Sandy Diao

Text-to-creation: the universal form of creation in the past, present & future

What is text-to-creation? Text-to-creation refers to the category of technologies that takes text as an input, and produces multimedia that span text, audio, images, or video as an output.
– Sandy Diao

Text is the universal form of creation

This statement may not be immediately obvious, but it is perhaps the most logical explanation for the widespread consumer adoption of LLM (large language model)-powered experiences that we are seeing today. As of writing, OpenAI has publicly disclosed that their ChatGPT technology has well over 100 million monthly active users. One third of Gen Z, millennials, and Gen X have tried generative AI technologies (Statistica 2023). The market for AI has grown to $6.6 billion this year, and is expected to triple to $21 billion by the end of the decade (Statistica 2023). It’s important to note that we are only scratching the surface with these numbers when measuring the impact of AI-only tools. When we consider the possibilities of using AI to supercharge industries such as pharmaceuticals ($1.4 trillion) or entertainment ($717 billion), the numbers become truly incomprehensible. So now, the question we all naturally want to understand is what happens from here and what opportunities lie ahead? To answer this question, we must look at how we got here to understand how our behaviors will evolve next.

Text is the primary medium of daily communication

To provide context on how I’ve developed my point of view, I’ve worked behind the scenes to launch several AI products. During my time at Indiegogo, I helped hundreds of founders launch hardware products that incorporated early versions of edge computing on consumer devices, such as security cameras and emotional companion robots. I’ve also worked at Pinterest and Meta, whose advertising platforms allowed end-users to access AI ad delivery through machine-learned audience models for conversion optimization. Currently, I work on growth at Descript, where we’ve built a text-based video editing software powered by generative AI models across transcription, voice synthesis, audio generation, and more.

At first glance, Descript may seem like a surface-level UX change to a video editor. However, when you try to understand why it works, you’ll start to realize that it’s not just a coincidence that Descript is as easily learned and accepted by content creators and businesses as it is. Beyond Descript, the common theme across all types of creation is that we create with text to communicate. Most communication happens asynchronously, and the way we do that is via text – handwritten, emailed, scripted, documented, in blog posts, books, engravings, and the list goes on and on.

When you layer on what’s happening in the landscape of adoption of ChatGPT and consider the incredible versatility and historical importance of the written language, it becomes clear that text is truly the foundation upon which our modern forms of communication are built. When we consider the incredible versatility and historical importance of written language, and layer on what’s happening in the landscape of ChatGPT adoption, it becomes clear that text is truly the foundation upon which our modern world is built. Text-based prompts through a chat interface are the initial form factor that allows us to talk to computers in a way that we’ve never been able to before.

Text can be used to create more than just text

In addition to text-based prompts that generate text-based outcomes, there are many other forms of text-to-creation that are becoming prevalent today. For instance, text-to-speech tools based on transcription models are being utilized in everything from virtual assistants and smart home devices to audiobooks and podcasts.

Text-to-image technology is also on the rise and has the potential to revolutionize everything from advertising and marketing to film and television. This technology can produce realistic, high-quality images from text descriptions, eliminating the need for expensive photo shoots and video productions, making it easier and more affordable for creators to bring their visions to life.

Lastly, text-to-expanded text creation is another area of development that could become even more powerful. As artificial intelligence and machine learning continue to improve, we may soon be able to create prompts with minimal context that are fine-tuned to create results indistinguishable from the work of humans. Imagine being able to write a book in precisely your own style and intent by providing just a log line. This could have far-reaching implications for industries such as journalism, advertising, and content creation, fundamentally altering the way we produce and consume written content.

The emergence of text-to-creation was inevitable

Considering text as the medium of creation throughout history makes a lot of sense. When thinking about the earliest forms of writing, one might envision cave paintings and hieroglyphics from history classes. Fast forward to the computing age, where humans use text to communicate, record, and create through typing. The written word remains the foundation of stories, systems of belief, and education, spanning from religious texts and philosophical treatises to novels and scientific papers.

In recent times, new forms of text-based creativity have emerged. The novel, which rose to prominence in the 18th and 19th centuries, marked a significant departure from traditional forms of storytelling and introduced new techniques for character development and plot construction. In the 20th century, the internet and digital technologies opened up new avenues for text creation, including blogs, social media, e-books, and online journalism.

Today, we continue to tell stories and share information primarily through text, even as we create short and long-form videos, explainer videos on YouTube, and self-created and hosted curricula across over 600 million blogs as of this year (Web Tribunal 2023). In many cases, even videos and movies originate as scripts. And while not all videos require manual text tagging, it’s worth considering the effort required to classify the topics and information in those that do.

The ideas that are expressed in written mediums originate in the minds of writers and creators before they are put onto paper. In order to effectively share and communicate these ideas with others, the text needs to be well-crafted and nearly finalized. Recently, LLMs have given us the ability to communicate sequentially and iteratively, much like natural language conversations. However, just as there are good and bad conversations based on the context provided and the clarity of instructions, there are also more and less effective ways to prompt an LLM. The effectiveness of text-based communication depends on the communicator’s abilities. In this new world of accessible LLMs, it is important to learn how to turn our thoughts into clear communication using natural language.

Now, tools like Descript enable users to create a custom voice model with just a few minutes of training data. In the future, some of these models could be instant and hidden within app experiences. Completing a computer task could be as easy as speaking to it in natural language and asking it to perform a task.

The emerging role of natural language speech

An important development in this area will be enabling speech, which is the most common form of natural language delivery today, to be converted into text so that we can use it for iterative creation. Significant progress has already been made in converting human speech to text through speech-to-text transcription models, such as Whisper, Google Cloud’s Speech-to-text, and Amazon Transcribe. Let’s take a look at the most important moments in the evolution of digitizing natural language speech by decade:

1950s: One of the earliest examples of text-to-speech technology can be traced back to Bell Labs, who introduced the Audrey system. While the technology was still in its infancy, it laid the groundwork for the development of modern text-to-speech systems.
1960s: Physicist John Larry Kelly, Jr used an IBM 704 computer to synthesize speech.
1970s: Itakura developed the line spectral pairs method. MUSA, a stand-alone speech synthesis algorithm used to read Italian out loud, was also released during this year.
1980s: Sun Electronics released Stratovox, a shooting-style arcade game that shook the video game world. DECtalk, the standalone acoustic-mechanical speech machine, was built, and Steve Jobs created NeXT, a system that was developed by Trillium Sound Research.
1990s: Ann Syrdal at AT&T Bell Laboratories developed a female speech synthesizer voice. Engineers worked to make voices more natural-sounding.
2000s: Quality and standards for synthesized speech became a working issue for developers. This coalesced in researchers using a common speech dataset along with deeper research into additional vectors of creation, such as emotions.
2010s: OpenAI released GPT-1, the first iteration of their generative language model. Researchers at the University of North Carolina at Chapel Hill introduced the AttnGAN model, which uses attention mechanisms to generate images from text descriptions. Since then, there have been numerous advancements in this field, with companies like OpenAI and NVIDIA developing their own image-generating models.
2020s: Open AI has continued to refine and develop the technology, culminating in the release of GPT-3 in 2020. With the ability to generate human-like text, GPT-3 has opened up new possibilities for text-to-text creation. In 2022, Open AI released ChatGPT as a public beta, and then released an updated model in 2023 that takes image inputs.

