Ecommerce and the demographics of online shoppers
We are on the cusp of high street Armageddon with 51% of shoppers choosing to purchase online. Calling it ‘Armageddon’ is probably a little extreme, but websites now give customers more choice, cheaper prices and next-day HOUR delivery (thanks Amazon). This means that online shopping will keep growing.
As a marketing professional, it’s important to understand your actual audience and not get bogged down in stereotypes. Fingers need to be on the pulse of current trends and not those of four years ago.
To demonstrate this, we examined website visitors’ demographics over the last four years to see how much frequent online shoppers have changed. By looking at the frequent shopper density of a number of websites we can see how much this user group have changed from 2015 to 2019.
As with any analysis, there are caveats.
- The first caveat pertains to the data: Website traffic, aggregated from Google Analytics accounts with demographic and interests data enabled. A number of websites with a UK-wide reach and a mass-market audience were analysed, using over 10 million data points. However, by their nature, the websites analysed had a Western-world bias, and this is reflected in the data. We’re confident however that there is minimal bias in the aggregate data towards a specific age group, gender or location.
- The vast majority of the traffic analysed is B2C, with a very small B2B audience. The data will be biased accordingly. If your business is B2B, we advise caution in your interpretation of the data.
- Univariate analysis only looks at one variable at a time and does not consider other external factors. It has the advantage of being quick and easy to understand and can give you instant insight into each variable in a database. It does not, however, allow insight from one variable to be combined with other variables. For example, if we found that male users were more responsive to a marketing campaign and rainy days have more campaign responses, we cannot say that male users on a rainy day will be more responsive; we can only say that males or rainy days are more responsive.
- This data analysis is based on website traffic from January 2015 to January 2019.
- Our findings are indicative and not gospel. This is a generalised analysis and is not specific to a particular company or campaign. It is designed to highlight, give tips and guidance to start those cogs turning. Any campaign strategy based on this data should be properly tested to see if your findings match ours for your particular target audience.
Who? User demographics of Online Shoppers
The characteristics of frequent shoppers may surprise you especially when you know how much they have changed over a short period of time. We will now take a look at their age, gender and in-market segments. To do this, the data below has been analysed using indexing (each data set shows how the frequent online shoppers differ from the overall population).
- If the value is greater than zero, it means that a specific trait is more common than the ‘average’
- If the value is less than zero, the reverse is true: that specific trait is under-represented on that particular search engine vs the average online audience.
By analysing the data in this way, we can show the differences in demographics between different search engines whilst avoiding being misled by the general demographics of online users.
Density of online shoppers
Looking at the proportion of the total population that are frequent shoppers and seeing how this has changed over time is very surprising:
Back in 2015, frequent shoppers only made up 20% of the total population, whereas in January 2019 they make up almost 70%. This shows that your website’s visitors are now much more likely to be frequent shoppers. They are more confident and more experienced with purchasing online then they were in 2015. This does not mean that you can scale back UX development, in fact, the opposite is true. Users expect a certain quality when it comes to buying online. If they can’t get that on your site then they will simply go to your competitors’ sites to get what they need.
It’s all well and good knowing that more of your audience is purchasing frequently online. But what do these users look like and how has this changed over time?
Index of age
As we can see from the chart above, online shoppers are far more likely to be between 25 and 44, and much less likely to be older than 55. However, when we look back in 2015, we can see that changes to the likelihood of being a frequent online shopper have occurred. In fact, if we look at the change in population density for each of the age bands, we can see how much the frequent shoppers are changing:
The probability of a frequent shopper being between the age of 25 to 44 is still true, however, the chart shows how much the over-55s group has grown. Since the start of 2015, the over-55s have grown by almost 500%. The stereotype of the older generation as technophobes is slowly becoming inaccurate.
Index of gender
The frequent shopper audience is much more likely to be female and this has changed very little since 2015. Although the index has dropped for females, the female audience has grown at a far greater rate than males. Only in the last few months has male growth overtaken the female audience.
Index of in-market
This has been created using the in-market segmentation in Google Analytics which will help identify the products people are interested in at that point in time. As we can see, shoppers are much more likely to be interested in clothing, consumer electronics, beauty products, computers, baby and children’s products.
