Marketing is often said to be an area of ​​uncertainty. No one knows for sure what will work. Marketers make hypotheses and set indicators (KPIs) to verify them. Then, based on the effectiveness verification, they make adjustments and optimizations.

Traditional marketing and planning involves conducting experiments based on hypotheses, stopping what doesn't work, optimizing what does work, and achieving results through trial and error.

Now, by leveraging a variety of data, marketers can use predictive modeling with greater accuracy and reliability to identify potential opportunities that "might work."

In this article, we'll explain what predictive modeling is, how it can be used in marketing, and how to get started.

What is a predictive model?

A predictive model is a mathematical model used to analyze historical data and predict future events or phenomena. The goal of a predictive model is to generate the most accurate prediction for a given data set.

Predictive models are developed using techniques such as machine learning and statistics that discover patterns and relationships within datasets and build models to forecast future events or phenomena.

For example, sales data can be used to develop models to forecast future demand. In this case, seasonality and trends in demand can be analyzed from sales history to forecast future demand. Similarly, data such as market trends and customer behavior patterns can be used to forecast future trends and customer demand.

Predictive models are used in many areas of business and marketing. For example, they have a variety of applications, such as demand forecasting, sales forecasting, risk assessment, product quality control, and marketing campaign optimization. However, predictive models are only predictions and do not necessarily guarantee accurate results. Therefore, careful attention must be paid to selecting appropriate data and developing models.

Predictive Models in Marketing

A predictive model in marketing is a mathematical model that uses techniques such as machine learning and statistics to learn from past data and predict future events or phenomena. It can be used to predict consumer behavior and the effectiveness of advertising strategies, optimizing marketing activities and improving ROI.

For example, a predictive model can be used to make predictions such as "if prices increase by XX%, future sales will decrease by XX%."

Types of predictive analytics used in marketing

There are several predictive models used in marketing, but here are the main ones, each with a specific purpose and using specific data:

Propensity score analysis

Propensity score analysis is one method used to accurately measure the impact of marketing initiatives.

For example, if a company implements a marketing strategy, and you want to measure how that strategy affected sales, it is difficult to compare an intervention group and a non-intervention group under completely identical conditions, and there is a high possibility that the results will be influenced by factors other than the marketing strategy.

Therefore, by utilizing propensity score analysis, it is possible to make the characteristics of the data of the intervention group and the non-intervention group closer and to make a more accurate comparison. For example, if there are groups A and B that implemented a certain marketing measure, and there is a specific bias in the people in group A, this can be eliminated to more accurately predict the effectiveness of the marketing measure.

Propensity score analysis is used to evaluate various initiatives, such as new product introductions and pricing changes.

Logistic regression analysis

Logistic regression analysis is a basic method for predicting a user's likely behavior based on their demographic and other information.

Specifically, based on a customer's attributes (age, gender, previous products purchased, actions taken, etc.), it is possible to probabilistically predict whether that customer will actually purchase a product and clarify which attributes are particularly important.

Logistic regression can be used to predict things like the probability of a customer purchasing or churn.

Collaborative filtering

Collaborative filtering is a method for recommending items that a user is likely to like based on the history of items that the user has rated in the past. It can also determine which items to recommend by referring to the item ratings of other users with similar tastes.

Specifically, the system analyzes the trends of items rated by a user, finds other users with similar trends, and can recommend items that have not yet been rated to those other users.

Collaborative filtering is used in a variety of fields, not only in entertainment such as movies and music, but also in product purchase history and website browsing history.

Time Series Analysis

Time series analysis is a method for analyzing changes in data over time. There are various time series analysis methods, but the most well-known method is the autoregressive integrated moving average (ARIMA) model.

Time series analysis can also be used to predict future trends based on past data, specifically known as demand forecasting, where forecasting the amount of a product that will be in demand can help optimize inventory and production planning.

Time series analysis is also useful for analyzing changes in KPIs such as sales and number of customers, and for evaluating the effectiveness of marketing measures. Time series analysis can also be used when external factors such as seasonal fluctuations and events have a significant impact.

Why is predictive analytics important in marketing?

Predictive analytics is important in marketing because it reduces uncertainty in marketing and leads to more accurate optimization of tactics and budgets.