Voice-to-creation is also text-based creation

Before we draw the firm conclusion that text underlies universal creation, we need to consider how voice-based creation fits into the picture. In the past few years, we have used our voices to activate AI assistants on devices powered by Alexa and Siri. In a 2018 interview with Vox, tech entrepreneur and investor Marc Andreessen predicted that “voice is going to be the primary user interface for the majority of computing over the next 10 years.” This prediction may still come true, but for that to happen, we need to understand that the engine powering voice interfaces is still based on text-to-speech models.

Voice-enabled technologies such as virtual assistants, like Amazon’s Alexa or Apple’s Siri, rely on text-to-speech models to convert spoken language into written language. This written language is then processed by natural language processing algorithms that help the technology understand and respond to the user’s request. Even the most advanced virtual assistants on the market, including Google Assistant and Amazon’s Alexa, are powered by text-to-speech models that are trained on vast amounts of written text. This is also true for other forms of media creation – the intermediate step is almost always text-to-creation at this time.

What happens next?

So, how do we evolve with text-to-creation from here? There are two key areas that will be innovated in in order to supercharge our ability to create with text:

More context as inputs, such as voice, vision, and motion. Relying solely on text can limit a model’s ability to infer all necessary context. While many models already support image inputs, we also need to incorporate video and other forms of real-world capture to enhance the results of our text-based prompts, and to extend into more creative fields that require understanding the nuances of emotion, intent, and preferences.
Edge computing will allow models to tap into individually unique and private data inputs that fine tune models to generate powerrful outputs that we may have never imagined.

While we can’t predict exactly how text creation will evolve in the future, it is clear that new technologies will inevitably be created to capture more context and compute with more personalized sources of data. There’s a universe of outputs that power use cases that we probably can’t even imagine yet, and I’m excited to see what those are.

Lensa app review: Are Lensa AI avatars worth the cost in 2023?

What is the Lensa app?

Lensa AI is a magic avatar generator app for iOS and Android that lets you create colorful, custom AI portraits of yourself. It takes less than 30 minutes to get a collection of 50+ avatars that are customized based on selfie images that you upload to the app.

First, my Lensa app review

3 out of 5 stars. The avatars weren’t bad, but also not great. 96% of the images were mediocre at best. However, I did like 2 out of the 50 images that I paid $3.99 for. Considering that it’s a 4K resolution piece of digital art hat I can now use as an avatar, I suppose it could have been worse. That’s about $2 for each digital photo, so I can’t say that I was totally scammed.

There are other cartoon-ify avatar generators that I’ve had better experiences with, such as Wonder. If you’re not interested in spending money on these yet, I recommend taking a look free apps with limited styles, like Snow or Beautycam.

Getting the avatars set up

In all, it took about 20 minutes to get from app installation to a folder of 50 avatars. It was about 5 minutes to set up, configure styles, and select selfies as training inputs. From there, it took approximately 15 minutes to generate the avatars.

22 styles across three categories of realism.

Here’s a step-by-step look at how I created my avatars with the Lensa app:

Sign up for a free 7-day trial to access the Magic Avatars feature.
Choose some styles. You can pick from 22 styles across the categories Essential, Art, and Time Machine.
Upload 10-20 selfies. Online guides suggest avoiding any face coverings, including hands that might be touching the face, since it impacts the training.
Choose to pay $3.99 for 50 generated images (10 styles) or $6.99 for 100 generated images. 50 sounded like a lot, so I went with 50 (though afterwards seeing how wildly in accurate and gross some of the photos were, I wish I had more options to choose from).
Hit the “generate” button, and it takes between 15-20 minutes for the images to finish.
You’ll see your pack generated, which you can download all at once in 4k or hi-res files. You can also download files individually.

Best and worst styles, according to my results

Before I share the rank ordering, I wanted to share that the biggest problem I observed in the worst style packs was that I think Lensa is probably missing training data for Asian faces. I found the more creative styles, such as Iridescent, Pop, and Comics to be grossly incorrect.

Stylish pack – looks very generic. Don’t know how this was trained using my selfies.

Comics style – doesn’t look like me. feature proportions are off, like the eye shapes.

Anime style – doesn’t look like me. feature proportions are off, especially the eyes.

Most accurate style: Cosmic

This was a bit surprising, since I assumed “Cosmic” would be a bit more abstract. But these avatars looked more like me than any of the other styles.

Most aesthetic: Iridescent

While the first two images don’t look like me at all, they’re still high quality, ethereal styles.

Most creative: Pop

Aside from the bottom right square that is just a cleaned up version of the selfie I submitted, these were unique, art-like renditions.

Most generically bad: Anime

These are so generic that you’d think I just randomly found these pictures while searching online. There are no indications that they were generated from my photos.

Most inaccurate: Focus

None of these look like me. Maybe slightly if I squint, but I look at it and feel terrified at how bizarre these are.

Don’t forget to check the privacy policy and the terms of use

Before you rush to download the app and start generating avatars, keep in mind that as consumers, we’re entering a whole new era of privacy and data rights management around the images that we entrust with generative AI companies. If this is something that you care about, make sure you understand the details in their privacy policy and terms of use to understand what the app does with your selfies, data, and generated images.

How to use AI writing tools to your advantage

Artificial intelligence (AI) has revolutionized the way we live and work, and writing is no exception. With the help of AI-powered writing tools, writers can generate ideas, improve their writing skills, and automate time-consuming writing tasks. Even prior to the advent of ChatGPT and other conversational AI bots that now act as personal writing assistants, several companies have for months and even year, developed some of the leading AI writing tools available today such as Writesonic, Jasper (formerly known as Jarvis.ai), Grammarly, ProWritingAid, Copysmith, Rytr, Articoolo, and more. These tools use machine learning algorithms to analyze large amounts of data, learn from it, and generate human-like content as the output. In this article, we’ll explore how you can use AI writing tools to your advantage, and which tools are the best fit for practical parts of the writing process.

