Use Cases
SQL-inf can be used as building blocks for many types of ML powered analysis and applications.
In the subpages we dig into a few detailed use cases of ML Analytics using SQL-inf:
Churn Analysis | Lead Scoring | Lifetime Value | Product Metrics
To give you some inspiration of how to build your own ML Analytics powered data application, we have put together a small database of possible use cases, grouped by SQL-inf commands used.
- Predict
- Explain-Predict
- TimeSeries
- Similar_To
- Cluster
- Sentiment/Topics
| Use Case | Description |
|---|---|
| Predicting Customer churn | Predict the probability of an existing customer churning from an e-commerce platform |
| Predicting User Churn | Predict the probability of an existing user churning from a gaming, social media platform or similar |
| Predicting Subscription Churn | Predict the probability of a subscription-based customer churning from a SaaS product or other subscription business |
| Predicting Product Demand (e-commerce) | Predicting the demand for specific products at one or several future points in time for an e-commerce site |
| Predicting Product Demand (retail) | Predicting the demand for specific products at one or several future points in time for a physical retail store |
| Lead Scoring for B2B Product | Score the quality of leads for a B2B SaaS business |
| Lead Scoring for PLG Product | Score the quality of leads based on interactions with website, marketing material and product |
| Lead Scoring for Consumer Product | Score the quality of leads based on user demographics and similar information for consumer service |
| Predicting User Conversions in Product | Predicting the probability of a free user/customer converting to a paying user/customer based on their in-product behaviors |
| Predicting User Upgrades in Product | Predict the probability of a user upgrading to a higher pricing tier |
| Predict specific Action by User in Product | Predict the probability of a given user performing a certain action in a product |
| Propensity to Buy in E-commerce | Predict the probability of a user purchasing a product from their previous purchase, in-product behavior and demograhics |
| Identify Upselling Opportunities | Predict the probability of upselling a particular product to a given customer |
| Classify Prospective Customers | Label prospective customers and group them based on their attributes and a set of previously labelled customers |
| Customer Life Time Value Prediction | Predict the life time value of a customer on an e-commerce platform |
| Seller Life Time Value Prediction | Predict the life time value of a seller on an ecommerce platform |
| Predict Product Engagement Score | Predict product engagement for any user based on behavioral traits or demographics |
| Predict In-Product Action | Predict the probability of any user performing a specific action in the product based on their demographics and previous actions |
| Predict Marketing Funnel | Predict the probability at each stage of a funnel for any given user to continue or drop-out given known traits and demographics |
| Detect Fraud in Transactions | Predict the probability of a given transactions being fraudulent |
| Credit Scoring | Based on demographic and behavioral traits, assign a credit score to each user |
| Use Case | Description |
|---|---|
| Analysing Customer Churn | Analyse and discover the main of past churn for an e-commerce platform |
| Analysing User Churn | Analyse and discover the drivers of past churn on a gaming, social media or similar platform |
| Analysing Subscription Churn | Analyse and discover the drivers of past churn for a subscription based business |
| Analysing Product Demand | Analyse and discover the drivers of demand for a particular product sold via an e-commerce site or physical retail store |
| Analysing Customer Life Time Value | Analyse and discover what has been the main drivers of customer life time value on an e-commerce platform |
| Analysing Seller Life Time Value | Analyse and discover what has been the main drivers of the life time value of sellers on an e-commerce platform |
| Analyse Product Engament Score | Discover the underlying drivers of product engagement |
| Analyse In-Product Action | Discover what drives users to perform a specific action in the product |
| Analyse Marketing Funnel | Discover what are the main drivers at any stage of a marketing funnel of a user either continuing or dropping out |
| Analyse Product Onboarding Process | Discover what are the main drivers at any stage of an onboarding process of a user either continuing or dropping out |
| Analyse Customer Journey | Analyse and understand the steps in a customer product journey that drives renewals and upsells |
| Define Customer Support KPIs | Derive CS KPIs that are more likely to drive overall customer LTV |
| Analyse Customer Support KPIs | Understand what effects given CS KPIs have on customer behaviors and LTV |
| Analyse Marketing Attributions | Analyse, understand and forecast what marketing channels drive user acquisition and LTV |
| Analyse Product KPIs or North Stars | Analyse, understand and derive Product KPIs and North Starts from behavioral data with the aim to drive LTV or engagement |
| Analyse Fraudulent Transactions | Analyse and discover the main indicators of a transaction being fraudulent |
| Use Case | Description |
|---|---|
| Forecasting Service Demands | Predicting the demand of subscribed users for a transportation or delivery service throughout a short term time period |
| Forecast Demand for Rentals | Predict the demand for rentals like hotels, holiday rentals or rental cars across a longer term time period |
| Forecast Demand for Bookings | Predict the demand for bookings for hospitality services like restaurants or other similar services throughout short term time period |
| Analyse Service Demands | Analyse and discover the drivers of demand of subscribed users for a transportation or delivery service |
| Analyse Demand for Rentals | Analyse and discover the drivers of demand for hospitality rentals |
| Analyse Demand for Bookings | Analyse and discover the drivers of hospitality bookings |
| Forecast Customer Support Volumes | Forecasts the volume of customer support calls, emails or tickets at any future point in time |
| Forecast Product Prices | Forecast prices set by sellers on an e-commerce site |
| Forecast Product Sales | Forecast the volume of sales on an e-commerce site |
| Forecast Prices in Hospitality | Forecast the prices at which rental properties are offered |
| Use Case | Description |
|---|---|
| Finding Similar Customers | Identify customers in your customer base similar to a specific customer, based on demographics or behavioral data |
| Finding Similar Leads | Identify leads that are similar to a specific lead |
| Finding Similar Users | Identify users similar to a specific user based on behavioral data or demographics |
| In-product Customer Recommendations | Suggest personalised in-product recommendations for customers or users based on previous in-product behavior |
| Product Personalizations | Personalise a user’s product experience based on what is most likely to drive a higher LTV or engagement |
| Use Case | Description |
|---|---|
| User Segmentations | Create segmentations of users of a gaming or social media platform based on their demographic and behavioral charateristics. |
| Customer Segmentations | Create segmentations of users of an ecommerce platform based on their purchase and behavioral charateristics |
| Account Segmentations | Create segmentations of customers/accounts based on meta data about each account, like industry, users, financials etc |
| User Cohort Analysis | Segment your users into cohorts and analyse the behaviors of each |
| Use Case | Description |
|---|---|
| Analyse Customer Feedback | Analyse text feedback from customers to understand their pain points, feature requests, usage etc |
| Analyse Customer Reviews | Analyse customer reviews to understand how customers view each product |
| Analyse Support Tickets | Analyse support tickets to understand the most common issues |