Engaging With History: What Machine Learning Can Do for Your Business and How to Figure It Out

Engaging With History: What Machine Learning Can Do for Your Business and How to Figure It Out

Everything feels unprecedented when you haven't engaged with history. The more you see a behavior throughout history, the more you realize how ingrained it is in human behavior, which makes you more confident that it'll be part of our future. It's the only way to forecast with accuracy. Economies have a long history of panic and sudden collapse, driven by a similar pattern of optimism leading to debt and debt leading to a crash.

Investing in machine learning (ML) is like investing in mobile 10 years ago — it can transform your business. ML is the next frontier in data analysis. It is a discipline where computer programs make predictions or draw insights based on patterns they identify in data and are able to improve those insights with experience — without humans explicitly telling them how to do so. ML has the power to go well beyond using internal data — it can be enhanced by marrying internal with external data to drive new insights that were not previously possible.

One of the most common use cases of ML is mass customization. ML can help you find the products that are most likely to be relevant for your customers more quickly and efficiently. It can save you time and make your resource investment more effective when it comes to business processes and decisions. However, ML is not a magic wand for your business. The first challenge in ML is figuring out the business impact the technology aims to drive. ML is a solution, but you need to first define the problem.

To maximize the value of ML for your business, an ongoing collaboration between product managers and data scientists is essential. Product managers should ensure that the problems being solved are the most impactful ones for the business. They can ask questions such as: Where do people in my company today apply knowledge to make decisions that could be automated, so their skills could be better leveraged elsewhere? What is the data that people in my company normally search for, collect, or extract manually from certain repositories of information, and how can this be automated? Do I have a clear segmentation of my customers based on their preferences, behaviors, and needs? Is my product/experience customized for each segment? Can I customize the experience for each individual customer based on what I know about them or their interaction with my site/app/product? How could I create a better, faster, or otherwise more delightful experience for them? How can I better identify good vs. bad customer experiences? Can I detect issues that will negatively impact customer experience or satisfaction before they happen or spread? What are the metrics or trends that, if I could correctly predict, would have a meaningful impact on my ability to serve my customers or otherwise compete in the industry? What are the key entities about which I gather data (people, companies, products, etc.)? Can I marry that data with any outside data (from public sources, partners, etc.) in a way that tells me something new or useful about those entities? How can I use this information to benefit my customers or my business?

By asking these questions and collaborating with data scientists, product managers can unlock the full potential of ML for their business. Here are three actionable pieces of advice for incorporating ML into your business strategy:

• 1. Clearly define the problem: Before implementing ML, clearly define the problem you want to solve. ML is a tool, not a solution in itself. By identifying the specific business impact you want to achieve, you can focus your efforts and resources effectively.

• 2. Foster collaboration between product managers and data scientists: Collaboration between product managers and data scientists is crucial for successful ML implementation. Product managers should communicate the most impactful problems to be solved, while data scientists can provide insights and expertise on how ML can address those problems.

• 3. Continuously evaluate and optimize: ML is not a one-time solution. It requires ongoing evaluation and optimization. Regularly assess the impact of ML on your business and make necessary adjustments to ensure its effectiveness and relevance.

In conclusion, engaging with history can provide valuable insights into human behavior and help us forecast the future. Similarly, incorporating machine learning into your business strategy can transform your organization and drive new insights and efficiencies. By clearly defining the problem, fostering collaboration between product managers and data scientists, and continuously evaluating and optimizing, you can harness the power of ML to drive business success. Just as history has shaped our present, ML has the potential to shape our future.