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2020 & beyond – where to in CX, UX and research design?

Robert Van Veenendaal explains how market research professionals can leverage AI to advance CX program maturity, ultimately leading to improved business performance.

Artificial intelligence is being embedded in more enterprise organisations, which presents a unique opportunity for Australian businesses to exceed consumer expectations in 2020. This year, businesses that leverage AI to collect, analyse and act on customer feedback will truly stand out from competitors that are stuck doing this manually. In fact, a recent MaritzCX study of more than 5,000 CX practitioners shows how companies that have reached the highest level of CX maturity are three times more successful at driving significant financial improvement and customer retention than companies in the bottom half of the maturity framework.

Companies that have achieved the highest level of CX maturity are able to leverage AI to demonstrate a focus on prediction and churn prevention and have evolved away from static business processes that negatively impact on customer experience.

With new AI advancements, researchers can expect AI to amplify and extend the skill set of the research team to free up their capacity to focus on high-value business opportunities.

Research teams have access to more data than ever before, so they don’t need help collecting more of it. Instead, AI advancements will point to actionable insights within this data, providing valuable recommendations on where to focus time, energy and business investment to see the highest impact.

There are three essential AI trends that stand out for researchers to evolve their CX program in 2020.

1. Improving our ability to predict customer intent

As surveys become shorter, researchers will need to pull richer insight from a smaller amount of feedback data. Text analytics software has recently evolved from categorising feedback into ‘sentiment’ groups (positive, neutral, negative) to pinpointing ‘emotion’ (excitement, anxiety, anger) and will soon be able to predict customer intent. This will give businesses who integrate the capability an extraordinary advantage. For example, technology will not only determine that a piece of unstructured qualitative feedback is negative but will also indicate the customer is feeling stress and intending to churn, which empowers front-line teams to pre-emptively close the loop with individual customers before they churn.

2. Calculating implied satisfaction scores

As the consumer becomes progressively overwhelmed with the volume of survey invitations, predictive capability becomes imperative. Researchers will need to leverage AI and machine learning to calculate an implied satisfaction score for non-respondents. This sort of predictive modelling gives businesses the power to action what customers need, using operational data such as age range, product usage behaviour, market segment and channel to predict behaviour.

One of Australia’s largest health funds utilised a predictive chi-square automatic interaction detector (CHAID) model to forecast which customers are more likely to cancel their policy based on operational and historical churn data alone. By implementing this model, the health fund was able to predict behaviour across the entire customer base, which allowed them to focus on those customers most likely to be retained. This allowed smaller teams with limited resources to focus on certain segments based on churn propensity and test how they can achieve the highest retention rates from different treatments.

For example, the health fund was able to notify the customer recovery team when a customer had a churn propensity score between 70 and 80 per cent and were then able to proactively contact the customers and retain their business. Moving into 2020, AI and machine learning will automatically optimise the model over time to improve accuracy and measure the impact of the closed-loop initiative.

3. Delivering more for less

The final trend we are seeing in Australia is a top-down consideration of the value of the CX function as a whole. Companies who can’t successfully demonstrate return on experience investment run into the problem of reduced resources and competing priorities. Therefore, in 2020, CX teams are likely to be expected to deliver more with less. In order to continue securing support for experience management programs, researchers will need to spend less time manually discovering the anomalies in data and more time understanding why and where the changes in performance data are coming from.

Leading Australian brands will soon have the capability to leverage intelligent machines to detect and report anomalies in customer experience data. Moving from rule-based alerts which are programmed by researchers, AI will soon be able to determine that key performance metrics have dipped and alert front-line teams to this shift alongside a recommended ‘next-bestaction’. For example, if there is a significant change to an NPS score, AI will not only flag the change but will alert you to the potential drivers related to change in performance to understand and remediate an issue at the earliest possible intervention point.

The year ahead

2020 will be an exciting year ahead for CX and research practitioners with new challenges and opportunities. Relying on tools like AI, text analytics and predictive modelling will empower market research professionals in this space to discover stronger insights than ever before, leaving businesses to act on these to make memorable customer experiences.

Author: Robert Van Veenendaal, VP of Service Delivery, Maritzcx

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Photo by Macau Photo Agency on Unsplash

This article also appears in the February – April 2020 edition of AMSRS publication, Research News – CX, UX & Research Design. Check out the rest of the articles in this edition.

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