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A friend is interested in hopping into the DAO rabbit hole... what learning resources do you send to them?

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DAO Jaclyn Lenee
12 months ago
12 months ago

Page 2


As part of my job at Stripe, I get to work with many early-stage startups and help them figure out their monetization and pricing strategies. While most founders have a clear product vision and have thought through things like their go-to-market strategy or hiring plans, surprisingly few have an idea about what their pricing should look like.

Interestingly, this is not just the case for early-stage startups but also more mature companies. Back when I ran pricing workshops at Google Play, many startups didn’t have an active pricing strategy — despite multi-million dollar run rates in some cases.

Pricing is not just perceived as a boring, but also as a complex subject matter that requires someone with a math PhD. Both of these assumptions aren’t true.

When I discussed the topic with Robin and Louis from Point Nine, we realized that we were seeing a lot of similar challenges. In this post we’ll show you 9 simple pricing and packaging hacks that we have seen work well - especially for early stage founders. We hope they’ll inspire you to experiment with your pricing strategy too!

Let’s get into it!

Christoph Janz, patio11 and others have been beating this drum for years: You can probably charge more for the product or service that you are selling. Startups often assume that their demand curve looks something like this:

Theoretically, this would mean that even a small price increase automatically leads to a pretty massive decrease in potential customers — but that is usually not the case (demand curves are generally not just simple straight lines as the examples in your economics 101 textbook might suggest). Especially in a B2B context, the willingness to pay is often significantly higher than people expect.

The other thing to keep in mind is that pricing is not just a finance exercise - it should also be part of the marketing and product strategy. High-quality products usually come with a high price tag. Conversely, price can also be an indicator of product quality. A high price point can help you build the image of a premium product.

Even if it sounds counter intuitive, a higher price tag might therefore actually lead to an increase in sales.

“But shouldn’t I focus on maximizing the amount of users first?”
This is usually the pushback I get from entrepreneurs when suggesting to charge more — and it is a good argument.

Especially when you are just getting started, it’s absolutely critical to get users to test your product and give you feedback. A high price tag will most likely be a barrier to getting those users on board.

Instead of lowering your price or giving the product away for free, however, consider using early adopter discounts. This way you still have the original price as an anchor and you clearly communicate the real value of your product offering.

This strategy does not just apply to companies who are just getting started. When experimenting with your pricing, promotions (ideally limited to a certain time period) are often better than simply A/B testing different price points.

Not every user has the same preferences. And not every user has the same willingness to pay. One way to increase the number of addressable users is to create different product packages for different user segments. SaaS products, for example, are usually segmented by user type (personal/hobby, SMBs, scale ups, enterprise).

While the core product is the same for everyone, premium plans come with additional features (that are more relevant for larger businesses) and thus a higher price tag (enterprise customers usually have higher willingness to pay than startups).

You’ll see this less in consumer software, but there are examples like Netflix:

The core idea behind these different packages is to find features that are proxies for willingness to pay. Screen quality is a perfect example: Users who are willing to spend $$$ on an Ultra HD TV, are probably also willing to spend more $$$ on a video streaming service.

Similarly, company size is a good proxy for willingness to pay when you are selling to other businesses. This is why many SaaS plans use the number of users as their pricing metric.

Charging on a per-user basis not only optimizes for willingness to pay, it’s also a price metric that correlates and scales nicely with the value you provide. Slack becomes more valuable the more people join an organization, so it makes sense to base the price on the number of users. As the organization grows (which, again, is a good indicator for company success and thus willingness to pay), so does Slack’s business.

It’s worth mentioning here that Slack only charges for users who are *actively* using the product: The pricing is perfectly aligned with the value it provides.

The thing to keep in mind is that your pricing needs to be predictable and simple. Slack could also base its pricing on the number of messages sent - another good proxy for the value it provides - but that would make it really difficult for new customers to predict how much the service will cost them.

Stripe, for example, doesn’t charge any setup fees or monthly subscriptions. The pricing is based on the value of each successful transaction it facilitates, which means it only wins if its customers win.

Earlier, we discussed building different product packages for different customer segments, which is tends to be best practice in the B2B SaaS space. In consumer tech, we often see a slightly different version of this strategy where companies charge different prices for different audiences - even though the product is exactly the same. This is called price discrimination.

Apps like Loom and Notion have a free tier for students, for example. The main driver is, again, willingness to pay. Students tend to be less affluent, so it makes sense to offer them the product at a lower price point to lock them in — and then increase the price once they have a steady income.

