{"id":17162,"date":"2019-08-26T14:13:33","date_gmt":"2019-08-26T12:13:33","guid":{"rendered":"https:\/\/yieldbird.com\/?p=17162"},"modified":"2024-07-22T19:27:14","modified_gmt":"2024-07-22T17:27:14","slug":"mobile-vs-desktop","status":"publish","type":"post","link":"https:\/\/yieldbird.com\/research-hub\/mobile-vs-desktop\/","title":{"rendered":"Mobile VS Desktop Seasonality"},"content":{"rendered":"\n

The performance of many websites changes during holidays and weekends and the fluctuations can sometimes be both huge and unexpected. With help from our data science team at Yieldbird<\/a><\/strong>, they have gathered data and shared their insights regarding the usage patterns below. <\/p>\n\n\n\n

Programmatic advertising is amazing. One of the main benefits is the accessibility to data. Here at Yieldbird, we are using the data to improve our mathematical models and machine learning systems in order to increase revenue for our publishers. <\/a><\/strong><\/p>\n\n\n\n

\"\"<\/figure>
\n


The team, Julia, Katarzyna, and Rafa\u0142 analyzed the performance of several news websites based on the ad exchange, ad requests and request eCPM indicators between June 2017 and May 2019. The data is unique and the amount also gives us a hint of what we can expect in the future.<\/p>\n<\/div><\/div>\n\n\n\n

TRAFFIC VOLUME, from more\nthan 100 different publishers in various countries<\/strong><\/h2>\n\n\n\n


First, we should look at all mobile and desktop traffic volumes \u2013 measured by ad requests:<\/p>\n\n\n\n

Chart 1.<\/strong><\/h2>\n\n\n\n
\"\"<\/figure>\n\n\n\n


Non-working days \u2013 Upper chart<\/strong><\/h2>\n\n\n\n


We can see that there is more traffic happening via mobile devices compared to desktop. Users tend to use their mobile devices during non-working days more and ever more frequently over the past two years. This phenomenon could be due to faster internet connectivity and more advanced mobile devices that allow users to surf the internet at ease.<\/p>\n\n\n\n

Working days \u2013 Lower\nchart<\/strong><\/h2>\n\n\n\n


The \u2018switching to mobile device\u2019 tendency first appeared in August 2018 and became stronger and stronger and still is so to this day. This change indicates that users also tend to use their mobile phones more often than desktop devices during working days.<\/p>\n\n\n\n

Conclusion<\/strong><\/h2>\n\n\n\n
\"\"<\/figure>
\n


This switch to using mobile devices more and more often was noticeable sooner on the non-working days’ chart but also became significant on the working-days chart later too. The tendency is deepening overtime on both charts and we can predict that it will continue to deepen even more so in the future.<\/p>\n<\/div><\/div>\n\n\n\n

RPM analysis<\/strong><\/h2>\n\n\n\n


The following charts show fluctuations in the inventory RPM\u2019s (revenue per 1000 impressions) in the last two years (2018-2019). Both charts present mobile (yellow line) and desktop (red line) trend lines.<\/p>\n\n\n\n

At first glance, we can see a higher RPM\u2019s dispersion between device\ntypes on holidays and weekends (upper chart) than on working days (bottom\nchart). Usually, higher unexpected traffic results in a drop in the RPM\u2019s\nindicator. This coincides with the data shown below and previous charts showing\nincreased mobile traffic during holidays and weekends. <\/p>\n\n\n\n


Do you like what you’re reading? Subscribe our newsletter for more content like this!<\/strong><\/h3>\n\n\n

[mc4wp_form id=”407″]<\/p>\n\n\n\n


We can also observe that during workdays both lines usually overlap but the yellow line shows a significant uplift over the red line during the period between May and September. This may indicate a higher demand for mobile traffic during the time that most people plan their long-term holidays.<\/p>\n\n\n\n

