What do the changes mean?
Feeling that they were missing out on key elements of their customer satisfaction picture, the client approached us wanting to understand what was influencing the survey results. Which other elements were related to the tracked metrics? What factors could help explain their variations over time? Based on their questions, we came up with an approach that integrated other (None Market research) data into their analysis.
Linking up relevant data sources for brand new insights
The first data source came directly from the client. Without realizing it, the client had tracked large amounts of respondent data through their CRM system and other internal data collection processes. Because of confidentiality agreements, we can’t disclose which additional data they had. Suffice it to say that the results of the analysis showed our client how respondent demographics and other captured data helped to explain a large portion of the effect.
The second source of data considered involved the weather conditions experienced during holiday periods. The client wanted to know which aspects of the weather relate to satisfaction – is it shining sun, temperature, or something else? In our analysis, we used information about actual weather conditions as well as data relating to ‘normal’ and expected weather for the destination being rated. We were able to retrieve this information for the majority of the destinations offered by our client.
Connecting the data dots through multiple information sources
Transforming data into useful forms
Before running the analysis, we needed to transform the data into a usable form. Each respondent stayed at a different destination on different dates and for different periods. This fragmented data had to be linked to the weather information, which was available at the destination level by the hour over a period of two years. And even before we could start transforming the weather data, some desk research was needed to ensure that our understanding of weather conditions was correct – since none of us here are meteorologists.
Based on this research, we were able to convert the hourly data into day-based data, which we then linked to the respondent data.
Unexpected insights, actionable results
Without revealing any details, we can say that the results were eye-opening to say the least – both for us and for the client. And by using this information, the client now has a better understanding of how all the tracked factors interact – and thus, a better idea of how to take action.
To sum up, by using data already in the hands of the client, we were able to help them shed light on underlying variables influencing its changes. In using more than one data source, we could colour in some of the white spaces in the client’s customer satisfaction picture to make the results more understandable, and more actionable.