Top 5 Powerful Analytics KPIs
Key Performance Indicators are important, and particularly important when performing text analytics with survey data. When conducting text analytics it is important to know which KPI metric you are interested in. There are many outputs when conducting text analytics and it is imported to have the output in mind before you begin. The output from text analytics can be a coded list of topics with the frequency counts. If the data is from hotel customers you may want to know how often customers mention; linens, comfort, TV, and room service. Another objective that uses the same hotel data could be to understand if mentions of room service have an impact on NPS ratings? Yet another KPI could be revenue. The objective might be to explore the relationship between mentions of room service and how much a customer spends at our hotel. In this example you are using the revenue amount as your KPI. In this post we will discuss the five powerful KPIs that we see used frequently when conducing text analytics.
KPI #5 is Sentiment analysis
Sentiment looks at the words and compares them to an established dictionary for what words are positive, which words are negative and which are neutral. When using Sentiment as your KPI the analysis is focused on uncovering which words, combination of words and which topics are increasing or decreasing sentiment and by what amount. Sentiment is one of the most popular KPIs in text analytics because it can be conducted on almost all unstructured text. This makes it useful on social media data where structured variables are limited and sentiment can be used to enrich other metrics such as percentage of mentions and reach.
KPI #4 is Customer Satisfaction Score or CSAT
CSAT is a broad term that describes many different types of customer service survey questions. The goal of any CSAT score is to measure a customer’s satisfaction level with your company. The scale for measuring CSAT isn’t strictly defined. Some have it on a 5 position scale ranging from very unsatisfied to very satisfied. Others use a score that is derived from calculating the number of happy customers divided by the number of customers asked.
KPI #3 is Customer Effort Score or CES Score
Customer Effort Score is a metric to measure customer service satisfaction with one single question. The belief is that service organizations create loyal customers by reducing customer effort.
KPI #2 is Net Promoter Score
The NPS score was created by Satmetrix in 2003. The NPS question is “How likely is it that you would recommend [company X] to a friend or colleague?” and the answer is offered as an 11 point scale. When consumers answer zero through six they are considered detractors. Customers that answer with a seven or eight are passive and those that answer nine or ten are the promoters. In the HBR article The One Number You Need to Grow, “By concentrating solely on those most enthusiastic about their rental experience, the company could focus on a key driver of profitable growth: customers who not only return to rent again but also recommend Enterprise to their friends.” According to NetPromoter.com (the website run by Satmetrix) “More than a decade after it transformed the business world, NPS® still stands alone as the only customer experience that predicts business growth.”
There are some that are critical of and question the predictability of NPS. “To this date there is not a sigle scientific (peer reviewed) study supporting that NPS predicts growth. The study that was used to launch NPS in HBR is also flawed (and HBR is not a peer reviewed magazine)." - Sven-Tore Bengtsson (Source: Link)
When using NPS as your KPI you can explore what topics are driving up or down your NPS score. Using the output to focus the team on what areas of focus will have an impact on the score that matters to you.
KPI #1 is Revenue
The KPI that is most sought after is of course revenue. It is the R in ROI and if your data can link customer comments to the amount of revenue that is generated by a customer than your text analytics will be able to provide insights into what is driving actual customer spending. We have a case study example on OdinText.com (here is the link: ). The case goes into detail on how Jiffy Lube was able to perform such an analysis in order to better understand which customer comment topics were driving revenue.
When performing text analytics it is important to know which KPIs are available for you to include in your analysis.
- Sentiment Analysis - https://en.wikipedia.org/wiki/Sentiment_analysis)
- Mining the Web for Feelings, Not Facts - http://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?_r=1