- Market research and communications specialists with the information they need to understand corporate issues such as positioning, image, preferred communication media and channels,
- Operations and Line Managers with information concerning services levels, emerging operating problems, and the customer impact of operating changes, and
- Executive Leadership with performance metrics suitable for balanced scorecard and dashboard integration.
Converge VoC uses advanced computer-based text acquisition and interpretation software that can read volumes of text data and provide unbiased sentiment analysis of the meaning behind the words. Relationships between words and phrases provide insight to the thinking behind what was said, improving our understanding of what really matters to our people.
This text analysis capability is important in our analysis of survey data and the qualitative information arising from focus groups and other sources of employee data. You have probably experienced having to read through mountains of employee comments. As entertaining as some of these may be, where’s the value? How do you move from what people have said, to evidence worthy of driving change? Even when summarized into themes, results are heavily influenced by the biases of those writing the summaries.
That’s where solid Qualitative/Text analysis comes in. By providing computer based text analysis, bias is removed and leaders can review the data free of interpretative spin. The Voice of the Customer is heard loud and clear and without bias. We can use a number of analytical tools to help. Simple word clouds provide a nice summary of text data and work very well as a communication feedback tool. People ‘get them’ instantly making them the perfect vehicle for quickly disseminating ‘what was said.’
. . . uses computer-based semantic reading engines that “read” the qualitative statements and interpret employee sentiment. In the example, areas of strong positive sentiment are listed in green while negative sentiments are expressed in red, and here, sorted by frequency of mention. This provides an unbiased way of interpreting and summarizing qualitative/text data.
Sentiment analysis requires the development of unbiased models of concepts and phrases to ensure the computer-based reading technologies return meaningful and relevant results. Converge have these models pre-built for qualitative data analysis in human resource and employee survey applications meaning we can run sentiment analysis for your with interpretative models that have already proved themselves in the field.
concept mapping . . .
. . . results in powerful graphical representations of how people are actually thinking about critical concepts.
Size of the ‘spheres’ represent the frequency of mention of critical concepts and lines represent connection points among these. You can also see less important concept points (in grey) operating within the spheres to which they are related. Even color is used to convey characteristics of employee sentiment towards the concepts expressed.
This capability supports our advanced data gathering technologies that can read through internal and external data bases and even scan social media, news and other sites on the web to capture what people are saying about you and gauging general sentiment.
Shouldn’t your customer research understand there is a difference between having your choice of beef or chicken and not crashing the plane?
A Three Dimensional Understanding
- Linear (more-is-better), where improved service or product performance yields improvement in customer satisfaction. Traditional customer research assumes that all characteristics are of this type.
- Basic (required), are those characteristics fundamental to the customer experience, only noticed when absent. Unless you deliver these basic characteristics, nothing else you do matters.
- Delighter (unexpected), are characteristics are those not expected by the customer, but once delivered, build engagement and loyalty.
- having your choice of beef or chicken, and
- whether or not the plane crashes,
Improved accuracy and reliability . . .
. . . because VoCAl uses tests of material significance over statistical significance.
Traditional customer research is based on polling techniques using statistical significance to identify important findings. When examining the differences between customer groups or segments for example, statistical significance is used to identify important differences, or used to distinguish important and unimportant product or service features. Similarly with results over time where statistical significance is used to identify important increases or declines in year to year results.
The problem is that statistical significance has nothing to do with practical or economic importance. That means most of what your research highlights as important, isn’t important at all.
- Those important product features, aren’t.
- Important differences between customer segments, meaningless.
- That significant increase in customer engagement, a fantasy.
Statistical significance gets the answers wrong about 90% of the time (false positive rate).
The misuse of statistical significance has devastating consequences for organizations in fields as diverse as law, health care, employee relations, economics and of course–customer engagement. (For a discussion on the disastrous effects of using statistical significance, see: The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice and Lives Highlight Lecture by Stephen T. Zilak and Deirdre N. McCloskey, posted on the Converge-Net website with permission of the authors.)
VoCAl’s use of material of material significance means the answers you get, are right, better than 99% of the time. That means marketing and operations can stop chasing statistically significant ghosts, and can back to the business of building solid relationships and improving product and services by focusing on what really matters to customers.