Forecasting Marketing Trends with Data Analysis and Machine Learning
The volumes of information in the world are enormous, and trends in digital marketing change at a staggering pace. Just as marketers finish studying the market and digital environment, they find themselves having to start their research anew.
It's crucial to think about the future in advance: how consumer behavior, fashion, and promotional tools will change. The first option is to "guess" and rely on vision, intuition, and experience, making marketing management unpredictable.
The second path is to use a data-driven approach that provides accurate forecasts. For example, choosing data analysis in marketing through machine learning to "predict" trends.
The article will explore why forecasting marketing trends is important, the role of it, and how machine learning works in this context.
Why forecast marketing trends
Audience preferences change rapidly, so marketers need to "catch the wave" before customer interests shift. This applies to both global trends and local changes. Lagging brands won't gain anything if they miss a trend. Knowing in advance what will be relevant allows for preparation. Then the company's marketing strategy can adapt to changes.
Forecasting trends is a path to precise planning and risk reduction. Marketing analysis will indicate where to direct resources and how to gain benefits. Trend forecasting is useful not only for creating advertising campaigns but also for the brand's products themselves. This approach simplifies promotion because the product already has an advantage. For example, a popular color, features, and the right values that meet society's needs.
"Looking" into the future provides insights into which creative marketing campaigns and promotion formats yield results. There's still time to come up with new ideas and explore new tools and communication channels.
Tracking trends in marketing is crucial for businesses to avoid becoming outdated. The competition for attention continues constantly, and those who can adapt to changes and offer something new emerge victorious.
Data analysis for marketing
When marketing is based on data analysis, forecasting becomes more accurate, and marketing becomes more manageable, reducing guesswork. Predictive analytics is a reliable way to plan marketing actions and advertising budgets.
A sufficient amount of information about past campaigns will also indicate weaknesses and guide efforts to work on mistakes. Every marketer should conduct data analysis before launching advertising, sending out campaigns, or creating a content plan to properly segment the audience.
This way, the development of the marketing strategy will be tailored to a specific user group, enhancing personalization, ultimately leading to greater predictability of results.
Machine learning for marketing
The volume of data is growing, and a human cannot manually process such vast information. The problem is solved by marketing automation, which includes machine learning — a method of artificial intelligence. Robots collect and analyze large datasets, and machine learning transforms the information into something useful for marketing purposes. Marketers begin to:
- Properly understand and interpret the gathered information. The application of machine learning eliminates human factors and subjective assessments. Information about orders, purchases, feedback, and preferences is not lost and is considered in data analysis.
- Explore the customer in 360 degrees. AI technologies can process terabytes of internal (customer database, email campaign results) and external (social media comments, likes, reposts, etc.) data to identify patterns. The result of such analysis is precise personalization of offerings.
- Predict trends. Machine learning in marketing is essential for identifying future trends in customer behavior and user preferences. This approach is valuable for refining products, services, and content.
Predicting trends through machine learning differs from trend watching, which is the method of finding trends here and now because ML forecasts trends are based on historical data and "looks" into the future.
How machine learning predicts trends
To apply machine learning in business, researchers go through the following stages:
- Gather information. Sources include internal databases from CDP, data from external third-party resources, and so on.
- Cleanse and enhance the information. It is crucial to minimize errors and collect comprehensive data to enable the AI to learn correctly. This stage involves adjusting the data to the required format.
- Determine which machine learning model to use for forecasting.
| Learning model | What it means | What kind of forecasting is useful for |
|---|---|---|
| With a trainer | A person provides the system with data labeled according to specific rules. | When results are limited in number; if categorization of data is needed. |
| Without a trainer | The algorithm retrieves the data without partitioning. | To predict future purchases, and to clean up information for the next steps. |
| Involving trainers in part | A small fraction of the data is labeled, but most of it is unlabeled. | When you need to process documents where there is a lot of information. |
- Initiate model training and monitor its success: check the results it produces. If the forecasting is successful, the model is potentially used for other predictions with new data.
Examples of trend forecasting
Since 2018, the Pyaterochka retail chain has been using machine learning to customize special offers and promotions in the future. The system relies on customer reactions and sales volumes, with the data anonymized. As a result, the company achieves a personalized approach.
Walmart predicts the likelihood of customers purchasing a product and personalizes product offerings on the website. Additionally, predictive analytics manages delivery times and other processes to ensure customers receive their products on time.
Platforms dealing with content and advertising also utilize machine learning. For example, on VKontakte, algorithms are essential for optimizing advertising campaigns, particularly for suggesting an audience similar to the advertiser's customers. In other words, the algorithm predicts who else might be interested in the brand's offer.
IBM's service generates personalized creatives for advertising campaigns and adapts variations for different displays to appeal to users and display correctly. User engagement data is used in this process, and brands like Chevrolet and Mastercard adopt this approach.
Netflix's recommendation algorithm is based on machine learning, considering user preferences and other factors. Content views, time, ratings, and other factors are taken into account. The Rive Gauche company also achieves personalization through machine learning tools, predicting customer behavior based on data and creating campaigns that enhance loyalty and sales.
Challenges of machine learning and data analysis
Dependency on data quality. Predictions about the future are based on existing information. If data is insufficient, incomplete, or incorrect, there is no way to correct it in real-time.
Limited coverage of offline business. Machine learning may not fully capture user actions in the real world, especially in offline scenarios. While tracking all orders, user actions, and reactions to content is feasible in the digital realm, it becomes more complex in offline stores. However, AI can still be applied to some extent.
Need for significant data volumes. Machine learning requires substantial datasets, often in the thousands, and insufficient sample sizes may lead to unreliable results.
Cost. The task of forecasting requires a trained specialist, and resources are also spent on the system itself, configuration, and integrations. Implementing ML is feasible only for businesses with a sufficient budget for these purposes.
The future of data analysis in marketing
Marketing is undergoing a transformation, integrating technologies as the average user engages in 20-500 interactions with a brand before making a purchase. This generates vast amounts of information that need to be tracked and processed accurately. Additionally, users expect a personalized approach and recommendations for suitable products (91% of consumers). Without IT tools, marketers cannot handle such tasks, so the use of machine learning, artificial intelligence, neural networks, and Big Data will continue to be implemented in marketing practices.
Summary
It is crucial for brands to stay on trend, tracking global trends, and anticipating customer behavior for budget planning, creating advertising campaigns, and generating ideas. Data analysis becomes a reliable way to "see" into the future, eliminating guesswork.
To predict trends, machine learning algorithms are employed, providing a comprehensive view of the customer. They guide what offers to make to encourage user purchases.
For trend prediction through ML, information is collected and processed (large datasets), a model is selected, and the algorithm's performance is launched and monitored.
Machine learning is a reliable method for prediction but comes at a cost. Significant data volumes are required for ML to function properly. There are also limitations for predictions in offline business. Marketing is already intertwined with IT and will not thrive in the future without AI, ML, Big Data, and other information processing methods.
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