How Predictive Analytics Can Improve Customer Experience

How Predictive Analytics Can Improve Customer Experience

Predictive analytics is a category of data analytics which aids in making predictions about future events, behaviours and outcomes based on historical data, feedback, and techniques including statistical modelling, AI and machine learning. Insights also get better over time, as the tools continue to learn and adapt based on the data fed into them. Think of how your phone predicts what your next word in a text will be or if you meant “its” rather than “it’s” – using the same technology you can improve your business operations and customer experiences. 

While 69% of shoppers want personalised experiences, only 40% of brands are offering them and predictive analytics can be an excellent tool to inject personalisation into your customer experiences. Regardless of your industry, introducing predictive analytics to your processes can immensely improve how you serve your customers and operate internally. A 2018 report from Zion Market Research found that the global predictive analytics market is projected to reach approximately $10.95 billion by 2022, with the market growing by 21% each year since 2016. And with that rate of growth, you can only expect your competitors are already getting involved, so it could be advantageous for any company to enter the market or expand how predictive analytics play into their business strategy. 

Using Predictive Analytics to Improve CX 

1. Needs Forecasting

Predictive analytics enables businesses to anticipate customer needs, oftentimes before the customer does, allowing companies to be proactive, curating marketing tools, messaging and product recommendations to inspire customer action. This is an element of the personalised experience which customers appreciate. Predictive analytics makes it possible for businesses to forecast customer needs based on previous purchases, viewed products or services, interests and other relevant data such as the purchase history of similar customers. 

2. Churn Reduction

High churn risk customers can be identified using predictive analytics so that you can pay close attention to the customer before they leave you for a competitor. When capturing customer feedback, you can identify whether or not a customer is likely to recommend you or not to others through an NPS score. Analytics can be used to predict how and when a promoter, someone who would recommend your business, may move into neutral or negative territory to become a passive or detractor (those who wouldn’t recommend you to others), requiring special attention or incentives to remain satisfied. A sophisticated system can rely on data elements such as customer effort, cycle time, retry rates and or clues such as bad feedback ratings to generate predictive alarms to drive recommendations as to how to maintain customer satisfaction before they churn or become detractors. You can create and analyse “What If” scenarios to predict customer behaviour

3. The Virtual Concierge

Customers expect real-time, highly personalised experiences as a result of so many companies they interact with daily doing it so well. It makes their experiences more convenient and enjoyable. So, just as Netflix or Spotify offer real-time highly personalised suggestions for films and music based on past a combination of factors such as likes, views, demographics and what customers with similar tastes have enjoyed, you can use predictive analytics driven by AI to offer customers instant gratification and personalised experiences. 

4. Improved Operational Efficiency & Resource Allocation

Besides its positive direct impact on Customer Experience, predictive analytics can significantly improve internal operational efficiency and how resources are allocated to improve CX performance indirectly as well. Having the cleanest, more efficient internal operations will help to ensure that the customer is never unnecessarily burdened. Using predictive models can assist in staffing a call centre or brick and mortar locations, forecasting inventory needs, and customer traffic all based on historical data and patterns. This streamlines costs and reduces wasted resources. 

Furthermore, by drawing on predictive analytics fed by phone calls, emails, social media sentiment, customer escalations and other data from other key touchpoints, businesses can determine the best way to cater to specific customer demands and exceed expectations. It can even streamline shipping processes, as predictive analytics helps businesses and their shipping partners ensure accurate and reliable arrivals by forecasting potential issues in the shipping journey or instances in which shipping can be expedited. 

Introducing predictive analytics to your CX and operational initiatives can enable you to better serve and understand your customers while improving productivity and efficiency. Ultimately, it will give you an edge in the competitive CX landscape with the ability to anticipate, predict and react in real-time to offer customers the best experiences available.