What is AI Writing?

AI writing is a technology that uses natural language processing (NLP) algorithms and machine learning (ML) models to generate content without human intervention. AI writing software are usually designed to help with specific writing tasks, such as generating blog articles, email newsletters, social media posts, and even creative writing.

AI writing tools have proven to be a game-changer for many creators and businesses. For instance, the team at HubSpot has used AI writing tools like Writesonic to create dozens of social media posts in just a few minutes. Content creators have also creatively applied AI writing tools – in 2020, a group of digital marketers used GPT-3 to generate several blog posts that received thousands of views and shares. The tool’s ability to generate content quickly and efficiently helped them reach a broader audience, resulting in increased traffic and engagement. Even traditional authors have used AI writing tools to generate outlines and ideas for their books. A popular tool for this is Articoolo, which allows writers to input a topic or keyword and generates a full-length article on that topic in just a few seconds.

Can an AI Write My Essay?

Yes, in theory and practice, AI can write your entire essay (and I don’t even want to think about what teachers and professors are doing now to grade students’ work with this as a much more accessible technology). Still, while AI writing tools can be useful in generating content, they’re not yet advanced enough to write an entire essay without any human input. Students and other essay writers will still need to guide the writing as much as possible to ensure that it addresses the objectives of the essay.

Where essay writers can use AI tools most effectively today will be in assisting with idea generation, improving writing, and providing suggestions for revisions. For example, tools like Grammarly may provide suggestions for essay structure or offer ways to improve the essay’s clarity, style, or grammar. Another creative application could also be to use AI to search for research sources to support an essay’s arguments.

Can an AI Write a Script?

AI writing tools can assist in scriptwriting by providing suggestions for character dialogue, plot development, and even story arcs. While an AI writing tool probably can’t write an entire Oscar award-winning script on its own, they can be useful in providing ideas and improving the writing process.

The entertainment industry has already started using AI writing tools to generate scripts for movies, TV shows, and video games. In 2016, a short film titled Sunspring was created using an AI writing tool called Benjamin. Benjamin, which was developed by filmmaker Ross Goodwin, used a recurrent neural network to analyze hundreds of science fiction screenplays and generate a script for the film. Even media giants like Netflix has been using AI writing tools like LDA (Latent Dirichlet Allocation) to analyze viewer data and generate script ideas for its original shows. The streaming giant also used an AI writing tool called AIVA to generate a musical score for one of its shows.

How Do I Create Content for My Blog Using AI?

Creating content for a blog can be a time-consuming process, especially if you’re looking to produce high-quality, engaging content consistently. Fortunately, AI writing tools have made it easier to create blog content quickly and efficiently.

One popular AI writing tool for blogs is Jasper. Jasper offers a variety of features, including content ideation, writing, and editing. For example, if you’re struggling to come up with a blog post idea, you can use their content ideation feature to generate multiple ideas based on a keyword or topic. The tool uses machine learning to analyze thousands of blog posts and suggest ideas that will perform well.

AI writing tools can also help with writing SEO-optimized blog articles. AI tools can help improve the quality of your blog content by suggesting relevant keywords, optimizing meta descriptions and titles, and analyzing the content’s readability. SurferSEO is an AI-powered SEO writing tool that uses machine learning algorithms to analyze search engine results and suggest relevant keywords and phrases for your blog posts. It can also provide feedback on the length of the content, its structure, and readability. Clearscope is another tool that uses AI to analyze your content, and the app will suggest keywords and topics that can help improve its relevance and ranking on search engines. It’s like a real-time grader for SEO.

Practical Ways to Start Using AI Writing Tools

Now that we’ve covered the basics of AI writing tools, let’s discuss how you can use them to your advantage. Here are some tips for using AI writing tools effectively:

Use AI writing tools like Writesonic or Jasper to generate ideas quickly. These AI-powered tools can analyze your keyword or topic and provide you with content ideas that you can build upon.
Refine your writing style and grammar using Grammarly or ProWritingAid. These tools use AI to scan your writing for errors and provide feedback to help you improve your writing.
Automate your writing process using tools like Copysmith or Rytr. These tools can generate content for you quickly, freeing up time to focus on other tasks. Copysmith can create high-quality product descriptions, blog intros and outros, and more, while Rytr can help you create blog posts, social media posts, or even ad copy.
Experiment with new writing styles and genres using tools like Articoolo or Writesonic. Articoolo can create high-quality articles based on your keywords, while Writesonic can generate content for social media, ads, blogs, and more.

While AI writing tools like the ones mentioned above are not capable of writing an entire book or essay without any human input, they can certainly assist writers in generating ideas and creating content quickly and efficiently. By using these tools to your advantage, you can improve your writing, save time, and even have fun exploring new creative ideas.

The untold story of startup success: building a company starts as a sidegig

It’s a common misconception that successful startups are born out of a single-minded focus and an all-consuming passion. We tell the story of founders who “make the leap” and give up everything – sell their house, move into a small apartment, work out of a garage – and end up believing that it’s 100% in or else there’s no hope for building a successful company. In reality, many of the most successful startups were started as side projects by founders who were full-time students or had day jobs.

Take Airbnb, for example – it was founded by three friends who were struggling to pay rent in San Francisco. They started renting out air mattresses in their apartment to local conference attendees, and the idea eventually grew into a billion-dollar company.

Even take Slack as another example, started as an internal tool for a gaming company called Tiny Speck. The founders realized that their tool could be useful for other companies, and Slack was born.

You might be surprised that Google falls into this pattern as well. Google, as one of the largest technology companies in the world, was started as a research project by Larry Page and Sergey Brin while they were PhD students at Stanford.

The point is that so many of the successful startups you know and love weren’t created as full-time endeavors. They were born out of a need or a passion that founders pursued in their evenings and weekends, often while working full-time jobs.

This contrarian view is important for traditional venture capitalists to consider, because it challenges the conventional wisdom that founders are only good if they have full commitment. That becomes true later, but if we didn’t have the explorers who were willing to build in their evenings, then we wouldn’t have a lot of the innovative companies that we have today. We tend to look for startup founders with single-minded focus and a team that is 100% committed to building a product. But the reality is that many successful startups start out as side gigs.

I’m really excited to look for startups that don’t fit the mold of a traditional full-time company. With trends like “The Great Betrayal” and full-time work looking less attractive, I’m willing to bet that we’ll see a huge wave of part-time entrepreneurs, some of whom will merely dabble, others becoming solopreneurs, but also some will build the the next biggest companies. You never know where the next billion-dollar idea may come from.

