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 future and 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. Know more visit our blog here : https://www.groupfio.com/7-ways-to-use-predictive-analytics-to-improve-customer-experience/
