Drawing business conclusions

Now that we've applied our BigQuery ML model, let's learn how the generated results can be used from a business perspective to improve the effectiveness of our sales strategy.

From the product_recommendations table, we can extract relevant information that we can use to improve our marketing campaigns or advertising strategy, and then target the users with higher propensity to buy a specific product.

For example, by executing the following query, we can extract the first 100 users with the highest propensity to buy a specific product from our e-commerce portal:

SELECT *
FROM
    `09_recommendation_engine.product_recommendations`
ORDER BY predicted_quantity_confidence DESC
LIMIT 100;

Executing this SQL statement returns the following result:

Figure 9.11 – The customers with the highest propensity to buy a specific product

The list that we've just extracted can be sent to our marketing...