30 software legends that started part-time

There’s a much, much longer list that isn’t captured on the Internet, but for starters, here’s a list of 30 software companies that you’ve probably heard of that were started as side projects:

Microsoft: Bill Gates and Paul Allen started Microsoft while they were still in high school, and continued to work on the company as a part-time venture while attending college.
Amazon: Jeff Bezos started Amazon as an online bookseller while working as a senior vice president at a hedge fund.
Google: Larry Page and Sergey Brin started working on the search engine that would become Google while they were Ph.D. students at Stanford University.
Slack: Stewart Butterfield and his team started working on the team communication tool while they were still working on a different project, and continued to work on Slack as a side project until it became a full-time venture.
Dell: Michael Dell started building personal computers in his college dorm room as a part-time venture before eventually quitting school to start Dell Inc. full-time.
Apple – Steve Jobs and Steve Wozniak created the first Apple computer in Jobs’ parents’ garage while working full-time jobs.
Airbnb – Founders Brian Chesky, Nathan Blecharczyk, and Joe Gebbia started renting out air mattresses in their apartment to conference attendees as a way to make extra money.
WhatsApp – Co-founders Jan Koum and Brian Acton created WhatsApp while working as engineers at Yahoo.
Dropbox – Drew Houston started developing the first version of Dropbox while working full-time at a startup called Accolade.
Evernote – Phil Libin, Stepan Pachikov, and Dave Engberg started Evernote as a part-time project while working at other companies.
Hootsuite – Ryan Holmes started Hootsuite as a side project while running a digital agency.
Wunderlist – Christian Reber started developing Wunderlist while working full-time as a designer.
Twitter – Jack Dorsey, Biz Stone, and Evan Williams created Twitter while working on another startup called Odeo.
Atlassian – Mike Cannon-Brookes and Scott Farquhar started Atlassian while studying at the University of New South Wales.
WordPress – Matt Mullenweg started developing WordPress as a side project while working as a consultant.
Trello – Joel Spolsky and Michael Pryor created Trello as a way to manage their own projects more efficiently.
MailChimp – Ben Chestnut started MailChimp as a side project while running a web design company.
Salesforce: Marc Benioff started Salesforce as a part-time venture while he was still working as an executive at Oracle.
Hubspot: Brian Halligan and Dharmesh Shah started Hubspot as a part-time venture while they were still professors at MIT.
Asana: Justin Rosenstein and Dustin Moskovitz started Asana as a part-time project while they were still working at Facebook.
Freshdesk: Girish Mathrubootham started Freshdesk as a part-time project while he was still working as a product manager at Zoho.
Basecamp: Jason Fried and David Heinemeier Hansson started Basecamp as a part-time venture while they were still working as consultants.
Airtable: Howie Liu, Andrew Ofstad, and Emmett Nicholas started Airtable as a part-time project while they were still working at various tech companies.
Canva: Melanie Perkins, Cliff Obrecht, and Cameron Adams started Canva as a part-time project while they were still students.
Pipedrive: Timo Rein and Davide De Guzman started Pipedrive as a part-time venture while they were still working as consultants.
Heroku – James Lindenbaum, Adam Wiggins, and Orion Henry started Heroku as a part-time project while working at different companies.
Typeform: Robert Finn and David Okuniev started Typeform as a part-time project while they were still working as designers.
Adobe – John Warnock and Chuck Geschke started Adobe as a part-time project while working at Xerox.
Red Hat – Bob Young and Marc Ewing started Red Hat as a part-time project while working at Cornell University.
Grammarly – Alex Shevchenko and Max Lytvyn started Grammarly as a part-time project while studying at UC Berkeley.

The dark side of AI-powered marketing

Artificial intelligence (AI) has become a major tool in the growth marketing arsenal, helping businesses drive growth and increase revenue. In fact, as the world has been introduced to consumer versions of AI through tools like ChatGPT recently, businesses have been using AI-powered campaigns and marketing channels for a long time. From TikTok video feeds to Facebook ad delivery, machine-learned targeting models have already seeped into our daily interactions and experiences on our phones and devices. Even Siri and Alexa (as primitive as they seem) voice assistants are deep learning TTS (text-to-speech) model rooted in AI technology. Businesses use AI-powered marketing rooted in machine learning algorithms to analyze user behavior and personalize marketing campaigns in real-time. This allows businesses to reach consumers more effectively and efficiently than ever before. However, as AI becomes increasingly prevalent in marketing, there are emerging pockets of ethical risks that potentially pose harm to you and me.

One of the leading concerns about AI-powered marketing is a tale that we’re all familiar with: the spreading of misinformation and fake news through AI-powered social media campaigns. AI algorithms can be easily manipulated to create fake news and spread false information to millions of people. According to a study by MIT Technology Review, false information spreads faster and more easily on social media than true information, which can have serious consequences for public opinion, trust, and democracy (MIT Technology Review, 2018). For example, the 2016 US Presidential election was influenced by AI-powered media campaigns on social media, which helped shape public opinion and ultimately influenced the outcome of the election (The Guardian, 2018).

Another area of concern is the manipulation of consumer behavior through AI-powered personalization. AI algorithms can track and analyze consumer behavior, including their browsing history, search history, and social media activity, to personalize marketing campaigns. While this can be a powerful tool for businesses to reach consumers more effectively, it also has the potential to exploit vulnerabilities and sway consumer behavior in potentially unintended ways. According to a report by the World Economic Forum, as AI algorithms become more advanced, they will have the potential to manipulate human behavior to a much greater extent, creating new risks for consumer welfare (World Economic Forum, 2021). For example, brands or organizations can use AI algorithms can be used to target vulnerable populations and exploit data to drive adoption or education, leading to negative consequences.

Big data could become another driving factor behind unethical practices in AI-powered growth marketing. AI algorithms are fed massive amounts of data to analyze consumer behavior and personalize marketing campaigns. According to a study by McKinsey & Company, big data analytics has the potential to generate significant value for businesses, but it also presents significant risks to privacy and security (McKinsey & Company, 2016). Vast quantities of data collected by algorithms can be easily manipulated to create false patterns and trends, leading to skewed results and unethical practices. Having worked in technology companies with access to large data sets, I know that there are several levels upon which data can be biased, both in how we determine which signals to track, how we define them, and ultimately how we interpret what the data is telling us. The scary thing is that we don’t fully realize that we’re making biased decisions, because we think the data is objective.

