With each Peakon question, employees have the option to provide a comment. These comments add colour and nuance to quantitative responses – and essentially the why behind the score.
Utilising this feedback effectively provides a detailed understanding of issues and how improvements can be made. There are, however, very different ways in which this feedback can be used throughout an organisation. For example, a team leader may read every comment from her employees. Whereas further up the organisational hierarchy, a department-head may see thousands of new comments a month, or tens of thousands for executives. Of course, there's no hope of reading all these – just as it would never be expected for a CEO to be involved in so many conversations throughout the company.
What leaders of leaders need to know instead, are the general themes and issues that people care about – and what the sentiment around certain subjects is. This is where topics come in.
What are topics?
Topics are way of aggregating many comments based on a common theme discussed within them.
For example, in response to a question around personal growth you would expect the topic of "Training opportunities" or perhaps the name of training initiatives you have within your organisation e.g. "Acme Excellence Academy".
You can review Topics by driver, open-ended questions and Values.
Looking at a particular Topic
Each topic will display a score, which is the average of the scores associated with the comments that make up each topic. This enables you to quickly judge the sentiment around each topic e.g. the 5.6 on the "Meeting Rooms" topic in the screenshot below shows there is room for improvement.
The Peakon topic algorithm
For a manager to begin seeing topics, her team will have provided more than 200 comments per driver. Peakon's proprietary machine learning algorithm can then begin to discover topics.
The challenge with finding topics in short text answers is that the same words are used in many contexts. Take two examples of comments in which the word manager is used:
- In response to one of Peakon's growth questions ("I feel that I'm growing professionally") an employee could comment:
"I'm definitely growing in a direction that's more interesting to me, thanks to the the development plan that my manager created."
- In response to an open-ended question ("If you had a magic wand what’s the one thing you would change about Acme Co?") an employee could comment:
"I really wish we would provide more line manager training, this would be really helpful."
In example 1, the word "manager" is used in relation to an employee’s interaction with the manager, whereas in example 2 it is used by a manager asking for more training.
The traditional (simple) approach to aggregating text is to count the number of times noun words are used. Often the counts of words can be represented in a word-cloud that, while visually compelling, gives you little information to act on. The reason for this is that it provides no context. Using example 1 and 2 we would find that ‘manager’ was important as it is used twice. But being told that ‘manager’ is mentioned often gives no context to act on. We get no interesting actionable information about why ‘manager’ is important.
The algorithm does five important things to make topics more actionable and interesting:
- Attach topics to a driver: The most innovative aspect of our algorithm is that it will attach the topics to a driver. This provides a context to the topic. Using the answer from example 1 the algorithm might find "Development plan" as a topic if it is a word often used in relation to growth. This means that you will know that "Development plan" is related to growth. This would inform the manager that employees value this investment, and may provide further insight on how it can be improved.
- Find multi-word topics: In example 1 the answer included the phrasing "development plan". If this is commonly used in many answers Peakon will highlight the topic as "Development plan" rather than simply just "Development". From example 2 the topic could be "Line manager training", "Manager training" or "Training" depending on how often the words are used across answers.
- Remove words from the question phrasing: People have a tendency to repeat the words of the question in the answer. In example 1 the answer includes the word ‘growing’ which was also in the question. It is not very interesting to find this word as topic since we already know that we are asking about growth. Therefore Peakon automatically removes the words from the question phrasing.
- Down-prioritize commonly occurring words: Peakon down-prioritizes words that are used throughout all the survey answers with no specific meaning. This means that "Manager" is only found as a topic if it has a specific meaning in a driver context. This provides topics that are specific to the driver, for example "Office" or "Room" for the environment driver or "Pay-rise" for the driver reward. If we did not down-prioritize commonly occurring words we may find "Manager" as a topic for all drivers because it is an often used word.
- Deliver a topic summary: Peakon generates a paragraph of the sentences that are most indicative of all the comments that make up a topic – giving you an insight to a broad conversation in a few seconds.
- Topics only analyses the last 3 months of comments: this ensures that your topics will change over time.