Why we’re missing the mark with consumer sentiment analysis
Sentiment analysis is widely used to supplement the analysis of text data in surveys, complaints, reviews and other customer feedback. In theory, sentiment analysis categorises opinions expressed in a piece of text just as a human might.
However, as the volume and complexity of customer data grows, growing issues with sentiment analysis are providing flawed information and stopping companies from getting the full picture from their data; key customer feedback that could drive positive business change is being missed.
For those who have used sentiment analysis it is likely that you have experienced these inaccuracies – from skewed sentiment percentages and incorrect tagging, to completely missing key business insights.
Furthermore, the accuracy of items sold as sentiment analysis that are billed as 90% accurate are sometimes as low as 40% accurate. This is a result of low recall and low precision.
Driving business change
So why is sentiment analysis such a poor business driver? Well it usually features basic output such as ‘neutral', ‘bad', or ‘good' with a score that hardly contributes to driving business change.
There is also the key issue that if there are multiple sentiments within a piece of text, sentiment analysis is unable to select both or identify the most important one, so key data is always being missed. For example, a review along the lines of ‘the food was brilliant and I loved the atmosphere, but the service was terribly slow!’ would have only one sentiment considered.
Therefore, the actionable insight, ‘slow service’ is ignored, leading the business to miss out on change that could potentially have turned this satisfied customer into a huge promoter.
Sometimes feedback is missed as sentences with either a negative of positive word do not express any sentiment at all. For instance, if the sentence ‘can you recommend a good tool?’ is included in a piece of customer feedback it doesn’t actually express any sentiment, but it uses the ‘good’ which is a positive sentiment. This leads to inaccuracy in both the categorisation and overall sentiment score, as well as the customer request potentially being missed.
There is also the fact that topics and sentiment are separable. Labelling a topic as “about billing” with the sentiment “unhappy” is not as useful or faithful as the output “customer expected pricing to be lower”. Companies are listing topics so that people only understand the “sentiment” of the part of the customer journey but it is losing all context with little insight into what the actual issue is.
The latest machine learning is being applied instead of sentiment analysis with great effect. It’s able to categorise and label multiple sentiments and label granular topics within a feedback/review.
In the case of the review ‘the food was brilliant and I loved the atmosphere, but the service was terribly slow!’ both sentiments will be picked up, in near real time, allowing both to drive business change.
Whilst sentiment analysis can identify single sentiments and speed up the human process, we need to consider the opportunity cost of missing key pieces of customer data.
The overarching fact with sentiment analysis is that you still have to physically read feedback to figure what’s going on and to find out why people are unhappy.
This takes huge amounts of time and resource. With machine learning you no longer have to read things, you simply act on the insight, a much better place to invest time and resources.
Interested in hearing leading global brands discuss subjects like this in person?
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