One could even see a world in which perfectly fine-tuned AI, which is based on mathematical models and algorithms that are designed to analyze and interpret patterns in data, can lead to a worse or more mediocre customer experience overall. These algorithms struggle to understand the complexity of human behavior and the context in which these behaviors occur. According to a report by Harvard Business Review, AI algorithms are still limited in their ability to understand human behavior and the context in which it occurs, which can lead to homogenous and generic experiences for consumers (Harvest Business Review, 2020). As a result, AI-powered growth marketing can create homogenous and generic experiences for consumers, ignoring important cultural and contextual factors that can influence consumer behavior. Imagine a world where every experience on the Internet is a 3.5-star Yelp restaurant one – homogenous, not too good nor too bad. Optimized right for the crowd.

To address these ethical concerns and limitations in AI-powered marketing, I believe that businesses must take a responsible and ethical approach. This might come in the from of more businesses putting ethical guidelines front and center, in addition to their terms of services. We need a cultural system (or even formal regulation) that can ensure the right incentives for transparency and accountability in AI-driven marketing campaigns, protect consumer privacy and data security, and create meaningful and respectful experiences for consumers. Our best bet will be to find the sweet spot in combining AI with human intuition and creativity, and then putting together the charter upfront for how these interact.

What the future of performance marketing holds

There is a lot of buzz around AI-driven marketing today. Tools like Jasper and ChatGPT have caused many go-to-market professionals to question whether their jobs will be replaced by robots in the near future. It can be unsettling to think about, but I’m also excited about the potential of these technologies to drastically improve our work and productivity. In my daily work, I’ve been able to use Jasper to quickly generate strategy statements and ad taglines with minimal input. It’s even helped me proofread parts of this write-up.

While this is all novel and exciting, AI-driven marketing is already making an impact on performance marketing channels. In fact, I’ve been using an ML-based performance ad channel over the last year in my role running growth at Descript, and these campaigns have broken our performance ceilings month after month. Let’s explore what’s happening now and how it affects our digital marketing and go-to-market decisions.

ML-based performance marketing is already here

Channels are all converging toward ML-based conversion optimization for targeting. Example: Google’s Performance Max campaigns; Facebook/Instagram’s broad targeting with conversion optimization campaigns
- For those who aren’t familiar: Google Performance Max is a paid search campaign type that uses machine learning to optimize ad delivery and targeting in real-time, which results in achieving better performance and ROI outcomes. Instead of setting up targeting, you set up ‘”anti-targeting’” by telling the system what’s not considered a valid conversion.
ML-based targeting is ideal from the lens of ROI – the machine-learned system will most certainly be more precise in targeting and use each ad dollar in more effective way than human allocation could. But, it’s not ideal from the lens of gaining customer insights.
Perhaps in 6-12 months time, all paid search and paid social will utilize blackbox targeting where we don’t define or know who the platforms are targeting, except understanding broad filters geography, demographics, or platform, to name a few examples.
This creates a reliance on ads, where you can get a ton of results but you don’t know what drove its success and you can only count on the blackbox targeting system to continue working well.
- A good parallel might be to understand what Amazon does with brands/sellers today. You pay Amazon to sell on your behalf, and the units sell or they don’t, but you don’t know who the customer is at the end of the day.
- Overall, the Amazon-esque system can work incredibly well, but it rules out a certain type of seller that isn’t savvy enough to learn and exploit the system.

Skills for the future are technical and strategic

Performance marketing becomes a technical sport

Less focus on hiring those with ads operating experience, which tends to be the background of most junior performance marketers or agency hires. The things that performance marketers spend time on today, such as building the post, creating targeting parameters like frequency caps and daily budgets, setting up an a/b experiment, or optimizing based on results, will all become automated. Instead, people will spend more time defining clear optimization signals and structuring campaigns across these signals. Think of it almost as prompt creation.
Performance marketing as a whole will become a technically performant function where the operators behind the scenes will need to be data-minded in order to understand how utilize targeting and ranking mechanisms.
Instrumenting marketing data systems isn’t a common skillset for data engineers. That’s why the performance marketer will need to become an acting product manager to help ensure accurate instrumentation, and define a data taxonomy that becomes useful for marketing teams.

Content marketing and product marketing work requires more precise customer segmentation

It will be imperative to develop strengths across multiple niches in order to gain scale. Otherwise, we’ll hit the ceiling within the constraints of our economics – i.e. willingness to pay for a conversion – and time horizon.
More micro-targeted content positioned to the customer that will be likely to convert. This could mean developing more landing pages with focused content that gives greater chance of fitting into the blackbox targeting match.
Having a multi-faceted product that spans customer segments should play to our advantage in that it allows us to compete in multiple audience segments at one time, which increases our chances of exposure and conversion.
This increases our need to have a clear data-driven signal customer segments that can be exposed externally somehow.

The chasm between digital performance and brand marketing tactics will widen, yet the function of these are intertwined

Today, performance marketers look toward increasing reach as a mechanism to improve the likelihood of finding a converting audience. Tactics include bidding on a CPM basis to achieve a broader set of conversion outcomes. However, in the world of ML targeting, the system will reach as few users as possible to reach conversion objectives, which minimizes total reach and frequency of reach.
Brand and performance assets are thought of as separate entities right now, but these are becoming inseparable parts of the customer’s experience. For example, a user that is abruptly shown a use case landing page will most certainly want to explore the brand’s larger offering on the homepage or other branded pages (in my experience, this happens 20-30% of the time). Both types of pages will be relevant to cold prospects who are looking to learn about the brand while trying to get more information about their use cases. It’s about identifying the ideal navigation path and understanding what happens after a user interacts with a brand for the first time, or vice versa.
The bridge from brand marketing to performance marketing can be built via a multi-touch attribution model that companies either develop themselves by centralizing all data within a CDP like Segment, or they can be outsourced and imported to a 3rd party tool that does this for you, like Branch.

Preparing for the future means hiring for technical skills & building for channel-market fit

In a world of ML-driven customer acquisition, digital marketing teams must create clear signals and high-quality user experiences to be successful. This means that the better you perform today, the better you’ll perform tomorrow – it’s a performance flywheel. This wasn’t the case in the past, when performance marketing was limited by target audience ceilings and manual optimization decisions. The more signals you have, the better these platforms will be able to acquire new customers for you.

To accomplish this, you should hire ad managers from diverse backgrounds, with an emphasis on those with technical expertise. Content quality is still paramount for delivering persuasive messages that convert, but product marketing, content marketing, growth marketing, and product teams must work together to create a well-managed journey from first touch to product entry.

Why monetization is one of the most important yet underutilized growth levers

What is monetization strategy?

