Unraveling Holiday Shopping: Using passive metering to understand how consumers shop online during the holiday season – Part III

In this third and final part of our holiday shopping case study, we take a deeper look into the purchases of our respondents as well as the online paths our participants took to reach their purchases.

Purchase rate

The chart you see here is the amount of people who visited a shopping site and also purchased an item in the same week across all domains.

At its peak, almost half of our respondents had made a purchase the week of Black Friday and Cyber Monday. Overall, at least 1/4th of our participants made a purchase every week. This very high purchase rate allowed us to gather data about our particpants' paths to purchase.

Paths to purchase

We compiled the most frequent paths (more than 15 sessions) our participants took to reach a purchase. We can see here from which categories people came most often before they made a purchase.

One of the main insights we can take from this chart is that many people went through a range of shopping domains before making a purchase – they were making comparisons between products on various websites before deciding where to purchase them from.

People who went directly from a domain to a purchase had a bigger variety of frequently visited categories than people who took longer paths. This indicates two behavior types – people who take a long time to make a purchase because they want the best deal (and thus are more likely to visit shopping and price comparison sites), and people who are more impulsive and buy directly from one domain.

In this chart we've seen paths to purchase on a big picture level – we can also use behavioral data to be more granular and identify individual paths to purchase, as you can see in the following examples.

Individual paths to purchase

We’re going to share some examples of users’ path to purchase we observed during the holiday shopping season.

Example: Toy

Our first participant wanted to purchase the 'Optimus Prime Power Surge' toy.

Our first participant wanted to purchase the 'Optimus Prime Power Surge' toy. She first googled the specific product, then clicked on three different search results – one from Walmart, one from Kmart, and one from Target. The result she found on Walmart is not the target product she was looking for. On Kmart, she found the appropriate product, but it was not in stock. The same goes for Target. She then opened another tab to Google for ‘Safe Active Toys’, perhaps because she was worried she would not be able to find her specific Optimus Prime toy anywhere for the price she had in mind.

She ended up on ToysRUs from her new search and then queried for Optimus Prime, which was in stock at $42.99 and has free shipping. At this point, she also remembered she received a Kmart promotion in her Gmail account, and went to check if she would be able to get the same product cheaper on Kmart when it is back in stock. She found the same product on Kohl's, but it was more expensive ($53.99 and free shipping).

Therefore, she returned to ToysRUs and searched the 'Optimus Prime Power Surge' again to get back to the product she saw before, which was $42.99 and free shipping. She checked out and filled in the in-the-moment survey, to which she responded that this purchase was for her child, and the product availability on ToysRUs was the determining factor in her purchase. In total, the decision to purchase took her 17 minutes and included 58 page views.

Example: Video game

In this example, our participant went directly to Amazon to search for the intended game, 'Tomb Raider' for PS4.

On the site, she found two different versions of the game – a physical copy of the game for $19.99, and a download code for the game for $29.99. Both these versions were the 'definitive edition' of the game, so she went to Google to find out what this means. From there, she also found the Wikipedia article for the game and read what it is about. She then returned to Amazon, added the physical copy of the game to her cart ($19.99), and researched delivery options. Perhaps she is not always at home to receive the package, or she would like to keep the package a surprise.
In her search, she found an option for 'Amazon Locker', so she researched it. She was directed to an Amazon help page about the Amazon Locker. However, she still wanted more information, and so returned to her search results to watch a few YouTube videos about the Amazon Locker.
After the videos, she went back to Amazon and her shopping cart, where she saw an offer informing her of a discount on her purchase if she applies for an Amazon credit card. She filled in the application. Right before she checked out, she went to Google for more information about an Amazon Prime Free Trial. Ultimately, she checked out and purchased her item. In the in-the-moment survey she indicated that this was a gift for her child, and that the delivery options were her determinant for purchase. In total, her purchase took 24 minutes and spanned 85 pageviews.

Example: Robotic vacuum cleaner

For our final path to purchase, we follow along a consumer that has been in the market for a robotic vacuum cleaner.

This search for a robotic vacuum cleaner started from an Amazon promotional email, based on a past search the participant had done for similar products. From this email, the respondent googled 'which roomba should I get'. In her search, she found a few different reviews from independent blogs and sites. These lead her to look for the 'iClebo' robotic vacuum cleaner. While on Amazon, she also considered a different brand, and found that she would like her robotic vacuum cleaner to have multi-room navigation. From there she went to Google and searched for specific 'Multi-Room Navigation' roombas.

She read more product reviews about robotic vacuum cleaners, then moved on to Amazon reviews of the 'iLife' brand of vacuum cleaner. She then looked up comparisons of the iLife and the iRobot. Returning to Amazon, she looked up the iLife specifically.

On Amazon, she continues on to read more reviews about the iLife. From this, she narrowed down her choice to a particular model that she was interested in - the iLife A4. Then, she googled the iLife A4 and read reviews about this specific model. She clearly had not completely set her mind to the iLife A4 yet, however, because back on Amazon she broadened her search again to see if she could find a different kind of Roomba, and read reviews for the iRobot.

She evaluated a few different models of iLife, looked at reviews, and finally decided on the iLife A4 Robot Vacuum Cleaner for $179.99 and free shipping. In the post-survey, she said it is a gift for herself and that 'price and delivery options' were the determinant for her purchase. In total, her purchase took 54 minutes and 109 pageviews.

Conclusion

These specific examples of paths to purchase show the new opportunities we can unlock by using a combination of passive measuring and survey data. Passive metering can tell us the 'what' of consumer behavior (such as which platforms they used, the products they bought, and when they made their purchases) by eliminating the limitations of self-reporting like memory, and recording actual online behavior with unprecedented depth and accuracy. Additionally, behavioral data can validate reported data.

However, behavioral data alone is not the secret to success. There are certain things that can't be measured. Surveying can tell us 'why' consumers behave the way they do. Opinions and intentions are better declared than measured, because they require subjective context. By integrating passive and survey data collection, we can create a holistic view of consumer's online behavior.