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Advanced Analytics to Predict Key Moments Across the Entire Customer Lifecycle
The customer lifecycle doesn’t begin on a website and end at a transaction. To stay competitive in today’s crowded market, companies must not only meet expectations, they must exceed them.

Brands are paying close attention to what customers are telling them - verbally and through actions. Brands use that data to inform their response. With analytics, companies can turn data into insights and anticipate customer needs.

The customer lifecycle has always been a part of all organizations, but modern technology has offered more sophisticated techniques and algorithms which allow for a deep behavioral analysis. Today’s on-demand, rapid-access world requires a quick, in-depth approach to predicting a customer’s next move in order to be successful.

As customers embrace the five stages of the customer lifecycle – reach, acquisition, conversion, retention, loyalty – even unknowingly, the need for organizations to proactively predict behaviors becomes inevitable. It’s no longer enough to simply rely on one-time behavior analyses at the point of entry, such as a website click or transaction; now, companies must take proactive steps to analyze the entire customer journey across all their channels, and throughout the entire customer lifecycle.

Advanced Analytics During the Customer Lifecycle

Building high-performing AI models from data or deploying those AI models to be widely used can become difficult. Maxia's AI are thoroughly trained and validated to serve live predictions from your latest data. Generally, analytics are applied in each stage by:

  • Stage one: Reach – Helps a company measure the initial interaction by quantifying brand metrics. Companies can predict which campaigns, marketing channels, and content resonate best with their customers and focus their time improving those campaigns.
  • Stage two: Acquisition – Earning the potential customer’s business. Of course, sales teams spend time guiding people to purchase, but that’s not always easy, or successful. Advanced analytics helps extend the reach to more people. This is where trends and behavior patterns become key to identifying new customers and their needs.
  • Stage three: Conversion – Advanced analytics helps convert a prospect into a customer. It helps analyze any roadblocks or hesitation, barriers that customers find during the conversion process, and friction between company and customer. This can help assess what support options and friction removal needs to be done.
  • Stage four: Retention – Keeping customers is beneficial to a business’ success. Knowing how to avoid at-risk customers and offering real-time results mediates issues before they might worsen. A company can implement retention strategies to maintain and foster customer relationships from advanced analytics.
  • Stage five: Loyalty – Identifying the most viable “super fans” of a company. Through a comprehensive predictive analytics model of behavioral analyses, past and future actions, and communication, a company is able to identify loyal customer behavior and activate them in many ways, driving more revenue.

Why Provide Advanced Analytics throughout the Entire Customer Lifecycle?

The time for point solutions for transforming the customer lifecycle has passed. Organizations now need a more advanced approach to increase customer outcomes and detect their future journey throughout the entire customer lifecycle. 

An effective predictive analytics approach should be able to detect a customer’s next move in real time, enabling organizations to predict the likelihood of the customer’s next event. It should perform seasonal, historical, behavioral and trait analyses of customer actions to predict the most probable outcome. Further, it should provide proactive, future user behavior, not simply one-off behavior assessments at particularly insightful single points of use.

The Maxia Difference

With Maxia, an organization is able to select the user action or trait that they are interested in predicting. From here, Maxia analyzes the data and optimizes a model that predicts the likelihood of this event for each customer. Maxia then sends the predictions to the organization’s analytics provider and other business integrations.

Maxia delivers a comprehensive predictive analytics model that processes customer data from Segment and transforms it into features for AI models. Maxia designs, validates and deploys AI models that predict user actions and user properties. Our AI and current hands-on process with our team takes a proactive approach to user predictions, identifying previously unknown patterns for rapid decision-making.

Our process is streamlined, allowing Maxia customers to sign up, set up their first Warehouse, and then jump on a call with us to access their specific AI needs, opportunities, and receive their Maxia API key. From there, we set up your Maxia Integration inside of Segment, allowing us to use your historic and live analytics data. Using that data, Maxia trains an AI model to conduct test predictions, and then sets it up to serve live predictions from newest data, sending the predictions back into Segment. To learn more about using advanced analytics during the entire customer lifecycles, reach out to us.