Customer attrition is the loss of clients or customers. It is commonly presented as a percentage over a period of time. This metric is critical when measuring a company’s performance. Customer attrition is also commonly known as customer turnover, customer churn, or customer defection

Customer attrition and customer retention are two sides of the same coin. Customer attrition, refers to the loss of customers, while customer retention focuses on keeping existing customers and ensuring their continued business. Effectively managing customer retention can reduce customer attrition, as strategies aimed at retaining customers—such as improving customer satisfaction, offering loyalty programs, and providing excellent customer service—help prevent customers from leaving. In essence, reducing attrition and increasing retention are both crucial for maintaining and growing a company’s customer base.

It’s one of the twelve crucial growth metrics each platform should monitor monthly. It is the rate customers stop doing business with a company over a specific period. It is typically calculated by dividing the number of customers lost during a given time frame by the number of customers at the start of that period.

Common causes of customer churn include poor customer service, high prices, better offers from competitors, lack of product or service satisfaction, and changes in customer needs or preferences.

A business can reduce customer churn by improving customer service, offering competitive pricing, enhancing product or service quality, personalizing customer experiences, and actively seeking and responding to customer feedback. All of the above are being addressed through the Product-led Growth Methodology.

Design thinking is a problem-solving approach rooted in human-centered design principles, emphasizing empathy, creativity, and collaboration.

Often used in product development, service design, and even business strategy, design thinking encourages teams to look beyond assumptions and explore solutions from the user’s perspective.

The Stanford d.school (Hasso Plattner Institute of Design at Stanford University) popularized a structured, human-centered approach to design thinking. Their methodology focuses on empathizing deeply with users and iterating designs based on continuous feedback. It’s widely used in product design, service development, and problem-solving across various industries. Here’s an overview of the core steps:
Empathize: Understand the users and their needs through immersive research. This stage involves interviewing, observing, and connecting with users to gain insights into their perspectives, desires, and pain points.
Define: Synthesize the findings from the empathize stage to pinpoint the core problem. Here, the goal is to create a clear problem statement that captures the user’s needs and the challenge the design will address.
Ideate: Generate a wide range of creative solutions. This phase emphasizes brainstorming without judgment, allowing for out-of-the-box ideas that may later be refined or combined into more practical solutions.
Prototype: Develop tangible representations of the ideas, often starting with low-fidelity models that can be quickly built and tested. Prototyping allows designers to explore different solutions and uncover potential issues early on.
Test: Present the prototypes to users, collect feedback, and observe interactions. This stage reveals which aspects work well and which need adjustment. Testing may lead to iterations, taking the design back through previous stages to refine the solution.

Yes, design thinking principles can be very effective in building AI solutions, as they foster a human-centered approach to developing technology that directly addresses user needs and challenges. When applied to AI projects, design thinking can help teams create more user-friendly, ethically sound, and impactful solutions. Here’s how each design thinking stage can align with the AI development process:
1. Empathize: Understand the User Context and Problem
Goal: Gain a deep understanding of the user’s needs, motivations, and pain points to identify where AI can provide value.
Actions: Conduct interviews, observe workflows, and engage directly with users who will interact with the AI solution. For example, if you’re building an AI chatbot for customer support, understand the types of questions users ask and the frustrations they encounter with current solutions.
2. Define: Clearly Identify the Problem and Scope
Goal: Translate user insights into a clear, concise problem statement that the AI will address.
Actions: Define what the AI solution needs to accomplish, such as improving efficiency, predicting certain outcomes, or enhancing customer experience. This stage also includes identifying any ethical considerations, biases in the data, or transparency needs the AI must meet.
3. Ideate: Explore Potential AI Solutions
Goal: Brainstorm different AI-based approaches and features that could solve the problem identified.
Actions: Involve data scientists, AI engineers, domain experts, and designers to propose multiple AI models, algorithms, and frameworks. For example, brainstorm whether a machine learning model, a recommendation engine, or a natural language processing algorithm best addresses the problem.
4. Prototype: Develop Early Versions of the AI Model
Goal: Create low-fidelity models or simulations of the AI solution to test its effectiveness and usability.
Actions: Develop initial, simplified versions of the AI model with a subset of data to explore performance and functionality. Build interactive demos or mockups for user testing, even before full-scale implementation, so you can gather early feedback.
5. Test: Validate with Real Users and Iterate
Goal: Validate the AI’s accuracy, usefulness, and ease of use with the intended audience, and identify areas for improvement.
Actions: Conduct user testing to observe how users interact with the AI, gather quantitative and qualitative feedback, and identify any bias or unintended outcomes. Based on this feedback, refine the model, adjust its scope, or retrain it with new data. Testing also reveals the importance of transparency, so users understand how AI decisions are made.

A “Good empty” state turns a moment of nothing into something.
Empty states:
* Encourage users to engage with our product
* Help them get comfortable by setting expectations for what’ll happen.
* Provide an obvious way to move forward.

For example, the first page that users see after signing up for Instagram is empty. Other profiles have photos, likes, and comments, but a first-time user’s account doesn’t have any information— 0 posts, 0 followers, 0 following. 

To make the activation easier, Instagram has turned this “empty state” into a learning 
& encouraging opportunity: where you would normally see your photos, it says “No Posts Yet — Tap on the camera to share your first photo or video” with an arrow pointing to the camera option.

It’s about creating a streamlined process of getting customers’ feedback.
As feedback it could be:
* Net Promoter Score (NPS)
* New feature votes
* Enjoying an ebook or material provided

Feedback on FAQ Items (kind of “did you find it useful”

The concept of product-led approach is to streamline collecting customer feedback and then use this feedback to optimize the user’s experience or your product. A product without a feedback loop is like “flying a plane without instruments”

It’s a streamline process, where you product includes the ability to mature through iterations.

Although adopting the agile methodology seems to be to correct approach, agile alone may lead you in loosing your focus of the overall picture to address one feature at the time.
You definitely need to build your product by having a Rapid, early validation of ideas that will allow you to mature through iteration.

Monolithic products will always become the blocking stone of a product-led growth strategist. Try to avoid them and make all the technical decisions towards an easy-to-change product
You need to build your product with the ability to expand

Product-led growth (PLG) is a business methodology in which user acquisition, expansion, conversion, and retention are all driven primarily by the product itself.
It creates company-wide alignment across teams—from engineering to sales and marketing—around the product as the largest source of sustainable, scalable business growth

A sticky product (a product that has a great usage cohort) is not synonym to a viral-product.

A sticky product is one that becomes part of people’s daily routine and is super-simple to onboard new users into – no matter how they find your product.

A viral product is a product allowing existing users to bring more users. It could be part of the product-core (i.e. in a messenger, inviting your friends to also use the messenger, or sharing a message out-of-the user’s network) or part of the product-ecosystem (i.e. refer a friend and reward schemes are the tinder for the fire of virality, as they make it easy to turn your customers into advocates.)

Enabling growth and creating virality comes down to ease and impact.

Enable your product to expand and allow your customers to invite their colleagues and peers to refer. Create exponential growth that is outside of your control – it’s not as scary as it sounds!

It’s about optimizing the first mile of your users’ product experience towards achieving their moment of value.

In other words it refers to the time spent by the users till the moment that they have understood the value that your product brings to them and their businesses. Also, called “the aha moment”

By utilizing tricks for best FTE (First Time Experience) and guides and empty states