What exactly might "consciousness" mean for AI in the real world?Philosophers have described consciousness as having a unique sense of self coupled with an awareness of what’s going on around you.However, applying those rules to AI is tricky.Sophisticated AI systems use deep learning to solve computational tasks quickly, using networks of layered algorithms that communicate with each other to solve more complex problems.Consciousness for AI would mean that neural networks could make those initial choices themselves, deviating from the programmers' intentions and doing their own thing.

AI And Consciousness

There are three levels of human consciousness, based on the computation that happens in the brain. <ul><li>The first, which they labeled "C0," represents calculations that happen without our knowledge, such as during facial recognition, and most AI functions at this level.</li><li>The second level, "C1," involves a so-called "global" awareness of information — in other words, actively sifting and evaluating quantities of data to make an informed, deliberate choice.</li><li>Self-awareness emerges in the third level, "C2," in which individuals recognize and correct mistakes and investigate the unknown.</li></ul>

The Three Levels Of Human Consciousness

How might artificial intelligence achieve consciousness?

philosophy

Philosophy

technologyandthefuture

Technology & The Future

artificialintelligence

Artificial Intelligence

Will AI Ever Become Conscious?

Everyday, us humans are focused on making a million little choices and over the past twenty years the amount of choices that are related to digital solutions has exponentially increasedOur work, personal, family and social lives have dramatically changed as a result of several waves of technological innovation.

The Future: Human Or AI

Web3 and the Metaverse are ushering in a new era of decentralization of data and community ownership.There are many questions regarding Web2 and where social media has left us and if there are lessons to be learned from Web3.The biggest one is whether Web3 will alter how we design solutions for human experience.A decentralized internet is exciting, but there are still many questions that need to be answered.If we can approach what we've done with <a href="https://www.entrepreneur.com/topic/technology">technology</a> at scale and take it to a whole new level with depth, then we can actually get closer to understanding what people feel.

Web 3.0 And The Human Experience

With excitement buzzing about web3, how can the tech community start to explore how this and emerging technology will impact the human experience?

The Future is Caring About the Human Experience

AI may be everywhere but is invisible to humans. Even if we can see it, people will most likely be ignorant of the fact that AI algorithms are used to make a product or service work.

Artificial Intelligence is an integral part of society

Social media relies on making users spend as long as possible on the platform. Therefore, algorithms make a dedicated study of each user and identify their interests so that they can continue to interact with the platform. AI's role is to suggest the right content and censor the vast amounts of content posted.

Social Media

AI can suggest the best products that are right for you based on the information available about you and your search history. Many online stores offer a photo-only product search service, where AI processes and analyzes the image and then suggests products similar to the one in the picture. One example is the Google Lens app.

Marketing

Many applications and programs rely primarily on AI to improve image quality and add some professional effects. Adobe Photoshop, for example, uses AI in processing and enhancing images. Smartphones are equipped with AI technologies to capture images in the best quality possible comparable to the quality of professional cameras.

Image Manipulation

Traditional games such as chess and electronic games generally use AI as a fierce competitor on the game's ground and provide a unique experience for every player in the game. In 1996, Gary Kasparov defeated an AI developed by IBM in a chess match, proving that no machine could beat the human mind. A year later, a new AI model defeated Gary, starting a new era of using AI in video games.

Video Games

Artificial intelligence is widely used in many commercial and industrial establishments. The most prominent application is the AI surveillance system which studies the surrounding environment, learns the patterns and behaviours in it, identifies any unusual activity or suspicious movement, and alerts the user directly.

Security and Surveillance

Many countries have been using AI in healthcare, for example, developing systems and devices to make diagnosing COVID-19 as fast, cheap, and accurate as possible. Another example is the device invented by Coala life, The COALA Heart Monitor System, a patch-free Remote Heart Monitor that provides real-time diagnostic-quality ECG analysis and heart sound recordings using AI.

Health

The large gap between the number of employees in the customer service department and the number of their clients has led to inadequate customer service and long waiting times. AI now helps guide customers to solve the problems they face while using the service directly and without delay.

Customer Service

From the beginning, there has been debate about AI and if it can be used in the field of creativity. One of the most prominent applications of AI in this field is museNet. This deep neural network can generate 4-minute musical compositions with ten different instruments that can combine music such as country, Mozart or the Beatles.