First, let’s understand: what is monetization? Monetization strategy refers to how a company decides to make money by offering its product or services to customers. This is a company deciding what they’re going to start charging users for them to use their products or services. Figuring out how you make money is tied in with what you’re naming it, when you charge, how often you charge, and how much it costs. For most software companies, revenue is the ultimate metric, and monetization is the strategy that touches revenue the deepest. The key reasons for a company to prioritize monetization strategy are:

Monetization allows companies to make money, which enables the team to reinvest into growth.
Monetization completes your product-market fit: it’s a reflection of your product’s positioning and packaging that ultimately allows you to build a sustainable and profitable business.
A focus on monetization strategy forces companies to focus on growing the set of users with the highest ARPC.
Rather than cost cutting, monetization strategy is a lever for accelerating revenue through its compounding effects on acquisition and activation.

To grow a company, product and marketing teams will think about several strategies, including but not limited to common ones such as acquiring paid users, reducing product friction, and improving product retention. When it comes to revenue, it seems like sales alone is somehow responsible for numerical dollar amounts, and it can feel as if product and marketing will dust their hands and work on user and retention problems (often times as proxy or leading indicator) rather than directly on revenue ones.

For product teams, the first default tactic to impact the bottom line is often to “cut costs”. This is also true for marketing teams that track metrics like LTV:CAC, where after a 4-5 years, LTV rarely grows and instead we look to decrease the cost basis of the users we acquire. Product teams often look at the traction of their time investments and determine whether products and features are additive or detrimental – if the latter, we shut down the feature, or roll it back. If the product or feature is neither good or bad, then usually nothing happens and is handed over to sales and marketing to grow adoption. These are problematic perspectives that are miss the mark on distributing a product to an end customer. Ultimately, a user will purchase a product that they need if the price and packaging are right in their moment of need. Companies can inspire users to get to that moment, but for the most part, a product’s core job is to get users to that point.

Pricing strategies today – SaaS & enterprise

Pricing is a very relevant topic today, where companies need to adjust prices due to the rising costs of doing business as a result of decades-high inflation rates. Companies like Netflix or Beamer for example, have announced their price increases. Some of these companies are taking extra care to word their messages by addressing value-added features to justify the increases (Netflix explained that they are providing a lot more value through an extensive new collection of movies and TV shows). Yet others simply saying that their costs have risen and they need to increase prices as a result (Beamer announced that COGS have increased). Regardless of the circumstances, price changes will impact users in different ways, such as affecting the satisfaction of existing users or changing the perceived value of new potential users. Prices also affect how users perceive a company’s competitive positioning. In the examples above, companies expressed that their prices were forced due to broader economic conditions, and that they had no choice but to begrudgingly change. But while these example are driven by an increased cost basis, there are many benefits to thinking about pricing and packaging proactively relative to positioning.

In a world where user growth is typically seen as a proxy to revenue growth, there are many missed opportunities when it comes to driving growth through the components of monetization. Let’s break those down into the raw pricing and packaging components that a monetization strategy represents:

If we only rely on growing the # and composition of users to grow revenue, we missing out on a big part of our conversion engine. For example, what if your business only offered two plans today – one for consumers and the other for businesses, and you found out that the prosumers using your product were looking more for business-type features, but were paying for the consumer plan? That’s both money and potential users left on the table, so there’s a ton of value in exploring how you could create a separate package for your prosumer users. The company Webflow, a low-code website builder platform, does a great job of identifying several distinct segments of users that would want to pay to create a website. Though the monthly prices are each within a low range of prices, they individually speak to the specific type of customer who is looking to create a website through the tool – a blogger would look at the CMS plan, whereas a small business would start with the business plan, and someone who is just getting started and doesn’t have the need to collaborate with others or utilize scalable page types would go for the starter or Basic plan.

Why don’t companies use pricing as a growth lever?

While it makes sense that we would want to package appropriately for our target customer segments, there are still reasons why companies don’t prioritize monetization as a growth lever. Based on my experience working at large companies like Pinterest and Facebook, to smaller startups like Indiegogo and Descript, these are the key reasons why companies don’t prioritize pricing changes as a growth lever:

It seems customary to focus on getting customers first, then getting monetization right later. For many consumer companies, we’re trained to think of the goal as a hockey-stick shaped user growth curve. SaaS companies track ARR, but even then, revenue growth is driven by assuming changes in the number of paid seats rather than increasing revenue per user.
Pricing and packaging isn’t a typical skill set for anyone on product or growth teams – it feels like an abstract decision that is driven by experienced people.
Pricing changes aren’t easy to make. Not only are these changes deeply embedded within existing product flows, such as checkout experiences, feature gates, or pricing pages, they are also decisions that no single team can make alone. Changing prices also risks the satisfaction of your core customers, if you’re not doing it right.

Given the potential hurdles of getting buy-in from internal stakeholders such as the CEO, product and marketing leaders, to your own customers, updating your company’s pricing can be hard to get started. But the changes of a company having gotten pricing and packaging right on the first try is low, and particularly as a company is scaling its growth, there are many reasons that make pricing changes incredibly advantageous.

How do you build a monetization strategy for your product?

There’s an art and science to landing a monetization strategy. There’s no one-size-fits all strategy, even as a baseline, that will apply to the specific context of the product and market that you’re building for. The best thing to do is to start with the intuition you have for your customer set, and begin to validate it through experiments. Start with your own baseline and go from there. There’s no one else’s pricing strategy that you can copy and instantly become successful with (in fact, it can often be detrimental to just follow what your peers are doing without considering how it impacts your customers). That said, here are a few of the most important considerations for creating your baselines and experimenting from that point:

Monetization isn’t just price changes, it’s about positioning relative to your understanding of customer value and how your product delivers it. Think about how packages are separated, and make the decision points clear for customers. Your goal isn’t to confuse them on what they want, but rather to offer them a clear way to make a decision on how pricing scales value for their usage of your product.
Don’t bet all of your eggs in one basket, test changes where you can. Geographical or audience roll outs are a great way to test without disrupting the entire user base.
Don’t reengineer all of your product SKUs right out of the gate. Painted door tests are a way to get real reactions that mimic true conversion rates.
If unit economics / profitability don’t work yet, don’t rely solely on cost-cutting. Look toward pricing and packaging as a lever proactively, as it could change your COGS considerations.

Implementing a monetization strategy at your company

So then how do you know if you even have the right monetization strategy before you’re a prime candidate for pricing and packaging changes? The reality is that pricing and packaging is never going o be a decision made in a vacuum. Being at the bottom of the funnel, monetization strategies will see the compounded effects of acquisition and activation for better or for worse. You might be charging the wrong price or on the wrong value metric for the right customer, even if it gates access to the right thing.

