Understanding the Multi-Channel Shopper


Consumers are making more of their purchasing decisions on mobile devices – whether on a smartphone or tablet – in addition to making it one of their primary channels to research products.

A recent survey by Telemetrics and xAd showed that 50 percent used their mobile devices to start the discovery process and 46 percent used mobile exclusively when performing research online. Even Google noted last year that 65 percent of online searches began on a smartphone.

This emerging multi-screen reality has led to CMOs worldwide to begin their own research – how do we capture and convert multi-channel shoppers? Good question.

The mobile shopper can act much differently than one browsing off of a PC. They’re at restaurants, on buses, or even in stores – often with only seconds to check out (and not necessarily “checkout”) a product out before moving on to the next task.

Tolerance for irrelevant content and slow/bad site-search functionality is much lower for mobile shoppers. Couple that with the fact that they move between devices, and you have a recipe for significant losses because even one bad experience at one of the points of discovery or conversion can lead lost customers.

However, before you even begin to execute any strategy of customer segmentation, you have to understand the different profiles of multi-channel shoppers and consider the right tactics to monetize each profile.

What follows are five distinct profiles of multi-channel shoppers, as well as some methods to convert them to customers. Please note that each profile isn’t mutually exclusive – many shoppers can exhibit behavior of multiple profiles, which is why gathering data across devices, channels and sessions is imperative to predicting what will be the most relevant content to present at this moment.

1. Need-It-Now Profile

This shopper has discovered the specific product they want and is ready to make a purchase; however, they’ll almost certainly want to find a physical retailer that has that product in stock or that can get it shipped very quickly, like Amazon’s same-day delivery service.

Having fresh web pages with the most up-to-date information is imperative.

For example – if a consumer is presented with a mobile search result for a “red strapless dress” that is on sale for $50, you better have location-specific data that this is, in fact, true. This means that your inventory information should be as close to real-time as possible.

Also, these users have very specific long-tail searches, making predictive-search capabilities extremely important. If available, know their browsing history and ultimately have their query available in a drop-down menu within a few taps. Typically, this is the profile of someone who shops on Amazon.

2. Bargain-Hunter Profile

This shopper uses their smartphone to compare prices either before or while they are in your store. Commonly referred to as “showroomers,” they are extremely price-sensitive and have very little brand loyalty.

It’s important to offer as much incentive information as possible. So, they should be provided with any warranty information or in-store special promotions.

Anonymously identifying and matching IP addresses of store Wi-Fi to see if they are in a store while accessing your mobile site is a good way to track these types of shoppers. Capture the data and track the behavior of other shoppers who visited your site while in store to gain invaluable insight.

3. Right-For-Me Profile

These shoppers visit your site at home on a desktop or laptop, on their smartphones and while in-store, and express very specific and consistent intent signals. They often search for and purchase certain sizes or brands/designers at specific price points.

It’s important to piece together individual experiences and present them with the most relevant content based off of their habits. You can compare the behavior of your authenticated users on mobile and web to learn about their preferences, and then try to apply it to their content.

Offering “More like this” widgets that take into account all of the previous expressions of intent can help keep that customer converting.

4. Time-to-Kill Profile

These shoppers are most likely your exploratory buyers, with a little extra time, where easy navigation and visual design elements that create a “fun” experience are important.

Using social-network data from Pinterest or Facebook, consider creating landing pages of popular or emerging products that can turn a browser into a customer. They’re going to explore more pages and provide you with a lot more data about what products could be linked.

By tracking bounce rates and time-on-page metrics, you can learn what products make better sense to present together.

5. Most-Valued Customer

These shoppers are the ones that engage with you the most across multiple channels or devices. They click through emails and discover a lot of your content, and should be the most important.

These customers shouldn’t be treated like any other customer – you should understand their intent and present them with the right offers at the right time. What time of year do they normally buy gifts, are they tied to a holiday, and are they only selecting sale items? While this may sound like something very simple, actually tracking this data at a granular level to scale for potentially thousands of people while knowing which product is right based on their previous history across devices – all accurately – is a tremendous task.


Understanding, processing and acting on all of this available data is a problem well beyond the scope of humans – it’s an issue for big data science. Amazon is doing a great job to capture the shoppers frustrated with experiences on other retailer’s mobile sites.

As consumers rapidly shift to mobile shopping on smartphones, Amazon is capturing an unbelievable 59.36 percent of mobile department store visits. And, they are investing a greater percentage of their revenue in technology and content – 7.9 percent up from 6.5 percent – because they realize that high-quality content that is relevant to each shopper is a problem that only a machine can address at scale.

In the last decade, search marketers have been faced with a gargantuan amount of online data and web analytics, and technologies that have helped process the information have barely allowed them to keep pace. Mobile data – and the different channels that it opens up – ups the ante exponentially. At the same time, consumers expect a seamless and relevant experience no matter their platform of choice.

The most successful companies will embrace a multi-channel initiative. Those that don’t will only face a bigger digital divide between themselves and consumers – ultimately losing them to the likes of Amazon.

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