A New Way to Think About Product-Market Fit: Practical Applications of NLP for Product Teams

Aviral Vaid

Aviral Vaid

Aug 03, 20234 min read


A New Way to Think About Product-Market Fit: Practical Applications of NLP for Product Teams

When it comes to achieving product-market fit (PMF), there are many misconceptions that can hinder your progress. One common misconception is that PMF is binary - either you have it or you don't. However, the reality is that PMF is more of a spectrum. You may be somewhere along this spectrum, but the goal is to continue iterating towards stronger and stronger PMF.

Another misconception is that once you reach the PMF mountain, you should become too conservative. This means that as things start working for you, you may be hesitant to introduce major capabilities until the existing ones are perfect. However, it's important to resist this temptation and keep moving quickly. After all, you've got a big mountain to climb.

To better understand how PMF actually behaves, it's helpful to imagine it as a landscape with three areas: the PMF Desert, the PMF Mountain, and the PMF Mountain Peak. The PMF Desert represents being too far off from PMF, where something fundamental is wrong. In this case, it's important to go back to the basics and make bold moves. Don't try to iterate your way out of this situation, but be bold and make big changes.

When you reach the PMF Mountain, you can see the peak, but barely. This is the time to go up the mountain fast. Remain bold while holding on to what's working.

Finally, when you reach the PMF Mountain Peak, it's time to build a real company. Hit the gas and take advantage of the momentum you've built.

Now let's shift our focus to natural language processing (NLP) and its practical applications for product teams. NLP has a wide range of applications, including chatbots, machine translation, text summarization, text generation, semantic search, and speech recognition. However, building a custom NLP system from scratch is a demanding task that most product teams avoid. Instead, they turn to third-party SaaS platforms that specialize in a subset of NLP applications to meet their business requirements.

One of the biggest concerns for product teams that are not specialized in NLP is how to select an NLP product that satisfies their needs. They need to consider which functionalities guarantee long-lasting success and how these products will be used in production.

Customization is an important factor to consider when selecting an NLP platform. Product teams should ensure that the system can ingest custom data and understand internal processes and limitations. The ideal NLP platform would be able to output business-specific NLP models that have been trained on business-specific data to solve the original business-specific need.

The amount and quality of data needed for training NLP models are also crucial considerations. While quantity is important, labeled data can be difficult to acquire and require a lot of time and effort. Therefore, product teams should look for NLP products that require as little data labeling as possible. Platforms that can utilize unlabeled data or require only a few labeled data points are preferable, as they minimize the time spent configuring and maintaining the models.

Maintaining NLP models can be a significant bottleneck in product development and a source of cost for companies. Therefore, product teams should consider how easy it is to teach new skills to the NLP system, how fast it learns, and how new flows can be added without breaking old ones. They should also think about how to detect bad customer journeys to improve the system and whether a dedicated person is needed to maintain it.

Scalability is another important factor to consider. Product teams should ensure that the Machine Learning models used in production can handle the increasing volume of customer data without compromising performance. They should also ensure that their customer's data is protected and not being used for improving NLP models without consent.

In conclusion, understanding and achieving PMF is a critical step in building a successful product. It's important to recognize that PMF is not binary but a spectrum, and to keep moving forward towards stronger PMF. When it comes to NLP, product teams should carefully consider their needs and select a platform that offers customization, requires minimal data labeling, and is scalable. By incorporating these principles and strategies, product teams can increase their chances of success and build products that truly meet customer needs.

Actionable advice:

1. Be honest with yourself about where you are on the PMF spectrum and make bold moves to get closer to PMF.

2. When selecting an NLP platform, prioritize customization, minimal data labeling, and scalability.

3. Regularly assess and improve your NLP system's performance, adding new flows and detecting bad customer journeys to ensure continuous improvement.

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