Monetization is an incredibly interdisciplinary sport. In this topic alone, you’ll end up running through at least these functions:

Product marketing view of different segments.
Qualitative and quantitative research to run pricing surveys such as Max diff or conjoint analysis.
Data science-led pricing tests, with test vs. control audiences, a/b variants, or other tests of statistical significance.
Customer support management to manage new and existing user questions and sentiment.
Sales & marketing will need to update pricing and packaging messages across the website, emails, and in external publications.
Finance to work through implications of how it impacts bottom line and other company numbers.
Product and design to identify the right flows to introduce pricing and packaging.

Not only does pricing require a strong general manager skillset, it also requires driving alignment amongst many stakeholders. Monetization isn’t going to be as simple as one person’s decision – because it ties to revenue and product, it will likely require working with the CEO in earlier stage and growth stage companies. This will involve bringing data, customer insights, strategic insights, risk management, and more to the table just in the process of writing a proposal alone. Given the complexity of pushing through pricing changes, testing every change isn’t always possible. Sometimes, you just have to roll it out and see how it works. If it doesn’t work well, you can roll back as long as you’re transparent and keep your customers updated each step of the way.

Always a seat away from Concertmistress

I want to share a telling and formative story from my childhood. This is a story about how I was always just a single seat away from being concertmistress in my orchestra.

Concertmistress: noun.
a female leader of the first violins in a symphony orchestra, who is usually also the assistant to the conductor.

I attended Roosevelt Middle School in San Francisco. It was the first time I had to travel away from the Tenderloin neighborhood area in order to attend school (previously, my elementary school was half an hour’s walk away) and got me venturing deep into the Richmond District. The way that classes worked was, there were certain required courses that you were assigned, say, math and language arts, but there were also electives. Without fully understanding exactly what those electives entailed, I signed up for the Symphonic Orchestra elective. Little would I know that this choice would shape my mindset so dramatically for years to come.

I started my first day of school bright and early, and I walked into Symphonic Orchestra which took place every morning for an hour starting at 7AM. The conductor, Mr. Jones, was a teacher who seemed stern yet soft-hearted, and he asked us all to grab the proper violin size from the lockers. While I was digging around for a suitable violin, I looked around at my classmates who appeared to be upperclassmen and upperclasswomen, as they were noticeably more charming in their conversations amongst each other and appeared to be confident in their instrument selection. At this point, I had only dabbled with violin during group classes in elementary school, as part of a music program that was lightly funded and focused on recreation more than actual music practice. As you can imagine, I became incredibly nervous at the fact that I was clearly younger, less experienced, and likely not as good of a violin player compared to my other classmates.

The conductor proceeded to seat some of the upper classmates based on his familiarity with their skill level, sorting us all into specific seating arrangements across first violin, second violin, viola, cell, bass, winds, brass, and percussion. Given that I was new and visibly mishandling the instrument, he seated me toward the back of the second violin section. He then passed around folders with sheet music and proceeded with a group tuning exercise. I was horrified looking around at my classmates tuning their instruments with such ease, but I intended to hide my anxiety and mimicked the gestures of the classmate next to me, by raising my violin up to my chin and fiddling around with the tuners. Luckily, I was able to grasp that twisting the tuner in one direction led to an increased sharpness in the pitch, and was able to get the violin tuned in sync with the group.

We pulled up the sheet music that we were going to play for that day, and upon looking at the sheet with lines and dots, I began to panic with a sweaty brow given that I did not know how to read sheet music. There was no time wasted as the conductor raised his baton and cued us all to start at the top of the piece. At this point, my survival instincts kicked in and all I could do was mimic the bow directions of the person next to me, gliding my bow in the air right above the string or pressing so lightly that no sound could be made. I somehow managed to pull this off for an hour going unnoticed, and by the end of the class, fooled the conductor and my stand partner into believing that I had been playing music the entire time. But while I got away for the first day, I knew I couldn’t go on fooling them for long. The best course of action would be to tell the instructor that I wanted to drop the course and register for the beginner level orchestra. But that was not a satisfying solution, and something within me told me to persist with the current class.

On the second day of symphonic orchestra, I got back into my actress mode and feigned playing violin once again. This time was different – after piecing together some annotations for finger positions on the score, with the sounds I was creating, and matching open strings with the dots on the page, musical notation suddenly made sense to me all at once. By the end of the class, I was following along and reading the music as quickly as the violinists around me. And by the end of the week, I was playing loudly and confidently. There were still confusing aspects of the sheet music, such as key signature, and in those situations I would listen to the relative ‘flatness’ or ‘sharpness’ of the music being played and mimic those sounds. I found a system that would work for me and was so excited to play even more music.

This went on for a month, and everyday I would learn something new about either reading musical scores or improving my violin technique. At the end of the second month, something unexpected happened: the conductor advanced me to the first stand of the second violin section. I was horrified and proud at the same time. I had practiced regularly at home and focused so much on improving that I could even play some of the more difficult passages, and often found that I could confidently play some of the harder passages with ease, whereas other classmates would play much quieter during the difficult parts. At this point, I told myself that I could not fake it any longer: I must improve. This led to me spending weekends at the library borrowing musical scores, VHS tapes, CDs, and violin books, where I could learn how to improve my skills as much as I could.

Symphonic orchestra filled my mornings with joy. I felt such a rush of excitement at the idea of being able to choose what I wanted to do (in this case, it was to play music), put dedicated effort towards it, and achieve visible progress (based on seating positions in the orchestra). It gave me a feeling of confidence that I had never experienced as a child, and I felt that I could be “good” at something for the first time. Half a year later, I was seated next to the concertmistress, a seat away from the top seat in the orchestra. I was so pleased with my progress and genuinely loved the music that I played. I did not know that, for the remainder of my middle school years, this would remain my seat and I would advance no further.

The concertmistress who took the coveted seat was both a friend and an amazing musician. She had practiced violin since she was in elementary school under the guidance of a private instructor. She was a musical prodigy when you considered her advanced techniques and sheer musicality, that I knew clearly exceeded my own skills in every possible way. Still, I gave myself permission to be ambitious and to learn to play as well as she could. Initially, this ambition was motivating and brought me great joy in my practice. As time passed, and I couldn’t see visible progress toward getting the concertmistress seat, I became disheartened, and violin practice seemed more and more like a chore.

Eventually, this bred within me a desire to prove myself without the help of others. I was both envious and resentful of other students who received any type of private instruction or help. My family was not financially positioned to hire a private music teacher, nor could I afford to buy a violin. I borrowed the school violin and practiced whenever I could, but during summers I was left without an instrument and felt saddened that my life situation left me this way. With those feelings deeply etched into my heart, so resulted my lifelong struggle to ask others for help, as from then on I felt that this battle was a tough and lonely one.