What is less well-known is the variety of other proxies many consumer apps use to price discriminate. Dating apps not only charge different prices for male and female users, some also show different prices based on the type of phone you use. Similar to the Netflix example we looked at earlier, the price elasticity of an iPhone 11 Pro user will probably look more favorably than that of someone who uses an older iPhone 7.

I have also seen companies who use user location as a proxy for willingness to pay and charge higher prices for people based in cities with higher GDP per capita.

It’s important to note that many of these practices aren’t legal and can easily backfire - especially when you simply display different prices to different users. A workaround I’ve seen are programmatic promotions: While the price is technically the same for everyone, users with lower expected willingness-to-pay are more likely to see a discounted offer.

As I have written extensively on before, most of our everyday actions can be traced back to some form of signaling and status seeking — especially when they involve a purchase decision. Whenever we spend money on an object or service it’s primarily because we want to signal something about our social standing.

Manufacturers of physical products already leverage signaling quite heavily. Just think about all the luxury fashion products out there. But what is the software equivalent of a Louis Vuitton handbag?

Software is at a disadvantage compared to physical products due to its intangibility. But that doesn’t mean that signaling is impossible.

  • Strava clearly signals which of their users have upgraded to a premium subscriptions with a little badge and premium-only leaderboards.
  • Superhuman is essentially a luxury email provider that charges $30 a month so you can show off with a “Sent via Superhuman” in your signature (Pro tip: You add the same text to your Gmail signature free of charge). Similarly, Hey charges $999 for scarce email addresses.

When you design your pricing packages, have a think about how you can help your customers signal their purchases to others. Not only does this help with word-of-mouth, it also increases the perceived value of your product.

People like to have choice … but they also like to be guided towards a specific choice. Many pricing pages will showcase different packages with different price tags that aren’t based on features (we covered those earlier) but time. The product offering remains the same. The only thing that changes is the duration of the subscription the user commits to.

The merchant obviously has a preferred package they’d like to sell: The one that promises the highest LTV (this is often an annual plan).

While the pricing page suggests that the user has a lot of different options, it’s really just one option that matters. An illusion of choice.

You may have noticed that pricing pages often feature three different options. That’s not (just) to appeal to three different types of customers, but because people prefer the middle option (also called The Center Stage Effect). Unsurprisingly, the middle option is therefore the subscription plan with the highest LTV.

Making that plan the default option (less friction) and labeling it as the most popular plan (social validation) helps to further increase its conversion rates.

Make sure to make the different options comparable and highlight how much cheaper the annual plan is (the churn rates of your monthly subscription will inform how much you can discount the annual package).

Companies often include a lifetime offer as their third option. Lifetime options tend to be significantly more expensive than the other plans. That’s because the goal isn’t actually to sell many of them, but to make the other options appear cheaper. Similarly, decoy prices can help you to make the default plan look more attractive.

One of the things I’ve always found most surprising is how little effort companies seem to put into their payment flows. While landing pages and apps are often pixel perfect, things get ugly once the user starts their final conversion step.

Most payment flows include way too many steps, ask for unnecessary information and are generally not well designed and visually appealing. And yet, reducing friction in your payment flow doesn’t have to be difficult:

  • Surface the most relevant payment methods
    When it comes to payments, user behavior is quite different from country to country. While US customers primarily use credit cards, local payment methods such as Ideal or P24 are the preferred mode of payment in the Netherlands and Poland, respectively.
  • Optimize for Mobile
    Similar to the previous point, the most frictionless payment path on mobile is often via Apple Pay or Google Pay. Making the form responsive and invoking the numeric keyboard were relevant also helps to speed up the payment flow on mobile devices.
  • Anticipate friction points
    Incorrect payment information is a one of the biggest reasons why users abandon the final funnel step. By automatically showing the right card logo and validating card details in real time you can minimize errors and thus reduce friction.

Of all the tips in this article, reducing friction in your payment flow is probably the easiest to implement and has the highest direct return on investment. Products like Stripe Elements and Stripe Checkout take a lot of the heavy load off of the developer.

Labor leads to love: Experiments have shown that people place a disproportionally high value on products they built themselves. A self-assembled piece of IKEA furniture has a higher perceived value than a ready-made version of the same product.

Similarly, people place a greater value on things once they own them (endowment effect). The pain of losing something you own seems to be more powerful than the pleasure of gaining it.

Why is this relevant for software monetization?

Well, it explains why free trials are so powerful. Not only do they allow you to get users on board who might not be willing to pay before giving the product a spin first (paywall friction is real!), they also increase the value (and thus willingness to pay) of your product.

