The GOATs – examples of differentiated disruptive and dominant strategy products
Now, we’ll turn our attention to the main market strategies and see some of the greatest of all time, or GOAT, examples for each strategy. A market strategy informs your go-to-market team’s efforts. Will you be going after customers that have too many options or not enough? Will you create a product that effectively works better or worse than your competitors? These seem like obvious questions when putting together a business plan, but once things get going for your company and you start getting some customers, suddenly these decisions might not be as concrete as when the company was first formed.
One of our greatest lessons from the start-up world was about getting comfortable with asking questions that seemed so baked into the company mission and ethos that they seemed obvious. We’ve asked these questions reluctantly in previous experiences but we don’t anymore. Companies can change their strategies if they’re tempted enough, but every change poses a potential risk to the company. Market strategy informs everything from how you build a product to how you sell it. A lack of clarity into these aspects of your market strategy could result in building a product that isn’t right. A lack of clarity in the communication and sale of a product could result in acquiring customers that aren’t right for the company you are.
The following chart from Wes Bush’s book Product Led Growth offers us a look at the four chambers of strategies companies can use in their growth:
Figure 4.1 – The chambers of strategies
This chart shows us the strategy quadrant that delineates the various areas of a go-to-market strategy and highlights the key areas between the differentiated, dominant, and disruptive strategies. Every company will need to decide what its go-to-market strategy will be because this will inform how it sells, how it markets its products, and the language it will use to reach its target customers.
In the following sections, we will be focusing on the differentiated, disruptive, and dominant strategies, and choosing one example for each that’s using AI to fuel these growth strategies.
The dominant strategy
In the dominant strategy, you are looking to win customers of all types: those that are looking for a superior product as well as those that are looking to pay less. This increases your market share overall and is why this strategy is referred to as a dominant strategy, because it’s a winner-takes-all mentality.
Let’s take the example of fast fashion, a highly competitive market if we’ve ever seen one. If you’re a fast fashion retailer, it’s not only important to have a cheaper product, but you must also have operations to support the delivery of that product more efficiently. Think Netflix, a good example in its heyday, but today we struggle to find a better example than SHEIN. The Chinese brand manifests its dominant strategy by leveraging AI to better anticipate new trends and predict demand for certain products in the market. It marries that data with its supply chain to ensure that it can deliver on changes in demand, to its customers’ delight.
SHEIN uses autoencoders to enhance its recommendation system and optimize user preferences, transformer models like BERT and GPT for sentiment analysis, chatbot responses and information extraction from text, CNNs and YOLO for image recognition in product tagging and trend detection, LSTMs and RNNs for demand forecasting, K-means clustering algorithms for grouping fashion trends, reinforcement learning for dynamic pricing, and regression models for predicting price adjustments based on demand. This impacts everything from its marketing efforts to its in-app experience to the reviews of its products. The evidence is very telling as well.
Previously, a Spanish fast fashion company, Zara, had the fastest turnaround time of 3 weeks for creating and delivering a collection. SHEIN brought that down to a new best-in-class of 3 days – a truly admirable use of AI and machine learning. Though it may be effective, this example is also a complicated one because it highlights the nature of AI’s effectiveness being in conflict with the world at large. Fast fashion already exacerbates environmental impacts and ethical concerns regarding the use of AI. Quick turnarounds and low costs lead to unsustainable practices, excessive waste, and poor labor conditions. But it’s important to remember that while AI can improve operational efficiency, and it certainly has for SHEIN, it doesn’t inherently address the fundamental problems fast fashion creates. In order to mitigate these concerns, it’s crucial for companies to use AI in conjunction with ethical and sustainable practices.
Another company dominating a highly competitive ecosystem is Hugging Face, which not only brings machine learning code repositories, models, datasets, and web apps to demo AI-powered products to smaller companies, but it does that through an open source community. They’ve also recently partnered with AWS to offer their machine learning capabilities to their open source community as well, features that are otherwise only available to the AWS customers that can afford them. The proof of the feasibility of the company is in the funding; it raised over $235 million in a series D round in August 2023.
A dominant strategy is one that undercuts the competition in the market by getting the job done better and costing less. This is effective because it maximizes its rewards on both ends. It gets to receive the customers that want the job done better as well as the customers that just want to pay less. That’s quite a lot of the pie you’re winning. When done right, companies employing these strategies can essentially print money. But with great power comes great responsibility. There should always be a discussion about the tradeoff between ethics and financial success, and companies of all sizes need to remain vigilant about the impact they’re having on the world.
