Predictive Analytics in Marketing: Automation Makes It Accessible
Predictive analytics is rightfully considered one of the most powerful tools of modern business, which provides it with a number of significant competitive advantages. In this article, we will explore what is predictive analytics in marketing and how it can be optimized using automation. You will also learn about the features of implementing predictive analytics in marketing campaigns, key scenarios for its use and common limitations.
Predictive Analytics: The Game Changer in Marketing
Predictive analytics is a type of advanced data analysis based on a wide range of techniques, including predictive modeling, data mining, machine learning, and a number of other methods. It demonstrates high efficiency when applied to key business processes, including such areas as marketing, sales, finance, HR, operations management, etc.
Companies often use predictive analytics tools to identify opportunities and risks by tracking relevant trends and relationships in data. It is often used with big data and data science, as well as artificial intelligence technologies and statistical models. All this helps specialists find characteristic patterns in current and historical data to successfully predict future results and events.
Below, you will learn about the benefits that businesses receive from using predictive analytics in marketing:
- Trend forecasting. Consumer tastes and preferences are constantly changing, so manually forecasting future trends can be ineffective. Predictive analytics tools process vast arrays of diverse data (both historical and real-time), helping marketers quickly and accurately identify new trends.
- Improved segmentation. Machine learning (ML) models used in predictive analytics can optimize segmentation processes by identifying subtle connections between customer data. This will allow marketers to effectively divide audiences into groups for better targeting and personalization of campaigns.
- Campaign customization. Predictive analytics helps marketers better understand the pain points, needs, and expectations of their target audience. By studying their forecasts and insights, they gain valuable information to intelligently customize their marketing campaigns. For example, what content is most likely to resonate with a certain customer segment, which platforms are best suited for maximum reach, when and how often to send messages, etc.
- Reduced customer churn. Another important benefit of predictive analytics marketing is that the tools mentioned help to quickly identify customer churn trends. With their help, marketers can find weak points in interaction with the audience and take measures to stimulate customer engagement/loyalty.
How Automation Brings Predictive Analytics to Everyone
Automated predictive analytics based on machine learning algorithms provides significant progress compared to traditional statistical models. AI technologies significantly improve the quality of the results they produce by detecting trends and patterns dynamically by processing large volumes of data from different sources. ML models can identify and process complex interactions and nonlinear relationships that are often missed by traditional analytical approaches.
Automation helps optimize predictive analytics in several ways at once, effectively eliminating its inherent barriers and limitations. Firstly, it significantly improves forecasting accuracy by detecting subtle patterns in multidimensional data arrays. It also makes research more adaptive, thanks to the ability of ML algorithms to self-learn based on updated data and adapt to dynamic conditions.
Automation also simplifies and speeds up data cleaning, quality assessment, and other data preparation steps. Finally, AI makes it possible to easily and quickly scale analytics resources to run large-scale campaigns that process huge amounts of information.
Below, we present to your attention a number of well-known platforms and other software tools designed to optimize the use of predictive analytics to solve business problems.
Microsoft Azure Machine Learning
The ML platform extends the functionality of the Microsoft Power BI analytical service by implementing machine learning algorithms. It allows users to flexibly manage the full cycle of predictive analytics: from data preparation, training and deployment of the model to evaluating the forecasts it generates. The platform is tightly integrated with Microsoft Azure tools for developing applications and automating robotic processes.
SAP Predictive Analytics
A specialized service from SAP is ideal for solving analytical problems in the areas of inventory management, logistics, and supply chain coordination. The main functions of this tool include data aggregation, predictive modeling, and model analysis. An important advantage of SAP Predictive Analytics is its in-depth automation, as well as options for manual data processing using coding and statistical methods.
ApiX-Drive
The ApiX-Drive online connector allows for flexible automation of numerous business processes through integrations between hundreds of systems and applications, connected in no-code mode. The tool can also be used for automatic collection and processing of analytical data. For example, integration with Facebook allows you to transfer different types of statistics (accounts, campaigns, etc.) to third-party services.