This is especially true for products that require some setup efforts or user input. A knowledge management system like Notion is the perfect example for this: It’s like a set of legos that you have to put together. The product becomes more valuable the more time you spent with it.

If your marginal costs allow it, always give users the option to test your product for free. The decision you have to make (and test) is whether it’s easier to convert users from a freemium plan (how do you get users to upgrade?) or a free trial (how long should the trial be?). In case of the latter you also want to test when to ask for payment details.

It’s worth mentioning that there’s no one-size-fits-all approach when it comes to pricing. Some of these tricks will work for you, some won’t. But the upside of the ones that will make experimenting with your pricing worth the effort.

All opinions expressed are solely my own and do not express the views or opinions of my employer.

As part of my job at Stripe, I get to work with many early-stage startups and help them figure out their monetization and pricing strategies. While most founders have a clear product vision and have…

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saas pricing Julian Lehr
12 months ago
12 months ago

16 New ML Gems for Ruby

New ML Gems

In August, I set out to improve the machine learning ecosystem for Ruby and wasn’t sure where it would go. Over the next 5 months, I ended up releasing 16 libraries and learned a lot along the way. I wanted to share some of that knowledge and introduce some of the libraries you can now use in Ruby.

The Theme

There are many great machine libraries for Python, so a natural place to start was to see what it’d take to bring them to Ruby. It turned out to be a lot less work than expected based on a common theme.

ML libraries want to be fast. This means less time waiting and more time iterating. However, interpreted languages like Python and Ruby aren’t relatively fast. How do libraries overcome this?

The key is they do most of the work in a compiled language - typically C++ - and have wrappers for other languages like Python.

This was really great news. The same approach and code could be used for Ruby.

The Patterns

Ruby has a number of ways to call C and C++ code.

Native extensions are one method. They’re written in C or C++ and use Ruby’s C API . You may have noticed gems with native extensions taking longer to install, as they need to compile.

 void Init_stats()
{ VALUE mStats = rb_define_module("Stats"); rb_define_module_function(mStats, "mean", mean, 2);

A more general way for one language to call another is a foreign function interface, or FFI. It requires a C API (due to C++ name mangling), which many machine learning libraries had. An advantage of FFI is you can define the interface in the host language - in our case, Ruby.

Ruby supports FFI with Fiddle. It was added in Ruby 1.9, but appears to be “the Ruby standard library’s best kept secret.”

 module Stats extend Fiddle::Importer dlload "libstats.so" extern "double mean(int a, int b)"

There’s also the FFI gem, which provides higher-level functionality and overcomes some limitations of Fiddle (like the ability to pass structs by value).

 module Stats extend FFI::Library ffi_lib "stats" attach_function :mean, [:int, :int], :double

For libraries without a C API, Rice provides a really nice way to bind C++ code (similar to Python’s pybind11).

 void Init_stats()
{ Module mStats = define_module("Stats"); mStats.define_singleton_method("mean", &mean);

Another approach is SWIG (Simplified Wrapper and Interface Generator). You create an interface file and then run SWIG to generate the bindings. Gusto has a good tutorial on this.

 %module stats
double mean(int, int); 

There’s also Rubex , which lets you write Ruby-like code that compiles to C (similar to Python’s Cython). It also provides the ability to interface with C libraries.

 lib "" double mean(int, int)

None of the approaches above are specific to machine learning, so you can use them with any C or C++ library.

The Libraries

Libraries were chosen based on popularity and performance. Many have a similar interface to their Python counterpart to make it easy to follow existing tutorials. Libraries are broken down into categories below with brief descriptions.

Gradient Boosting

XGBoost and LightGBM are gradient boosting libraries. Gradient boosting is a powerful technique for building predictive models that fits many small decision trees that together make robust predictions, even with outliers and missing values. Gradient boosting performs well on tabular data.

Deep Learning

Torch.rb and TensorFlow are deep learning libraries. Torch.rb is built on LibTorch, the library that powers PyTorch. Deep learning has been very successful in areas like image recognition and natural language processing.


Disco is a recommendation library. It looks at ratings or actions from users to predict other items they might like, known as collaborative filtering. Matrix factorization is a common way to accomplish this.

LIBMF is a high-performance matrix factorization library.

Collaborative filtering can also find similar users and items. If you have a large number of users or items, an approximate nearest neighbor algorithm can speed up the search. Spotify does this for music recommendations.

