Predictive Customer Analytics: How AI Works for Analyzing and Predicting Customer Behavior

The Process of Customer Analytics Trends and Behavior Modelling

Building predictive behavior models is all about the synergy of quality data and clearly defined goals for behavior analysis. When businesses want to predict customer behavior for propelling their retail marketing efforts, the following process of developing appropriate models will be a part of eCommerce development services.

Data Collection

The process begins with the collection of relevant data about customer behaviors. This data must be gathered following privacy regulations (e.g. GDPR) to establish the lawful basis for predictive insights on consumer behavior. The following data is usually collected:

• Customer Demographics: Age, location, gender, income;
• Website Activity: Product views, search history, time spent on pages, clicks;
• Purchase History: Products purchased, order value, frequency of purchases;
• Reviews and Ratings: Consumer feedback or customer surveys on products;
• External Data: Social media sentiment, market trends.

Data Preprocessing

When data is collected, it doesn’t mean it is ready for analysis. Moreover, it is considered to be raw and most likely to contain errors or inconsistencies. Therefore, the next step of predictive modeling is preprocessing of this data. This helps to clean and prepare it for analysis. During this stage, data scientists and ML engineers handle missing values, identify and correct errors in data entries, and ensure data is consistent in terms of format and units.

Data cleaning is followed by data normalization or standardization. This technique is responsible for scaling numerical features to a common range. As a result, all features contribute equally to the model’s performance, leading to more accurate predictions for future marketing efforts. 

Feature Engineering

Deriving significant characteristics or variables from the raw data to reflect various important aspects of how customers interact with the eCommerce business is the next step in predicting customer behavior. In feature engineering, new variables are made from preexisting data, current features are combined to generate new informative ones, or the most pertinent characteristics that help with the prediction task are selected.

The goal of feature engineering is to create features for models that may be used from data originating from unstructured sources, such as text and images. Using image-to-text and image recognition technologies to eliminate superfluous words and photos from product descriptions was one of our eCommerce initiatives. Our customer was able to expand their product range and boost traffic by 56% by seamlessly incorporating over 1,000,000 products with precise and pertinent item descriptions.

Artificial Intelligence/Machine Learning AWS infrastructure Data Analytics UI/UX Design Web development

Leveraging AI/ML for Expanded Product Range and Enhanced eCommerce Product Listings 

Explore Case

Model Selection

During this stage of AI/ML development services, ML engineers decide which machine learning models will be the most suitable for customer behavior prediction tasks. For that, they define the nature of prediction tasks (e.g. suggest additional products to buy, include in marketing emails, etc.). Then, they evaluate characteristics of the data. And afterwards, they can finally choose the right algorithms based on the findings from the previous stages. 

Some of the commonly used algorithms for predictive behavior modeling include regression models, decision trees, random forests, and neural networks. Below, we describe each of them and highlight their use cases in the context of predicting consumer behavior in eCommerce.

Regression Models

Use Case: Customer Lifetime Value (CLV) Prediction

Regression methods, including logistic and linear regression, work well for predicting CLV. These models forecast the total income a customer is anticipated to create over the course of their lifetime with the eCommerce business by analyzing customer data, including historical purchase history, demographics, and product preferences.

Regression models open the following possibilities for the marketing strategy in retail:

• Identifying customers who bring the most value to the business to personalize marketing techniques for their retention;
• Optimizing marketing cost thanks to centering marketing and sales efforts on segments of customers with high CLV; 
• Creating reward campaigns or customizing loyalty programs for important clients.

Decision Trees

Use Case: Customer Segmentation

Decision trees can check demographics, purchase history, and website behavior to produce specific rules and criteria for meaningful segmentation. Consequently, segments will contain customers with similar behaviors and preference or purchase characteristics.

