How Predictive Analytics Powers Your Customer Journey Analytics | Pecan AI

How Predictive Analytics Powers Your Customer Journey Analytics

Customer expectations continue to change each and every year. Without a proper customer journey analytics, it’s nearly impossible to know precisely how your customers are changing. In that case, it’s tough to adjust your business model to better meet their needs.

Leading research says using data to understand your customers’ journey is the best way to identify behavior changes and note important insights. This analytic approach allows brands to adjust product features, messaging mixes, and communication strategies. Then, you can meet customers where they’re at and exceed their expectations.

But what is customer journey analytics?

According to CX Today, customer journey analytics is the science of assessing customer behavior across touchpoints and over time. Above all, this information allows you to determine how customer behavior affects company outcomes. Leveraging customer analytics allows brands to take a more proactive approach to customer management, improving loyalty and lowering churn.

Why should your business use customer journey analytics?

A number of factors have influenced the growing adoption of customer journey analytics over the last few years:

  • Customer sell cycles are lengthening: The COVID-19 pandemic caused serious supply chain issues, now combined with high inflation. Customers are shopping longer and weighing options prior to making a purchasing decision. Brands have adopted customer journey analytics to better understand these changing shopping behaviors.
  • Growth of online data: According to McKinsey, data is exploding. Five years ago, we had around 30 million zettabytes of data globally. Now we have 80 million. In another five years, we’ll have 300 million.
  • Customer preference: According to a May 2022 Salesforce report, 73% of respondents expect companies to understand their unique needs and expectations. In addition, 62% think companies should anticipate their needs.

What do AI and machine learning contribute?

In addition to the items above — and a considerable drive in the adoption of customer journey analytics — is the introduction of AI and machine learning. With flexible, accessible predictive modeling software, brands can now analyze all of their customer data and every stage of the buyer journey.

install engage loyalty cross-sell

With advanced analytics and predictive analytics software, you can analyze every step of the customer journey, giving you insights into likely future events. Equipped with this foresight about customers’ future behavior, you can empower your customer loyalty team. Provided that information, they can drive reward programs that extend the lifetime value of your customers. You can also give customer retention teams advance knowledge about high-flight-risk customers to support proactive strategies to lower churn.

As your organization embraces predictive analytics, and the health of your customer database improves, you can even create predictive models using information about your most valuable customers. Collaborate on this modeling with your marketing team to improve conversion rates and aid in prospecting campaigns with online look-alike ad targeting.

In conclusion, with a predictive approach to customer journey analytics, you can do more than look at what customers did in the past. With predictive analytics, you see and predict their journey. With this foresight, you can make decisions proactively and boost business outcomes across the board.

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