NGT is an approximate nearest neighbor library that performs extremely well on benchmarks (in Python/C++).

ANN Benchmarks

Image from ANN Benchmarks , MIT license

Another promising technique for recommendations is factorization machines. The traditional approach to collaborative filtering builds a model exclusively from past ratings or actions. However, you may have additional side information about users or items. Factorization machines can incorporate this data. They can also perform classification and regression.

xLearn is a high-performance library for factorization machines.


Optimization finds the best solution to a problem out of many possible solutions. Scheduling and vehicle routing are two common tasks. Optimization problems have an objective function to minimize (or maximize) and a set of constraints.

Linear programming is an approach you can use when the objective function and constraints are linear. Here’s a really good introductory series if you want to learn more.

SCS is a library that can solve many types of optimization problems.

OSQP is another that’s specifically designed for quadratic problems.

Text Classification

fastText is a text classification and word representation library. It can label documents with one or more categories, which is useful for content tagging, spam filtering, and language detection. It can also compute word vectors, which can be compared to find similar words and analogies.


It’s nice when languages play nicely together.

ONNX Runtime is a scoring engine for ML models. You can build a model in one language, save it in the ONNX format, and run it in another. Here’s an example .

Npy is a library for saving and loading NumPy npy and npz files. It uses Numo for multi-dimensional arrays.


Vowpal Wabbit specializes in online learning. It’s great for reinforcement learning as well as supervised learning where you want to train a model incrementally instead of all at once. This is nice when you have a lot of data.

ThunderSVM is an SVM library that runs in parallel on either CPUs or GPUs.

GSLR is a linear regression library powered by GSL that supports both ordinary least squares and ridge regression. It can be used alone or to improve the performance of Eps .


I wanted to also give a shout-out to another library that entered the scene in 2019.

Rumale is a machine learning library that supports many, many algorithms, similar to Python’s Scikit-learn. Thanks @yoshoku for the amazing work!

Final Word

There are now many state-of-the-art machine learning libraries available for Ruby. If you’re a Ruby engineering who’s interested in machine learning, now’s a good time to try it. Also, if you come across a C or C++ library you want to use in Ruby, you’ve seen a few ways to do it. Let’s make Ruby a great language for machine learning.


All code examples are public domain. Use them however you’d like (licensed under CC0 ).

Disco is a recommendation library. It looks at ratings or actions from users to predict other items they might like, known as collaborative filtering
"fastText is a text classification and word representation library. It can label documents with one or more categories, which is useful for content tagging"

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Saved to
rails ruby Andrew Kane
12 months ago
12 months ago

Everything that's wrong with technical recruiting in one diagram https://t.co/6gWngDh8be

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hiring Aline Lerner
12 months ago
12 months ago

SaaS positioning is hard and people get confused by it I do the positioning work for all of Powered By Search's clients Here's the process I follow for getting clear positioning every time:

12 months ago

Zapier's SEO Strategy: How They Use Reviews Of Other Products To Drive Organic Traffic https://t.co/hUgcEhxhBA https://t.co/7rGobFsqJy

12 months ago

How Zapier growth hacked SEO The strategies used to grow from 0-600K users in 3 years A thread👇👇

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12 months ago
12 months ago

A guiding path forward at Touch of Modern

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Saved to
tomo Tyler Guillen
12 months ago
12 months ago

Here we go! 🚀 Unofficial Notion Webhooks are live and free to use! Receive webhooks when your @NotionHQ pages are created and updated. I built fully functional webhooks in a few hours over the weekend and was only possible because of @hostedhooks https://t.co/2kxuEQCuWb

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notion APIs
12 months ago
12 months ago

Extreme HTTP Performance Tuning: 1.2M API req/s on a 4 vCPU EC2 Instance | talawah.io

Saved to
devops Marc Richards
12 months ago
12 months ago

Context for the DAO · metacartel/MCV Wiki

The MetaCartel community started during the September of 2018 as a technical working group around meta transaction technologies, a solution that allows users to interact with decentralized applications on the Ethereum Blockchain without the need to own Ether. This working group consisted of participants from a range of projects including Universal Logins, Gnosis, Status.im, ChronoLogic, SpankChain, Shipl, ENS, Argent, and ETHWorks — all of which, coordinated together through online video calls and offline workshops to standardise technical specifications and solve cryptoeconomic research problems surrounding meta transactions (Github: Harbour MVP). In late 2018, TabooKey make a key research breakthrough to the relay collision problem (EIP-1613). MetaCartel’s community then came together to build the Gas Station Network, a decentralized network of gas relay nodes. This project later spun out from the community as the Gas Station Network Alliance.

