How to Use Predictive Analytics in Data-Driven Marketing

Marketing involves promoting products and services to customers. The end goal is to generate sales and retain a healthy customer base. Marketers use a host of strategies to attain their objective, from identifying potential clients to running ads. However, a successful outcome is never guaranteed. Ineffective marketing campaigns can easily drive customers away and erode revenues.

This is when predictive analytics is useful. Predictive analytics uses data to identify patterns and forecast potential outcomes.¹ In a marketing context, organizations use predictive analytics to refine customer segmentation, optimize ads and generate a higher ROI on campaigns. This post explores the fundamentals of predictive analytics in marketing.

Why Data-Driven Marketing Matters

Traditionally, companies relied on a less informed approach to marketing. Lacking extensive data, marketers used print ads, TV commercials and radio to reach a broad audience. This costly approach didn’t guarantee that their target customers would see the ads. It also offered little personalization or direct interaction with individual clients.²

Today’s marketing campaigns are much different. Using data-driven strategies, marketers tailor ads to a much smaller audience base. They target customers based on demographics and behaviors and craft ads to convert individual clients. Some marketing tools that require heavy data involvement include online ads, email marketing and social media.

As an example, assume a customer visits a clothing retailer’s website. They view a few products and sign up for the company’s email marketing list. Online marketers could use data gleaned from the customer’s interactions to design a tailored marketing funnel aimed at making a sale.

Key Predictive Analytics Techniques

Predictive marketing analytics uses statistics and mathematical formulas to identify patterns. Some common techniques include regression analysis, classification and clustering.

Regression analysis examines relationships between multiple dependent variables and an independent variable to model potential outcomes.³ It’s useful for forecasting. As an example, a marketer might study how a product’s price could impact future sales.

Classification and clustering models allow marketers to segment customers based on specific characteristics and relationships. Using the data, marketing professionals personalize campaigns for each group.

Implementing Predictive Models

Organizations require accurate data for predictive analytics. The data comes from a combination of internal and external sources. Marketing analysts review the data, removing irrelevant data points and preparing it for analysis.

Selecting the proper algorithm for analyzing data is critical. If a marketer uses the wrong method, it yields incorrect results. Companies that rely on skewed data for their campaigns may waste money on ineffective marketing strategies.

After implementing an algorithm, marketers continually refine it to reflect situational changes and new data. The refinement process improves forecasting accuracy over time.

Tools and Technologies

Predictive analytics for marketing requires complex analysis of vast amounts of data. To support the analysis process, marketers rely on a combination of programming languages and ready-made tools.

Python and R programming languages offer extensive libraries for data manipulation and analysis, such as Pandas and NumPy. Marketers use them to create tailored analyses and derive insights from available data.⁴

Predictive analytics for marketers may also involve using automated machine learning tools. Such tools simplify analysis and require far less coding to develop and implement predictive models.

Best Practices in Predictive Analytics

Like any mathematical analysis, predictive analytics requires accurate inputs for accurate results. Selecting incorrect data can easily skew the outcome and cause marketers to make faulty decisions.

Analysts must use quality data to avoid erroneous outcomes. That involves closely scrutinizing the data for missing information and outliers. Marketers must also select their data analysis models carefully, as using the wrong model can impact accuracy.

Predictive analytics insights should align with the marketing objectives. This requires a careful consideration of the question a marketer wants to answer with data. Once a marketer fully implements a predictive analytics model, it requires continuous monitoring. Ongoing analysis and refinement allow marketers to incorporate new data and decision-making factors.

Common Pitfalls to Avoid

Using predictive analytics in marketing can produce many benefits for companies interested in maximizing marketing results. However, it’s not without limitations. Certain mistakes can elevate campaign costs and sink returns.

One area to be cautious with is model selection. Predictive analytics requires a finely tuned model for the best results. Overfitting or underfitting models won’t interpret data correctly. Even worse, they may fail to understand new data or accommodate changing scenarios.⁵

Data privacy compliance is another concern. Various laws, such as the California Consumer Privacy Act (CCPA) and the European Union’s General Data Protection Regulation (GDPR), limit how companies collect and use customer data. Marketers should abide by data privacy laws to avoid legal repercussions, such as costly fines and penalties.

Another potential pitfall is the misinterpretation of results. Failing to understand the data’s meaning or apply it to relevant marketing objectives may yield incorrect insights and lead to uninformed decision-making.

Measuring and Optimizing ROI

Marketers can greatly benefit from insights gleaned from predictive analytics. However, it’s important to track returns so that decision-makers see the value derived from the tools. They can do so with the help of key performance indicators (KPIs).

KPIs are metrics used to evaluate results over time. Organizations can select relevant KPIs and benchmark them against future results. Changes in KPI results alert companies to potential opportunities or threats so they can optimize for strong future returns on their marketing campaigns.

Enhance Marketing Campaign Performance with Predictive Analytics

Predictive analytics is upending how organizations plan and implement marketing campaigns. With its help, organizations can make critical marketing decisions based on actual data rather than guesswork. As a result, organizations using predictive analytics for marketing campaigns may see a higher ROI on their spend.

Marketers eager to expand their predictive analytics skills can benefit from the Online Master of Science in Business Analytics (OMSBA) at William & Mary. The program provides in-depth education and training with the newest technology tools that organizations use for data-driven decision-making. Students learn from W&M’s world-class faculty and benefit from flexible online courses with no campus commute required.

To jump-start your career in predictive analytics marketing, speak with an admissions outreach advisor about the admissions process and application requirements.