A recent report that appeared in an article on Business Advice, revealed that Monday is the most popular day for online shopping, while Saturday surprisingly came out the least popular. Reports on the US market suggest the same phenomenon exists. So we decided to compare the industry figures with the data generated by our own retailers on the Brightpearl platform throughout Q3 2017 (July to September), to find out whether the same pattern emerges and, more importantly, how prevalent it is.
Here’s a quick overview of what we found out with more detailed results to follow:
We looked at the total orders placed each day as a proportion of the overall month, which revealed the ‘heartbeat’ of the UK market. By adding a marker for every Monday in the series, we can see that a pattern of behavior emerges, especially at the end of July and throughout most of August; namely, that most orders are placed on a Monday and there is a gradual decline in orders throughout the week, before it starts to improve on Sunday:
However, the rhythm is not always uniform – there are murmurs, or outside events, that change the pattern and impact the behavior of shoppers. The chart above reveals that, for one week in July, Tuesday became the highest day of activity (namely Amazon Prime Day), a day that had proportionally higher order volumes than all the others throughout the whole of the quarter, with the exception of one Monday in September. For the following two weeks, the behavior is slightly disturbed, like a ripple effect, before it settles again. Similarly, we can see at the end of August, a public holiday in the UK, the activity shifts to Tuesday.
While local events impact the pattern, both the UK and the US follow the same macro trend during an average week, with the weekends being markedly quieter than the days at the beginning of the week:
The US has a slightly flatter rhythm than the UK, although overall, online retailers face around a 15 to 20% drop in order activity when it reaches the end of the week, while Mondays are on average 15 to 17% more active.
When customers are most likely to place an order and the likely amount they are going to spend provides important information about the profile of customers that place orders at different times of the week. The heartbeat will vary from vendor to vendor, for example based on factors such as the vertical in which they operate.
Therefore, this information provides a profile of different customers, which can be used by retailers to determine the best dates and times to run specific promotions or campaigns, based on set objectives. For example, certain campaigns may target a higher number of conversions in order to boost the order number; meanwhile others might involve certain product lines that, based on the average order value could meet with better success at certain times.
Following on, it means that retailers can implement experiments based on previous benchmarks, to measure whether they are able to improve areas such as the drop off rate at weekends, or the drop in average spend. You can look at a series of objectives and set measurable targets against these, making use of day of week and average order value data points.
Finally, being able to use more data to enhance outcomes enables a more sophisticated approach to planning. For example, it goes without saying that vacations will have an impact on buying behavior and this is, of course, reflected in the analysis. The more important question, however, is what level of impact does it have? How much does it impact you in your business? Additionally, how will it alter patterns of behavior in the following weeks?
This type of information enables online retailers to better predict the impact of influential events in future and to allow for this in the planning of inventory or the running of campaigns. Longer term, it means that retailers can make use of data to build better forecasting models, ones that allow for more factors and that include more sophisticated customer profiling.
It’s important to remember that data analysis cannot reveal why something is happening, it simply tells us what is happening. There is always a temptation to interpret results with conclusions such as “people spend more time with their family, so they spend less time and less money online,” or “people like to order on Mondays having researched purchases over the weekend,” and so on. The fact is, we can’t know this and the danger is, it leads to business decisions being made on gut feeling. The key thing is, what does the data tell us that are areas of weakness against the benchmark averages – and is there a way we can experiment with different offers, product promotions or marketing campaigns to see what improves this?
It will be these subtle sophistications and the intelligent use of ‘Big Data’ that will enable those retailers who apply it to their business to gain advantages over others who do not. In a time of ever increasing competition, the ability to experiment and tweak the areas of weakness will separate those who gain share against rivals, versus those who fail to achieve this. There’s a reason why Amazon picks the day of the week for ‘Prime Day’ that it does. It will have been based on an assessment of data points that support a business case for why a certain day is best. The actual reasons why are likely to be unknown to us, and probably unknown to them. It will simply be that the data says it should be so – and retailers who want to emulate similar success will be well advised to follow similar logic.
But what do you think? Do your trends follow similar patterns? We’d love to know whether you think this could impact your organization – so do leave us your comments, thoughts and feedback below.