As this spin-out happened in early 2019, MetaCartel evolved into a community focused around driving UX innovations and discovering new crypto use-cases on the Ethereum application layer. During February and March of 2019, the community worked on plans of launching of a DApp incubator. In early April, these plans transformed into the idea of MetaCartel DAO and would become known as the first fork of Moloch DAO. It’s purpose was to setup an ecosystem grant fund that would drive experimentation around crypto use cases and cryptonative business models.

MetaCartel DAO’s smart contracts were deployed to the Ethereum mainnet on the 5th of June with initial backers including Matic Network, NuCypher, SpankChain, Gnosis, AdEx, The Graph, Abridged, Odyssey and Giveth, along with 10+ other individual contributors. From the start of the DAO in July, the DAO has since raised over 1,100+ ETH and deployed $44,450 to over 13 projects, with experiments including NFT conference ticketing, DAO reputation systems, DeFi based business models for DApps, and DAOs for coordinating meatspace events. With now over 60+ DAO participants, and over 800+ community members, we are taking what we have learned from launching DApp experiments and DAOs to launch MetaCartel Ventures. Incubated by the SpankHouse, MetaCartel Ventures aims to be a project that aims to deepen MetaCartel’s existing commitment to furthering the progress within the Ethereum DApp ecosystem and Web 3.

MetaCartel’s community is in a unique position to facilitate MCV.


Since inception, MetaCartel has emphasized the importance of community and has spent significant effort on community building, content creation and ecosystem knowledge sharing. Since its inception, it has:

  • Held over 30+ online virtual community calls around technical research problems, working group agendas and development topics.
  • Held over 20+ offline meetups all around the world across most of Ethereum’s major community conferences in cities such as: SF, Berlin, Prague, Denver, Paris, NYC, Osaka.
  • Recorded and hosted over 26+ weekly podcast episodes with the creator of DApps (Wizard of DApps), including the release of over weekly 30+ newsletter editions.
  • Held its first Demo Day conference during Berlin Blockchain Week 2019 focused exclusively on DApps, which over 200 of its community members attended.

Grants DAO

In July 2019, the MetaCartel launched the Ethereum community’s first ever DApp focused grant program focused on funding and supporting the launch of minimum viable DApps and furthering experimentation of new crypto use-cases and cryptonative business models. It has since:

  • Provided grant funding and support to over 13 projects, that range from: NFT conference ticketing (Mintbase), DAO Infrastructure (PocketMoloch, DAOHaus), DAO reputation systems (Pepper4D), DeFi based business models (rDAI), Smart contract automation (Gelato Finance), and Events sponsorship coordination DAOs (Orochi DAO)
  • The operators of MetaCartel provides assistance and support alongside grants to projects in sourcing initial pilots opportunities as well as initial customers and partners.

(written december 2019)

With its current efforts to support such projects, MetaCartel Ventures is in a unique vantage point to identify as well as attract high potential investment opportunities at the earliest stages.

“Start by doing what's necessary; then do what's possible; and suddenly you are doing the impossible” —Francis of Assisi

Ethereum's Venture DAO. Contribute to metacartel/MCV development by creating an account on GitHub.

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metacartel DAO
12 months ago
12 months ago

Launched a quick Ruby @Replit example repo for @ShipmatesHQ. Check it out and let me know your thoughts! https://t.co/85eJviZKnf

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shipmates David Marquez
about 1 year ago
about 1 year ago

What's the best way to monetize a product? If you stick with the first thing you try, you leave money and growth on the table. 🧵 Here's everything we tried and learned with @mailbrew and @typefullyapp.

What's the best way to monetize a product?

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about 1 year ago
about 1 year ago

How to add SaaS revenue very fast:

 Avoid competitive channels like Google ads and cold emails. These 9 founders found “SaaS Gold” using 3 specific channels to add over $1b in value to their companies: A thread 👇👇

Saved to
saas Nathan Latka
about 1 year ago
about 1 year ago

CourseDog - All-in-one Curriculum and Schedule Planning

A Product Suite Built for Forward-Thinking Campuses

Our products were designed to work together. Coursedog is the only solution that manages schedule optimization, curriculum and catalog management in one platform. Driving efficiencies across campus to accelerate student success.



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Automate form & workflow-based curriculum approval processes and configure reports to eliminate bottlenecks.

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Power syllabi automatically with your curricular source of truth to delight faculty and students; consistent, compliant, and accreditation-ready.

