Predictive marketing is a powerful tool for enhancing the user experience of e-buyers and increasing conversions for e-merchants. This burgeoning concept is gaining traction among companies. But what exactly is predictive analytics? How does it apply to marketing, and what benefits can be derived from it? This article explores the advantages of predictive marketing and presents a few tangible use cases in e-commerce.
Understanding Predictive Marketing
Traditional marketing strategies often revolve around data collected and analyzed retrospectively to plan future marketing actions. If a product proves successful, the marketing strategy will typically promote this same product at the same time the following year.
Predictive marketing, however, takes a different approach. It uses past and present data to identify and anticipate the future actions of customers or prospects. It’s about delivering the right product or service to the right person at the right time, using the most fitting message and communication channel. The primary goal? To boost the conversion rate and, subsequently, the company’s revenue.
In addition to enhancing customer acquisition strategies, predictive marketing is widely used for customer retention, primarily by improving user experiences.
How Does Predictive Marketing Work?
1. Data collection: The first step in predictive marketing involves data mining or data collection. Data about visitors to a website can be collected through cookies, dedicated user monitoring solutions, account and registration forms, etc.
2. Data analysis and behavior forecasting: Once data collection has commenced, the next phase involves predicting users’ expected behaviors based on their previous actions. This step aids in optimizing their experiences.
3. Real-time user experience optimization: After identifying potential purchasing scenarios for users, the final step is to optimize the user experience for these scenarios. This step is often facilitated through machine learning and advanced recommendation algorithms incorporating substantial data.
Although predictive marketing can be applied across various sectors, certain industries already make substantial use of it. These include e-commerce, distribution, industry (particularly automotive and pharmaceutical), energy, telecoms, finance, insurance, and the public sector.
Three Practical Applications of Predictive Marketing
1. Predictive Marketing for Optimizing Product Recommendations
Many e-commerce sites employ predictive marketing to anticipate customer actions. For example, if a customer views a specific type of product, predictive marketing might display complementary products to add to their basket once they’ve shown interest in the initial product.
With the help of machine learning, predictive marketing can offer more refined and personalized product recommendations based on the user’s navigation, search history, or profile.
2. Predictive Marketing for Identifying Customers Switching Brands
Predictive marketing is beneficial for businesses looking to optimize customer loyalty. It helps identify customers ready to shift to competitors – those in the “churn” phase.
By analyzing numerous parameters, such as the last login date to the customer area, date of last visit, date of last email campaign opening, or date of newsletter unsubscription, predictive marketing helps businesses identify when to deploy reactivation and loyalty campaigns.
3. Predictive Marketing for Inventory Optimization
Predictive marketing can also aid e-merchants in managing their inventory more effectively. It anticipates customer demand based on the period to minimize stock shortages. Predictive marketing makes it possible to mathematically predict, with higher accuracy, the volumes of products that should be in stock at a given time.
Predictive Marketing FAQ
Can All Types of Businesses Utilize Predictive Marketing?
Predictive marketing can be adapted to meet the needs of businesses of varying sizes. However, smaller businesses might have limited resources to invest in specialized software or advanced technical skills, while larger enterprises can employ predictive marketing on a larger scale to predict market trends, optimize marketing campaigns, improve customer segmentation, and personalize the customer experience.
What are the Limitations or Risks of Using Predictive Marketing?
While predictive marketing can offer valuable insights into customer behaviors, it is not without its limitations and risks. These include potential bias, privacy concerns, prediction errors, costs, complexity, and customer resistance.
What are the Most Commonly Used Software Tools for Predictive Marketing?
Several software tools are available for predictive marketing, such as IBM SPSS Modeler, RapidMiner, SAS Predictive Analytics, Microsoft Azure Machine Learning, Google Cloud AI Platform, H2O.ai, and Alteryx.
What are the Ethical Challenges Related to Predictive Marketing?
Predictive marketing poses several ethical challenges, including potential discrimination, manipulation, and lack of transparency. It is vital for businesses to ensure transparency about their data usage and offer customers control over their data.