Creativity

Up to now, one thing remains clear: whatever the future holds, AI will most definitely play an important role in it. The core of this statement should be “surrounded by science we don’t understand”…

computerscience

Computer Science

How Artificial Intelligence Keeps The Internet Running Without Us Knowing

If we look at the recent developments in AI, it can seem as though the developers and creators of AI applications have lost control of its powerful potential.But these reports lose sight of the more effective and well-governed developments that are supporting and optimizing real work and the interchanges happening every day between humans and AI. When AI is approached comprehensively for how it can optimize an entire system-- including the humans within that system-- it has a higher chance of delivering meaningful impact.

AI And Customer Experience: The Premise

Customer care was one of the top use cases for AI.60 percent of executive respondents believe AI will play a role in 11 percent to 30 percent of their processes-- a considerable but not necessarily dominant influence on how most businesses operate. The overall acceleration of digital adoption has likely changed these metrics post-pandemic and the need to implement digital and AI solutions is now imperative to be competitive.

The Facts

One of the fastest areas of adoption for AI in the enterprise is chatbot applications.What customers really want is instant access to someone (or something) that understands what they need. Self-service customer engagement is trending towards delivering that-- but companies need to work with a partner that can deliver at scale.

ChatBots: The First Experience With AI

Employees need systems that support a balance between complex tasks and easy-to-execute deliverables that satisfy a sense of accomplishment and completion of work at the end of a workday.The design and implementation of AI must consider empathy and how it augments and supports the self-reported, lagging skill set for listening and understanding.

Supporting a Growing AI-Native Workforce

The customer engagement employee is an AI worker, their knowledge and contributions are essential to AI implementation. You cannot separate the two.The next generation will have moved beyond being 'digital natives' they will be 'AI natives.'Contact centre employees are at the cutting edge of an AI-literate workforce in our society and we have the opportunity now to provide the technology that will support their work and better serve customers.Companies need to expand the aperture in how success is measured against the human-side of the AI equation.

The Bottom Line

The need to implement digital and AI solutions is now imperative to be competitive.

Discover the world of business and economics.

economics

Economics

marketingandsales

Marketing & Sales

business

Business

Artificial Intelligence Is Not a Strategy. It Is a Customer Experience Accelerator

The Metaverse will likely drastically change the way we act interpersonally online by mimicking the serendipity and spontaneity of real-world interaction.However, developing these new social modalities will require AI-assisted and Fully AI-created content.

Metaverse: “A persistent, infinitely-scaling virtual space with its own economy and identity system.”

<ol><li>Professionally-created content</li><li>User-generated content</li><li>AI-assisted user-generated content</li><li>Fully AI-created content</li></ol>

The Four Stages of Content Creation:

We have only reached the first two of content creation in which single person and small multiplayer experiences are the norm.In this format, socializing remains focused on engaging in specific activities - e.g. completing tasks in a game.

The Present:

The volume of professionally-created content is limited because it is expensive to produce. Thus, user-generated content offers a promising new avenue forward, but content quality is an issue.Instead, using AI as a co-creator will allow for greater volumes of content, as lay people will be equipped with the tools to create quality content. With recent and continued developments to AI, building the Metaverse seems imminent.

The Future:

Socializing will shift away from task/plan specific entertainment toward spontaneous shared activities.AI-powered characters will be virtually indistinguishable from actual individuals, and social interactions will more closely resemble those in the real world.

Social Effects:

Through UGC and AI, we’re inching closer to the Metaverse. But the most exciting part of that vision is its potential to reinvent our social interactions.

Meet Me in the Metaverse | Andreessen Horowitz

Leadership churn indicates the number of senior team members who leave an organization over a given period of time. Leadership churn is calculated using a simple “churn rate” formula:(Leaders who leave / Total new leaders) X 100 = Leadership Churn Rate

Leadership Churn

<ul><li>Wasted time. When an employee at the leadership level leaves, a business must account for the cost of the time invested in their recruiting, interviewing, hiring, and onboarding. </li><li>Decreased productivity. No matter how talented a new hire is, nobody can reach maximum productivity on their first day of the job.</li><li>Lower employee morale. Employees across a business look to the senior leadership team for guidance and direction, so when the C-suite appears unstable, constantly in flux, or low-performing, there’s an unavoidable trickle-down effect. </li></ul>

The Hidden Costs of Leadership Churn

Focus on three key areas:<ul><li>The Organizational: Organizational onboarding consists of showing new hires how the logistics of the company function. This is where they’ll learn things such as where to park, how to enroll in health benefits, the language of the company such as specific acronyms, and company norms. </li><li>The Technical: This is where clear goals and expectations for the new hire are laid out. </li><li>The Social: Connecting them with people from different teams and varying seniority levels will help them feel more deeply integrated in the company culture, and give them valuable insights.</li></ul>

Reducing Leadership Churn: It all Starts with Onboarding

Any outdated onboarding processes need to evolve for a modern working environment. Instead of manual practices that take up excessive time and resources—yet fail to show results—advances in technology mean you can boost your onboarding success while reducing time and effort spent.

