Today I shamelessly re-blog a post from my friend and colleague - Matti Airas from Etuma. The post in its entirety can be found here:
Matti's Blog post on Etuma.com should be required reading for anyone looking to implement a customer feedback process, a customer experience management process, or text analysis process. He makes 8 key points that discuss why and when organizations can benefit from CX / Text Analysis technology. And also, when they maybe can't or shouldn't take on CX. The Title of his post is:
Matti participated in a large number of text analysis projects - Companies bringing datasets into the Etuma text analysis system. I have been involved personally in some of them. So, I know what he is speaking about. And, the truth in his words. I highly recommend reading it.
So, without further ado, here's the text of Matti's post:
The key is continuous feedback: B-to-C companies with solid NPS survey process in place for over a year, can always extract both strategic and operational insights out of customers' unstructured feedback. And reversely, without a solid feedback process, the benefits for analyzing customer feedback are limited. Good data is paramount!
Contact center complaint analysis is also becoming more important. The process, which made sense in a world in which complaints came via phone, might not work for electronic feedback. When complaints are coming in via Tweets, FB postings, emails and webforms, it is evermore difficult and expensive to manually categorize them. Semantic social media analysis has been talked about for many years but we are yet to see great demand for it.
I wrote a white paper called CX Professionals Guide to Text Analysis. It helps you find the right method and tool for your CX and EX text analysis needs and requirements.
Most companies, even large enterprises, don't have enough time and resources to develop niche horizontal competencies. Customer and employee experience analytics is one of these niche horizontal competencies. The learning curve is quite steep. It takes months of full time effort, background knowledge in statistics, and experience in visualization and analytics tools to be able to effectively extract insights. And even if one person has become good at it, they either move to a new position or change employers.
Customer experience analysis results are like any other data: actionable insights don’t jump out and announce their existence and importance. Actually, CX analytics is even harder because most of the feedback is text and it is unstructured. CX analysis results are raw material that you have to enrich and refine. That's why you need to implement a CX analytics process.
I wrote a white paper called CX Professionals Guide to Extracting Insights to assist in solving this problem. Follow this process and you will be extracting actionable insights and sharing them with CX and EX stakeholders in weeks.
Matti's Blog post on Etuma.com should be required reading for anyone looking to implement a customer feedback process, a customer experience management process, or text analysis process. He makes 8 key points that discuss why and when organizations can benefit from CX / Text Analysis technology. And also, when they maybe can't or shouldn't take on CX. The Title of his post is:
Matti participated in a large number of text analysis projects - Companies bringing datasets into the Etuma text analysis system. I have been involved personally in some of them. So, I know what he is speaking about. And, the truth in his words. I highly recommend reading it.
So, without further ado, here's the text of Matti's post:
1. Every single dataset has insights in it
In these 159 datasets I have still to find one that doesn't have some operational or strategic value. I have to admit that sometimes the value is anecdotal–finding minor operational issues and getting product/process improvement ideas–and that it is difficult to calculate a solid ROI for CX text analysis investment (I mean, what is the value of better information?!).The key is continuous feedback: B-to-C companies with solid NPS survey process in place for over a year, can always extract both strategic and operational insights out of customers' unstructured feedback. And reversely, without a solid feedback process, the benefits for analyzing customer feedback are limited. Good data is paramount!
2. Net Promoter System is the main driver for CX text analysis
Net Promoter System drives CX text analysis demand but especially during the past year, with the need to retain great employees increasing, and the emergence of eNPS we've seen lot more demand for employee experience analysis.Contact center complaint analysis is also becoming more important. The process, which made sense in a world in which complaints came via phone, might not work for electronic feedback. When complaints are coming in via Tweets, FB postings, emails and webforms, it is evermore difficult and expensive to manually categorize them. Semantic social media analysis has been talked about for many years but we are yet to see great demand for it.
3. Most companies struggle with text analysis
There are plenty of tools to analyze structured data but unstructured data analysis is still a relatively new industry. This combined with the lack of competent text analytics people leads companies to struggle with on how to solve the text analysis problem.I wrote a white paper called CX Professionals Guide to Text Analysis. It helps you find the right method and tool for your CX and EX text analysis needs and requirements.
4. The reason not to buy is not text analysis quality
I remember only two or three cases in which the reason not to buy was text analysis quality. Most CX and EX text analysis solutions categorize feedback accurately and sentiment analysis works way beyond any statistical relevance requirements. The reason not to buy boils down to these three issues:- Lack of top management commitment: no CX strategy (=>no budget, no resources);
- Insufficient or bad feedback gathering system (=low volume or bad quality); or
- Not having the right people and competencies for CX analytics.
5. Extracting insights is difficult
Even if every single feedback dataset has actionable insights, it doesn't mean that extracting insights is easy. Developing the right competencies in order to turn the analysis results into actionable insights seems to be a challenge.Most companies, even large enterprises, don't have enough time and resources to develop niche horizontal competencies. Customer and employee experience analytics is one of these niche horizontal competencies. The learning curve is quite steep. It takes months of full time effort, background knowledge in statistics, and experience in visualization and analytics tools to be able to effectively extract insights. And even if one person has become good at it, they either move to a new position or change employers.
Customer experience analysis results are like any other data: actionable insights don’t jump out and announce their existence and importance. Actually, CX analytics is even harder because most of the feedback is text and it is unstructured. CX analysis results are raw material that you have to enrich and refine. That's why you need to implement a CX analytics process.
I wrote a white paper called CX Professionals Guide to Extracting Insights to assist in solving this problem. Follow this process and you will be extracting actionable insights and sharing them with CX and EX stakeholders in weeks.
