Feb 10 2022
Help with decision making
Machine learning can help companies convert their data into value-adding insights. Humans cannot evaluate information and run many potential scenarios at the size and speed required to take the best course of action. As summed up by Lean Manufacturing Research LLC's creator and chief analyst, Dan Miklovic, machine learning "doesn't replace people, but rather helps people do things better".
Companies can use historical price data and other data sets to learn how certain conditions impact goods and services' demand. Insights from machine learning algorithms help businesses dynamically price their products depending on many factors, helping them maximize revenue. This pricing method is most often seen in the transportation sector, such as surge pricing at Uber or sky-high airline ticket costs during school holiday weeks.
Chatbots are a popular type of automation. They have bridged the gap between people and machines by allowing us to interact with machines that can then perform activities based on specific requests. The earliest chatbots were programmed to obey predefined rules that told them what to do depending on keywords. Machine learning and natural language processing, a subset of machine learning, allow chatbots to be more productive and engaging. Alexa, Google Assistant, and Siri are all notable instances of modern chatbots. These new chatbots react better to consumers' requests and interact more like actual people.
Jan 06 2022
Let’s now look at some of the best practices when it comes to applying machine learning for business decisions.
1. Define a large problem instead of focusing on a minor one Businesses should avoid using machine learning just for its novelty. This results in teams lacking motivation or dedicated resources to achieve tangible results. Instead, start with an issue that matters a lot and has a good chance of getting handled. Then, narrow down that issue by thinking about what business information is highly desired but not currently accessible.
2. Come up with the context; data on its own won’t suffice While ML algorithms are good at finding correlations, they don't grasp the context of the data. So, choosing the data to feed your algorithm can be complex. Here are three ways the “context” may hinder the development of ML solutions.
Predicting the lifetime value of an eCommerce customer Let's suppose that many of the customers with the greatest lifetime value were reached through a phone outreach campaign that lasted for over two years, but failed to break even, despite generating new sales. If a phone follow-up program like this isn't going to be a component of future eCommerce sales development, then this data is irrelevant for the algorithm.
Medical recovery time estimation A computer may use data to decide therapy for first- or second-degree burns. As a result, the computer may anticipate that many second-degree burn patients will only require as much time as first-degree burn victims. Because the context wasn't in the data, the computer assumed second-degree burns heal as quickly as first-degree.
Product Recommendations A recommendation engine for an eCommerce store over-recommends a product. However, these promotional purchases were sold more based on the “deal” and the cheap price, and less based on the real alignment with the consumer.
3. Be ready for continuous adjustments Choosing algorithms, data, cleaning data, and testing in a live setting takes considerable thought and testing. Unique and complicated commercial use cases need custom machine learning solutions. Iteration and modification are required even for very typical use cases. A business that embarks on an ML project without sufficient resources may never produce a meaningful outcome.