Reducing Leadership Churn with Technology

Why worry about leadership churn? Because 40% of executives fail within 18 months.

career

Career

humanresources

Human Resources

leadershipandmanagement

Leadership & Management

AI works with a simple set of instructions that creates many rules and strategies. AI is used, for example, to help doctors diagnose patients, pilots fly commercial aircraft, and help predict traffic.

Artificial intelligence  (AI) is often self-taught

<ul><li>Unsupervised learning. This approach is ideal for analysing untagged data to find general similarities and patterns. </li><li>Supervised learning. A computer algorithm is trained on input data to create the desired output.</li><li>Reinforcement learning. The program uses an Iterative approach to gather feedback, then compares the data and suggests an optimal outcome. </li></ul>

Different ways a machine learns

Each technique has its strengths and weaknesses, but when combined, researchers can build complex AI systems. However, these models become more self-directed, making it hard for computer scientists to determine how these algorithms arrive at their solutions. As machines continue to develop, we must consider how to teach them to teach each other to operate ethically.

Transparency is needed with self-directed Ai

Explore the three major methods of machine learning, which allows computers to write their own rules to problem solve and process data. -- Today, artificial intelligence helps doctors diagnose patients, pilots fly commercial aircraft, and city planners predict traffic. These AIs are often self-taught, working off a simple set of instructions to create a unique array of rules and strategies. So how exactly does a machine learn? Briana Brownell digs into the three basic ways machines investigate, negotiate, and communicate. Lesson by Briana Brownell, directed by Champ Panupong Techawongthawon. Support Our Non-Profit Mission ---------------------------------------------- Support us on Patreon: http://bit.ly/TEDEdPatreon Check out our merch: http://bit.ly/TEDEDShop ---------------------------------------------- Connect With Us ---------------------------------------------- Sign up for our newsletter: http://bit.ly/TEDEdNewsletter Follow us on Facebook: http://bit.ly/TEDEdFacebook Find us on Twitter: http://bit.ly/TEDEdTwitter Peep us on Instagram: http://bit.ly/TEDEdInstagram ---------------------------------------------- Keep Learning ---------------------------------------------- View full lesson: https://ed.ted.com/lessons/how-does-artificial-intelligence-learn-briana-brownell Dig deeper with additional resources: https://ed.ted.com/lessons/how-does-artificial-intelligence-learn-briana-brownell#digdeeper Animator's website: https://pleasecallmechamp.com/ Music: https://ambroseyu.com/ ---------------------------------------------- Thank you so much to our patrons for your support! Without you this video would not be possible! Nevin Spoljaric, Christine, Yvonne Feijoo, Sid Chanpuriya, Arjay Arcinue Dineros, RAD, Anoom Yasmin, Laura Johnson, Anoop Varghese, David Yastremski, Noah Webb, Zoë Tulip, B, Erica Guerrero, Richard Manklow, Roberto Chena, Luke Pisano, Andrea Gordon, Aleksandar Donev, Brendan and Nicole's summer job, Ryan Weiler, Jesse Lira, Ezekiel Raui, Itay Levi, Cameron Chakraverty, Petr Vacek, Rhys Patterson, Dennis, Margaret King, Olivia Fu, Katrina Adams, Regina Post, Mary Collins, Kari Teffeau, clumsybunnie, Adam Leos, Jeremy Laurin, Cindy Lai, Liz, Hannah Nommé, Rajath Durgada Manjunath, Dan Nguyen, Chin Beng Tan, Alejandro Gomez, Tom Boman, Karen Warner, Isorn Sookwanish, Iryna Panasiuk, Diane Gallin and Aaron Torres.

videos

Videos

Explore the three major methods of machine learning, which allows computers to write their own rules to problem solve and process data.

