Components of predictive analytics

BI_Sep08_BWhen it comes to the success of your business, you likely rely, at least in part, on predictions made off of existing data. While simple forecasts are easy enough, it can be more difficult to set down long-term theories about what the future may or may not entail. That’s why many businesses employ predictive analytics. While long used by enterprises, many smaller companies are also now starting to use these methods as well. At first glance, predictive analytics can be overwhelming, so, to help, here is an overview of the three main components.

Together, these three elements of predictive analytics enables data scientists and even managers to conduct and analyze forecasts and predictions.

Component 1: data

As with most business processes, data is one of the most important and vital components. Without data you won’t be able to make predictions and the decisions necessary to reach desired outcomes. In other words, data is the foundation of predictive analytics.

If you want predictive analytics to be successful, you need not only the right kind of data but information that is useful in helping answer the main question you are trying to predict or forecast. You need to to collect as much relevant data as possible in relation to what you are trying to predict. This means tracking past data, customers, demographics, and more.

Merely tracking data isn’t going to guarantee more accurate predictions however. You will also need a way to store and quickly access this data. Most businesses use a data warehouse which allows for easier tracking, combining, and analyzing of data.

As a business manager you likely don’t have the time to look after data and implement a full-on warehousing and storage solution. What you will most likely need to do is work with a provider, like us, who can help establish an effective warehouse solution, and an analytics expert who can help ensure that you are tracking the right, and most useful, data.

Component 2: statistics

Love it, or hate it, statistics, and more specifically regression analysis, is an integral part of predictive analytics. Most predictive analytics starts with usually a manager or data scientist wondering if different sets of data are correlated. For example, is the age, income, and sex of a customer (independent variables) related to when they purchase product X (dependent variable)?

Using data that has been collected from various customer touch points – say a customer loyalty card, past purchases made by the customer, data found on social media, and visits to a website – you can run a regression analysis to see if there is in fact a correlation between independent and dependent variables, and just how related individual independent variables are.

From here, usually after some trial and error, you hopefully can come up with a regression equation and assign what’s called regression coefficients – how much each variable affects the outcome – to each of the independent variables.

This equation can then be applied to predict outcomes. To carry on the example above, you can figure out exactly how influential each independent variable is to the sale of product X. If you find that income and age of different customers heavily influences sales, you can usually also predict when customers of a certain age and income level will buy (by comparing the analysis with past sales data). From here, you can schedule promotions, stock extra products, or even begin marketing to other non-customers who fall into the same categories.

Component 3: assumptions

Because predictive analytics focuses on the future, which is impossible to predict with 100% accuracy, you need to rely on assumptions for this type of analytics to actually work. While there are likely many assumptions you will need to acknowledge, the biggest is: the future will be the same as the past.

As a business owner or manager you are going to need to be aware of the assumptions made for each model or question you are trying to predict the answer to. This also means that you will need to be revisiting these on a regular basis to ensure they are still true or valid. If something changes, say buying habits, then the predictions in place will be invalid and potentially useless.

Remember the 2008-09 sub-prime mortgage crisis? Well, one of the main reasons this was so huge was because brokers and analysts assumed that people would always be able to pay their mortgages, and built their prediction models off of this assumption. We all know what happened there. While this is a large scale example, it is a powerful lesson to learn: Not checking that the assumptions you have based your predictions on could lead to massive trouble for your company.

By understanding the basic ideas behind these three components, you will be better able to communicate and leverage the results provided by this form of analytics.

If you are looking to implement a solution that can support your analytics, or to learn more about predictive analytics, contact us today to see how we can help.

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