The connection between Customer Experience and Data Science!!

Let me be honest, I wrote this post year back but wasn't able to publish it 😀. Have been in the Customer Experience (CX) and Data Science space for quite some time. It's quite understable that the world is hungry for one thing - "DATA". The language this world speaks in answering business problem is the same - "data visualisation created based on data".

Correlation of Data with CX and DS?

The answer to this is fairly simple and straightforward. Companies are spending millions to have a process, and educate the key people to onboard with importance and impact in terms of investment returns. They want to understand recent interaction experiences with the brand to synthesize sentiment and emotional state (happy or unhappy) with the service provided by frontline employees interacting on daily basis. In short, they want to know the quality, performance, service, satisfaction, reliability and experience with the brand. This helps businesses from driving decision-making, re-targeting customers, reducing customer attrition, and engaging audiences across channels for a consistent, seamless customer experience, data drives modern business. The brand connects with customers in various mediums most commons among all are Email and SMS.

Data generation process in CX?

Companies connect with customers by sending out surveys which consist of questionaries which are related to Likelihood to Recommend, Overall Satisfaction, Key Driver (important for the company), and Verbatim question based on industry best practices and interaction touchpoints. When the customers respond to those surveys it creates real data which can be used for analysis creating reports and dashboards. The most important part of survey is it should be short, targeted, specific to the touch point being engaged.

Answer to a business question, Visualisation.

Almost all companies who survey customers have Customer Experience Management (CEM) tools which can be used to automate the process of survey creation, sending surveys, receiving and storing in the tool, and creating reports and dashboards (inbuilt feature) to understand customer's experience sentiments and emotional state with the experience. Over a period of time, this data becomes huge to massive in number speaking what customers have to say to you. Tools like Medallia Experience Cloud, Qualtrics, and others can be used to create reports showing Net Promoter Score, Overall Satisfaction, Key drivers health report over industry benchmark and are the KPIs for the business. Many brands do export the data from CEM tool consuming data in external reporting tool like Tableau, Power BI etc. creating amazing reporting dashboard helping understand customers painpoints. CEM is getting better-improving algorithms to understand customer's sentiment and emotions which is very powerful and necessary.

Connection CX with Data Science

The date has been gathered can easily be imported by many tools used in data science to do advanced levels of reporting and analysis which some of the CEM tools fail to offer like prediction and forecasting visuals. Data scientists can process, and transform the data available to understand the pattern and insights to know more about what business what to learn. The CEM as a product is emerging and improving constantly. The industry has to build products that can also predict and forecast their customer's interaction patterns, how often they do business with you, what are the probability of the next interaction. The answer to the question is currently available only when we use ML and data visualization tools like R, Python, Hadoop, Spark, Tableau, and PowerBI to massage available data to bring more insights to the table for the business.

Not all tool available in market provide you good visualization in reproting. They use the CEM tool as a warehouse for data gathering, importing to their in-house solutions, and doing advanced levels of analysis by creating advanced reports and dashboards. The positive part is they are getting the information they are seeking from the best CX enablers but the down part is the cost and the resource involved to answer the business question which is unanswered!!

I'd conclude saying, tools and technologies should do what they are meant to do. CEM and reporting tools should be focused on the experience processes, and reporting tools respectively. They are engaged and used for a common stuff - Data. On the other hand, it's an opportunity for aspiring data scientists they can play with more diverse data (structured and unstructured), and learn and explore.

Hope this was helpful. Open to the thoughts, feedback, and discussion on the connection and correlation.

#CustomerExperience #CX #Data #DataScience #DataScientist