How to Use Data Studio to Identify High Intent Holiday Shoppers

by Tara McClinchey

The holidays sneak up on me every year. As Black Friday approaches, I’m starting to do research on what to buy my close friends and family. I’m taking my time, clicking on the URLs that have been compiled as wish lists and sent to me, and perusing the product lists for other gift ideas. I know I’m not the only one who does this ahead of holiday season, so what can digital analysts and marketers gain from understanding this behaviour?

Traffic that is driven from a wish list has a higher chance of converting to a purchase than general traffic and, given the holidays are approaching, the portion of this traffic that will convert will likely convert soon. This “high intent” traffic can be effectively nurtured through the purchase path through retargeting … if you know how to spot the right intention signals.

In this post, I will show you how you can build a Data Studio report to identify holiday shoppers with high intentions of purchasing. You can use this report to build audiences that you can retarget and convert to sale before the season is over. (Tip: turn these audiences off after the holidays — no one wants to see ads for that gift they didn’t end up buying!)  

The report will answer the following questions:

  • How is my site performing at acquiring potential customers?
  • How much traffic is acquired through friend shares of product pages?
  • How are people sharing my product pages?
  • Do shares to my product pages lead to more sales?
  • Are customers who browse product lists more likely to buy?
  • Which products in product lists are selling well?
  • Do any products from product lists have a high rate of getting added to cart, and then getting abandoned?

Here is a sample report based on Google’s Merchandise Store that includes two pages with a set of scorecards in the header providing an overview of activity made by traffic with a high intent to buy (these are set at report-level), and 2 to 3 widgets on each page that focus on either Product Page Shares (page 1) or Product Performance (page 2).

Intention Signals

Someone who navigates through product lists and adds to cart or someone who shares a product page URL is a good candidate for retargeting. However someone who bounces after the first page or navigates for less than a minute indicates little interest in any of the products. You’ll want to first determine what qualifies a potential customer on your website. In this example, potential customers are those that:

  • navigate the site (excluding traffic that bounces) and stay on the site for a longer period of time (in this example, excluding traffic that navigates for less than 3 minutes, but this time will depend on your own website);
  • arrive on the site directly (they are already brand-aware);
  • arrive on the site from an unpaid source that is not campaign-specific (e.g., unpaid referrals and non-campaign-specific emails);
  • navigate to multiple pages;
  • have previously placed an order (in the example, this is based on Google’s User dimension, so will only count those who have not cleared their cookies and have previously placed an order using the same device and browser; if you have configured a User ID custom dimension, this metric will be more accurate by stitching user activity across devices and browsers);
  • have registered or signed up to your newsletter (this metric will require event configuration).

Now that we’ve defined our customer, it’s time to understand them better. Below is a guide on how to build this report, if you want more information about the metrics and dimensions included. Want help building your reports? We’ve got more ideas. Send us an email.

Guide to Reporting on Intention Signals

Scorecards are great for showing top-line metrics that can provide quick insight into your website’s performance. They are great for monitoring 5 to 6 key performance indicators (KPIs) but, in this case, I’ve focused on monitoring the top indicators of potential customers.

Scorecards

1. Non-bounce Sessions

Metric: create a new metric, Sessions + 0 (segment to be applied)

Segment: create a segment, sessions with > 1 pageview, session duration longer than 3, sessions with 0 transactions

Date Range: auto (in this example, last 14 days) compared with same period last year

2. Direct Traffic

Metric: create a new metric, Sessions + 0 (filter and segment to be applied)

Filter: include sessions from direct source

Segment: apply same segment as scorecard #1

Date Range: auto (in this example, last 14 days) compared with same period last year

3. Friend Shares Traffic

Metric: create a new metric, Sessions + 0 (filter and segment to be applied)

Filter: include sessions from email or social sources; exclude campaigns that have been tagged; include sessions that landed on product pages (for Google Merchandise Store, product pages include /google+redesign/ in the URL)

Segment:  apply same segment as scorecard #1

Date Range: auto (in this example, last 14 days) compared with same period last year

4. Pages / Session

Metric: Pages / Session

Segment:  apply same segment as scorecard #1

Date Range: auto (in this example, last 14 days) compared with same period last year

5. Previous Customers

Metric: create a new metric, Sessions + 0 (segment to be applied)

Segment: create a segment, sessions with > 1 pageview, session duration longer than 3, sessions with 0 transactions, and users that have more than 1 transaction

Date Range: auto (in this example, last 14 days) compared with same period last year

6. Registrations

Metric: goal completions or total events for the registration or sign-up

Date Range: auto (in this example, last 14 days) compared with same period last year

Guide to Reporting on Product Shares

Knowing which sources are used to share your products can help you build an audience for your retargeting campaign through the season. The assumption here is that traffic from these sources that lands on a product page receives a link from a list of gift ideas, whether from a close friend, family member, or even a blogger.  

This data answers the following questions: how much traffic was acquired through friend shares of product pages? How are people sharing your product pages? Do shares to your product pages lead to more sales?

Widgets

  1. Scorecard

Metric: Friend Shares Traffic (copy of the widget in the header with changes in formatting)

2. Time Series

Dimension: time dimension of choice (I kept the default) broken down by Source (limit to 5 sources to keep the graph clean and focused on top sources)

Metric: Sessions

Date Range: from beginning of holiday season shopping until end of season (e.g., November 1 to December 25)

Filter: exclude traffic from campaign-specific email and paid sources

3. Bubble Chart

Dimension: Source (limit to 5 sources to keep the graph clean and focused on top sources)

Metric X: Sessions

Metric Y: Transactions

Bubble Size: Revenue

Date Range: Auto (set to the last 14 days in example)

Filter: exclude traffic from campaign-specific email and paid sources

Guide to Reporting on Product Lists

The second page of the report provides analysis on product list performance. Are customers who browse product lists more likely to buy? Which products in product lists are selling well? Do any products have high list-to-cart and low list-to-buy rates? The answers to each of these questions are useful for building retargeting audiences, even beyond the holiday season.

Three customized metrics are required for this type of analysis. In Data Studio, create new metrics with the following names and formulas:

  1. List-to-Cart: Product Add to Carts / Product List Views (shows the rate of products that were added to cart from a product list)
  2. List-to-Buy: Quantity / Product List Views (shows the rate of products that were bought from a product list)
  3. List View Value: Product Revenue / Product List Views (shows the value of products in a product list)

A Time Series chart can be used to show the trends of product list performance throughout the season, and a table and Bubble Chart can show the top performing products in lists.

Widgets

  1. Time-Series

Dimension: time dimension of choice (I kept the default)

Metrics: List-to-Cart and List-to-Buy

Date Range: from beginning of holiday season shopping until end of season (e.g., November 1 to December 25)

2. Table

Dimension: Product

Metrics: List-to-Cart (sort), List-to-Buy (secondary sort), and List View Value

Date Range: Auto (set to the last 14 days in example)

3. Bubble Chart

Dimension: Product (limit to 5 products to keep the chart clean and focused on top products)

Metric X: List-to-Cart

Metric Y: List-to-Buy

Bubble Size: List View Value

Date Range: Auto (set to the last 14 days in example

Tara McClinchey

Analyst, and Data Studio Practice Lead

As a member of Napkyn's Analyst Team and Practice Lead for Google Data Studio, Tara is an expert in telling the story behind the data through data analysis, rich visualizations, and client consultation and training.

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