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Partnering With a Diverse Set of Institutions

Since our founding in 2018, Coursedog has partnered with almost 100 Higher Education institutions that serve nearly 1 million students.


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Registrar & AVP of Student Success, Chaminade University

The team was able to answer technical questions, and in IT industry terms that I could understand. It’s been great to work with them.

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"... an intuitive, modern user interface compared to the existing solutions on the market."

Office of the University Registrar

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CIO, Dallas Baptist University

“Coursedog is an exceptional product that meets the needs of schedule and catalog development. The customer service and relationship building are the game changers.”

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Blue Ridge Community College


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Knowing the high number of ultimate end-users and stakeholders, BRCC wanted a tool that's as powerful as it is easy to use, and that could integrate with Colleague.
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Increasing need to automate paper-based processes and to better leverage demand analytics and data in the scheduling process.
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Search for a single solution to streamline and improve all curricular processes in an integrated environment.
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Dallas Baptist University



Dallas, Texas


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Search for a single solution to streamline and improve all curricular processes in an integrated environment.
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Illinois Central College


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Need to better align academic services and institutional resources to student need, and to establish a single source of truth for curriculum.
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Monitor enrollment in real time and assess the efficacy of your course schedule. Track seat utilization and faculty preference satisfaction.

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Learn Best Practices in Curriculum Success

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The only solution that manages schedule optimization, curriculum and catalog management in one platform. Accelerate student success & improve campus financial health.

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landing page
12 months ago
about 1 year ago

THREAD How to get *ANY* Fortune 500 SaaS or Tech Sales Job AND Make $5-10k/MO on the Side The secret? Leveraging Artificial Intelligence, software, & a proven formula for success. Not on Gumroad so RT & like if you want part 2! Here’s the blueprint:

A really long thread on how to do sales

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saas SaaS Capo
12 months ago
about 1 year ago

I believe there is a fundamental disconnect between the way designers and business people think. A disconnect that causes a huge amount of personal dissatisfaction. Let me try and explain...

12 months ago

Thoughts on the Stonk Market and the Memefication of the financial industry Key takeaway: The market is no longer driven by fundamentals - it’s driven by memes. No longer a metaphor, but a living structure – the stonk market. https://t.co/MudHO1g4Gx

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stonks Kyla Scanlon
about 1 year ago
about 1 year ago

If you're considering competing in a red ocean, modern business strategy has very clear advice for you:


Red oceans are zero-sum, closed games where everyone is competing along the same dimensions. They're brutal. You have to be prepared to deploy a stunning amount of capital. Customers view you as a commodity and have no loyalty.

If you enter the market too late, you'll go up against competitors with more momentum, more brand awareness, and a desire to end you before you become a real threat.

This was the challenge Sea Group was staring down when they launched Shopee in one of the reddest oceans in the world – ecommerce in Southeast Asia.

But thanks to a well-executed red ocean strategy, Shopee has grown to become the leader across multiple markets.

How To Build a Red Ocean Strategy

To have a chance at winning in a competitive industry, you'll need:

  1. Insights about the industry (that incumbents have overlooked)
  2. Lots of capital to withstand a war of attrition
  3. Better and faster execution than your competitors
  4. The boldness to strike when the leader makes unforced errors

Simple, but not easy. Let's see how Shopee did it.

First, Develop a Unique Thesis

If you're going to enter a competitive industry that already has several players, the only way you even have a chance is if you have insights that other competitors have overlooked.

Think of red oceans like sports. The best players are always finding ways to eke out any competitive advantage.

Their "insights" might be offseason training routines, immaculately monitored diets or ice baths to speed up their recovery time. Or it might be a completely new way to do things, like the Fosbury flop .


The winners in a red oceans are the ones who've figured out something about their industry that everyone else missed. You could earn those insights by having domain experts on your team, or in Shopee's case, leveraging their late-mover advantage:

"...there was a lot of (potential) growth and many areas that weren’t being addressed well by existing players ... That’s one of the advantages of being late (to the industry), because you can see what’s out there, what the trends are and see what you can do differently or better," ( Zhou Junjie, Shopee CCO )

Your insights should lead you to create a model of the dynamics of your industry, revealing what your path to victory would need to look like.

When we look at some of Shopee's moves, it's likely that they derived a model that resembles this one:

This is the drawing that Jeff Bezos used to guide the Amazon ecommerce business for decades. I'm not sure what's more impressive: that he drew this on a napkin over coffee, or that this model is relevant whether it's being used in America or Southeast Asia.

