Sales and Inventory Data Management

Any competent data strategy starts with astute data storage. Here are scenarios you typically want to avoid, especially if you are a growing organization:

Machine Learning: Sales Prediction

Trying to predict sales ―or any real-world outcome, for that matter― based on gut feeling alone has its flaws. This is primarily because humans are subject to biases towards their own experiences; taking a data-oriented approach, however, will ensure that we are evaluating a globally-representative sample of information. Data scientists employ a type of computer programming called machine learning to make this sort of data-driven prediction. Machine learning refers to the process of teaching a computer how to identify and extrapolate upon patterns in data, as opposed to a human completing these tasks. In this case, a data scientist can use a large quantity of historical sales data to make a prediction of future sales for the short, medium, and long term.

There are several algorithmic methods that data scientists use to predict sales.

The first method, with which you may already be familiar, is regression analysis. Regression analysis determines the statistical relationship between sales (or some other target variable that you are trying to predict) and any number of other factors; if you understand these relationships, information about the other factors will help you predict the sales. As I will explain later, an added benefit of regression analysis is that it also provides diagnostic insights.

The second method is time-series forecasting. Time-series forecasting is similar to regression analysis, except instead of using external factors to predict the target variable, you use patterns in the historical data of the target variable itself to predict future data of the target variable. An advantage of this method is that it can recognize complex patterns such as seasonality, volatility, inflection, and others, which are usually missed by regressions; the downside is that time-series forecasting doesn’t provide any diagnostic insights.

The third method involves a special machine-learning model called a neural network. Neural networks learn patterns in data by mimicking the anatomical process by which your brain learns information. Specifically, a neural network consists of a series of “neurons” which each contain information about how to predict a particular outcome. As data is then read by each neuron, the neuron sends its best guess about the future outcome to the next neuron in succession, until the final neuron makes the final decision as to its prediction. The final neuron can then let previous neurons know whether they got the prediction correct in a causal tracing pattern known as backpropagation.

Machine Learning: Sales Diagnostics

Customer Churn Prediction

So you’ve started sourcing new clients using data analytics. Now comes the next challenge: how can you retain as many of our current clients as possible? Once again, the answer lies in your data; implementing an effective machine learning algorithm or general data analysis will allow you to identify which clients are at risk of terminating their contracts, a month, a quarter, or even a year ahead of time. This will allow you to shift your intervention measures from reactive to proactive, in a way that none of your competitors can match.