--

Today, artificial intelligence helps doctors diagnose patients, pilots fly commercial aircraft, and city planners predict traffic. These AIs are often self-taught, working off a simple set of instructions to create a unique array of rules and strategies. So how exactly does a machine learn? Briana Brownell digs into the three basic ways machines investigate, negotiate, and communicate.

Lesson by Briana Brownell, directed by Champ Panupong Techawongthawon.

Support Our Non-Profit Mission
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Keep Learning
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View full lesson: https://ed.ted.com/lessons/how-does-artificial-intelligence-learn-briana-brownell
Dig deeper with additional resources: https://ed.ted.com/lessons/how-does-artificial-intelligence-learn-briana-brownell#digdeeper

Animator's website: https://pleasecallmechamp.com/
Music: https://ambroseyu.com/
----------------------------------------------

Thank you so much to our patrons for your support! Without you this video would not be possible! Nevin Spoljaric, Christine, Yvonne Feijoo, Sid Chanpuriya, Arjay Arcinue Dineros, RAD, Anoom Yasmin, Laura Johnson, Anoop Varghese, David Yastremski, Noah Webb, Zoë Tulip, B, Erica Guerrero, Richard Manklow, Roberto Chena, Luke Pisano, Andrea Gordon, Aleksandar Donev, Brendan and Nicole's summer job, Ryan Weiler, Jesse Lira, Ezekiel Raui, Itay Levi, Cameron Chakraverty, Petr Vacek, Rhys Patterson, Dennis, Margaret King, Olivia Fu, Katrina Adams, Regina Post, Mary Collins, Kari Teffeau, clumsybunnie, Adam Leos, Jeremy Laurin, Cindy Lai, Liz, Hannah Nommé, Rajath Durgada Manjunath, Dan Nguyen, Chin Beng Tan, Alejandro Gomez, Tom Boman, Karen Warner, Isorn Sookwanish, Iryna Panasiuk, Diane Gallin and Aaron Torres.

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TED-Ed

How does artificial intelligence learn? - Briana Brownell

Stereotypes create a “believing is seeing” situation where people distort reality to fit their biased view of a given group. To resolve such bias, create transparency around who contributes what and how, so that actual performance is quantified fairly and visible. Popular tech collaboration platforms like Slack, Dropbox, and Zoom unobtrusively capture real-time performance information that can be mined with AI to reveal unprecedented windows into the drivers of organizational performance: who leads thinking around a specific project, resolves key problems, and so on.

Quantify to Rectify Stereotypes

The famed <a class="idea-content-link" href="https://www.amazon.com/Moneyball-Art-Winning-Unfair-Game/dp/0393324818">Moneyball</a> Much business today hinges on teamwork.A basic question might no longer be whether teams of all men or all women perform best, but whether more gender-balanced teams perform better than those heavily weighted toward men or women. A 20-year period research found that controlling for individual past success, mixed-gender, balanced teams are more likely to publish more influential ideas than all-male or all-female groups.

Look through the Lens of Teams, Not Individuals

Data is critical to creating effective, innovative people policies, especially as labor markets and work arrangements change. One company recently <a class="idea-content-link" href="https://www.marketplace.org/2021/04/19/for-companies-trying-to-diversify-their-workforces-good-data-might-be-key/">used data</a> The company is now able to make better offers, reinforce a culture of support, and increase their yield of top talent, with no real increase in costs. Take a similar, open-minded approach to examining your policies and practices and you may be pleasantly surprised by what you find.

Innovate Your People Policies

Your company already holds the data that can lead to sustainable change.

corporateculture

Corporate Culture

artsandculture

Society, Arts & Culture

3 Ways AI Can Help You Improve Diversity

Machine learning (ML) is a subfield of artificial intelligence (AI). The goal of ML is to make computers learn from the data that you give them. Instead of writing code that describes the action the computer should take, your code provides an algorithm that adapts based on examples of intended behavior. \
The resulting program, consisting of the algorithm and associated learned parameters, is called a trained model.

A brief description of machine learning

To develop and manage a production-ready model, you must work through the following stages:
<ul>
<li>Source and prepare your data.</li>
<li>Develop your model.</li>
<li>Train an ML model on your data:
</li>
<li>Train model
</li>
<li>Evaluate model accuracy</li>
<li>Tune hyperparameters
</li>
<li>Deploy your trained model.
</li>
<li>Send prediction requests to your model:
</li>
<li>Online prediction
</li>
<li>Batch prediction
</li>
<li>Monitor the predictions on an ongoing basis.
</li>
<li>Manage your models and model versions.</li>
</ul>
These stages are iterative. You may need to reevaluate and go back to a previous step at any point in the process.