The disruptive strategy
With a disruptive strategy, you’re still selling a product for less money, but it offers you less too. Who wants that? People who are overserved and bombarded with options, but who actually need relatively simple tools compared to the competing options they have to choose from. We can see no better example of that than Canva. You can edit photos and create anything from a social media post to a resume using Canva, and the tools to do so are incredibly user-friendly and simple.
The Australian creativity platform leverages AI to offer customers more of the kinds of templates and content they’re looking for. They do this by using CNNs for image processing tasks like background removal and object recognition, transformer models like BERT and GPT for auto-generating design suggestions and extracting text from images, GANs for creating new design elements and enhancing image quality, and reinforcement learning for optimizing design recommendations and layout suggestions. While it offers less than Adobe’s Photoshop or the Microsoft Office suite, it does offer what its users are specifically looking for, and it’s either free or cheap to use.
It quickly rose to unicorn status for expertly meeting its customers where they were. Canva did this by conducting extensive market research to understand the needs and pain points of potential users and analyzing existing design tools to identify gaps in the market and discover areas for improvement. It also segmented their audience based on design complexity, industry-specific needs, and whether they were professional or non-professional users, allowing the company to tailor offerings specific to different user groups. One of the biggest contributors to Canva’s success was the freemium model, which attracted a wide range of users. Those who had more specific needs could address them with paid features, but the freemium model allowed Canva to meet users at different stages of their design journey.
What’s most interesting about disruptive strategies is the ability to influence your potential customers by showing them a different angle. Disruption changes the paradigm of the existing power structures in a market and a new tastemaker arrives to inspire their competitors and customers alike to try new things with regard to how they ask for and use a product. This change in paradigm offers the entire market the gift of novelty and specialization. Maybe the job gets done worse, but this is beneficial for a new group of proposed customers. Perhaps this new group is one none of the other competitors thought to try and serve. Canva also offers us a glimpse into the potential danger disruptive strategies can face. Recently, the unicorn company was in the news for raising annual prices on their business-focused subscription service by 300% or more, driving users to threaten to abandon the software for competitors and claiming it’s no longer the “simple and affordable alternative” that got them to buy it in the first place. Canva’s response was that the new prices reflect the true value of their new AI-powered product experience. In this case, Canva appears to be abandoning its disruptive strategy for a more… differentiated strategy. This example is also a reminder that investing in AI is likely to incur costs that your company will then have to pass on to your users and customers. This decision may, in turn, affect your go-to-market strategy.
The differentiated strategy
In a differentiated strategy, you may sell a superior product that specializes in some niche, but you also charge more for it to account for this specialization. My differentiated strategy example is a company that hits close to home: a British machine learning-based property tech company I worked with named Beekin. Beekin isn’t a property technology platform that offers cheaper services than its competitors, but at the time, it offered a next-generation platform no other property tech company was able to offer. We built a machine learning-native platform that did everything from making market evaluations to predicting future behaviors to offer an optimal price for renters. Our customers didn’t have many options for competing tools because their alternatives were property tech giants with antiquated rule-based engines, and the competitive landscape in property tech is only just being disrupted by AI.
Differentiated strategies flourish in environments where they can best communicate the strength of their products. Because the price tag is also higher, there needs to be a compelling reason why someone would pursue differentiators in a market. Unique products that differ greatly from their competitors thrive in this market strategy. In the case of Beekin, there weren’t many other competitors using machine learning as the foundation of their product, and it was able to help many people carry out their jobs. This is the greatest strength of a differentiated product: to satisfy a niche and advancing market. The need for differentiation is what furthers and matures industries. Once customers with more particular and pressing needs arise, differentiators pop up to show their competitors how they might begin specializing and rising to meet their customers where they are.
Let’s take a moment to appreciate all the examples we have seen so far. The work and dedication that it takes to align a viable functioning product with a business model, a product strategy, and a loyal customer base is nothing short of a triumph, and success should be shouted out and celebrated. We learn so much from the successes of others, and we had a lot of fun choosing fitting examples for this chapter.
Whether you are trusting the brightest minds in AI or you’re getting the help of a machine learning intern just out of bootcamp, know that a lot can be done with very simple models. Striving for algorithmic perfection and relying on simple tools that are commonly relied on and used are equally admirable. It’s largely a question of preference and use case.
