How cross-device shopping can kill your ecommerce analytics, and what you can do about it

By Erin McElwee

July 20, 2016

This is a guest post from Monetate partner Blue Acorn.

Account holders. People who opt-in to your email list. Cart abandoners. Those who frequently convert. Those are some of the ways you might categorize your customers and prospects in an attempt to better market to them.

The problem? When you evaluate your shoppers based on broad categories, you only see a fraction of their total experience with your brand. That means you’ll struggle to build a clear and comprehensive picture of how each part works together to create a conversion.

Ecommerce Quarterly for Q1 2016The latest Monetate Ecommerce Quarterly report stresses the importance of understanding these segments and their entire journey toward a purchase. It’s right on the money, especially when you consider their finding that only 42% of purchases occur within the first hour of a shopper’s browsing session. With 58% of users taking a less direct path when working toward your end goal—obtaining an order—there’s clearly a need to understand the rest of the picture.

Indeed, while your users research, compare, and (perhaps) hesitate about making a purchase, you’re judging them based on multiple sets of cookies, across multiple device IDs, and across many stages of the buying process. Each inconsistency and failed attempt to reconcile these idiosyncrasies adds a layer of friction that makes your users feel less like appreciated patrons and more like ecommerce floaters. Conversely, having a consistent shopping experience brings greater understanding, and when people feel understood, loyalty flourishes. In commerce, brand loyalty = per-user recurring revenue stream. And who doesn’t like recurring revenue?

The right experience on the right device.

In the just-released Ecommerce Quarterly report, Monetate asks: “Wouldn’t it be cool if you could make sure those shopping experiences pick up where they left off, no matter what?”

I think that would be cool!

In order to serve your users an online experience that is engaging, you must first anticipate their needs. One of the ways you can do that is by looking at when and how people shop. Here’s what the aggregate data in the Monetate EQ found:

Device type graph

As interesting as that is, page views only illuminate a part of the story. Here’s an example of how you can utilize cross-browser tracking to create a personalized, start-to-finish shopping experience.

Let’s say, after an initial investigation into your data, you uncovered a scenario similar to this one:

  • 3am: You send a promotional email
  • 5:30am–7am: Get an influx of mobile traffic and your analytics show that most of it came from your early-morning email. But email is deemed a poor performer because last-click attribution shows it didn’t generate much money.
  • 12–3pm: Desktop traffic peaks along with site conversion rate. No traffic source anomalies are detected, yet direct traffic seems to be on the rise month over month.
  • 7pm: Mobile traffic and conversion rates peak. No traffic source anomalies are noted.

Based on the above scenario, you might conclude that 7pm is the prime time for mobile conversions, and that you should adjust your email schedule accordingly. But this thought process makes a lot of faulty assumptions. It also fails to show how the actual buying journey went down. In reality, it may have gone a lot more like this:

  1. Customer woke up and checked her email via her mobile phone…
  2. Went to work and spent her five-minute break researching the purchase she was considering, then abandoned her cart…
  3. Got home from work and decided to complete her purchase on her personal laptop.

Adding to the confusion: the shopper was identified as a new visitor and treated as such for all three interactions.

But there’s a better way.

With cross-browser IDs and profiles for this shopper, you could identify that she regularly checks her email in the morning and that this is the most common first touchpoint before a conversion. You’d also know that it typically takes three touchpoints to finalize a sale. Throw in a few other data points like past order size and frequently visited product category data, and you’re on your way to delivering a full-fledged, multi-device, personalized shopping experience.

Instead of creating a simple A=B experience, you can make something more like this, using the nuance and real-world intelligence of customer data and machine learning.

  1. Create a rule that if a shopper has not converted in the past month, serve her an email promoting new arrivals from her most frequently visited product categories.
  2. On the second touch, curate the homepage to highlight the most recently viewed product and sort the category page by products within the shopper’s historical product price range showing at the top of the page.
  3. On the third touch, highlight the cart contents and remind the shopper of any promotion she might be eligible for, such as free shipping.
  4. Continue to personalize the experience through when communicating with her via confirmation emails and customer service touchpoints (if applicable).

The Monetate Ecommerce Quarterly report reminds us that although more shoppers are turning to online shopping than ever before, they’re not necessarily getting the best experience. Shopping online does not need to feel cold and repetitive. Anticipating the needs of your shoppers while applying a personal touch might help your shoppers feel more at home, which will increase brand loyalty.

Treat your loyal customers like newbies, and your newbies like longtime customers, and you risk leaving valuable dollars on the table.

This was a guest post by Erin McElwee, a conversion consultant at Blue Acorn

Erin McElwee is a conversion consultant at Blue Acorn.

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