When you have a blueprint like this, one that illustrates your industry's mechanics and helps you understand your competitor's actions, you can better identify the untapped opportunities in your red ocean.

This helps you direct the significant resources you're about to deploy.

Second, Build a Stockpile of Capital – Then Know Where to Use It

With venture funding, access to Garena's cash flow, and their parent company doing an IPO in 2017, Shopee has always had lots of ready capital to deploy.

But so do their competitors.

That's why red oceans demand you to develop your own unique thesis and sketch out your own model of the industry, so you know exactly how your capital should be spent.

Using the Bezos industry model as a guide, we can look at a few examples and understand why Shopee continues to invest in them:

Zero Commissions for Sellers

Sellers and merchants are platform-agnostic: if they have any loyalty, it's usually to whoever provides the most benefits and takes the least commission.

And it's really hard to beat zero commission.

From this perspective – and when you look at the industry using the Bezos model – this is a no-brainer move in a red ocean. To keep up, Lazada was forced to remove their marketplace commission fee in 2018.

Free Shipping Subsidies

Reducing the cost of shipping adds positive momentum to two points on the model: it improves the Customer Experience while attracting new Sellers to your platform because they know they can make more sales.

Everyone was losing money, but Shopee spent their dollars on the right spot: free shipping. Consumers obviously love it, but sellers love it too because it increases their sales.

That's a former Lazada employee talking about how Shopee was ready to deploy their capital to get (and keep) sellers on the platform. This was a critical advantage when Alibaba experimented with stopping free shipping altogether to try to improve Lazada's economics.

Third, Out-Execute The Competition

Red oceans are defined by constant battle and hand to hand combat. Sure, you need to have unique insights and the right model, but consistent, high-quality frontline execution on that model is just as important.

In some cases, this means coming up with a different approach and following through on it, like the Fosbury flop. In others, it means cloning what your competitors are doing, then taking it to the next level.

There are examples of both from Shopee's history:

Leaning Into Mobile as the Primary Shopping Device

Shopee, blessed with the timing of their late entry, launched right in the middle of the smartphone adoption wave in Southeast Asia.

The platform’s bet on a mobile-first approach has paid off: more than 90 per cent of its transactions are on the app. ( Yahoo , 2019)

This differentiated them from Lazada, who launched in 2012 as a web-first shopping experience.

Shopee's mobile-first approach meant more than just starting off as an app. Designing the customer experience primarily for a mobile context gave them a headstart on things like in-app games and social features, tactics that kept shoppers coming back.

Here's how a Shopee alumni described one such feature:

Shopee went heavy on gamification to get people to open the app, especially around monthly campaigns. You saw this with Shopee Shake. You would see people everywhere shaking their phones at dedicated times to get the most coins.

Being first on features like these got shoppers to think about Shopee a little more often than the competition, ingraining a very useful habit that they could leverage over time.

Going Hyperlocal Before The Competition

The mobile-first context also led to launching a separate app for each country, which gave Shopee country teams the freedom to create more relevant customer experiences depending on the market:

In Indonesia, for example, Shopee launched a dedicated section of Islamic products and services to cater to the majority Muslim market. In countries like Thailand and Vietnam, where celebrity endorsements hold sway over consumers’ buying habits, Shopee features online stores that sell items curated by top celebrities. ( Today )

As I've written about before, going hyperlocal unlocks parts of the TAM that competitors can't access. It turns parts of your red ocean a little bit bluer.

Running a Relentless Drumbeat of Campaigns

Taking a page from Chinese ecommerce, Lazada brought the 11/11 shopping festival to Southeast Asia. Qoo10 did the same thing but claimed October 10th. When Shopee entered the picture, they decided to own September 9th.

These flagship shopping festival campaigns are massive, whole-company efforts. They're worth it: they drive enormous amounts of traffic and app downloads, and crucially, consistently spike the all-important metric of ecommerce: GMV.

A former leader at Shopee revealed that the typical GMV spike they expected from 9/9 was "5-10x a normal shopping day", but this is likely a conservative estimate. Some ecommerce enablers plan for a surge of 30x, indicating that these campaigns have a significant effect not just for Shopee, but for the entire ecosystem.

With those numbers, it's no surprise that Shopee soon went after 11/11 and 10/10, eventually executing shopping festivals every single month (except 1/1, presumably to give their team a chance to breathe.)

The Shopee team realized that these monthly campaigns were painful to execute, but amongst all of their marketing efforts, they were the most effective ways to rise above the cacophony of their red ocean and get noticed.

