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Companies are investing more in tools and technologies that will help them understand customers deeply. With the realization that it is 6 – 7 times more expensive to acquire customers than to retain them, companies are shifting their focus on the existing customers.
The most effective way to improve retention and loyalty is by delivering an exceptional customer experience. For years, companies relied on surveys to generate data on customer preference & behavior, which they used to track their CX performance. However, as customers’ needs changed, companies realized that survey data was not enough.
Companies are now investing in a more advanced approach that takes full advantage of all the available data from internal and external sources.
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Today, companies have access to a data pool on customer interactions, transactions, and profiles; customer attitudes, purchase behaviors and preferences, social media activity; and new data sets generated by the Internet of Things.
Those companies focusing on becoming customer-centric are harnessing the analytics capability of predictive insights to build more deeper relationships with customers. With the help of AI & ML, these companies can now forecast issues in the customer journey and prepare the most impactful solution for them. They can shape the CX with effective strategies to reduce churn, improve loyalty, and boost revenue.
Brands require a comprehensive view of the entire customer experience & journey. They also need to be able to dig deep and obtain granular details of what drives customers’ purchase decisions & nuances of customer behavior. While surveys are the simplest way to interact with customers directly and gather their opinions, surveys are still flawed because they fail to unveil the root cause of customer sentiment.
Predictive analytics helps companies go deeper, understand, and track the key drivers in customer satisfaction. Predictive analytics uses ML & AI to assign scores to individual customers. It enables brands to predict what customers want or may want before even customers realize what they want. It also empowers brands to forecast issues in the customer journey or CX before it escalates. Advanced analytics empowers companies to gain a competitive advantage and discover growth opportunities.
Boost loyalty, customer lifetime value & revenue.
You can segment customers by comparing past and current customer behavior. You can personalize product/service recommendations and messages using these segments to improve customer experience.
For example, an eCommerce website can utilize predictive analytics to estimate when customers purchase less from their website. The eCommerce company can predict customer behavior based on historical data and offer personalized recommendations and promotions/discounts during those periods. Delivering relevant and promotional product offers can positively impact re-engaging the customers and improving the revenue during the off-period.
By leveraging predictive analytics, you can reach customers at the most relevant time in the customer journey.
Predictive analytics monitors customer behavior and interprets data in real-time. It analyzes the customer transaction and interaction and creates audience segments to deliver targeted content based on the customer segments.
Forecasting customer needs one of the common usages of predictive analytics. You can employ predictive analytics to anticipate how particular customers react to a particular campaign or offer. With the knowledge of each customer’s behavior and needs, you can adapt your offerings accordingly.
Predictive analytics also enables you to tailor a customer’s experience as it happens. With real-time analysis of customers’ actions, you can make recommendations on product offering or song/ shows recommendations.
Companies often focus more on the idea of acquiring new customers than they ignore the dropping-off of existing customers.
Companies fail to realize that after gaining a new customer, they have the ability to deliver an improved and perfect customer experience by forecasting their specific needs based on the data collected from their journey.
You can analyze and interpret their previous purchases, viewed products, and abandoned carts with predictive analytics. You can create unique customer profiles and segment customers by combining and comparing their data with broader profiles. You can personalize their experience and increase your customer loyalty and ROI value.
You can also leverage predictive analytics in a customer support setting by forecasting a potential issue and solving it proactively before customers complain about it. With predictive analytics, you can increase the customer lifetime value.
By using predictive analytics on data, you can identify loyal customers and those at risk of leaving. With predictive analytics, you can interpret data in real-time and adapt your customer services, offers, messages, and marketing campaigns to meet customer needs.
You can enable a churn model that will automate analysis of customers’ transactional and non-transactional data to generate risk scores. Being able to predict potential attrition can help you work on re-designing your strategies to make the most impact on customer satisfaction and experience.
According to BCG, corporate banks can reduce customer attrition by 20 – 30% by employing a predictive analytics-based churn model.
Starbucks is using predictive analytics to improve customer loyalty.
American Express makes use of predictive analytics to predict potential fraud and identify at-risk customers. Leveraging predictive analytics, the company can warn its customers about fraud attempts via emails or phone calls.
By discovering potential fraud attacks before it occurs, the company can help its customers stay safe. Doing so, American Express gains loyal customers. The company also maintains the lowest rates for fraud loss in the industry.
We log in to Spotify to listen to our favorite song. And that’s what we do; we search for our favorite song and create our playlists. Spotify uses this information to recommend you songs every week.
Using predictive analytics, they create the “Discover Weekly” playlist with songs similar to the genre based on what you listen to daily on the app. Spotify offers a more personalized playlist the more you use the app.
It is overwhelming and stressful for a beginner or new user to look for beauty products online. Sephora makes the new user’s experience flawless by using predictive analytics.
The brand uses customer footprint on the app/website, such as interest, purchases, preferences, Skin-ID technologies, and more to create a unique profile of individual customers. Using the profiles, Sephora designs a personalized recommendation page for each of its customers.
Predictive analytics allows companies to measure better and manage their CX performance. It also helps to improve strategic decision-making.
CX leaders can accurately view the key drivers that influence customer experience. Additionally, create a holistic view of each customer’s satisfaction and value potential to act upon it in real-time.
Predictive analytics tool has made delivering exceptional customer experience efficient by adding value to each customer and CX performance.
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