A few weeks ago when playing around with voice of the customer data for a client, I found a great stat that their customer satisfaction score highly correlated to the day of the week. People’s satisfaction with the website and products varied significantly depending on the day that they filled in the survey. Can you guess the day of the week that produced the lowest scores? People hate Mondays. And they were taking that fact out on our survey. More people were satisfied on Thursdays, Fridays, and Saturdays before the rate started a depressing decline to a Monday low.
The rate on Mondays was really dragging down scores for overall satisfaction and NPS (Net Promoter Score). It almost felt that people’s Monday blues in getting back to work after a great weekend was biasing the results. This was a new revelation as the data had only ever been provided at an aggregated monthly level before. We extracted two years’ worth of data and did some stats tests to prove the data was unbiased; we proved that there were no outliers producing false averages and that the differences we were seeing in the scores were statistically significant.
We created a nice data visualization showing smiley happy customer icons running up to the weekend and sad depressives on Monday. We played around with the axis to highlight the differences while ensuring we didn’t over-egg the results. The visualization generated lots of hilarity, engagement, and debate. Were people just really hacked off on a weekend? If you asked the same person the same question on a Thursday would they reply in a different way than on a Monday? Was it that just moody people answered surveys on a Monday? Would we see this trend for other brands? Were happy people off spreading the love and didn’t have time to answer surveys until the end of the working week? Was this actually useful for our client?
We didn’t know the answers to those questions. The services our client sold need to be available all week; it wasn’t the sort of product or brand where communications tailored for the day of week would be appropriate. It was daft of us to recommend only launching new products on a Friday. The client couldn’t tailor products to the day of the week. We had a great, funny 15-minute debate with the client; he was more engaged with the results than we have seen for a while. And then we wondered what to do next.
But there were definite learnings from this exercise:
1. Useless but true isn’t always bad. Rightly so we try and live by the mantra of focusing on “actionable analytics” but sometimes it’s fun to dig into data to find trends that wow people – even if you can’t get your head around how to take action on it. We don’t often get a chance to be playful and that’s something as analysts we shouldn’t lose or we risk delivering staid analysis. In this case we found how to present a regular report in a new way. And, that kept our client engaged and assured they weren’t looking at the same report we’d shown them last month.
2. The power of analysis is in the detail. Once we got our hands on daily data, we could find a lot more information. With any piece of analysis, get down to the lowest level you can. Just running some distribution and x-tab charts across the whole data set didn’t just uncover a single funny trend. We now know to check there are no biases in the future. More importantly though, by understanding all the variations in the data set an analyst has more confidence in the overall results. This leads to being able to be more forceful with recommendations and so the analysis becomes more actionable.
3. No surprise that visualizing these distributions was key to getting people debating. The visualization was simple and to the point. It made one statement, “I don’t like Mondays” and triggered a debate. Everyone got engaged because everyone could imagine themselves in that situation saying, “Yeah, I tend to be in a bad mood on a Monday too.”
4. Finally: Is this a trend for every type of survey? I’ve seen other analysis that backs up the distribution we saw. Why not try it out by asking for a pay raise on a Friday? Your boss is probably in a better mood and will be more satisfied with your overall work!
As an organisation, finding the right marketing channels is an essential part of your marketing strategy.
When measuring the effectiveness of discount codes, retailers often get it wrong. In this article, we'll look at how data-driven attribution can help businesses better understand where discount codes produce the best ROI.
Many businesses invest a great deal of time and effort into knowing their customers - but too few focus on understanding them.
Data. It’s the latest ‘buzzword’ in the digital marketing world when it comes to content.