Altair AI Studio
Altair AI Studio (formerly RapidMiner Studio) provides a comprehensive set of predictive analytics tools for marketing and other areas. With its help, professionals can simplify and speed up data extraction from multiple sources, its cleaning and application in modeling processes. The low-code platform improves the accessibility of designing, testing and deploying predictive and prescriptive models. It also offers options for automated model creation, as well as their visualization, management and monitoring.
H2O Driverless AI
Developed on the open-source ML platform H2O, the service is designed to automate the development of AI models and predictive analytics. Its capabilities include advanced options for selecting models, designing features, setting parameters, semantic analysis and natural language processing. H2O Driverless AI helps interpret the results of predictive analytics using causal graphs, local interpretable explanations, Shapley values and surrogate decision tree methods.
Top Marketing Tasks Enhanced by Predictive Analytics
In this section of our article, you will learn about the processes that predictive analytics in digital marketing helps to optimize.
Audience Segmentation and Campaign Personalization
Thanks to predictive analytics, marketers do not have to spend a lot of time and other resources on segmenting the customer base manually. Instead, they only need to use specially trained machine learning algorithms to automate this task. By processing a lot of user data from various sources and finding subtle connections between them, ML models quickly and accurately distribute customers into relevant target groups.
Segmentation results and other predictive analytics capabilities prove to be very useful in personalizing marketing campaigns. With their help, marketers receive useful forecasts and insights that contain valuable information for customizing the messages sent to the audience through certain communication channels.
Predicting Customer Behavior
Another important task solved with the help of predictive analytics in marketing is forecasting customer behavior. Thanks to this option, marketers and entrepreneurs get the opportunity to plan their campaigns more effectively, directing them to the most relevant audience segments.
Predictive analytics tools allow you to accurately predict the actions that an existing or potential client will take at a certain stage of interaction with them. By analyzing this information, a business can use its resources more rationally to increase the return on investment.
Optimizing Marketing Budgets and Increasing ROI
Finally, an equally popular area for the application of predictive analytics is the optimization of marketing budgets and financial management in general. The above-mentioned tools have proven themselves to be excellent in automating dynamic budgeting processes, helping to track company expenses in real time and accurately predict budgets for future campaigns.
Entrepreneurs and their employees can quickly adjust marketing budgets depending on various trends, changes in market dynamics, and other aspects. As a result, companies using predictive analytics will be able to minimize unnecessary costs and increase the ROI of their investments in marketing.
Challenges Marketers Face with Predictive Analytics
At the same time, predictive marketing analytics has not only significant advantages, but also certain disadvantages that make its implementation difficult. Among them, the following can be highlighted:
- Insufficient volume and poor quality of data. High-quality, voluminous and relevant data are critical to developing accurate forecast models. Failure to do so will undoubtedly reduce the reliability of forecasts.
- Difficulty of interpretation. When using complex ML models (especially deep learning algorithms), specialists often encounter the “black box” problem. The reason for this is the lack of understanding of what the AI conclusions and predictions are based on, which makes it difficult to interpret the results.
- Complexity of integration. Another common challenge is the integration of predictive analytics into marketing campaigns. Modern marketers use a wide stack of software tools, not all of which are easy to integrate with predictive systems.
- Under- or over-training of the model. An ML model that is too simple is unable to track all the important trends, connections, and patterns reflected in the data. While excessive complexity of the algorithm also negatively affects the quality of the forecasts it generates.
- Personnel training. Many companies have difficulty training their marketing and sales staff to use forecast models correctly. Insufficient personnel qualifications are an equally serious obstacle to the full implementation of these tools.
Bottom Line
Marketing automation with predictive analytics provides businesses with significant benefits by helping them effectively analyze and manage data to achieve relevant goals. Using predictive ML models, companies accurately predict future trends, segment audiences, and personalize marketing campaigns, as well as track customer behavior and optimize interactions with clients. Implementing predictive analytics solutions is often associated with certain difficulties and limitations, the consequences of which can be minimized by choosing the right stack of software tools.