What is predictive analytics in marketing? Explanation of key methods, application examples, and how to get started.

Predictive analytics is an analytical method that uses statistics and machine learning to analyze past data and predict future sales, customer behavior, and the effectiveness of marketing strategies. This article explains the basics of predictive analytics that marketing professionals should know, the types of representative predictive models, application examples including MMM (Marketing Mix Modeling), and the steps to start predictive analytics in your own company.

What is predictive analytics in marketing?

Predictive analytics in marketing is an approach that uses customer data and performance data from past initiatives to predict future purchasing behavior and the effectiveness of those initiatives, and to use that information for decision-making. It provides data-driven forecasts for questions such as, "How will sales change if we increase advertising spending by 10%?" or "Which customers are likely to churn?"

When performing predictive analytics, a "predictive model" is constructed. A predictive model is a mathematical model that learns patterns and relationships from past data to estimate future values ​​and events. In other words, the predictive model is at the core of predictive analytics, and the two are inextricably linked.

Let's look at a specific example. By modeling the relationship between advertising spending and sales over the past three years, we can forecast sales for next year's advertising plan. Similarly, we can predict the probability that a particular customer will purchase a product based on their attributes and behavioral history.

The difference between predictive analytics and effectiveness evaluation (descriptive analysis)

Predictive analytics is an analysis aimed at "predicting the future," and its purpose differs from descriptive analysis and effectiveness evaluation, which are aimed at understanding the past. Data analysis can be broadly classified into four types depending on the type of question it seeks to answer.

→ Click here for more details on the four categories of data analysis:What are the four categories of data analysis? The role and use of "descriptive," "diagnostic," "predictive," and "prescriptive" analytics to answer marketers' questions

Predictive analytics lies between analysis that understands the past and analysis that determines the next course of action. Looking back at the past alone is insufficient for making future decisions; incorporating predictive analytics improves the accuracy of decision-making.

What can be done with predictive analytics (benefits in marketing)

The greatest value of predictive analytics lies in enabling marketing decisions that do not rely solely on intuition or experience. Specifically, it offers the following four benefits:

1. Optimizing budget allocation

By using predictive models to estimate the future effects of each initiative, you can reallocate your budget to initiatives with a high ROI (Return on Investment). Within a limited marketing budget, you can consider allocations that maximize incremental sales.

2. Pre-implementation simulation of policy effects

You can estimate the effects of a measure before implementing it. For example, if you can simulate "sales if you double the amount of TV commercials you spend," you can reduce the risk of failure associated with large investment decisions.

3. Deepening customer understanding

By predicting purchase probability and churn risk on a customer-by-customer basis, it becomes possible to tailor approaches to each individual. This allows for a shift from uniform strategies applied to all customers to strategies based on priorities.

4. Reducing uncertainty and improving explanatory power

Data-driven forecasts provide valuable justification for explaining decisions to management and relevant departments. They allow you to demonstrate, with numbers, "why this budget allocation is what it is," making it easier to build internal consensus.

Main predictive analytics methods used in marketing

There are several predictive models used in marketing, depending on the objective and data. First, let's organize the overall picture in a table.

regression analysis

Regression analysis is the most fundamental method for predicting continuous numerical values ​​such as sales and demand. It expresses the relationship between explanatory variables (advertising costs, price, temperature, etc.) and the dependent variable (sales, etc.) using mathematical formulas, and estimates future values.

For example, by modeling the relationship between advertising costs and sales, it's possible to create sales forecasts based on advertising plans. Multiple regression analysis, which deals with multiple explanatory variables, is a fundamental technique for predictive models in marketing, and MMM, which will be discussed later, is an extension of this concept.

Logistic regression analysis

Logistic regression analysis is a method for predicting binary outcomes, such as "purchase" or "not purchase," as probabilities. Based on customer attributes (age, gender, past purchase history, etc.), it calculates the probability that a customer will purchase a product or churn from a service.

Because it allows you to understand which variables strongly influence the results, it is useful not only for making predictions but also for interpreting "why" those predictions are made. It is a standard method for purchase forecasting and churn forecasting.

Time Series Analysis

Time series analysis is a method that models how data changes over time and predicts future trends. A representative model is the Autoregressive Integrated Moving Average (ARIMA) model.

Because it allows for forecasts that incorporate seasonality (such as sales increasing in summer or at the end of the year) and trends, it is widely used for demand forecasting and inventory planning. It is also effective for analyzing trends in KPIs such as sales and customer numbers, and for capturing changes before and after the implementation of measures.

