As we’ve published this series on email measurement and ROI, we’ve received a lot of great questions from our readers. So I’ll use this opportunity (as well as a post next week) to provide full responses. Note that where questions have a largely common theme, I have grouped them together and responded to them under a single heading.
What is the best way to track or measure inbox delivery versus just generic delivery rates?
I’m going to have to give a Return Path-specific answer to this question. We measure inbox placement rate rather than just delivery, which is typically emails sent minus emails bounced and fails to identify the percentage of emails that actually end up in subscribers’ inboxes, where they see and engage with them. Inbox placement rate measurement leverages the broadest and most accurate seed list coverage for 150 domains worldwide, and then complements the seeding data with real subscriber data for enhanced accuracy. In this way, program owners can report on inbox, spam, and missing rates, and then use this data to optimize their campaign performance.
How do you measure read rate for email campaigns?
Open rate is typically recorded by a tracking pixel that is embedded in the email. When the client or browser used to display the email requests that image, then an “open” is recorded for that email by the image’s host server. The open rate percentage is typically calculated against emails delivered. At Return Path, we also talk about read rate. This is based on the actual behavior of our panel subscribers, and is not skewed by automatic image enablement/disablement.
Are we assuming all metrics have equal weight? If not, what’s the weighting like for improving inbox placement rate versus improving click-through rate?
Metrics have greater weight as you move further into the conversion funnel. An open is worth less than a click, and a click is worth less than a conversion. If you know your average order value, you can reverse-calculate this. Let’s consider a simple example:
On average (and allowing for a small rounding error), this email program will need to send 640 emails for each transaction generated. As an address passes through each metric, the chances of conversion increase, so the associated value increases accordingly.
For cases where access to revenue and cost data is restricted or completely inaccessible, what are the best KPIs to report on specifically for reports the CMO is interested in?
Probably the right starting point is a selection of the benchmark reports that I have referenced. Many of them provide industry-specific metrics, so you use the set that most closely reflects your own sector to build a generic model. I would then build a deliberately conservative scenario to make your business case. In this way, you make the point that ROI will still be positive even if the generic model overstates true value, or the investment only delivers a comparatively small return.
What are some numbers in the financial services industry and how to apply in that space?
Many of the benchmark reports I’ve referenced provide more detailed drill-down by industry. For example, the 2014 Silverpop Email Marketing Metrics Benchmark Study contains the following:
The DMA Response Rate Report 2015 has conversion rate date for financial services:
It’s interesting to note that financial services is the only major industry where prospect list data converts better than house list data, and may reflect the fraud-related challenges endemic in this sector.
Is the conversion pipeline a tool we can use in Return Path?
The conversion pipeline/funnel is not a Return Path tool, but the ROI calculator we have referenced can be used for much the same purpose. The concept is a simple one: we are thinking about every potential point of interaction, from initial send to final transaction. Whereas the focus is normally on just average order value, every stage has a value that can be mapped onto it, For example:
This list isn’t exhaustive. We could also consider the value of gross opens/clicks vs. net opens/clicks, as well as post-click browsing behavior. This approach lets senders focus on proving the value of a specific objective.
For example, let’s assume a list of 100,000 emails which are sent once per week and the sender wants to increase average open rates from 25% to 26% (of delivered). That would be an extra 900 opens per send or 46,800 opens per year. The incremental value of these additional opens is $29,250 (46,800 x $0.625), so a $25,000 email optimization investment to achieve this target would be ROI-positive.
Senders can also take a similar approach to mapping financial values onto events such as bounces, complaints, and unsubscribes. In this way, ROI conversations can be built around reducing these rates through improved deliverability and a better understanding of how subscribers engage.
More Q&A to come in Part 2 of this blog post!