The information gained from predictive models allows marketers to create more effective, customer-driven marketing activities, resulting in higher ROI.

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The Challenges of Predictive Analytics in Marketing

The main challenges with predictive analytics in marketing are twofold:

Data quality

Data quality plays a big role in predictive modeling: poor data quality increases the chances of incorrect predictions and misinterpretations of the facts.

In the context of predictive analytics, poor data quality refers to the accuracy and quantity (including availability). For example, if sales data is available for a period, but not for half of the stores, using time series analysis to forecast future sales will produce results with low accuracy.

Companies are better off taking a broad approach to data collection and management, including data from CRM, analytics tools like Google Analytics, storefronts, ad agencies, etc.

Cost and time

Predictive modeling tools are generally difficult to use unless you are an expert, and can be costly. Also, while the tools can perform predictive analysis, they cannot visualize data, so you must do additional work or use other tools to explain the results internally. Cost-conscious or budget-constrained companies may not be able to invest in building predictive models without training an in-house data analysis team or introducing external services.

In addition, it can take a considerable amount of time before a highly accurate predictive model can be completed and actually used effectively in marketing activities, so it is a difficult initiative to pursue without the understanding and buy-in of management. If a company is looking for a quick solution to an immediate problem, predictive modeling is unlikely to be suitable.

Use cases for predictive modeling in marketing

Predictive modeling can be used in a variety of ways in marketing. Below are some examples of how it can be used:

Marketing mix modeling (MMM)

MMM is a statistical analysis method used in marketing that analyzes the relationship between sales and other KPIs and data on various marketing initiatives (such as advertising volume and advertising costs) over time. The purpose of MMM is to identify the optimal combination of marketing initiatives and budget allocation to maximize ROI (return on investment).

Based on the results of MMM's analysis, marketers can predict incremental sales by optimizing advertising strategies and reallocating budgets.

MMM is a predictive model that allows marketers to make future decisions based on past data. Sales forecasts can be made by reallocating marketing budgets to more efficient measures based on the analysis results.

Recommendation Engine

A recommendation engine works by analyzing users' past behavior and preferences and, based on that information, predicting products and services that may interest them.

For example, if a user has previously purchased a camera from an online store, they can be recommended relevant camera accessories.

Most successful e-commerce sites (such as Amazon and Rakuten) and streaming services (such as Netflix and Spotify) have become experts in using collaborative filtering to recommend relevant products, shows, songs, etc. to users.

LTV (Lifetime Value) Prediction

Lifetime Value (LTV) forecasting is an analytical technique used by businesses to assess the revenue they are likely to generate from specific customers.

LTV prediction is used to forecast future revenue for new customers by analyzing past customer behavior (metrics such as average purchase value, purchase frequency, purchase cycles, and length of time they remain with us).

Using LTV predictions, businesses can prioritize customers and plan their marketing efforts more strategically, balancing customer acquisition costs and LTV.

Churn prediction

Churn prediction is an analytical methodology that allows a business to assess whether a customer is likely or unlikely to churn in the future.

This prediction uses data like past customer behavior, trends, preferences, etc. to forecast future behavior. Churn prediction helps businesses to more accurately identify target customers to reduce customer acquisition costs and retain existing customers.

Companies can also improve customer loyalty by identifying customers who are likely to churn and implementing appropriate retention measures.

Get started with predictive modeling in marketing

For marketers, predictive modeling can help them understand the factors that influence customer behavior and business outcomes, reducing marketing uncertainty and helping them implement more precise strategies and tactics.

However, incorporating predictive models into marketing activities is difficult because it requires specialized knowledge of data, machine learning, statistics, etc. (or it requires significant cost and time).

Our company, XICA, has over 10 years of consulting experience in the field of data science in marketing, and uses proprietary technology and algorithms toMMM ServiceWe have provided this service to over 250 companies, mainly large corporations. In addition to MMM, our team of specialized consultants and data scientists help clients solve their problems through marketing data analysis according to their needs and objectives.

Our MMM serviceFor more information about "MAGELLAN", click here.

Also, if you would like to consult with us about implementing MMM services and predictive analysis in marketing, please contact us.Contact us.

Author information

Marketing team of XICA Co., Ltd.