Clustering

Clustering is a technique that automatically groups customers based on the similarity of the data. It is classified as "unsupervised learning," where no correct labels are provided in advance.

By grouping customers with similar purchasing tendencies and behavioral patterns, it's possible to design optimal strategies for each segment. Unlike "classification models," which distribute data into predefined categories, clustering is characterized by discovering group structures from the data itself.

Collaborative filtering

Collaborative filtering is a technique that predicts products a customer is likely to like based on their past behavior history and the behavior of other customers with similar preferences.

This method is what works behind the scenes when you see recommendations like, "People who bought this item also bought these items." It's widely used in recommendation engines for e-commerce sites and video/music streaming services.

Examples of predictive analytics applications in marketing

Predictive analytics is used in a wide range of situations, from allocating marketing budgets to implementing customer-specific strategies. Here are five representative examples of its use.

Marketing mix modeling (MMM)

MMM is an analytical method that statistically models the relationship between KPIs such as sales and data from various marketing initiatives (TV commercials, digital advertising, sales promotions, etc.), and visualizes the sales contribution of each initiative. From a predictive analytics perspective, what is important is that the predictive model built with MMM can be used to simulate "sales when the budget allocation is changed."

MMM is particularly effective in cases where online and offline initiatives are mixed, making it difficult to measure the effectiveness of individual initiatives. As user-level measurement becomes more difficult due to the progress of cookie regulations, MMM is attracting renewed attention as a statistical approach that does not rely on personal data.

By reallocating the budget based on the analysis results, it is possible to maximize incremental sales even with the same total budget. Because it allows for a seamless review of past initiatives and simulation of future strategies, MMM (Marketing Magazine) is a representative application of predictive analytics in marketing.

XICA provides analytical services centered on MMM (Multi-Memory Management), primarily to large corporations.Supporting decision-making within marketing organizationsI have done it.

Demand forecast

Demand forecasting is the process of predicting future demand based on data such as past sales performance and seasonal factors. It is one of the most classic applications of forecasting models, utilizing time series analysis and regression analysis.

Accurate demand forecasting prevents both lost opportunities due to stockouts and increased costs due to excess inventory. For marketing departments, it's essential for coordinating supply plans with supply chains that anticipate peaks in demand caused by campaigns.

LTV (Lifetime Value) Prediction

LTV forecasting is an analysis that predicts the revenue a customer will generate for a company over time. It builds a predictive model based on past customer behavior data such as average purchase amount, purchase frequency, and customer retention period.

Being able to predict LTV (Lifetime Value) allows for investment decisions that balance LTV with customer acquisition costs (CAC). Knowing "which channels produce customers with high LTV" also clarifies the prioritization of acquisition strategies.

Churn prediction

Churn forecasting is an analysis that predicts the likelihood of customers churning in the future. It captures signs of churn from behavioral data such as decreased usage frequency and changes in login intervals.

Identifying customers at high risk of churn in advance allows you to focus retention efforts, such as coupon distribution and follow-up communications, on those customers who need them most. Generally, retaining existing customers is less expensive than acquiring new ones, making churn forecasting a valuable tool for increasing revenue.

Recommendation

Recommendation systems predict each customer's preferences and present products and content that they are likely to be interested in. Predictive models, including the collaborative filtering mentioned earlier, form the foundation of this system.

It has become established as a means of simultaneously improving the customer experience and increasing sales, such as suggesting bundled purchases on e-commerce sites and recommending works on streaming services.

Challenges and considerations of predictive analytics

The main challenges of predictive analytics boil down to two things: data quality and the organizational structure for effectively utilizing predictions. Here are three key points to keep in mind before implementation.

Data quality and quantity

The accuracy of a predictive model depends heavily on the quality and quantity of data used for training. Missing or biased data increases the risk of incorrect predictions.

For example, attempting to predict company-wide sales with only sales data from a few stores will not yield reliable results. Building a foundation for collecting, integrating, and organizing scattered data both inside and outside the company—such as CRM data, web analytics tools, advertising reports, and in-store data—is the starting point for predictive analytics.

Expertise in interpretation and application

In recent years, the usability of analytical tools has improved, creating an environment where even non-experts can build predictive models. However, even with readily available tools, expertise is still required for data preparation, judging the validity of models, and translating prediction results into actionable strategies.