The ML workflow

Before you start thinking about how to solve a problem with ML, take some time to think about the problem you are trying to solve. Ask yourself the following questions:
<ul>
<li>Do you have a well-defined problem to solve? Many different approaches are possible when using ML to recognize patterns in data.</li>
<li>Is ML the best solution for the problem? Supervised ML (the style of ML described in this documentation) is well suited to certain kinds of problems.</li>
<li>How can you measure the model's success? One of the biggest challenges of creating an ML model is knowing when the model development phase is complete.</li>
</ul>

Evaluate the problem

You must have access to a large set of training data that includes the attribute (called a feature in ML) that you want to be able to infer (predict) based on the other features.For example, assume you want your model to predict the sale price of a house. Begin with a large set of data describing the characteristics of houses in a given area, including the sale price of each house.

Source and prepare your data

Having sourced your data, you must analyze and understand the data and prepare it to be the input to the training process. For example, you may need to perform the following steps:<ul><li>Join data from multiple sources and rationalize it into one dataset.</li><li>Visualize the data to look for trends.</li><li>Use data-centric languages and tools to find patterns in the data.</li><li>Identify features in your data. Features comprise the subset of data attributes that you use in your model.</li><li>Clean the data to find any anomalous values caused by errors in data entry or measurement.</li></ul>

Data analysis

In the preprocessing step, you transform valid, clean data into the format that best suits the needs of your model. Here are some examples of data preprocessing:<ul><li>Normalizing numeric data to a common scale.</li><li>Applying formatting rules to data. For example, removing the HTML tagging from a text feature.</li><li>Reducing data redundancy through simplification. For example, converting a text feature to a bag of words representation.</li><li>Representing text numerically. For example, assigning values to each possible value in a categorical feature.</li><li>Assigning key values to data instances.</li></ul>

Data preprocessing

TensorFlow has several preprocessing libraries that you can use with AI Platform. For example, tf.transform.You can deploy and serve scikit-learn pipelines on AI Platform to apply built-in transforms for training and online prediction. Applying custom transformations is in beta.You can deploy a custom prediction routine (beta) to make sure AI Platform preprocesses input at prediction time in the same way that you preprocessed data during training.

Google Cloud support for data exploration and preparation

<ul><li>Vertex AI Workbench user-managed notebooks are Deep Learning VM Images instances pre-packaged with JupyterLab notebooks and optimized for deep learning data science tasks, from data preparation and exploration to quick prototype development.</li><li>BigQuery is a fully managed data warehouse service that allows ad hoc analysis on real-time data with standard SQL.</li><li>Dataproc is a fully-managed cloud service for running Apache Spark and Apache Hadoop clusters.</li></ul>

Google Cloud services

Develop your model using established ML techniques or by defining new operations and approaches.Start learning by working through TensorFlow's getting started guide. You can also follow the scikit-learn documentation or the XGBoost documentation to create your model. Then examine some code samples designed to work with AI Platform.

Code your model

AI Platform provides the services you need to train and evaluate your model in the cloud. In addition, AI Platform offers hyperparameter tuning functionality to optimize the training process.When training your model, you feed it data for which you already know the value for your target data attribute (feature). You run the model to predict those target values for your training data, so that the model can adjust its settings to better fit the data and thus to predict the target value more accurately.

Train, evaluate, and tune your model

During training, you apply the model to known data to adjust the settings to improve the results. When your results are good enough for the needs of your application, you should deploy the model to whatever system your application uses and test it.To test your model, run data through it in a context as close as possible to your final application and your production infrastructure.Use a different dataset from those used for training and evaluation. Ideally, you should use a separate set of data each time you test, so that your model is tested with data that it has never processed before.

Testing your model

AI Platform provides tools to upload your trained ML model to the cloud, so that you can send prediction requests to the model.In order to deploy your trained model on AI Platform, you must save your trained model using the tools provided by your machine learning framework. This involves serializing the information that represents your trained model into a file which you can deploy for prediction in the cloud.Then you upload the saved model to a Cloud Storage bucket, and create a model resource on AI Platform, specifying the Cloud Storage path to your saved model.

