Identifying High-Value Customers
The most valuable insight into your users is who among them are your high-value customers, the ones who have interacted with your content or purchased recently, who interact or purchase frequently, and who engage in high-value conversions.
You can create a Recency-Frequency-Monetary Value (RFM) segment that identifies those customers, and then apply it to your reports.
Recency. Users who have interacted with your content or purchased recently (for example, within the last two days or last week) are more likely to interact or purchase again.
Frequency. Users who interact or purchase frequently (for example, every week or month), as well as recently, are more likely to interact or purchase again.
Monetary Value. Users who engage in the most conversions, as well as have converted recently and frequently,are more likely to convert again.
When you’re able to identify those users and understand their behavior, you can tailor your business decisions to focus on them. For example, if your high-value customers respond to a narrow set of keywords or make purchases during specific times of day, you can focus your bidding on those keywords or day parts. If they tend to be from a few specific geographic areas, you can emphasize advertising in those areas and diminish or eliminate advertising in other areas.
You need to identify the RFM thresholds that identify your high-value customers.
To create an RFM segment, base it on filters like the following:
Sessions per user greater than 5
Days Since Last Session less than 5
Revenue per user greater than 100
Conditions > Filter Users:
Goal Value > 100
You can build segments to identify cohorts, for example, new users to your site on a specific date or during a specific date range who arrived as the result of a specific campaign:
Date of First Session: the date range of your campaign
Traffic Sources: Campaign exactly matches name of your campaign
You can classify your users by any dimension, metric, or combination thereof that is important to your business.
If you run an ecommerce site, revenue might be your most important metric, followed by goal completion, campaign, ad group, and keyword.
If you run a content site, your biggest concerns might be pageviews, time on page, and time on site.
If you’re classifying by revenue, you can create tiers to indicate different value customers:
Conditions > Filter Users:
- Low Value: Revenue per user < 6
- Medium Value: revenue per user > 5 AND revenue per user < 10
- High Value: revenue per user > 10
You can get an immediate sense of how your customers break down among the different tiers, and you can track how customers migrate from one tier to another over time by comparing date ranges: for example, if the number of low-value customers decreases from one date range to the next while the numbers of medium- and high-value customers increase, you can infer that you’ve made a successful appeal to those consumer appetites and convinced them to spend more money on your products.
If you’re classifying by campaign, you might want to include both the campaign and revenue or goal completions:
Conditions > Filter Users:
- Campaign containing campaign name AND Revenue > 5
- Campaign containing campaign name AND Goal 1 (Goal 1 Completions) > 1
You can use this method of segmenting users as an exploratory approach through which you both validate expectations (e.g., users from California do buy more beachwear than users from Minnesota) and uncover new information (e.g., users from social channels convert at a higher rate than users from organic search).
Once you’ve identified the segments whose behavior you want to analyze, you can apply those segments to any of the reports.
Understanding the users who don’t convert on your site can help you focus resources by either ignoring those users if they don’t represent real potential, or by targeting them more aggressively and precisely if they do represent real potential. For example, a Ferrari or Porsche dealership might get a steady stream of people walking through the lot on a weekend, but not all of them represent potential buyers. The salesmen need to quickly assess who has the means and intention of buying and focus their efforts on those people.
Users who don’t convert are going to represent the vast majority of your users, so you need
to focus your attention on more specific behavior.
For example, you can identify users who started down a goal path, but did not convert:
- Users who viewed the product page
- Users who then viewed the Shopping Cart page or who clicked Add to Cart
- Users whose revenue = 0
You can further segment to identify things like users from specific sources who start down a goal path but don’t convert, for example, two segments that compare non-converting users from two different campaigns or two different social channels.
Users who add items to their shopping carts represent a greater inclination to buy than other users who simply browse without converting.
When you’ve identified the users who demonstrate an inclination to buy, you can then develop strategies that encourage them to complete the purchase. For example, you can remarket to those segments with a discount on the specific items that languish in their carts, or adjust the related campaign or social content.
For non- converters who browse without adding any items to their shopping carts, you can build segments of the page sequences you thought would encourage sales (e.g., product home page > product details page > payment-options page) to see whether users followed those paths as you expected. If they did follow those paths, then you might adjust your content to include more incentives to purchase. If they didn’t follow those paths, then you might adjust other site content to encourage users down those paths.
Understanding who converts on your site can help you focus your resources on expanding that segment of your audience.
For example, you can build segments to compare conversion rates between users from different age groups according to gender.
Age: 18-24, 25-34
Age: 18-24, 25-34
When you apply these segments to the Ecommerce Overview report, you can see how ecommerce activity compares between the two groups. You might find that the total revenue from females is only a fraction of the revenue you get from male users, but the average transaction value from females is nearly 90% of that from males. With that information in hand, you have the opportunity to increase your presence in female-centric venues, and craft specific messaging for that audience.
You can also find the conversion rates for things like users who had a minimum number of sessions, viewed a minimum number of pages, or spent a minimum amount of time on your site. Or you can create tiers for each of those classifications to see whether there’s a particular sweet spot you need to focus on.
For example, you might see that conversions go up dramatically when users view at least five pages, but don’t increase at a greater rate the more pages they view beyond that.
Or you might see that conversions make a big jump when users stay on the site at least 15 seconds, and then increase proportionally with each time increment beyond that.