Text analytics really is becoming "Good enough" to change the way we collect feedback.
Until recently, Enterprise Feedback Management systems have been mainly web-survey based technology without integrated text analysis capability. Text analysis vendors emerged and made claims that their analytics could replace customer surveys. I've argued that Text Analysis alone couldn't provide the depth of insight and ability to formulate actions that a well designed survey provides. Yet, I've also made the case for text analysis as a means to provide qualitative context to survey results. And, that it therefore is a useful tool for helping to manage customer feedback.
Societal and technology changes, particularly the time pressures people face, the increased complexity of daily life and the ubiquity of mobile devices with internet access, I believe, are forcing feedback professionals to consider alternatives to exhaustive (and lengthy) customer surveys. Survey response rates have been in decline. And, companies have reacted by moving to shorter surveys, making up for the lack of survey insights by doing more with analytics of all kinds. Concurrently, text analysis technology has become more capable. This has enabled the increased use of open answer questions in surveys, typically replacing multiple attribute oriented questions with single open answer questions.
Trends and technology, I believe, now make Text Analysis a key component in any larger effort aimed at managing customer feedback. That said, I still think text analysis is most effective when applied to survey based verbatim text. Readers of this blog know that I use Etuma360 (www.etuma.com). And, having used it to evaluate survey verbatim text on several data sets, I’m now of the opinion that text analysis has the potential to substantially change how we do customer feedback.
For instance, in most customer surveys, we try to carefully design question sets that help customers tell us about various attributes of our product or services offerings. For firms with lots of products or services this often makes for cumbersome and complex customer survey projects with lots of back-end analysis work needed to get to actionable results. Needless to say customers today don't want to spend the time required answering all the questions we want to have answered. And, as importantly, executives today don't want to spend money on surveys that may take weeks or months to garner insights from.
So, what to do? Obviously, without customer feedback data there can't be any actionable customer insights. We need an approach to customer feedback that is both powerful, yet concise. Text analysis helps us get to that place.
The chart above comes from verbatim text responses in a survey (using the net promoter question) I did for a local soccer club. The text analyzed came from a question asking for “any additional feedback” the customer wanted to supply, at the end of the survey. The survey asked specific questions regarding product / service attributes. In this case, asking about things like coaching, communication, venues, etc. When I examine the topics identified via text analysis, they look a lot like the topics we identified as needing survey question based input. If I had designed the survey to ask for open answer feedback specific to customer’s experiences, I think the text analysis results would have been even more closely aligned with the product/service attributes we were interested in.
Text analysis tools also provide additional analysis capability (Etuma360 does anyway). For instance, Etuma has the ability to compare topic / sentiment for different groups within the survey using background variables to filter open answers. This generates data that closely aligns with loyalty drivers and provides a measure of each topic’s relative value to selected customer subsets. For companies with customer behavior data (revenue tier, tenure, etc.) embedded as background variables in their surveys, even more granular insights can be generated. The example below shows a topic comparison of the data presented in Figure 1, filtered by “Promoters”. Getting this data was easy using Etuma360 in combination with QuestBack. More importantly, these insights can generate in real time, without weeks of analysis work, so executives can see and act on them quickly.
Companies today have to deal with the reality that their customers want their time respected and their feedback to be heard. Asking for 5 or 10 minutes to collect feedback a couple times per year is about all they are willing or able to give. Using EFM systems that employ web surveys and background data in conjunction with text analysis helps maximize action taking and insight discovery while minimizing the time commitment required of the customer.
This seems like a combination that most companies want. With QuestBack and Etuma, it seems to me that they can get it at a very reasonable cost.
If you are interested in trying Etuma360 they have a free trial program just Click Here. QuestBack does too. E-mail me if interested.
Stewart Nash
stew.nash2010@gmail.com
www.linkedin.com/in/stewartnash .
Figure 1 - Example Etuma360 Topic with Sentiment list
The chart above comes from verbatim text responses in a survey (using the net promoter question) I did for a local soccer club. The text analyzed came from a question asking for “any additional feedback” the customer wanted to supply, at the end of the survey. The survey asked specific questions regarding product / service attributes. In this case, asking about things like coaching, communication, venues, etc. When I examine the topics identified via text analysis, they look a lot like the topics we identified as needing survey question based input. If I had designed the survey to ask for open answer feedback specific to customer’s experiences, I think the text analysis results would have been even more closely aligned with the product/service attributes we were interested in.
Text analysis tools also provide additional analysis capability (Etuma360 does anyway). For instance, Etuma has the ability to compare topic / sentiment for different groups within the survey using background variables to filter open answers. This generates data that closely aligns with loyalty drivers and provides a measure of each topic’s relative value to selected customer subsets. For companies with customer behavior data (revenue tier, tenure, etc.) embedded as background variables in their surveys, even more granular insights can be generated. The example below shows a topic comparison of the data presented in Figure 1, filtered by “Promoters”. Getting this data was easy using Etuma360 in combination with QuestBack. More importantly, these insights can generate in real time, without weeks of analysis work, so executives can see and act on them quickly.
Figure 2- Example Etuma360 based Topic / Sentiment Distribution filtered for “Promoters”
Companies today have to deal with the reality that their customers want their time respected and their feedback to be heard. Asking for 5 or 10 minutes to collect feedback a couple times per year is about all they are willing or able to give. Using EFM systems that employ web surveys and background data in conjunction with text analysis helps maximize action taking and insight discovery while minimizing the time commitment required of the customer.
This seems like a combination that most companies want. With QuestBack and Etuma, it seems to me that they can get it at a very reasonable cost.
If you are interested in trying Etuma360 they have a free trial program just Click Here. QuestBack does too. E-mail me if interested.
Stewart Nash
stew.nash2010@gmail.com
www.linkedin.com/in/stewartnash .
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