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06/17/2022

How to Use Predictive Analytics to Improve Customer Experiences

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Icons representing predictive analytics

It’s been a long time since companies discovered that superior customer experience (CX) is a critical factor in gaining a competitive advantage. Striving to win customers’ hearts, organizations are continuously trying to innovate and come up with new sophisticated CX strategies. 

Forrester reveals that 67% of companies consider CX improvement as a critical priority. However, the traditional methods that they employ to evaluate CX prove insufficient today. 

Surveys have long been the only feasible way to gather feedback on CX. While being a valuable research tool, surveys often fall short of identifying CX issues.

First, companies have to spend a considerable amount of resources to make sense of questionnaires and improve conventionally low response rates, only to make ambiguous conclusions about CX performance.

Second, surveys provide insights with significant delays, hindering companies from timely addressing issues. Finally, surveys generally can’t pinpoint the root cause of the problems in the customer journey. 

Listen to Kellogg’s Take on All Things Analytics

With today’s availability of predictive analytics services and access to large amounts of consumer data, companies can get a much clearer picture of customer journeys, precisely identify CX issues, and personalize customer experiences with various systems on the fly. Proactively monitoring CX performance and reacting to customer problems in real time is what often distinguishes leading companies from the rest. 

Similar to how predictive analytics have transformed marketing, it can also change the way companies orchestrate their CX efforts. Social media sentiment, purchase patterns, customer attitude datasets, consumer wearables, and many other data sources allow companies to tell exactly where, when, and why a certain customer has CX-related issues.

This way, you don’t need to ask customers about their experiences but simply draw granular insights from their interaction data. 

Implementation Tips 

Predictive analytics will bring tangible results only when you have a clear business objective. Far too often, companies build excellent predictive analytics models that don't bring any business value. To mitigate this common pitfall, define what exact problem you are trying to solve with predictive analytics. In the case of CX improvement, it can be decreasing costs, increasing customer retention, reducing churn, or enhancing brand loyalty. 

Afterward, it’s critical to get a 360-degree view of your customers by establishing a comprehensive database that consolidates all types of customer data. Quite often, different business departments work in silos, meaning that they collect and process customer data without sharing it with the rest of the organization.

This can lead to an incomplete customer database and hinder the full potential of a CX platform. A full-fledged customer data lake is one of the most critical prerequisites for an all-around understanding of customer experiences.  

Most importantly, a common reason for failed predictive analytics-based CX initiatives is low-quality data. After identifying business goals and ensuring that you have a unified database of both customer and business data, it’s time to start training your predictive analytics model. 

[Read more: Retail and Consumer Goods Analytics Study]

The accuracy and business value of your model is almost entirely dependent on the volume and quality of training data. Small datasets won’t suffice as they often cause unwanted variability, while false information about your customers leads to model inaccuracy. For example, companies tend to include information from incorrectly filled out surveys into their training datasets, causing data scientists to draw inaccurate insights. 

Finally, it’s also paramount to continuously monitor and enhance your predictive analytics model. At least from a business standpoint, companies’ objectives change and evolve, calling for a continuous optimization of the system. 

Conclusion

Comprehensive and well-thought-out ML-based CX platforms can enable proactive customer engagement, ensure adequate levels of customer satisfaction, reduce churn, and enable accurate revenue predictions. However, the transition from the conventional survey-based CX performance measurement is never an easy task. 

It’s critical to treat CX platform implementation not as a one-time project but an ongoing company-wide transformation. In the majority of cases, it becomes apparent that employees need significant upskilling to leverage predictive analytics and, most importantly, a mindset shift. When it comes to such drastic changes in workflows, cultural change becomes the backbone of success. 

This is why it’s wise to start with easy-to-implement projects that produce quick results. This low-hanging-fruit approach allows for an overall boost of morale and a more gradual introduction to holistic data analytics. 

As with the majority of other business disciplines, the future of CX is data-driven. Understanding your customers’ needs and wants has always been the core element of successful business, so those who can reap the benefits of predictive analytics for CX enhancement will have a significant competitive edge. 

Andrey Koptelov, Itransition’s Innovation Analyst 

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