Customers are hard to predict.
Sometimes you think you haven’t made the sale, but the customer ends up placing a huge order. Other times, customers seem like they’re happy with your service, but they vanish and take their business to your rivals.
It would be useful to have a crystal ball that allowed you to see into the future and accurately predict customer behavior. You could take all the guesswork out of your marketing and customer relationship management.
Sadly, enterprise-ready crystal balls aren’t commercially available yet. But you do have access to a set of tools and techniques that help you make extremely accurate predictions about what’s going to happen next in your business. This is what’s known as predictive analytics.
What Is Predictive Analytics?
In a nutshell, predictive analytics is the art of extrapolating possible future trends from available data.
This is nothing new of course. Car dealerships can use things like income data to make a rough guess about when a previous customer might be considering a trade-in. The dealership can reach out to potential leads and try to interest them in a new model.
What’s different about predictive analytics is the sheer volume of data available and the analytic techniques involved. Predictive analytics requires enormous quantities of data, plus you need sophisticated data mining techniques to find useful insights. Get it right, and you can form a reasonably clear picture of future customer activity.
Predictive analytics is transforming the automotive industry from top to bottom. On the manufacturing side, Mercedes-AMG is using analytics techniques to improve production efficiency. Mercedes-AMG engines are hand-built, part of that brand’s commitment to excellence. By using predictive analytics, they’ve been able to build next-gen quality assurance tools that work alongside the engineers, optimizing testing processes and identifying potential faults before they occur. It helps to improve efficiency without impacting the Mercedes-AMG’s exceptional quality.
On the marketing side, predictive analytics can drive sales at a dealer level. Volkswagen has built an analytics engine that takes customer data from sources such as financial records, socio-demographics, and consumer lifecycle information, and crunches it all into a single number from 0 to 100. A high score means that the customer will probably buy a Volkswagen product; a low score means that they won’t. It’s a transformational system for dealers, who can now focus their efforts on likely buyers.
Whether you’re a big or small organization, you can make spookily precise predictions about your customer’s future behavior. All you need is the right data.
Where Does the Data Come From?
Customer data is everywhere.
In fact, many companies struggle with the amount of available data. They try to capture too much data or data that isn’t relevant to their needs.
You need to focus on what’s useful.
Before you think about sources of data, you need to think about what you hope to achieve with predictive analytics. What kind of customer behavior do you intend to model? Is your focus going to be on marketing, customer experience, or some other area? What kind of data best suits your goals?
Once you know this, you can look for data sources within your organization, as well as external data. Potential sources include:
- Sales records
- Inventory data
- Customer relationship management (CRM) reports
- Internal spreadsheets and databases
- Survey data such as feedback forms
- Brand sentiment on social media
Even things like the weather can be a legitimate data source. Retail is often impacted by weather conditions, so meteorological data can be a crucial part of predictive analytics.
What Can I Do With Predictive Analytics?
Predictive analytics has endless real-world business applications.
Manufacturers use predictive analytics to predict when machinery is likely to break down. This allows them to get in and perform repairs before things go seriously wrong.
The financial sector uses predictive analysis for everything from loan decisions to fraud prevention. When you have an understanding of a customer’s spending patterns, you know if they will be able to afford loan repayments.
Predictive analytics isn’t just reactive, however. It can also help you to proactively make decisions and grow your business. For example, you can:
Getting new clients is one of the toughest parts of any business. Most companies direct the bulk of their marketing spend to lead acquisition. But once you have the leads, how do you know which ones are likely to convert?
Predictive analytics offers a robust method of lead scoring. Effectively, it’s a matter of looking at historical data to find previous leads that match the profile of the current lead. Doing this gives you an indication of the likelihood of converting the new lead, as well as what the new relationship might be like.
Much of this process can be easily automated so that potential big clients are immediately flagged to go to your best salespeople.
What’s easier than trying to convert a lead? Selling to an existing customer. Whether you’re B2C or B2B, you probably have customers who would be willing to spend more if you made the right offer.
Predictive analytics can help here in several ways, such as by making better recommendations (which we’ll discuss below). You can also predict the likelihood of an upsell by looking at data such as Share of Wallet (SOW).
SOW is the proportion of the customer’s budget that they spend with you. A happy customer with a low SOW is an ideal prospect for upselling.
Why do businesses lose customers? Often, it’s due to something entirely avoidable, like a product that doesn’t completely suit the customer’s needs, or they’re just not engaged with your company.
You can identify this type of customer by looking at the data. A classic example is how often a customer brings their car in for service at a dealership. At first, they come on schedule, then less frequently, and then they stop coming entirely.
Predictive analytics allows you to identify behavior patterns that might indicate that a customer is thinking of moving on. You can then take pre-emptive action, like offering them a discount.
Recommendation engines are at the heart of e-commerce. These are the algorithms that offer tailored product suggestions to visitors. The more data you have, the more useful your recommendations.
For example, if someone purchases a new car today, then they will probably need scheduled maintenance in a few months. Once you dig into the data, connections such as these begin to emerge.
Accurate recommendations are also a useful tool for your salespeople. Not only is it a chance for a profitable cross-sell, but it also shows an understanding of the client’s needs. That builds trust and strengthens the customer relationship.
What Do I Need to Get Started With Predictive Analytics?
When you’re thinking about any new high-tech venture, you should leave the technology part until the end.
Instead, start by looking at your own organization. You’ll need to consider the following things:
1. What are your goals?
Before you begin, agree on some realistic goals for your predictive analytics project. Think in terms of return on investment (ROI). If your focus is marketing, what kind of growth do you anticipate? If it’s about providing an improved customer experience, how will you measure that?
2. Do you have a data culture?
Does your staff capture every customer detail in the CRM? Or is everything stored in Excel files and handwritten notes? Data is the lifeblood of your company, so you need to consider the way it flows through the office. In some cases, you might need to instigate a culture shift in order to capture the required data.
3. Do you have a skills gap?
Evaluate your team. Do you have anyone in your organization with these skills available? Will you need to hire new people, upskill existing staff, or hire an agency to assist?
4. Are your people empowered to make decisions?
This is a crucial step that people often forget. Data can tell you some important things, but what happens then? Can your staff make quick decisions based on data? Do they know how to respond to predictions?
The actual technical side of analytics is usually done on a Software as a Service (SaaS) basis. There are a number of competitors in the analytics space, from big names like Google and IBM to smaller companies offering a bespoke service.
The Real Purpose
The real purpose of predictive analytics is to maximize resource utilization.
Predicting customer behavior allows you to focus your resources where they matter. You want your marketing to focus on people who will be receptive to your brand messaging. You want your salespeople to call people who are likely to buy something. You want your service to focus on customers with whom you will have a high-value relationship.
Without analytics, you’re flying blind. But with the right data, you can guess what a customer is going to do next, maybe before they know themselves.