Changing customer expectations and what drives satisfaction is changing at a faster pace than ever before. There is a reason why the world’s most successful companies have a laser focus on customer satisfaction. Poor customer service has been the top reason for customers leaving organisations. Emergence of new products with new and differentiated capabilities is continuously increasing, but customer still tend to stick with companies that have serviced them well over time. This is why companies focus so much on customer loyalty and advocacy. Net Promoter (NPS) system continues to be a very reliable and consistent measure for assessing companies’ future growth potential as well as projections of revenue.
Application of Data Science and Analytics to help improve satisfaction
With the emergence of new technologies and application of data science methods on large amounts of data, it is becoming more and more commonplace for companies to put a stronger focus on factors that help increase customer loyalty for their specific products.
Data science, analytics, statistical testing and machine learning are growing at an astronomical rate and companies are building capabilities or hiring experts to sift through the goldmine of data and help them drive the right business decisions swiftly. It is no longer enough for companies to believe that they understand what the customers want. It is not so simple anymore. Companies now understand that they don’t just have one common group of customers. There are various sub-groups of customers who frequently have varying and sometimes diametrically opposite needs. Some customers may be interested in the product features and another set of customers may be with them for great customer service or convenience. These could be two very different groups who have different motivations for using the company’s products or services.
Data scientists are able to create different customer profiles and understand what drives satisfaction or dissatisfaction for various customer segments. Each customer is unique in their expectations and desires, however data modelling can create groups of customers using Cluster analysis to segment those that have similar expectations and needs from the product. Cluster analysis is a class of techniques that are used to classify objects into related groups called clusters. It can also be used as a predictive analytics tool. New customers can be profiled and if they belong to a well understood cluster, an assessment can be made that can predict customer preferences and buying patterns. The models continue to evolve to keep updating the clustering patters as more data becomes available.
The other frequently used application is sentiment analysis. Customers are constantly giving feedback on surveys, social media and other channels about the services and products. Sentiment analysis can automatically calculate a sentiment score of each comment. Automated actions can be taken to improve responsiveness on poor feedback. Natural Language processing (NLP) is commonly used in chat bots and automated emails or IVR responses.
Importance of doing it right
While there is a lot of buzz about latest data science and AI techniques, one shouldn’t deviate from the basic logical understanding of their business environment. Majority of the techniques available today help us do what we want to do faster and better. These techniques do not tell us what we should be doing, not do they tell us which techniques are more applicable for our business. Technology isn’t the final solution, it’s an enabler. The data scientist’s role is therefore not only to understand the benefits and limitations of various analysis, but more importantly to understand when they should or should not be used. They need to understand that correlation does not mean causation. Incorrect data analysis interpretations can do more harm than good because most management teams would not know the inner workings of the analysis. This is the reason we need experienced, reliable and trusted data science experts. A small error could lead to big impacts, especially in more automated scenarios where people may not get an opportunity to apply common sense before taking decisions.
Solving the puzzle
Most organisations are beginning to use data science, analytics and machine learning tools in different areas. When it comes to using these tools to improving customer service, it needs to start with collecting true customer feedback
Online surveys are the most commonly used tools for collecting and understanding customer preferences as well as feedback. Online surveys provide automated calculation of survey sample size to enable better analysis at the end of the surveys. Customer satisfaction surveys are adopted by almost all large organisations to constantly be in touch with customer requirements. The Data Potential online survey platform makes it seamless and effective for organisations with an easy tool to build beautiful surveys including automated sampling, calculations and statistical reports. Successful organizations constantly take feedback from their customers at different stages of their customer life cycle.
Once data is collected, the first step is a high level analysis of the data including data cleansing, preparation and exclusions. The next step is usually to apply descriptive statistics, segmentation and visualisations. At this stage we have a high level understanding of trends but usually not enough to take intelligent business decisions. It would be fair to say that at this stage we have information, but not knowledge. Data science tools than help take it to the next level by really understanding the trends and preferences not only to establish accurate satisfaction drivers, but also in being able to highlight areas of improvement and opportunities for building customer loyalty. Data science experts help do all these with experience, mathematics and statistical skills to help identify the actions that need to be taken to improve customer satisfaction and loyalty in a scientific manner. Data potential helps companies through the complete process from gathering customer feedback on our survey platform to using expert data science skills to provide the distilled knowledge and intelligence to take smart business decisions.
About the Author:
Ashutosh Sharma is the founder of datapotential.com (an advanced online survey platform) and Aselector.com (an Artificial Intelligence based knowledge management platform). Data Potential also provides six sigma based process optimisation, CX improvement and data science consulting services. Ashutosh is a writer, speaker and thought leader on AI and statistical methods.
