Why Predictive Analytics is the Future of CX Marketing

The digital marketing and customer experience landscape is ever-changing. Traditional methods of data analysis, such as surveys, cannot always keep up with new standards. Meanwhile, the long-awaited developments over Predictive Analytics are gradually seeping into the future of customer experience, thanks to advancing technologies.

Let’s discover why.

The ability to foresee the future is a superpower most people would like to have, but marketers most of all.

We spend most of our business resources attempting to figure out “where the market is heading” to adapt our strategies strategically and successfully. 

The general market is massive, containing billions of people with different needs and wants that drive their usually unpredictable spending habits. Therefore, the need to know the customers and understand them better than they know themselves leads to predictive analytics. 

Any good company is always striving to improve its customer experience while also anticipating new and unexpected changes in consumer behavior. Historically speaking, we haven’t been able to come nearly as close to accurately guessing the customer’s next move until now. Instead, we have mostly just asked people what they think and how they feel, adopting survey-based methods.

Nowadays, we are dealing with something much better, and which not many CX managers and experts have yet to understand fully.

Predictive analytics promises to revolutionize the way we perform impact analysis and the customer experience. However, the field is not growing with enough momentum and speed.

Italian philosopher Antonio Gramsci famously referred to the 1920s-30s period as an “interregnum,” or a gap in time when there’s no social structure. He described the crisis with the phrase, “the old is dying, and the new cannot be born.” The saying can also reflect the ever-changing digital marketing landscape. 

That is, traditional business practices like survey-based analysis methods are dying out. Like zombies, they still lurk among us even when more and more industry professionals are trusting them less.

A recent study found that 93 percent of respondents use a survey-based metric (e.g., Customer Satisfaction Score, Customer Effort Score) as their primary way to measure CX performance. However, only 15 percent of executive leaders claimed to be fully satisfied with how their company measured customer success.

Although CX managers are very familiar with the limitations of surveys-based metrics, the idea of implementing more modern analysis methods still seems far too complex for many and daunting. Yet, this is actually not the case, and we will later explain why, but let’s first dive into the limitations of survey-based analysis methods to find out exactly why “the old is dying.”

We can arguably identify at least four different main reasons why survey-based analysis methods are far from perfect. They are numerically limited, ambiguous, unfocused, and often provide slow and insufficient reactions.

 

• Quantitatively Limited: As the first apparent flaw in their design, surveys generally do not provide as much numerical value or results as data-driven analytics. Only about seven percent of customers actually answer our surveys. 
• Ambiguous Response: As detailed more in our previous articles, directly asking the customers does not always ensure as many sincere and detailed answers as we may want. Only 16 percent of CX leaders agreed that surveys provide enough granular reports.
• Unclear Outcomes: Many executives can agree that there’s no sure way or concrete evidence of how survey-based analysis methods transform their business. In other words, they are unsure whether such methods bring positive benefits or outcomes. The ROI for each company decision is hard to estimate when only asking customers. 
• Slow and Insufficient Reactions: Even when we can identify prevalent customer issues through survey-based methods, the approach does not always allow for a sufficient amount of immediate reactions and actions. In the digital environment, customers expect as many real-time interventions as possible.

On the other hand, data-driven methods can build a more trustworthy customer journey, which is the foundation of predictive analytics.

Instead of asking the customers, we can see them and follow them through what we call the customer journey.

The customer journey is a story containing all the different interactions that the customer had with a business, either directly or indirectly.

In the digital landscape, we can now track, process, and understand the customer journey better than ever. It is known that understanding the past allows us to prepare for the future, and this belief is what powers predictive analytics.

Analyzing all the interactions that occur during the customer’s experience allows us to identify where and when something went wrong and then react quickly and precisely.

Yet, to really understand the customer journey, we must also consider the different types of customer interactions. There are two main types: direct interactions and indirect ones.

Let’s next observe their differences.

The most straightforward approach to successfully implement a data-driven customer experience is through providing direct interactions. Tracking this type of interaction can be fully automated, requiring little human effort. Roughly speaking, we can identify the direct interactions a customer has had with our business using financial and operational data, which are both aggregated and covers details about individual customers.

As such, the database allows us to understand our customers better and design better products for the future. Plus, to intervene in real-time, all thanks to feedback loops.

Textbook examples of using this predictive database in real-time include major companies like Spotify and Netflix, both of which deliver suggestions based on what you have been watching or listening to.

Even though direct interactions seem to be the most ordinary strategy to enhance the customer journey, and even if they are already in place, we still suggest thinking bigger and bolder about better integration between the CX and IT teams. A more holistic approach is needed to design the algorithm following a clear customer experience vision.

Indirect interactions with the customers are significant as well and possibly even more impactful. Through indirect communications, we refer to any or all the mentions of our brand in social media comments, online chat rooms, and online reviews. Texts or content from these channels is collectively known as unstructured feedback because they have clearly been written in a natural language, while the vast amount of these kinds of feedback makes it hard to systematize.

However, they are fundamentally important because the feedback allows us to listen to the customers’ voices and understand their complex sentiments or feelings toward our services and products. It also becomes why survey-based methods are still ‘alive’ and needed today, for they are the best-known way to collect structured feedback about customer sentiment. Albeit, such practices still carry the limitations we mentioned before.

What if there is a way to structure the unstructured feedback that comes directly from the source?

This is where Wonderflow comes in.

Wonderflow can convert all the indirect customer interactions into actionable insights. 

Our top-notch A.I. can process natural language and transform all the social media comments and online reviews (among many more) into ‘clean’ data to complete the entire picture of the customer journey.

CX managers will have a more precise and better understanding of customer behaviors and their publicly voiced needs and wants.

Asking the customers for their opinions belongs more to the past. It is now the time to allow “the new” to be born.