Many people, probably misled by internet articles, are convinced that data science and machine learning are only relevant for companies that routinely process big data or make a large volume of decisions frequently and consistently, like approving or denying a loan application.
But the reality is that machine learning can be a high ROI solution for various problems in companies of all sizes. The investment required is getting lower and lower. For a machine learning solution to have the potential to move the business forward, the problem must be well defined and understood. The other condition is the existence of enough data that exemplifies the information necessary to make a decision--which doesn’t necessarily mean big data, or when it does, that it is generated internally.
Understanding customer behavior from small data
The famous Titanic competition offered by Kaggle is a good illustration of how machine learning can be successfully used on small data. In this competition, participants gain access to two datasets containing passenger information like name, age, gender, socio-economic class, etc. One of them has the details of a subset of the passengers on board the Titanic (891 to be exact) and, importantly, reveals whether they survived or not.
With data from 418 passengers, the other data set offers the same information but does not disclose the "ground truth". It's the participant's job to predict which of those passengers did survive.
It's true that this particular exercise doesn't translate into any actual business application. However, it helps showcase the effectiveness of predictions made by a machine model trained on fewer than 900 records. In the contest, the best scores were in the 83-84% range, which might be a perfectly reasonable accuracy for the intended purpose, as previously discussed here:
We can apply the same approach to various business problems even in the most resource-constrained environments. For example, when an online boutique used a survey to learn about customer preferences, in minutes, using free resources, I was able to train a machine learning model to predict the answers for the customers who left a question unanswered using the data of customers who did respond.
Combining small data with models pre-trained in big data to support better decisions
A common application is fraud detection.
Armed with petabytes of data collected every hour from customer transactions, big e-commerce players like Target and Walmart can leverage sophisticated deep learning systems to minimize the incidence of fraudulent purchases.
While fraud has a more significant impact on mid/large businesses, the volume and sophistication of fraud attacks keep rising for small businesses as well. A small online store is unlikely to be in a position to acquire the same volume of proprietary data and perform the large-scale analyses made by large retailers. However, it doesn’t mean it can’t leverage a model pre-trained on an extensive third-party data set to achieve similar results.
Take, for instance, the AWS fraud detection system. A small e-commerce website concerned with fraud can use the system for several things, including spotting potential fraudsters among new customers to minimize fraud loss. By sending as little as two pieces of data from a guest checkout order (e.g., email, IP address), the business can get back a risk score and use it to automatically accept a transaction, place it under review, or collect more customer details.
Another great example can be found in precision agriculture.
Small farms are using agricultural technology that leverages ML to deliver recommendations that increase yield and reduce the emissions released into the atmosphere that add to global warming. For instance, growers now can avoid hit-and-miss results and save time and money using a service that takes local soil samples and feed their data into a pre-trained model to answer questions like, "Which seeds should I plant?" and "Where do I need to spread fertilizer?"
Even in the smallest of organizations, the quality of decisions can mean the difference between success and failure.
Do I give this customer a special price? Do we approve this transaction, or request more information to prevent fraud? What is the right quantity of herbicide to use during this application window to avoid crop damage?
By themselves, these individual decisions may have little impact on business performance. Taken together, they influence everything from profitability to reputation. For that reason, smart businesses of all sizes are leveraging machine learning to gain a competitive edge by achieving a decision yield that is better than the industry norm.
Not every business problem will benefit from machine learning or justify the investment. However, free tools and affordable third-party solutions make it increasingly easy for any organization to adopt ML when it can improve decision-making. Businesses will either embrace this fact or be replaced by others who do.