How predictive analytics is changing the future of CX

In a post-pandemic world, the landscape of business and, subsequently, customer expectations are changing drastically. While almost every industry is trying to put its foot on the digital platform, it’s increasing the difficulties of maintaining customer experience for the organizations. In this situation, the traditional survey-based system can never be enough. So, leading companies are finding better ways to dive deep into the pain points, gaps, and positive notes from customers to serve better and keep the growth curve upwards. This article will shed some light on the shortcomings of the survey-based CX system and the possible future.

Measuring CX based on Customer Feedback: A large chunk of leading corporations still believes in the traditional way of measuring the CX, which is a typical survey-based method.

In this method, the companies have a dedicated team to prepare questionnaires about the customers’ purchase experience, and the same are distributed to catch a post-purchase experience. Though this is an age-old method and has many benefits, a recent survey conducted by McKinsey & Company shows a different statistical picture about the thought of the leadership team of companies on survey-based cx.

	Limited	Reactive	Ambiguous	Unfocused
	7% of the customer voice is shared with CX leaders.	13% -think they can take real-time measure on CX issues	16% – they can reach to the root cause by using survey	4%- Calculates a decision return on investment

So, based on the survey, it can be concluded that companies are looking into better solutions to measure CX.

Shortcomings of the Survey-Based Measure: Lower response rates, lack of data, and data ambiguity are the primary reasons that pushed companies to think otherwise. In a nutshell, the disadvantage of a traditional procedure could be the following:

The survey doesn’t provide the complete data: Typically, even a survey link is sent to all consumers, but only a certain proportion of people participate in the survey. So, it’s not the 100% of data that a company is receiving.
Less Response Rate: Sometimes, answering the survey questions after every purchase can be redundant. In that case, there’s a chance of getting inappropriate data from a survey.
Data Accuracy is Questionable: There’s no guarantee of an authentic and honest response from a survey.
Might result in Incorrect Conclusion: The lack of accuracy in the collected data can hinder the process of decision-making for any company. Without accurate data, any organization might come to an incorrect conclusion that can affect future customer policies, internal employee-related decisions, and future investment planning.

CX Metrics other than Survey-Based: There are a few different ways of measuring the CX other than the survey-based.

CES(Customer Effort Score): It simply measures customers’ effort during a purchasing process. For example, the options could range from very easy, easy, difficult, and very difficult.
NPS (Net Promoter Score): This asks the customers how they would like to recommend the same product to others, and there is a range of ratings from 1-10.

Again, these two metrics continue the exact data authenticity and accuracy problems. The rating is subjective to a customer’s emotion at that point and can’t be relied on entirely.

A Newer Approach to Measure CX- Predictive Customer Analysis: In the present day, CX is the spine of any business success. It pushes the companies to get granular insight into a CX cycle. To achieve a vast and detailed view, any organization needs a massive array of data not limited to some conducted surveys.

Thankfully, every work now depends on a pool of data, and there are many ways to access them lawfully.

Companies can collect data from:

Browser and search histories of users from their smartphones and other devices.
Third-party data sets can be used to understand customer attitudes and purchasing behavior.
From customers’ social media activity, like what kind of ads they are interested in while using the social media.
The Internet of Things generates new data sets on customer health, sentiment, and location.

Besides, any business’s marketing and revenue management sectors have already transformed due to the collection and analysis of these massive data sets.

How the newer approach can transform the industry: Customer predictive analysis promises a much more accurate data collection, which helps the business in other decision-making. Organizations are more likely to make correct investment plans to achieve better CX, and thus customers will remain loyal and maintain a long-term relationship with the company. This would be a business growth factor.

Case Study: There are many practical examples where Predictive Analysis proved beneficial.

Can Lower the Delayed Payment Rate for Financial Sector: Missed payment can cause heavy disruption in any financial sector. Thus, it enhances the business factors and increases customer satisfaction.
Can boost performance on digital channels: One of the top credit card companies wanted to boost their performance on digital platforms, implementing an omnichannel prediction strategy. It gathered data from customer interactions and transactions through different channels to understand their satisfaction drive. The data includes various factors like lead times and hopping frequency between other digital media. It also recorded adverse outcomes and how the company handled those then.
This systematic analytics-driven approach leads the company to enable an organized strategy towards a journey improvement cycle and better customer service.
Example of OTT platforms: A go-to example of predictive analysis can be any OTT platform, like Netflix. It continuously monitors the data of users’ search and binge history and suggests shows accordingly.

Predictive Analysis Theory to Action: The transition from a traditional survey-based system to the predictive-analysis one is not an overnight thing; Below are the four key factors that can be considered before implementing a data-analysis-based system.

Mindset is vital: It takes a paradigm-shifting perspective for a leadership team to take a call to change the existing CX system. Implementing predictive analysis to CX is an amalgamation of both marketing and technology. So, in that case, the CX team, technology team, and leadership team need to understand this coordination and agree to try the new system.
Cross-Functional Team Set-up: The prime element of Predictive Analysis is data. Data gathering and filtering are vital tasks of this system. e.g., the data team should be responsible for the data gathering and the algorithm to filter them out. And the leadership team would be making decisions on strategies based on the data, and the CX team would be taking care of providing services according to the decision process.
Start from the Basic: The entire process should be divided into multiple stages, and it should start from the very basic one- gathering data from various channels. Finally, the business decision and implementations would come.
Focus on Use-Cases: The data helps understand many factors from potential long-term customers to their pain points and reason of dissatisfaction.

Missing Pieces: Predictive analysis is all about collecting data of users, and it demands monitoring a user’s activity through various channels. So the tool will collect data from browsing histories to their chat histories to any comments made by the user, which can call out for data privacy and breach. So, any organization implementing the new tool to enhance CX must have a solid conscience for collecting data lawfully. Otherwise, it would upset the customers instead of making them happier.

About the author:

Jay Anthony, Marketing Head, TECHVED Consulting