Host your model in the cloud

AI Platform provides the services you need to request predictions from your model in the cloud.There are two ways to get predictions from trained models: online prediction (sometimes called HTTP prediction) and batch prediction. In both cases, you pass input data to a cloud-hosted machine-learning model and get inferences for each data instance.

Send prediction requests to your model

Monitor the predictions on an ongoing basis. AI Platform provides APIs to examine running jobs. In addition, various Google Cloud tools support the operation of your deployed model, such as Cloud Logging and Cloud Monitoring.

Monitor your prediction service

AI Platform provides various interfaces for managing your model and versions, including a <a href="https://cloud.google.com/ai-platform/training/docs/reference/rest">REST API</a>, the gcloud ai-platform <a href="https://cloud.google.com/sdk/gcloud/reference/ai-platform">command-line tool</a>, and the <a href="https://console.cloud.google.com/ai-platform/">Cloud Console</a>.

Manage your models and model versions

Deep-dive into google's machine learning models and how it relies more on logic than data

softwareengineering

Software Engineering

Machine Learning With Google

Machine learning workflow  |  AI Platform  |  Google Cloud

When startups talk about percentage churn(say 5 percent) they ignore too many variables: Does this include users who signed up during this month, or only lost customers from the previous month?What is the revenue churn vs user churn? 5% is okay if it’s all free users you’re losing, but what if that 5% happened to be all your premium customers or 50% of your revenue? What is your activity churn? How many users became inactive this month? This is your leading indicator of future churn, and usually the most actionable insight.

Customer Churn: The Questions To Ask

Activity churn is the best indicator of future problems. Typical churn stats use something like account cancellations as a measurement but cancellation is only ever a trailing indicator, it’s literally the last thing that happens. When you identify previously active customers who are slipping, a well-timed personal email can help to re-engage them. The best case is that you recover a customer; the worst case is you learn why your users leave. Either of these leaves you in a better position than doing nothing.

Activity Churn Is What Matters

There is a difference between someone who didn’t convert after a 30-day trial and a year-long user who has started to slip away. The difference results in two different jobs, conversion and recovery. Don’t let them both fall into a naive “we miss you” bucket. Treat them differently.

Strategies For Re-Engaging Customers: Target The Right Ones

Don’t auto-mail a customer who has several open support issues to remind them to login and don’t begin your mail with “Dear Customer”. Such activities do more harm than good. Introduce yourself in the email, and make it clear that you’re a real person who really wants to hear what they have to say about your application.

Strategies For Re-Engaging Customers: Be Personal

Customers don’t forget to use your product, they lose interest in it. So you have to motivate them. There are some features that will bring customers back, or keep them from switching. By offering churning customers a glimpse of what’s coming down the line, you can excite them about future releases. Things that inspire people to stick around are usually features which save time (e.g. better importing), increase adoption or efficiency (e.g. integrating with third parties), or offer additional value for no extra work (e.g. weekly reports).

Strategies For Re-Engaging Customers: Being Interesting And Useful

If you have new features coming down the line, screenshots or gifs can be particularly interesting for long standing users. If the improvements are in terms of workflow, sometimes a clear diagram will be seen and understood far better than paragraphs of text. Never forget you’re just one of many emails in the inbox, and they’re all making promises. Any effort that makes your email stand out will help.

Strategies For Re-Engaging Customers: Be Visual

Some amount of churn is natural. Businesses come and go, as does the demand for your product. When a customer decides to leave, make sure they leave on good terms. If they have legitimate problems with your product, acknowledge them. Don’t delude yourself and fight back. Thank them for their custom and let them go. The easiest way to screw this up is to continue to spam them months or years into their departure. It does more harm than good.

Strategies For Re-Engaging Customers: Be Thankful, Honest, And Respectful

If you’re a SMB(Small Or Medium Business), you could expect an annual churn rate of anything from 31-58%. As startups acquire more customers and target the enterprise market, churn falls to a more respectable 6-10%. In their early days, companies are still finding their customers and will inevitably attract the wrong customers for whom their product isn’t a good fit, hence churn should decrease for startups as their product and brand become more focused and mature.

The Impact Of Churn: Your Mileage May Vary

Discover easy to implement strategies to reduce your churn rate

startup

Startups

product

Product & Design

Ultimate Guide to Reducing Churn

Churn is one of the most important metrics for today’s startups. It's also the most misunderstood. Learn how to understand churn properly for your business.