30-Second Summary:

  • Big data requires specialized tools to collect and process while small data can be handled in a few spreadsheets.

  • Using big data to personalize your services can make your customers feel known and increase their brand loyalty.

  • Problems can be identified and solved even before they arise with big data predictive models.

  • Customer expectations are understood with information from big data and their journey can be optimized to fit accordingly.

  • Online surveys are incompetent for understanding customer needs. Using big data can reveal more accurate insights to help improve customer experience.

Introduction

Are you asking “why is data-driven analytics of interest to companies when customer experience is concerned?”

Customer reviews could be nightmarish or euphoric for brands because good reviews keep companies in business and bad reviews make existence a living hell. The importance of big data in improving customer experience (CX) has become quite obvious in the last decade. Companies using big data to improve customer experiences are rewarded with loyalty and increased revenue.

In this article, we provide comprehensive insights on how these companies are using big data to deliver personalized customer experiences. We also share tips on how big data and knowledge management can change your customer acquisition and retention game.

But first, let’s catch up with a quick comparison.

Big data vs. Small data

Big data is described as large data sets often measured in petabytes, zettabytes, or exabytes. Big data sets often contain varied elements and they can be structured, semi-structured or unstructured data. In addition to volume and variation, big data increases as more and more people interact with its sources (e.g. apps, websites, etc).

Small data, on the other hand, are small data sets with a maximum volume of gigabytes or terabytes. Data like this can be put in an Excel file or stored in centralized storage on one server. Items in small data sets are organized in such a manner that they’re immediately accessible and actionable. Small data can be derived from local sources or big data metrics and is often presented visually. It is used to influence everyday business activities and is not for long-term planning.

Unlike small data, managing big data requires special big data tools because traditional data processors can’t handle the large volume.

How understanding and use of big data can improve customer experience

Regardless of the methods that you have used in acquiring your first set of customers, it would take excellent customer experience to keep them. In today’s business landscape, data-driven strategies are the most efficient and effective. It enables companies to analyze each touchpoint in the customer journey and optimize accordingly. Here are some specific ways that big data can help improve your customer experiences.

Personalize services

A personal touch makes customers feel seen and special. That’s the beauty of customer experience. These personalized customer experiences are carefully curated for each user. In return, these users are more likely to remain loyal to your brand because they feel known. According to a report from Accenture, 91% of people are more likely to patronize a brand they feel knows them well enough to present them with relevant recommendations.

Big data-driven personalized services can improve sales and brand reputation significantly. Amazon, Netflix, and Spotify all use big data tools to recommend new products and features to customers. Your business can consider doing the same. However, you have to be careful about the data collected and the information you communicate with your customers. You don’t want to spook them like Target did when it used big data to predict that people were pregnant even before they started noticing symptoms.

Early crisis management

Negative reviews spread like wildfire on the internet. One disgruntled customer can turn the hearts of millions of potential customers against a brand with one post or comment on social media, Trustpilot, Yelp, or wherever your customers are gathered. To avoid the crisis that comes with a dented brand reputation, it’s crucial to manage these complaints at the earliest stages.

With big data tools, you can track mentions of your brand on the internet. There are special social listening tools that will notify you of both good and bad mentions. Customer service representatives can swoop in almost immediately when the bad comments go up and save the day with some classic PR.

Another thing big data can do is spot problematic patterns humans would probably never notice. For instance, different individual customer reps could get singular calls reporting a minor issue and disregard them. Big data analytics, however, can easily find the correlations across thousands or more of call logs, identifying and prioritizing those scattered problem reports, and drawing your attention to them.

Predictive maintenance of equipment and processes

Fast-food giant McDonald’s has incurred much dissatisfaction from customers over its constantly broken ice cream machines. This shows how strongly faulty machinery can hurt the customer experience. To avoid this nightmare, big brands now use big data to carry out predictive maintenance.

Data analytics can track error messages, log entries, and sensor data. Then, cross-reference them with weather data, equipment model and year. All that data can reveal patterns that indicate that machinery is wearing out or breaking down. Preventive measures can then be taken to prevent quality control issues and hiccups in customer experiences.

Create a smoother customer journey

Big data facilitates easier customer journey models. It helps brands to stop guessing pain points, goals, interests and needs because it makes the whole buying process transparent. Big data is the ultimate customer feedback tool and it should be used to inform better customer journeys. By implementing sensor data collection, brands capture users’ interactions and associated metadata to build beginning-to-end customer journey maps.

For example, if customers keep adding to the cart and taking items out before completing an order. The problem may be the method of payment, the channel of delivery, and the time of delivery. A detailed sensor monitoring report should be able to point you in the direction of the real problem.

Understand evolving consumer expectations

Since the Covid-19 pandemic, consumers have changed significantly. A lot of customers have adapted to digital, contactless customer relations. They now expect faster response time and at least some elements of digitalization.

It behooves customer service professionals, marketers and sellers to integrate these innovations and restructuring them into customer experiences. There is a call to apply new tech that will provide digital-first differentiated customer engagement. And without big data, this would prove difficult to implement and measure.

Companies are beginning to successfully break down data silos by pulling all their information into one place. It results in data layers that are transparent and provides visibility to every department and activity. With such data structures, they are able to leverage data to understand and anticipate customer expectations, especially during rapidly changing times.

Top big data analytics tool for improving customer experience

From the plethora of big data analytics tools available today, there are few that are specifically built to analyze customer behavior and inform decisions that could transform customer experience for companies. These are our top recommended tools for doing just that.

  • SPSS
  • MongoDB
  • Microsoft Power BI
  • Tableau

Why you should use big data tools to improve customer experience

Other than keeping up with the Joneses big using the slickest technologies in the market, there are a ton of reasons why you must consider incorporating big data into your business processes. These are three of the most cogent reasons.

Personalization over automation

A lot of businesses –big and small– are embracing automation in all areas of operation including customer service and experience. But automating processes without big data applications will only create a one size fits all experience for customers. Companies need more than a chatbot for a complete and pleasant customer experience. With big data insights, companies can create automated processes that focus on individual customer values as opposed to generic alternatives.

Customer advocacy

Customer advocacy is a product of great customer experience. Customers are loyal to your offering, but if the experience is mind-blowing, they will pull their friends over to take a bite of the experience.

To ensure these experiences are as personal and elating as possible, you need big data. With it, you can identify the demographic, geography, and social-economic compositions of your target consumers, among several other things. Your company can predict and solve issues that are dear to the hearts of their customers. And, that can be heart-touching.

The incompetence of online surveys

According to a McKinsey report, the typical customer experience survey samples only 7% of a company’s customers. And about 25% of these respondents don’t read the survey questions before answering. Using the data collected from these online surveys may be misleading at worst, and addressing the needs of a very small proportion of your customers at best.

Big data tools collate tiny bits of insight from almost every customer. This eliminates the need for customers to even read survey questions. And they don’t have to spend any extra time filling out forms. Consequently, you don’t need them to tell you what their issues really are; they will simply show you where exactly your products could use improvements.

Conclusion

Applying big data strategy to customer acquisition and retention helps businesses develop the resounding customer experience. Understanding who your customers are, what they like or dislike, and anticipating their needs can be competitive leverage, used to deliver personalized and hyper-personalized experiences that can translate into increased customer loyalty and growth.

However, companies need to be careful with big data use. It can get disastrous when the wrong data is collected, processed, and used to communicate the wrong information. Remember, there is a thin line between data collation and privacy infringement.