Have you ever gotten caught up on Facebook clicking one after another on the videos and articles it throws up on your page? Or possibly found yourself on Amazon where you end up buying something you didn’t even know you needed 10 minutes ago? If you have, than you’ve already been welcomed into the world of predictive analytics. Many online sites and brands have been using predictive analysis to target and retain customers by creating personalized content for them.
Data is no longer a level playing field. Companies that leverage AI and machine learning software have a leg up over competitors who are still only using data to look backwards. Research shows that 77% of high-performing customer service teams rate their ability to leverage artificial intelligence as excellent or above average. Companies that get predictive analytics right can greatly improve their customer experiences and in turn leverage their data to offer a better CX and realize true customer lifetime value.
With the rise of Big Data, along with Machine Learning, Predictive Analysis is becoming the status quo. This technology applies to every industry where returning customers are a vital part of their strategy. Companies that are consolidating their customer behavioral and purchase data with SSoV on social media and other public platforms are finding that an omnichannel approach to data can help them build predictive analytic tools that can offer exceptional customer satisfaction and a better customer journey.
Think of predictive analytics it like a weather forecast for your business that can use existing data to predict future outcomes. Predictive analytics leads to higher engagement, increased customer retention, and higher lead generation, which ultimately results in increased sales.
There are seven types of predictive analytics to pay attention to when it comes to customer experience. Each type helps gain better understanding of customers and improve the overall brand experience.
1. Predicting Customer Needs
The most basic, but perhaps the most important, type of analytics is predicting customer needs. This is something many cosmetic brands have utilized brilliantly. For example, online and retail cosmetic giant Sephora keeps track of when their customers have purchased certain products and through additional behavioral information captured via customer service questionnaires, they know how often the customer uses those products, so they have a rough estimate of when they will run out. They can therefore begin sending email and retargeting ad prompts to remind the customer that it is time to re-order the item so they don’t run the risk of running out.
By using the same model of combining usage information gleaned from customer surveys with products that your customers have ordered in the past you can also provide this valuable “time to reorder” prompt. This goes a long way to making the customer feel as if you care about their needs and want to provide them with a personalized experience.
2. Real-Time Product Feedback
Predictive analytics move so quickly that they can help tailor a customer’s experience as it happens. This feature is built into the algorithms of services like Netflix and Spotify. A customer’s actions, such as watching a certain show or skipping certain songs, impacts the next recommendations they’ll receive. Things change quickly based on customer feedback and preferences so brands can capture what customers want at that exact moment.
By using predictive analytic models of past behavior to inform recommendations, a brand can provide a high-touch model of care. Think of how you feel when you receive a personal and unexpected gift from a friend who really knows you; the “I was shopping and saw this and thought of you” type gift. It’s not just the gift itself but the fact that you have a friend who knows you and your preferences and tastes that makes you feel special and valued. This is the same experience that a customer has when you make recommendations for them based upon your knowledge of them. You become more than a brand and more like a treasured part of their lives.
3. Identifying Flight Risk Factors
Data can pinpoint which customers are most at risk for leaving in time for you to take steps to try to hold onto them before that happens. Companies that use predictive analytics to identify flight risk factors can greatly improve their customer retention. For example, FedEx uses data to predict which of its customers will defect to a competitor within 60-90% accuracy based upon a predictive analytic model that combines complaints made both internally and on social media, usage decreases and changes in internal personnel. By using data to identify the factors that lead to churn and the groups most likely to leave, companies can reach out with targeted messages to get the customers to stick around.
Churn is something that all companies fear and also struggle with solving. Predictive analytic models gathered from data points gives brands one of the best lines of defense for churn around. Establishing past predictors of behavior demonstrated by clients lost in the past can help you build a predictive model for the futureand allow you to identify when those behaviors are starting up in current clients, giving you a chance to plug the holes in the dam wall before a flood occurs.