The challenges of "having predicted values ​​but not knowing how reliable they are" and "being unable to explain the analysis results to management" cannot be overcome with tools alone. You need to consider a system that suits your company, whether it's developing internal talent or collaborating with external experts.

Predictive analytics in the age of generative AI

With the spread of generative AI and AutoML (automated machine learning), the barrier to entry for predictive analytics continues to decrease. You can try out predictive models without writing code,AI assists in interpreting analysis results.Having them do that has become a realistic option.

On the other hand, some things remain unchanged. These include the principle that the quality of training data determines the quality of predictions, and the human judgment required to distinguish between "correlation" and "causation" and use that knowledge in decision-making. As tools evolve, the ability to design "what to predict" and "how to use the prediction results" becomes a differentiating factor for organizations.

5 Steps to Getting Started with Predictive Analytics in Marketing

The implementation of predictive analytics proceeds in five steps: (1) setting objectives, (2) preparing data, (3) building a model, (4) verifying accuracy, and (5) improving operations.

Step 1: Setting Objectives and KPIs

First, define "what you are forecasting and how it will be used for decision-making." Is it sales forecasting, or churn forecasting? By deciding who will use the forecast results and how, you can prevent rework in later stages.

Step 2: Data collection and preparation

We identify, collect, and integrate the data necessary for our objectives. This often requires cross-departmental data integration, such as CRM, advertising reports, access analytics, and in-store data, and is generally the most time-consuming process.

Step 3: Method Selection and Model Construction

We select a method and build a predictive model according to the nature of the data and the target of prediction. Rather than aiming for a complex model from the start, it is standard practice to begin with simple methods such as regression analysis and then make them more sophisticated as needed.

Step 4: Accuracy Verification

We verify the accuracy of the prediction model by comparing its predicted values ​​with actual results. It is important to design a verification mechanism in advance, such as setting aside a portion of historical data for verification or checking the results at regular points during operation.

Step 5: Operation and Continuous Improvement

Because market conditions and customer behavior change, the accuracy of predictive models deteriorates over time. Incorporate an operational cycle that regularly updates data and retrains the model.

Frequently Asked Questions (FAQ) about Predictive Analytics

Q. What is the difference between predictive analytics and AI-based prediction?

A. Predictive analytics is a general term for efforts to predict the future using statistics and machine learning, and AI-based prediction is one of the means to achieve this. Both statistical methods such as regression analysis and machine learning methods such as deep learning are included in predictive analytics.

Q. How much data and how long of a period is needed for predictive analytics?

A. The amount of data required varies depending on the subject and method of prediction. For example, in time series analysis that captures seasonality, data covering periods that include multiple seasonal cycles is desirable. It is impossible to say definitively "how many data points are needed," so we recommend consulting with an expert to determine the appropriate amount based on your objectives.

Q. What is the relationship between MMM and predictive analytics?

A. Marketing Mix Modeling (MMM) is one of the leading applications of predictive analytics. MMM statistically models the relationship between a campaign and sales, and uses that predictive model to simulate sales when the budget allocation is changed. Its key feature is that it can simultaneously review past effects and predict future results.

Q. What level of accuracy can be expected from the predictions?

A. Predictive models are merely estimates based on past data and do not guarantee 100% accuracy. The important thing is to understand the limitations of accuracy and use them as a more reliable source of information than relying solely on intuition. Operate them with the understanding that you will continuously improve accuracy through repeated testing.

Q. Can predictive analytics be performed even if there are no specialists within the company?

A. Yes, it is possible. By utilizing user-friendly analytical tools and collaborating with external analytical partners, even companies without a dedicated team can begin using predictive analytics. However, since data preparation and result interpretation require a certain level of expertise, it is a practical option to proceed with the support of external experts in the initial stages.

Summary: Use predictive analytics to enhance marketing decision-making.

Predictive analytics is a powerful approach to reduce marketing uncertainty by using historical data to foresee the future. By using predictive models such as regression analysis and time series analysis appropriately for each purpose, you can improve the quality of decision-making, from optimizing budget allocation to implementing customer-specific strategies.

On the other hand, data preparation and interpretation of prediction results require expertise, and there are many situations where it is difficult to proceed solely within a company.

XICA combines data science and consulting expertise to support companies' marketing decision-making through analytical support, including MMM solutions.

If you are considering implementing predictive analytics or utilizing MMM, please feel free to contact us.Contact us.

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XICA Co., Ltd. Marketing Team