Creating Personalized Product Recommendations with Power BI

Question

We have built a Power BI dashboard that analyzes the main influencers for higher sales. For the current year, the factors that are main influencers are:

• Sales item quantity 2.9 or higher
• Distinct items 1.8 or higher
• Item family 0.78 or less (concentration on a given family of services)
• Number of customer visits 1.64 or higher
• Number of distinct service professionals 0.89 or less (concentration of visits to the same professionals)

Our three identified customer segments are:

1. Sales item quantity 6 or higher, average sales $635, 496 higher than overall average of 139.
2. Sales item quantity between 4 and 6, average sales $279, 140 higher than global average.
3. Distinct items greater than 2, sales item quantity between 2 and 4, average sales $197, 58 higher than global average.

What are the next steps to increase sales? Apparently, the best customers are loyal to a particular set of services within a family and a service professional and buy 2.9 or more item quantities of about 1.8 distinct items or more.

How can we create personalized recommendations based on customer purchase history? Based on what we have found so far, it is possible to achieve personalized product recommendations using Python and Azure ML.

Answer 1

Hi @Rodrigo Xavier

Thank you for using the Microsoft Q&A forum.

To answer your first question on how to increase sales and the next steps, it would be nice to focus on the identified factors that are main influencers for higher sales, which are sales item quantity, distinct items, item family, number of customer visits, and number of distinct service professionals. Based on the customer's identified segments, the best customers are loyal to a particular set of services within a family and a service professional and buy 2.9 or more item quantities of about 1.8 distinct items or more.

To increase sales, you can consider the following steps:

Targeted Marketing: Use the identified factors to target specific customer segments with personalized marketing campaigns. For example, you can offer promotions or discounts to customers who purchase a particular set of services within a family or who visit the same service professional.

Upselling and Cross-selling: Use the identified factors to upsell and cross-sell relevant products or services to customers. For example, if a customer has purchased a particular set of services within a family, you can recommend related services or products that complement their purchase.

Loyalty Programs: Implement loyalty programs that reward customers for their loyalty to a particular set of services within a family or a service professional. This can incentivize customers to continue purchasing from your business and increase their overall spend.

Customer Feedback: Gather feedback from customers to understand their needs and preferences better. Use this feedback to improve your products or services and tailor your marketing campaigns accordingly.

To create personalized product recommendations based on customer purchase history using Power BI, you can leverage Python and Azure ML. Here are the steps you can follow:

1. Data Collection: Gather historical customer purchase data including items purchased, quantities, and other relevant information.
2. Data Preprocessing: Clean and preprocess the data to remove any inconsistencies or missing values. Ensure that the data is in a format suitable for training machine learning models.
3. Feature Engineering: Extract relevant features from the data that can be used to train the recommendation model. This may include customer demographics, purchase history, item attributes, etc.
4. Model Selection: Choose an appropriate machine learning model for generating personalized recommendations. Popular models include collaborative filtering, content-based filtering, and hybrid models.
5. Model Training: Train the selected model using the preprocessed data. Use techniques such as cross-validation to evaluate the model's performance and fine-tune hyperparameters if necessary.
6. Deployment: Deploy the trained model to Azure ML for serving recommendations in real-time. This may involve creating an API endpoint that accepts customer data and returns personalized recommendations.
7. Integration with Power BI: Integrate the personalized recommendation service with Power BI dashboard using APIs or connectors. This allows you to display dynamic product recommendations directly within the dashboard based on customer insights.
8. Monitoring and Optimization: Continuously monitor the performance of the recommendation system and gather feedback from customers. Use this feedback to further optimize the model and improve the quality of recommendations over time.

To personalize the product recommendations even further, we can leverage the capabilities of both Intelligent Recommendations and Azure Personalizer. By combining these tools, you can offer customized suggestions tailored to the specific needs and preferences of individual customers.

Contextual Recommendations: One key aspect of personalization is considering the context in which the recommendations are being made. For example, if a customer is currently browsing for groceries online, take into account factors such as the weather, time of day, or any special occasions to make relevant suggestions. This contextual information can be obtained through various sources, including user profiles, historical data, or real-time environmental data.

Real-time Ranking and Prioritization: Azure Personalizer allows us to dynamically rank and prioritize product recommendations based on the current context and user preferences. By continuously learning from user interactions and feedback, Personalizer can adapt its recommendations in real-time to maximize user engagement and satisfaction.

Iterative Learning: To further improve the effectiveness of the recommendation system, we can employ iterative learning cycles where Personalizer learns from user feedback and adjusts its recommendations accordingly. This iterative process helps refine the recommendation models over time, leading to more accurate and relevant suggestions.

1. Reward Mechanism: After making a recommendation, collect feedback from the user to evaluate its effectiveness. Azure Personalizer provides a reward mechanism that allows us to assign a score based on whether the user accepted or rejected the recommendation. This feedback loop helps Personalizer continuously learn and improve its recommendation models.

By following these steps, you can create personalized product recommendations that drive sales and enhance customer satisfaction. Additionally, based on your Power BI dashboard analysis, you can tailor the recommendations to target specific customer segments and optimize sales further. Combining the power of Intelligent Recommendations with the real-time personalization capabilities of Azure Personalizer, create a highly effective and adaptive product recommendation system. This system can not only deliver relevant suggestions based on the customer's current context but also learns and improves over time to provide an increasingly personalized shopping experience.

For a detailed implementation of this approach, you can refer to the provided notebook here. Additionally, you can explore the Microsoft documentation on Intelligent Recommendations here and the Azure Personalizer API here.

I hope the provided information helps! Thank you.

---

Please don't forget to click Accept Answer and Yes for was this answer helpful.