In conclusion, this data can be used to predict patterns in mobile and\ndesktop demand and assist with drawing appropriate flaws in certain time\nperiods.<\/p>\n\n\n\n

Chart 2.<\/strong><\/h2>\n\n\n\n
\"\"<\/figure>\n\n\n\n


The black dotted lines are trend lines, which show a clear correlation that the RPM drops more significantly during holidays than desktop usage does. Why? The traffic increase is normal with a drop in RPM. You can also see that pre-holiday has a high eCPM<\/a><\/strong> and after New Year the RPM drops severely. Another pattern noticed is that during workdays in the summer it is a quieter period for desktop usage.<\/p>\n\n\n\n

AD\nREQUESTS DIFFERENCE<\/strong><\/h2>\n\n\n\n


Both the presented charts (Chart 3.) show the percentage ad requests<\/strong><\/a> difference – during holidays or weekends (upper chart) and workdays (lower chart). This is the comparison between desktop (red line) and mobile traffic (yellow line).<\/p>\n\n\n\n

The disparity was counted as the difference between the number of ad requests for the current day and its lag (the day before that). It describes how the website traffic fluctuates and provides information about how the traffic changes from day to day, the strength of this change and its direction (all of the downfalls shows the shrinkage of ad requests amount and all of the increases present the traffic uplift). <\/p>\n\n\n\n

Values over the line (over 0%) show that the trend is increasing \u2013 this case is noticeable for mobile traffic during the holidays and weekends. On the other hand, the values below 0% represent the decreasing trend of traffic (specifically for desktop traffic during holidays). While comparing the work-free days with workdays, the conclusion arises that the gap between the traffic changes is stronger during holidays\/weekends than during typical weekdays. <\/p>\n\n\n\n

The workdays are increasingly similar but the strength of the differentiation is not so eye-catching. However, it is worth mentioning that the growth of mobile traffic and the downfall of desktop traffic is typical for the work-free days (on some days it even doubles) and as the mobile traffic becomes stronger, it can be deduced that the users switched from desktops to mobile devices.<\/p>\n\n\n\n

Chart 3.<\/strong><\/h2>\n\n\n\n
\"\"<\/figure>\n\n\n\n


Mobile usage increase is a macro trend<\/strong><\/h2>\n\n\n\n


The usage and accessibility of mobile phones increased in the last ten years, as we already know. What we now see is that since the usage of high-end smartphones and generous data plans, we now start using our phone, as the graphs above show, also as our main device – it doesn\u2019t matter if it is a holiday day or a normal working day. Cisco predicts 47% of data traffic growth until 2021.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Image source.<\/strong><\/a><\/p>\n\n\n\n

Feel free to contact me if you have any questions.<\/p>\n\n\n\n

Data\n& insights provided by; <\/strong>Julia Boczuli\u0144ska, Katarzyna Zgoda-Ferchmin\n& Rafa\u0142 Barylski at yieldbird.com in May 2019.<\/p>\n\n\n","protected":false},"excerpt":{"rendered":"

The performance of many websites changes during holidays and weekends and the fluctuations can sometimes be both huge and unexpected. With help from our data science team at Yieldbird, they have gathered data and shared their insights regarding the usage patterns below. Programmatic advertising is amazing. One of the main benefits is the accessibility to […]<\/p>\n","protected":false},"author":16,"featured_media":17299,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[28],"tags":[],"content-category":[],"class_list":["post-17162","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ad-tech"],"acf":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/posts\/17162","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/users\/16"}],"replies":[{"embeddable":true,"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/comments?post=17162"}],"version-history":[{"count":1,"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/posts\/17162\/revisions"}],"predecessor-version":[{"id":33457,"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/posts\/17162\/revisions\/33457"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/media\/17299"}],"wp:attachment":[{"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/media?parent=17162"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/categories?post=17162"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/tags?post=17162"},{"taxonomy":"content-category","embeddable":true,"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/content-category?post=17162"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}