Better product decisions

Machine learning is there to help.

Better product decisions

“Without machine learning, our decisions are based on guesses. With machine learning, we can make evidence-based decisions to get us the best outcome.”

Bill Gates

Product managers and product owners are increasingly looking to harness the power of machine learning to automate mundane tasks, make decisions and unlock business opportunities. With an ever-growing number of data sources, machine learning can be a powerful tool for product managers to make more informed decisions. By leveraging the power of data, product managers can better understand the market they are operating in and the potential of their product.

  • Product managers and owners can implement machine learning to analyse data faster, draw more accurate insights and optimise processes. With its ability to spot trends or patterns that would escape the human eye, machine learning can help you gain valuable insights from extensive and complex datasets to help you make better product decisions.

  • With machine learning, you can also measure complex correlations among different product elements and gain insights into their relationships. For example, machine learning models are used to study how different types of product features and customer behaviours influence adoption, engagement and retention. This knowledge can help you build better product strategies by uncovering the factors that drive user behaviour and loyalty.

  • You can also deploy machine learning to enable early-stage forecasting and predictive analytics when making product decisions. The insights gained from machine learning can help you identify the right customer behaviours and signals that result in optimal outcomes, allowing you to make informed product decisions in a timely manner.

  • Beyond predictive insights, machine learning can also help prioritize product investments and identify cost-effective solutions. By uncovering complex data points, you can break down ROI for different target customers and understand what drives their purchasing behaviour.

Simple example of predictive analysis

Imagine you are the product manager of a telematics platform in your country wanting to use machine learning to estimate their TAM, SAM, and SOM. If unaware, TAM is Total Addressable Market, SAM is Serviceable Available Market, and SOM is Serviceable Obtainable Market.

  1. You can start by collecting data about the types of vehicles in their fleet, the geographical area they are targeting, and the amount of competition in the market.

  2. Using predictive models trained by ML algorithms, you can analyse the data to identify potential customer segments and their specific needs. By analysing the data, you can gain insight into the size of the product’s TAM, SAM, and SOM.

  3. Then, using machine learning, you can identify trends in the data and refine their targeting. By identifying the most lucrative customer segments, you can ensure they target the right markets and make the most of your product.

With more and more understanding of machine learning, now powered by several AI-based platforms, more and more use cases are becoming mainstream practice for product professionals.

Ultimately, machine learning is emerging as a powerful tool for product managers and owners to discover relationships, uncover insights and make better product decisions. By leveraging its analytical capabilities, you can make fact-based decisions that maximise ROI and minimise risk. This will not only help you make the right product decisions but also position your organisation ahead of the competition.

Please share your thoughts and use cases in how machine learning has helped you make better product decisions.

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