By the end of middle school, I continued to sit next to the concertmistress, and while I learned a lot from watching her play, I always carried a heavy burden in my heart. I promised myself that if I could make my own money someday, I would never spend it on a teacher. Instead, I would be as self-sufficient and resourceful as I could be, and would never succumb to the privileged methods that others used. This led to a toxic mindset as I transitioned into high school, which was even more challenging when it came to coursework and social relationships. I ended up suffering a bout of depression and mental illness towards the end of high school, which weakened me physically as well.

Moving into college, I came to see that my ways of thinking were wrong. This was thanks to the kind and giving people I met, the mentors and friends who were willing to wait for me, help me, give me things without expecting or wanting anything in return. From then on, my mindset shifted, and I began to open to others about my struggles. Still, I often felt myself being in the position of second chair, whether it was in regards to internship offers, exam grades, social influence, and the like. I never got the coveted concertmistress seat, and it felt unfair. During those moments, I would recede back into my self-pitying mindset to avoid the help of others. Then, friends and mentors would reach out and break past that barrier with me, and I could accept the help of others and succeed as a result. It was a hard transition for me, and I’m still working on it even today.

This started as a story about my childhood, but it’s actually a thank you note for my mentors who showed me so much compassion and kindness. Mr. Jones who conducted my orchestra surely belongs in that list, and I’m sure that I didn’t actually fool him into thinking I was playing violin that first day. He saw my progress and gave me the chance of a lifetime, and the never-ending gift of love for music. To all of my other mentors, thank you for continuing to give and share (even if I feel that I don’t deserve it sometimes). Thank you for changing the course of my life.

Use Python to study foreign language vocabulary on your Desktop (aka “digital flashcards”) – Python code included

Are you studying a foreign language, such as Chinese, Japanese, Korean, French, etc., and want an easier way to quiz yourself? Look no further, I’ve written a Python flashcards program that cuts through all of the complex designs and ads so you can study locally on your computer in peace.

Flash Cards Python Program Demo

Download Python and CSV files

I’ve included the Python script here. You’ll be directed to Google Drive, where you’ll be able to safely down the .py file. I’ve also included a CSV template sample so you can ensure that you’re using the right headings to run the code.

Python code

CSV Template

If you have any questions on how to use the code or feedback on how I can improve it, shoot me a note as I’d love to hear from you!

How to Backup WeChat Files, Chat History, and Contacts List (Screenshots Included)

For whatever reason, you may be looking to backup, download, or export your WeChat (微信）files, chat logs, or contacts. After scouring the Internet for hours, I wasn’t able to find comprehensive tutorials for my needs, so I decided to pull together these step-by-step instructions to help those who are facing similar problems. Without further ado, here are the best ways I found to backup my WeChat profile data:

3 Ways to Backup WeChat Data

For chat history and files: Use WeChat’s PC App or Mac Desktop App to store logs on computer (for device to device transfer only)
For chat history, files, contacts, or moments: Request personal data backup files (EU, US only)
For chat history and files: Use third party desktop apps

For chat history and files: Use Wechat’s PC/Mac Desktop App to store logs on computer (for device to device transfer only)

Time: 15 minutes+, depending on how much chat history and file data you have
Preparation: Laptop (WeChat PC or Mac Desktop App), Phone (iOS or Android WeChat App)
Cost: Free

Go to Wechat’s official site to download the Mac or Windows version of the Desktop app.
- Mac download link
- Windows download link
Open the Desktop App and scan with your WeChat phone App to log in.

4. Once you’ve logged in, find the menu icon at the bottom left corner and click ‘Backup and Restore’.

5. Choose ‘Back up on Mac’ to store the files within the Desktop app on your computer.

6. Take out your Phone and follow on-screen guidance to ‘Backup All’, or select specific chat records that you want to save. This process will take you anywhere from a few minutes to hours, depending on how much data your chat logs have (i.e. image files, large attachments, etc). Keep both the computer and phone Apps open the entire time, or else the backup process will pause.

7. That’s it! The data is now stored on your Desktop WeChat. When you need to restore this data to a new phone or mobile device, just log in to the Desktop App, go into ‘Backup and Restore’ and choose ‘Restore on Phone’.

For chat history, files, contacts, or moments: Request personal data backup files (EU, US only)

Time: 10-15 minutes
Preparation: Phone only (iOS or Android WeChat App)
Cost: Free

Open your iOS or Android WeChat App
Go to ‘Me’ → ‘Settings’ → ‘More Settings’ → ‘Export Personal Data’

3. You’ll meet one of two scenarios:

A) If you haven’t linked your Wechat to an email address, you’ll need to verify your email first before you can request a personal data export:

B) If you’ve already linked and verified an email address, click ‘OK’ to begin the data request process. IMPORTANT: Do not close this Wechat window – minimize the App to check your email, and then come back to this screen. The email verification must happen while this window is running. If you click out of this screen, you’ll have to start the process over again. You’ll receive the verification email in your inbox about 2-5 minutes later.

After you click ‘OK’ in the email and see the success screen, go back to the App, where you’ll see that your email was successfully verified. Click ‘Next’.

4. Select which data types you want to export. Hit ‘Next’ to begin the data request process.

5. You’re done! Depending on how large the data file is, it can take up to 72 hours to receive an email with the data download link. You’ll need to open the email on your Desktop, and the download link will expire in 72 hours from the time it was generated. Note that the downloaded data is all stored in (.js) extension files, so you’ll need a way to parse through that.

Wechat data page must be opened in a Desktop browser to download.

For chat history and files: Use third party desktop Apps for backup

Time: 30 minutes or more
Preparation: Laptop, Phone
Cost: Depends on App (Free, $10-$50 is the typical range I’ve seen)

Of the three featured backup methods, this is the least consistently effective one. There are dozens of third-party software providers out there that claim to backup Wechat files, but depending on whether the original developers have updated their software, version compatibility, and many other reasons, the experience will be less reliable. Do your research prior to purchasing and make sure that other users can vouch for a positive experience (you can do by searching for user reviews on Google, Reddit, etc). I do not endorse any of the following options, but am laying out some options that I came across in my research:

MobileTrans – Backup Wechat history and account contacts to a PC before transferring to another mobile device. The same developers also created dr.fone, which offers similar functionality on their website.
iCloud – Backup your iPhone (for iOS users) including WeChat data, and you’ll be able to reinstate all of your chat history and contacts after you log into iCloud on a different mobile device.

Anything else?

That’s it! I hope one of the methods above worked for you. If not, do not fret – there are alternative (but less straight forward) ways to backup your data, including several that require reading the Chinese language. If none of the above worked for you, send me a message and I’ll help recommend an alternative solution.