Anticipating your customers’ needs can keep them coming back for more. While no business has a crystal ball, there are ways that you can predict customer behavior statistically. It might sound a little dry, but even math-challenged people can master predictive analytics. Here is what you need to know.
Predictive analytics is a type of data analysis that attempts to forecast or predict the outcome of something. It relies on historical data and statistical modeling to gain insights into future events. This tool can be very sophisticated, using machine learning to anticipate behaviors, or it can be remarkably simple.
Predictive statistics use several statistics techniques:
Data mining involves collecting massive amounts of data, then looking for patterns within that information. Their frequency identifies correlations and relationships.
Example: Around 60% of customers who put an item in their online shopping cart will complete the purchase. Visitors are 50% more likely to buy something when shipping is free.
Predictive modeling involves using statistics to predict what will happen. This form of predictive analytics is focused on what will happen.
Example: A fitness center finds that when clients go from working out five times a week to once a week, those members will not renew their memberships.
Text analytics is the art of converting text into data that can be used in an analysis.
Example: When a blog article has a 3% density of a certain keyword, readers are more likely to comment or say that the article was useful than if a 1% keyword density was used.
With so much functionality and potential, predictive analytics play a vital role in modern business. Reports from Zion Market Research suggest that the global market for this technology will reach $10.95 billion by the year 2022.
While predictive analytics can take many forms, it generally refers to technologies that enable users to detect trends in practice. In general, predictive analytics allows companies to get insights from their historical data and forecast a specific timeframe in which an action will happen.
Companies use these tools to find patterns in the data they collect and boil them down into trends and patterns. They can also spot risks to their future success as well as identify potential opportunities. It just depends on how the model is developed.
Here are some real-world examples:
Some businesses may find that a particular product is sold in disproportionate numbers to members of a certain demographic. By knowing what percentage of their customer base this demographic makes up, they can predict repeat sales.
Retailers might use predictive analysis to plan how much inventory they need to meet demand. They may also use predictive analysis to determine how many salespeople they need on staff at particular times during the week.
Hotels may set their pricing based on the expected occupancy, offering deals to encourage travelers during slow times and increase prices during peak seasons.
If predictive analytics sounds compelling, that’s because it is. These numbers can help companies of all sizes understand their markets better as well as which actions actually contribute to sales and which miss the mark. However, you must know what to look for. Before you can do any sort of predictive analysis, you need to collect your data and clean it up so that it is in a format that you can use mathematically. There are several methods of data collection and mining. You may use each one in your predictive analytics.
Data collection is the easy part. Many of the tools and systems you already use are collecting data behind the scenes. Some of this information may even be boiled down into insights for you by those programs, like Facebook letting you know which demographics like your content most or the best time of day to post. However, if you stop at automatic data collection, you are going to miss some of your most significant insights.
One option is to classify the data, adding labels to describe which insight you want to see. For instance, when you tag a blog post, you are essentially engaging in data classification. An accounting firm may tag blog posts as being focused on “small business,” “personal finance,” “taxes,” or “retirement planning.” These tags help that company understand what type of content is getting the most readers as well as which gets the most conversion – it’s not always the same thing. From there, that accounting firm can develop new content that focuses on the most effective tags.
You can also use data clustering. This places information into related groups. To use the accounting firm as an example, it could divide its content into two categories – business and personal. This way, instead of seeing that specific tags are effective, the company could get a clue as to which class of customers it is connecting most online. Likewise, a retail company could look at how many dresses it sells during a sale, as opposed to specific silhouettes, fabrics, colors, and price points.
Next, you need to decide how you will analyze the data you’ve collected. There are dozens, maybe hundreds, of different techniques that you can use, but they generally fall into these categories.
Decision trees are a popular option. They follow the visitor’s or customer’s journey to the end interaction. Each decision point results in a new branching so that the end result resembles a tree.
Example: A person visits a website. That person adds something to his or her cart or doesn’t. From there, that person chooses to shop for additional items, checks out, or abandons the cart.
Naïve Bayes is another option. This method involves assumptions. In the example above, you might assume that the reason the person abandoned the cart was that the shipping cost was too high. In reality, it could be that the item wouldn’t be received in time or that the customer was shopping on his or her smartphone while sitting on a bus, and they reached their stop, but you are assuming that it was the shipping cost because it is the most likely scenario.
Companies with a singular product may be able to use a single decision tree, but most businesses are better served by taking a random forest approach. The random forest model works like a decision tree, but it goes a step further. It divides the customer journey into several decision trees that operate individually, each one with a different prediction.
Example: Creating a decision tree that starts with clicking on an emailed coupon as well as a decision tree that starts with clicking on an ad, one that begins with a social media post, and so on.
Simple statistics involves looking at the data and forming correlations between the classifications. This is the most simplistic analysis technique, but it is an effective part of predictive analysis.
There are several predictive analytics models. The right one depends on your needs. It is not unusual for a company to use several predictive models at once.
One approach is to calculate the lifetime value of each customer you attract. When you only look at how much you earn from the sales directly associated with an ad, you are missing the bigger picture – how much you earn from each customer you attracted over the lifetime of that person. In most cases, assuming your product and your customer support are good enough, that individual will make many purchases over a given time window, say six months or two years. Some predictive models take this into account to predict future revenue from customer acquisition.
You can also look at how much you earn from a particular segment of your customer group. This strategy helps you understand which of your customer segments are the most profitable and which may spend less. Customer segmentation helps you understand who your customers really are. Sometimes, your biggest sales are to the people you least expect. Knowing how big the populations are in each segment and their relative propensity to purchase can lead to revenue/sales predictions.
Some companies choose to focus their predictive analysis on where they need to take preventative measures. This could involve noting which customers need extra incentives to stay loyal or figuring out when bits of machinery need attention.
Regression looks at a single target variable, like sales, and it does so in several ways.
The first is a simple regression. This method finds the relationship between a single predictor and the variable. For instance, you might assume that the more time someone spends on your website, the more they will spend. In this case, you would ploy time spent on your site versus the amount spent on that visit, then figure out the trend line between those variables. This is normally assumed to be a straight line. Simple regression is most accurate when there are many data points, and they follow a linear course. Exponential trends may be more difficult to spot.
There is also multiple regression. This technique uses several variables to predict the end result. It is a little more complicated but tends to be more accurate. For instance, you might look at the time of day, day of the week, whether or not there is a coupon, the customer demographic or segment, and the number of items in the cart to determine how much that person is likely to spend.
There is no reason to choose one predictive analytics model. Most companies use several, if not all of them, at various points in their analysis. Sometimes, you may use one to get the information you will use in another – such as figuring out the most profitable customer segment and what attracts them to your site. Other times, you may use them separately, looking at the success of an ad in attracting visitors, then examining what your website visitors do, whether or not they arrived at your site because of a particular ad or not.
Predictive analytics is one of the most effective tools you have to improve the way your company operates and identify new opportunities. While you could attempt to figure out the relationships between variables and outcomes on your own, you can also use tools to do the heavy lifting for you. ironFocus consolidates your data and extrapolates actionable insights from that information, so you don’t have to lift a finger. Our data agency presents all the information you need to know in dashboards and reports that are easy to understand and react to. Contact ironFocus today to try it for yourself.