Finally, Pounce When The Leader Stumbles

Red oceans are industries with thin margins and an even thinner margin of error. Customers are price sensitive, fickle, and distracted.

So when the leader in a red ocean inevitably makes a mistake, or gets complacent, you need to have the agility and boldness to make big moves to gobble up market share.

In 2018, a window of opportunity opened for Shopee:

Lazada’s actual operations ground to a halt ... the company did “almost nothing” in the first six months after (newly-installed CEO) Peng Lei’s arrival ... Lazada even suspended some of its key promotions during this period. ( Kr-Asia )

Going Big With Brand Ambassadors

Alibaba took control of Lazada in 2016, quickly putting their homegrown leadership in charge. After decades of fine-tuning Taobao's monopoly economics in China, one can imagine the difficulty they must've faced when adapting to the red ocean of Southeast Asia.

This had direct implications on Lazada's marketing, specifically with the risks they were willing to take.

When Lazada execs proposed growth ideas to Hangzhou, they were pitching to an infrastructure that was built around optimizing quantifiable performance:

Lazada ... eventually resorted to its old playbook of paid ads on Facebook and Google ... because ad-driven traffic in Southeast Asia is cheaper than in China, the return on investment for this action is high, which translates to better performance reviews in Alibaba’s internal evaluation system. ( Kr-Asia )

The conditions were set for Shopee to make a big move.

In 2018, Shopee hired k-pop megagroup Blackpink as their regional brand ambassador to help promote their 12/12 campaign.

This was the first regional brand ambassador campaign by any ecommerce platform in Southeast Asia, and marked the beginning of Shopee's repeated use of branding as a competitive advantage.

Viewed from the Alibaba/Lazada optimization perspective, spending big on celebrity brand ambassadors is a wasteful, unnecessary risk of resources.

But viewed through the lens of Bezos' ecommerce marketplace model, it's completely rational, even obvious: traffic is a key part of your growth flywheel. High-profile celebrity ambassadors increase brand awareness, leading to higher mental availability, leading to more traffic not just during the campaign period but also over the long term.

Shopee's brand ambassador campaigns, with their earworm jingles, would go on to make waves all across Southeast Asia.

The other ecommerce platforms eventually caught on. 2019 saw Tokopedia hiring their first regional brand ambassador. Lazada finally followed in 2020.

Epilogue: The Paradox of Red Ocean Strategies

When Dick Fosbury debuted his new high jump technique in the 1968 Olympics, he was the only person doing it. Few expected it to work. He proved its efficacy by winning the gold medal.

In the 1972 olympics, 28 high jumpers used his technique.

There's a kernel of irony within every red ocean strategy that leads its authors to victory: once revealed, the strategy can then be used on you.

Therefore, the only way to create a sustained advantage in a red ocean is through something that can't easily be copied: your company culture.

Monoculture as an advantage in a red ocean

I was going through Sea Group and Shopee's Glassdoor reviews, and I noticed one theme kept coming up: they're terrible at diversity.

Meetings held in Chinese. Product documentation written in Chinese. Getting promoted faster and getting more responsibility if you're Chinese.

This is going to sound weird, but I think this is a huge advantage.

Red oceans are wartime. In wartime, normal rules don't apply.

In wartime, monoculture gives you an edge. With reduced cultural and communication overhead, you can execute faster, giving you the learnings needed to iterate your strategy faster than your competitors.

Don't get me wrong, I feel for unhappy employees and alumni who were collateral damage. But Shopee is a company fighting battles in one of the reddest oceans in the world.

With Alibaba and Lazada starting to regain its footing after being "asleep for two years" (alumni's words, not mine) and with Gojek/Tokopedia putting the finishing touches on their merger, they'll need all the advantages they can get.

Thanks to Alin Dobrea , Huiqi Low , Vinita Penna , Nathaniel Yim , Huey Yun Teo , Zishuang Cheng , Li Zhiliang and many more friends from the SEA ecommerce ecosystem for reading earlier drafts of this post.

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If you're considering competing in a red ocean, modern business strategy has very clear advice for you: Don't. Red oceans are zero-sum, closed games where everyone is competing along the same dimensions. They're brutal. You have to be prepared to deploy a stunning amount of capital. Customers view you as

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NFTs are rewards for doing things...really like this concept emerging. Usually platforms pay users to use their stuff via emissions. However you can incentivise usage without the unsustainable emissions via gamifying NFT rewards and keep your core tokenomics focused elsewhere

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about 1 year ago