As machine learning (ML) becomes more prevalent, organizations are eager to adopt this technology to enhance their business processes. However, many organizations face uncertainties about where to begin and which data to use for training their ML models. This guide provides an overview of how ML works, the types of data required, and presents four practical examples showcasing ML applications in various business domains.
Our subconscious mind excels at recognizing patterns. For instance, a marketing analyst might intuitively know that customers who purchase running shoes are also likely to be interested in fitness trackers and sports apparel. Similarly, a social media platform can infer that users who engage with travel content and post pictures from scenic locations are more likely to be interested in vacation deals.
Likewise, machine learning algorithms search for patterns within data to make predictions. Unlike humans, machines can rapidly analyze large amounts of data with greater objectivity.
To understand how machine learning works, let’s explore a simple example. Suppose we want to develop a machine learning algorithm to predict customer preferences for movies.
To achieve this, we collect data on past customers and their movie preferences. This data might include information such as the customer’s age, genre preferences, ratings given to movies, and frequency of movie-watching.
With this data, we can train a machine learning algorithm to uncover patterns. For example, the algorithm might discover that customers who are young adults, enjoy action movies, and frequently rate movies positively tend to prefer sci-fi films.
Once the algorithm has learned these patterns, we can utilize it to provide personalized movie recommendations to new users based on their age, genre preferences, and other relevant factors.
Machine learning algorithms operate with the common goal of minimizing error, regardless of the specific algorithm employed. Error refers to the disparity between the predicted outcome and the actual outcome.
Let’s illustrate this with an example: Imagine we have a machine learning algorithm that predicts the price of a house based on various features such as square footage, number of bedrooms, and location. If the algorithm predicts a house price of $300,000, but the actual selling price is $320,000, the error is $20,000. The objective of the algorithm is to minimize this error.
To achieve this, the algorithm starts with an initial state and iteratively makes adjustments to reduce the error. One common approach is known as “gradient descent.” Using gradient descent, the algorithm evaluates the error of the current state and seeks a new state that decreases the error. This is done by taking small steps in the direction that leads to the most significant reduction in error.
The algorithm continues this iterative process until it converges to a state where the error is minimized, representing the best possible prediction. A useful analogy for this process is envisioning a landscape with hills and valleys. The algorithm’s objective is to locate the lowest valley, which corresponds to the state with the minimum error, thus providing the most accurate predictions.
Working with complex algorithms that involve numerous parameters makes visualizing the optimization process more challenging. However, the objective remains the same: finding the minimum of the error function. In neural networks, a widely used method called “backpropagation” is employed to achieve this goal.
Backpropagation involves a backward propagation of information from the output layer to the input layer. During this iterative process, the weights of connections between neurons are updated to minimize the error. By iteratively adjusting the weights based on the calculated error, the neural network gradually converges towards a state of minimized error.
In the field of machine learning, the adage “correlation is not causation” holds true and underscores the importance of working with relevant data. Simply observing a correlation between two variables does not imply a causal relationship.
Let’s consider a different example to illustrate this. Suppose we have data showing a correlation between the frequency of exercise and academic performance in students. Does this mean that exercise directly causes better academic performance? Not necessarily. The correlation exists because both exercise and academic performance are influenced by factors like discipline, motivation, and overall well-being. Engaging in regular exercise might be an indicator of a student who maintains a healthy and disciplined lifestyle, which can positively impact academic performance.
This emphasizes the need to identify and utilize data that is specifically relevant to the task at hand. For instance, when predicting customer preferences in the fashion industry, it would be crucial to gather data from sources such as online shopping histories, fashion trend analysis, and customer reviews.
In another example, let’s consider demand forecasting for a retail business. Relevant data sources for this task could include historical sales data, promotional activities, weather patterns, and economic indicators. By analyzing these relevant data sources, accurate predictions can be made to optimize inventory management and meet customer demand effectively.
Each machine learning task has its own specific requirements for relevant data. Identifying and utilizing the most relevant data sources improves the accuracy and reliability of the machine learning models, leading to valuable insights and better decision-making.
Understanding the business task. Regardless of the key performance indicator (KPI) you aim to optimize, it is essential to consider the factors influencing that metric. This step requires domain knowledge and a comprehension of the business context. For instance, if you want to predict customer churn, you may need to analyze data on customer service interactions or product usage.
Identifying available data sources. This includes both internal sources like enterprise databases and spreadsheets, as well as external sources such as social media data.
Assessing data quality. Once relevant data sources are identified, it is crucial to evaluate data quality, including factors like completeness, accuracy, and timeliness. Complete, accurate, and up-to-date data tends to yield better results compared to incomplete, inaccurate, or outdated data.
The next step is to connect the data to Octai. Octai seamlessly integrates with popular business platforms like Salesforce and Snowflake, simplifying the process of accessing the required data. You can also export data to a CSV file or directly connect to your database.
Once the data is connected, Octai automatically identifies suitable algorithms and runs the machine learning models. All you need to do is select the column, or KPI, you’d like to predict, whether it’s churn, conversion, attrition, fraud, or any other metric you have in mind. Octai will handle the rest by automatically identifying suitable algorithms and running the machine learning models.
Of course, accuracy is not the sole determinant. We must also take into account factors such as false positives and false negatives. A false positive occurs when the model predicts customer churn that does not actually occur, while a false negative happens when the model fails to predict customer churn that does occur.
In this case, our model exhibits a relatively low rate of both false positives and false negatives. Octai simplifies the process of venturing into machine learning, even for individuals with limited experience in data science. With just a few clicks, you can connect to your dataset, automatically construct and evaluate multiple machine learning models, and identify the most effective one.
Moving forward, we can deploy our churn prediction model into production, allowing it to autonomously make predictions and empower businesses to take preemptive actions against churn. For instance, if the churn model anticipates a customer is likely to cancel their subscription, proactive outreach efforts can be initiated, such as offering them exclusive discounts or compelling incentives to encourage retention.
Octai provides a user-friendly interface that simplifies the initial steps of AI implementation.
While we have discussed various KPIs like churn, attrition, fraud, and cross-sales, Octai offers numerous opportunities to uncover and optimize additional KPIs.
To begin, take a look at our applications page, featuring demonstrations and sample datasets for tasks such as:
As emphasized earlier, finding relevant data and organizing it in a useful format is a crucial first step in data science. With Octai’s integrated data cleaning functionality, a significant portion of the tedious groundwork is automated, enabling you to focus on achieving tangible results.