by Ryan Harrington
“Predictive analytics”. If you've paid any attention to the business or tech world over the past several years, then you’ve heard the phrase. It's a phrase that has likely been accompanied by other intimidating phrases like “big data” or “artificial intelligence” or “machine learning”. Over the past decade, these phrases have only become more popular and even more important for businesses to understand. Don't believe us? Here's what the Google Trends for both "big data" and "machine learning" look like over the past 13 years.
As a business, it's important to understand how these technologies can be helpful for your company. While the applications of any of these technologies is only limited by creativity, in practice, there are a few simple question that every business can answer by using predictive analytics. We'll get you started with seven of those questions:
1. Which leads are most likely to become paying customers?
Every business has a list of potential customers or leads that they are looking to convert into paying customers. Perhaps your company has a sales team or a call center and you are looking to prioritize their time. It is a classic predictive analytics problem to rank which leads are likely to convert into customers. The same types of predictive analytics techniques can also help to determine which customer characteristics are most likely to lead to a conversion.
2. Which customers are likely to churn?
If you aren't familiar with the term, "churn" refers to when a customer stops doing business with a company. For the most part, companies want to minimize customer churn as much as possible. Being able to identify which customers are likely to churn can help a business to keep the customer and retain their revenue.
3. Which marketing channels are providing the most lift?
Every business has a marketing strategy. Some businesses use multiple strategies - from social to print and everywhere in between. Naturally, it's important to maximize spend on marketing channels. Predictive analytics can help to ensure that marketing dollars are being spent on the channels that are giving the most bang for your buck. Similarly, predictive analytics may help you to identify the type of content on a specific channel that is driving the most engagement.
4. What customer segment does a new customer fall into?
Understanding customer segments is extremely important for a business. Customer segments can be extremely useful for a company by helping to define strategy for the business - from developing new products to selecting a marketing strategy. Among many other things, data mining techniques can help a business with two key pieces of this puzzle. First, if the business has not already identified their customer segments, this can be done using a set of techniques called "clustering analysis". Second, once the segments have been identified, predictive analytics techniques can help identify which segment a new customer might fall into, allowing the business to give the customer the best experience possible.
5. What will our profits look like over the next two years?
One of the most important things for any business is whether or not it is making a profit. As we've discussed previously, one of the things that predictive analytics is excellent at doing is making forecasts. There are many different types of forecasts and many different applications of them. A business that is able to put together a strong forecast is able to make better decisions about their company's future and more effectively plan ahead.
6. Which products are customers likely to buy together?
We've all been there. We scroll down the page, click "Add to Cart", and then we're immediately presented with several items that are "Frequently Bought Together". We decide this is a great idea, and so immediately put those other items in the cart as well. Our $49.99 purchase is now $107.92. Nearly every online shopping website features some form of this experience for its customers. The engine behind this experience is a form of predictive analytics known as "association analysis". Any company that sells multiple products has an opportunity to perform a similar type of analysis, regardless of whether or not it has an online retail presence. Ultimately, understanding the products that customers are likely to use together will help to give customers a better experience and to drive growth for the company.
7. How does sentiment about my product effect revenue?
"Sentiment" describes how customers feel about a company or a product. For a company, it is important to understand how sentiment may effect revenue in the future. This can be done using a broad range of predictive analytics tools known as "text analysis". In order to make it possible for sentiment analysis to be completed, a company needs a consistent source of customer feedback. For example, this may come in the form of social data or from customer phone calls. Harnessing this information allows for a company to be more responsive and to ultimately make better decisions about their products.