best data scientists on the planet

Just like us, they not only talk the talk. They are also spending their own money

They have also built their own SaaS product, a unique platform, Culturate, which aids in identifying key traits and behaviours in employees that correlate with high performance. This platform is designed to help organisations transition their workforce from good to great by leveraging AI-driven insights to optimise employee performance.

Thinkers, and educators on Data and AI

TalentFirst.ai has published resources like the "Zero to Practical AI Playbook," which guides organisations on the journey of integrating AI into their operations, starting from scratch and moving towards practical applications.

Check out their resource on unlocking Human Potential in the AI Age!

https://culturate.ai/unlocking-human-potential-in-the-ai-age/ 

LET’S START WITH THE BASICS

To calculate Customer Lifetime Value, you need a few essential pieces of information, or fields, such as:

• Customer ID: A unique identifier for each customer.
• Purchase Date: The date when a purchase was made.
• Purchase Amount: The amount spent on each purchase.
• Customer Acquisition Date: When the customer first made a purchase.
• Customer Exit Date: If applicable, when the customer last made a purchase.
  

Where do these fields come from?

 These fields are usually stored in different systems. For instance, Customer ID and Acquisition Date are often found in a Customer Relationship Management (CRM) system. Purchase Date and Amount are stored in your sales or transaction database. Customer Exit Date might be in a customer retention database or inferred from the transaction data.

A data scientist’s job is to gather all this information from different places and combine it into a centralised database. This process is essential for calculating CLV and understanding customer spending patterns over time.

CUSTOMER SEGMENTATION

Now, let’s consider something more complex: customer segmentation and personalisation. Here, you might need hundreds of fields, including:

• Demographic Data: Age, gender, income level, location.
• Behavioural Data: Browsing history, purchase frequency, product preferences.
• Interaction Data: Email open rates, click-through rates, social media engagement.
• Transaction Data: Purchase history, average order value, total spend.
• Feedback Data: Customer reviews, survey responses.
• Loyalty Program Data: Points earned, rewards redeemed.
• Customer Support Data: Number of support tickets, types of issues, resolution time.
  

DEMOGRAPHIC & BEHAVIOURAL DATA

That sounds like a lot of data, right? And it’s stored in various places. Demographic and Behavioural Data are in marketing automation platforms and web analytics tools. Interaction Data is found in email marketing software and social media platforms. Transaction Data comes from sales databases and e-commerce platforms. Feedback Data is in survey tools and review platforms. Loyalty Program Data is managed by loyalty management systems. Customer Support Data is stored in customer support software.

DATA ANALYSIS

The data scientist has to gather and combine all this data. This process involves several steps:

1. Data Cleaning: Ensuring data quality and consistency.
2. Data Transformation: Converting data into a common format.
3. Data Merging: Combining data from different sources.

Once the data is centralised, the data scientist can perform complex analyses. For example, they might create detailed customer segments. They could identify high-value customers who frequently purchase high-margin products and respond well to email promotions.