Ready to unlock the true potential of your marketing data and transform your customer retention strategies?
In Part 1 of this series, we demystified the process of finding the right data with the intuitive combination of BigQuery Canvas and Gemini. Now, prepare to dive even deeper and harness the magic of Marketing Science with a no-code approach to churn prediction.
Imagine effortlessly building powerful predictive models that reveal which customers are most likely to leave, allowing you to take proactive steps to keep them engaged. No complex coding required! We'll explore how BigQuery ML and generative AI democratize access to advanced analytics, empowering data scientists, engineers, analysts, and marketers alike with an intuitive and powerful tool, regardless of their technical expertise.
This seamless integration of data querying, feature engineering, and machine learning model development all within a single platform opens up a world of possibilities.
But first, we need to lay the foundation for our predictive model. In this article, we'll first create a 'flat table' – a crucial step in preparing your data for machine learning magic and then prepare the data for Machine learning model to use it as an input
Step 1 : Join the output queries to get a single FLAT table, which is generally what you need when you want to build a machine learning model
The above joined table now has the following Key variables
- user_pseudo_id,
- total_purchases,
- total_revenue,
- average_order_value,
- days_since_last_visit,
- sessions_last_month,
- total_sessions
You'll notice we don't have a readily available "Churned" variable. This is where the magic of data engineering comes in, and where your marketing expertise is essential! To build our churn prediction model, we need to define what "churn" actually means for your business. In this case, let's assume we want to identify customers who are likely to stop engaging with your website or app based on the number of days since their last visit. By pinpointing these at-risk customers, we can proactively engage them with targeted campaigns to prevent churn and increase retention.
With that in mind, we create a new variable called Churn. I would again use Gemini’s Gen AI capability to create SQL using Natural Language.
With our labeled variable in hand, we're ready for the grand finale: building our machine learning model! And guess what? We can accomplish this incredible feat without leaving the comfort of our BigQuery Canvas or writing a single line of SQL. Get ready to witness the power of Gemini as we create a sophisticated classification model using only natural language. But first, a quick touch of data normalization and engineering to ensure our model performs at its best 🙂
And finally we take the big step of asking Generative AI to create a classification model (Supervised learning in action here!)
And we got a strong output
This model empowers marketing analysts to proactively identify and engage website visitors who are likely to churn. By integrating this predictive capability directly into their workflow, analysts can:
- Gain real-time insights: Instead of relying on historical data, analysts can assess visitor behavior in the moment and intervene promptly.
- Personalize the user experience: By understanding the factors that contribute to churn, analysts can tailor website content, messaging, and offers to individual visitors, increasing the likelihood of re-engagement.
- Optimize marketing campaigns: With a clearer picture of which visitor segments are most likely to leave, marketers can refine their targeting strategies and allocate resources more effectively.
- Improve website design and functionality: Analyzing the behavior of at-risk visitors can reveal pain points in the user experience, leading to data-driven improvements in website design and navigation.
- Boost customer lifetime value: By reducing churn and fostering long-term relationships with customers, businesses can increase revenue and brand loyalty.
This real-time predictive capability allows for a range of targeted actions, such as:
- Triggering personalized email campaigns: Sending customized messages to at-risk visitors, offering exclusive discounts or highlighting content relevant to their interests.
- Displaying targeted pop-ups or notifications: Presenting visitors with compelling offers or incentives to encourage them to stay on the site or complete a desired action.
- Initiating live chat sessions: Proactively engaging with visitors who exhibit signs of disengagement and offering immediate assistance or support.
- Providing personalized recommendations: Suggesting products, services, or content that align with the visitor's browsing history and preferences, increasing their engagement and encouraging them to explore further.
By leveraging this model, marketing analysts can transform their approach to customer retention, moving from reactive to proactive and driving significant improvements in website engagement and overall business performance.
So folks, there you have it! Hope you enjoyed both the parts. Please let me know if you have any questions!