Using decision trees for customer segmentation can offer:

• Executing marketing strategies targeted specifically for certain customer segments;
• Creating customized product bundles and upsell/cross-sell tactics for various market categories;
• Designing focused email campaigns that offer pertinent product recommendations for every market segment.

Random Forests

Use Case: Churn Prediction

Login frequency, history of purchases, and interaction with support can be analyzed with random forests. Along with aforementioned customer engagement metrics, these algorithms also evaluate demographics and subscription details among other factors. In such a way, eCommerce companies can get insights into reasons behind subscription cancellation.

With the work of random forests, retailers can fine-tune their marketing efforts to make customers stay thanks to:

• Defining customers who are likely to churn and offering them targeted incentives;
• Offering personalized promotions for at-risk customers;
• Improving customer retention rates and reducing customer acquisition costs.

Neural Networks

Use Case: Customer Purchase Intent Prediction

Deep learning models like neural networks help with detecting purchase intent. They analyze browsing behavior thanks to tracking clicks, time spent on pages, interactions with products, items in abandoned carts to understand what customers prefer. Also, these algorithms provide insights into demographic data, including gender and age. With all the data collected and analyzed, they can indicate whether a customer is likely to purchase a product or not.

Benefits of using neural networks for predicting customer behavior include:

• Displaying personalized product recommendations based on real-time purchase intent;
• Optimizing product placement in the digital store to show only relevant items to customers with high buying intent;
• Reducing cart abandonment rates by offering targeted incentives at the checkout stage.

Serhii Leleko

AI&ML Engineer at SPD Technology

“Choosing the right model to predict behavior requires understanding customers’ goals. We pay attention to data: structured data works well with most models, while unstructured data might need pre-processing. Interpretability is also important. If you need to understand why a customer might churn, simpler models like decision trees are better than complex neural networks.”

Model Training

After data scientists and ML engineers choose the appropriate models, they feed models with the prepared data to learn the patterns and relationships between features and target variables. During training, data is strategically split into two sets: training data (used for model learning) and a testing set (used for evaluation). This split helps to detect overfitting, the case where the model memorizes the training data too well but performs poorly on unseen data. 

Next, the types of machine learning models are used to determine which particular training pipeline to choose. In order to maximize performance, hyperparameters, meaning the parameters that govern the behavior of the models, are also changed. In order to reduce prediction errors, models need the modification of their internal parameters after learning patterns from the training set.

Evaluation and Validation

Once trained, the model performance needs to be evaluated. This can be done on the unseen testing data set. Such an approach allows data scientists to see if models can correctly generalize to new data. Also, this helps detect overfitting.

Classification, regression, and AUC-ROC curve are commonly used metrics for evaluating model performance. They are used to check the results of predicting customer categories, numerical values, or assessing model imbalanced datasets respectively. Thanks to evaluation, it becomes possible to see if there are any weaknesses in the models before deployment.

Prediction and Deployment

If the evaluation stage went successfully, and machine learning performs well, it means they are ready for deployment and can be integrated into the eCommerce website. Once deployed, models take in features representing consumer behavior data and output predictions about their future actions or decisions.

There are two main ways this can be used: 

1. The store can generate real-time predictions, which contributes to personalized recommendations or discounts. With real-time prediction, customers are more likely to take immediate action and buy right away. 
2. The store can collect numerous predictions and use them for reporting, predicting future trends, or meticulously crafting marketing campaigns. 

Feedback and Iteration

Customer behavior is not something stable. Therefore, it is considered essential to monitor models’ performance over time and make adjustments when needed. Moreover, there are also additional factors that come into play and change the eCommerce environment. New merchants that become competitors, new trends that change customer preferences, sudden changes in the global economy that influence purchasing power – all that requires addressing when fine-tuning ML models. 

To ensure models provide relevant predictions, you need to monitor their performance based on the feedback loop. It gives you the possibility to find areas for improvement, such as feeding new data for re-training, adjusting hyperparameters, or developing new models to meet your business requirements more efficiently.