4. Optimizing A Better Pricing Model
Many companies used to change their pricing models based on age or gender, but they can now do it with predictive analytics. This is especially common with insurance companies. Leading insurance companies uses telematics programs and in-car sensors to gauge how well and how often customers drive. That data personalizes the rates for each individual person based on their likelihood of getting in an accident. For example, someone who drives less often and stays close to home will have a lower rate than someone who is always in the car and likes to speed or spends a great deal of time on the highway in stop and go traffic.
While this practice at first blush might seem slightly invasive, in the long run you’re providing your customers with stronger, more personalized pricing models and even incentives for them achieve a better price break for services that fluctuate based on performance metrics. Who doesn’t like to be rewarded for “good behavior”? Giving people discounts for being a loyal customer can also be a way to optimize a better pricing model and keep customers incentivized and happy.
5. Staffing Up Or Down
Predictive analytics can help brands anticipate high or low call volumes. Data from the website’s browsing patterns can tell a company if it needs to staff up or down. If a lot of people have been shopping, there is a greater chance of more calls, but if traffic has slowed, there likely won’t be as many calls. Staffing appropriately saves companies money by not paying people when there isn’t any work and creates a better customer experience by ensuring there are enough people to help customers during busy times.
Predictive analytics can also help businesses know what kind of staffing they need. For example, if a recent product release is discovered to have an operational misfunction or hard to understand directions in assembly, businesses can know that they need to staff customer service with subject matter experts who can properly explain directions or workarounds. This type of excellent service goes a long way to boosting customer loyalty. In fact, according to a recent customer poll, 87% of consumers indicate that having a positive customer service trouble shooting experience is a major factor in whether or not they stay faithful to a brand.
6. Pre-emptive Service Model
If you’ve ever welcomed a new addition to your family, you might wonder how those Gerber Life Insurance people found you and why you’re suddenly getting so many mailings from them. Or possibly a recent stop at the Humane Society meant that you were flooded with ads for pet insurance.
Predictive analytics can be used to predict important events in a customer’s life cycle and increase their revenue during those times. For example, insurance companies frequently use this model by predicting when kids will get their drivers’ licenses or when customers will move to a bigger house. able to predict life events means the company can proactively approach customers about new products right when they need it most.
This type of real-time marketing is incredibly effective, but it can’t happen in a vacuum or by throwing darts at a board; you need deep analytics on both your current customers and target audience in order to make this type of marketing work for you. To have this kind of data at the ready, you need to already be up and running on an intelligent customer data platform like FiO’s Insight Marketing Platform (IMP). Having access to comprehensive intel like Zero Party Data on your customer base allows you to build predictive analytic models that perform and create real ROI.
7. Real-Time Marketing Bets
Personalized marketing can be effective, but it has to be based in data. Predictive analytics can run through data nearly instantly to help companies make real-time marketing moves. Hotels and casinos regularly use this application.
For example, predictive analytics can help marketers predict what it will take for customers to stay at the casino. Data from repeat customers can suggest what it takes to get that customer to stay overnight, such as a free room, food or free chips based on their history and preferences. They can also use that same data to build demographic analytic models that predict what new customers with similar backgrounds and biographical data will do, when, and why.
Marketing bets can pay off by personalizing an offer the customer will enjoy without offering more than it will take the customer to stay. It’s not a smart play to offer the world if all you get in return is a small plot of land. Using data models allows for marketers to craft highly personalized offers that give JUST ENOUGH incentive to get the desired result but not so much that it ends up being a net loss for the business. This type of marketing offer showcases how predictive analytics benefit both the customer and the brand.
Predictive analytics helps brands look towards the future and improve their customer experiences. These seven types of predictive analytics show just how much new data can do when used correctly and strategically, and how they can continue to enhance your CX strategy. The first step to utilizing these powerful marketing tools is always data. In today’s personalized customer journey-centric marketplace, there is no such thing as too much data on your customers. The more you know about them the better you market to them, and the stronger you can build your customer lifetime value percentages.
If you have customer data but don’t know how to use it to build predictive analytics or haven’t even begun collecting the types of customer data you need to stay competitive in today’s market, Group FiO can get your started on the right data-driven path IMMEDIATELY. Take advantage of the FREE TRIAL OFFER of our Insight Marketing Platform today to start using the power of predictive analytics now.