Today, desktop computers drive the most clicks on search ads and conversion events on websites. But tablets and mobile phones aren’t chopped liver, either.
These devices “assist” sales and can be very important depending on the time of day and/or where your customer is in the overall buy cycle. They are important to consider in the overall shopping journey/experience.
How can marketing managers or agencies get more credible information on what’s contributing to purchases when consumers are regularly splitting their activity between two or three devices?
I myself use a phone, a tablet, and a computer for personal use, and I use all of them at different times of the day. On top of that are intra-family (multiple phone, multiple computer) purchase considerations, not to mention work vs. home vs. school.
Attribution won’t be something we can “nail” anytime you’re dealing with a high ticket or high consideration purchase. All we can do is improve. There’s also no attribution for customers obtained through word of mouth.
Attribution modeling is still in its infancy, and the industry isn’t that far along yet. This leaves it up to us (the agency or the individual company) to figure things out.
To understand this, we need to latch onto any reasonable scenarios, studies or data we might have, including new estimates that Google or others might provide.
This situation reminds me of when I started in online marketing. We looked for studies conducted by others and sought to understand what others were doing to inform our online strategy. We understood that nothing was carved in stone and things would change based on new information, new developments, and additional insight we got from our own company data and testing.
Notably, attribution breaks when people change devices. So, are there methods to help us demonstrate the phenomenon of “lost attribution” due to device switching?
Let’s look a little more at shopping/browsing on different devices.
Below is some basic information on browsers and shoppers:
The more devices a person owns, the more likely he or she is to trust and to make purchases faster.
(Note: The above data is from a session at Seattle SIC Conference in October 2014, “Thinking Beyond the Shopping Cart: Making Cross-Device Shopping Experience Work.”)
For certain market segments, people convert better on tablets than on desktop devices.
For example, conversions in the health vertical have the best ROI during work hours and into the evening on tablet devices. If you think about it, this makes sense. You don’t particularly want your employer to know your personal medical information, even if it’s as simple as having a sore neck or back.
For other folks, tablets are more about discovery than transactions. Tablets are influential, but people are still purchasing more on computers.
In some recent research presented at SMX Advanced 2014 by Lars Hirsch, Bing showed that tablet clicks are undervalued and that tablet clicks in the morning can lead to purchases later in the day.
The research showed that if both “good clicks per conversion” and “good clicks/conversions” are high, then tablet clicks that occur in the morning can lead to purchases later in the day on any device, as shown in the image below from Bing.
Mobile users are similar in many ways to tablet shoppers (refer to 1-3 in the tablet section above). Following is some information on how people search now and buy later on mobile devices.
In general, mobile users look for quick pieces of info on mobile devices like reading reviews, looking up sizing, etc. Or they look for immediate info like directions to a store, locate a phone number, etc.
Mobile purchases are usually inexpensive and are often impulsive. For example: kitchen and housewares tend to convert well on mobile devices in the evening after work.
It’s not uncommon for mobile traffic to be the lowest converting traffic. This is certainly the case for Zappos and other larger retailers. People conduct initial research on their phones and go on to do more complex activities/complete purchases on their computers.
Assuming I can get some of the information, or have some reasonably accurate way of getting information, what is my best way of acting on it so that I can acquire the most customers at a profit?
The way mobile and tablet devices factor information into marketing campaigns will be different for every company. Below are some general tips.
There are many ways in which we prevent people from having a good experience on both tablets and mobile devices. Here are some questions you could ask:
Following are some tips for your mobile sites:
Work on eliminating friction to increase the likelihood of conversions online.
Let’s say you’re Zappos and you know that 40% of your mobile visitors go to a computer to make a purchase. You can take this info and bake in KPIs. Here are some factors to take into consideration when doing this:
One example is Google Analytics’ multi channel funnel info. It won’t give you complete attribution information but has some useful nuggets to help inform your decisions. In various reports, you’ll likely see keyword “not provided” which is a significant stumbling block.
I find the following reports useful: Assisted Conversion, Top Conversion Paths and Path Length. This information may get better and better as time passes, given all the user data Google has (logged-in users, etc.). But that won’t work for iPhones as often. It won’t work in Europe very often. And Google is reluctant to truly implement such solutions for privacy reasons.
You can also look at assist information in the Google AdWords interface for insight. For example, take a look at the following data: Estimated Cross Device Conversions, Phone Call Conversions and Estimated Total Conversions.
Estimated Total Conversions provides Google’s best estimate of website conversions, cross-device conversions, and phone call conversions. It’s not uncommon to see Estimated Total Conversions figures come in at 2X+ the number of conversions you’re seeing in your account.
The challenge of attribution across devices will not be resolved soon. Google and other companies will be reluctant to truly implement such solutions for many reasons including privacy. Until then, we’ll have to work with the information we have along with our own extrapolations to figure things out.
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