Business?

Predictive analytics has become extremely popular for its ability to increase customer retention, anticipate users’ next action, rel="noopener noreferrer" and better prospect potential customers. In fact, Forbes suggests that by 2020 prescriptive and predictive analytics will attract 40% of enterprises’ net new investment in business intelligence.

For industries that have a wealth a customer data such as financial services, retail, travel & hospitality, technology, and even higher education, predictive analytics provides a significant opportunity to strengthen both business and digital marketing initiatives, such rel="noopener noreferrer" as:

Customer Churn Prevention –
It costs five times as much for a business to attract a new customer than it does to keep an existing customer, which is why many companies make it a priority to limit customer churn rates as much as possible.

By applying predictive analytics, companies are able to identify signs of dissatisfaction among their customers and identify which of those customer segments are at the most risk for leaving. Using that information, companies can make the necessary changes to mitigate risk and keep those customers happy, thereby protecting their bottom line.

Upsell & Cross-Selling –
As we just alluded to, a company’s current customer base is the best source for both existing revenue and future revenue growth. Because of this, it’s critical to maximize the revenue opportunities that are possible within your market segment and product set.

One of the most profitable ways to utilize predictive analytics is providing customers with suggestions on additional products or services that are most likely appeal to them (i.e. an airline offering higher-income passengers the opportunity to upgrade to first-class). This not only increases your brand’s value to your customers, but also the revenue derived from your customers.

Account-Based rel="noopener noreferrer" Marketing –
Many businesses, especially in the B2B space, have adopted an account-based marketing approach to better identify and engage business prospects. Predictive analytics takes ABM a step further.

The first phase of Account-Based Marketing is to develop a list of target accounts that fit your Ideal Customer Profile (ICP). Traditionally, marketers and sales teams have generated these lists through educated guesses or using filters such as company size, industry, or revenue to narrow potential accounts.

With predictive analytics, businesses can leverage existing data from their Customer Relationship Management platform rel="noopener noreferrer" (CRM), Mobile App Tracker system (MAT), as well as external data sets to better analyze patterns, attributes, and signals that characterize their best customer. Once these patterns are identified, data scientists can begin prepping data sets such as customer behavior, contact logs, and profile information and input these variables into a predictive model to determine individual predictive scores.

These insights enable Marketing and Sales to shrink the universe of potential target accounts into a manageable group that represents the most promising opportunities, which builds alignment between the two teams while also creating a target account list that is more accurate and less likely to result in missed opportunities or wasted resources.

Predictive analytics is also highly useful for B2B marketers using an account-based marketing strategy for its ability to:

  1. Prioritize the right contacts within their target account list
  2. Recommend the right products to sell to those products
  3. Suggest rel="noopener noreferrer" the right content/messages that will resonate and enhance the prospect’s buying journey

For any company that’s looking to elevate their marketing maturity, predictive analytics offers organizations a valuable tool to leverage customer data in a way that produces tangible business results. While there is no doubt that predictive analytics can help businesses increase customer retention, anticipate a user’s next action, and better prospect potential customers, it’s important to note that this is not a ‘set it and forget it’ solution. To be successful requires equal parts machine learning and human insight, as well as a commitment to constantly re-evaluate the process in order to deliver results.

About the author: Kerrie Davis (Wuenschel)

Kerrie Davis (Wuenschel) is the Director of Analytics for R2i. With 10 years experience in digital marketing, she's passionate about helping clients apply best practices to their analytics programs through audits, analysis, configuration, implementation, optimization, measuring, and reporting on success based on business goals and KPIs. Kerrie holds extensive experience with Adobe Analytics, Dynamic Tag Management, Google Analytics, and Google Tag Manager. Prior to joining R2i, Kerrie was a digital marketer in both the staffing and technology industry and received her undergraduate and graduate degrees from McDaniel College.


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