However, viewing the rate of change over the last few years we can see that travel and software are growing within the frequent shoppers segment.
What? Device and browser Information
So far, we have discovered that frequent online shoppers tend to be young females, looking to purchase clothing and beauty products. But we have discovered that this is changing over time as the audience is getting older, more male and includes growing trends towards travel and software. Now we know who these users are, we can now look into what they are using to shop online with.
Index of device
Interestingly, frequent online shoppers are much more likely to visit sites via desktop devices compared to the overall base. This has only been the case in 2018, prior to which, frequent online shoppers were much more likely to be on mobile. This is not to say that the volume of mobile users is dwindling, in fact, the proportion of mobile shoppers has increased by 200% since 2015.
Index of browser
We can see that frequent online shoppers are much more likely to use Chrome, Samsung Internet (Samsung Internet refers to the Samsung browser typically on Samsung Android devices) or Amazon Silk. However, this was not always the case. In 2015, shoppers were more likely to use Apple Safari than other browsers. In 2016 it switched to Samsung Internet. In 2017 the probability of using Chrome increased significantly, and in 2018 we saw a growing probability for Amazon Silk, Edge and Firefox as well as Chrome and Samsung. When looking at the growth rate we can see both Chrome and Amazon Silk continue in popularity:
Index of channel
In 2015, frequent online shoppers were more likely to use social media or organic channels to visit a site. This has changed significantly over the last four years with the probability of online shoppers reacting to display advertising and paid search. (This may be influenced by the bias of the websites we used in this analysis which have all increased their media spend and tend to bid on more commercially relevant keywords – the keywords that shoppers would typically search for. It’s also worth mentioning that Google shopping ads have also grown over the last year which may be influencing bias).
Understanding what methods frequent online shoppers are using to visit your site is important when considering the design of your website. Understanding what device or browser will help ensure the site works in all scenarios.
When considering what channels to use to target shoppers, paid advertising is still a powerful tool – more so when you consider the additional targeting features of advertising platforms such as display, remarketing and shopping ads.
When? Day of Week and time of day
When are frequent online shoppers more likely to visit your site? Is it at weekends? At lunchtime? Or only monthly? The data shows:
Hour of day: percentage of total shoppers
As we can see in 2018, shoppers are spending more time online throughout the day and into the evening. This is a significant change from 2015 when more shoppers were shopping in the morning and over lunchtime. But how does this compare to the typical web visitor?
Index of hour of day
In 2018, frequent online shoppers are much more likely to visit in the evening compared to the total population. They are much less likely to visit in the early hours. As the data shows, this is a big change from 2015:
Index of day of week
Compared to the total population, frequent online shoppers are much more likely to visit on Mondays, Tuesdays and Wednesdays. They are much more unlikely to visit at the weekend. This is a change to behaviour when we compare 2018 data to 2017 and 2016 when frequent online shoppers were more likely to visit towards the end of the week.
Since more people have started to shop online, the overall behaviour of frequent online shoppers has changed significantly within four years. This is important as understanding when shoppers are online can help guide the targeting of paid marketing ads such as display advertising. Targeting these ads at times you know your users are shopping will help improve click-through and conversion rates.
The demographics of frequent online shoppers are consistently changing. From four years of data we can see big changes within this segment, from their age profile to their shopping patterns, and our analysis shows that they continue to change.
This analysis has shown that frequent online shoppers used to be young females looking to purchase beauty products at the weekend. But this has significantly changed as those who traditionally used the high street came online to shop – older people and men. Whether this is due to trust, technical ability or other factors, the data remains the same.
We would like to reiterate that our findings are based on a number of businesses with whom we work, so, whether you’re an agency or business, you might choose to undertake your own research to see what can be learned and used. A test-and-learn approach to new marketing activity is essential to ensure the best performance and avoid waste. If a statistically significant improvement on a test happens and you have confidence in the data, only then should a campaign be rolled out.
Data analysis like this forms a key part of our activity from strategies for growth to tactical advertising campaigns. As such we know that understanding and taking advantage of the changing demographics for your digital marketing campaigns could prove very beneficial from targeting in advertising campaigns to UX and web development.
Do your findings differ from ours? Do you have your own experiences to share? Let us know in the comments section below.