Which KPIs should you track over email? Part 2

In Part 1 of this series, I made the case for why looking at Revenue Per Recipient (RPR) and Revenue % attributed to email as an inherently flawed system and how we need to move away from judging email marketing success from them.

In the second part of this series, I’ll make a case for what metrics you should track as well as some of the nuances and complexities of measuring email marketing success throughout.

To begin, it’s critical that I caveat this piece by saying that even though we track all of the following metrics rigorously inside our agency, it’s not black and white by any means and may differ across your company or brand’s email marketing program.

This is because email is primarily a communication medium, and in the context of eCommerce being focused on revenue generation, intertwining the two together can often skew judgement dramatically.

With that being said, there are definitely high-level statistics we report on to let us know we’re moving in the right direction.

For certain flows triggered by onsite behaviour, we can definitely measure success quite efficiently.

And for campaigns, there are also metrics we can measure on a monthly, quarterly and yearly basis that are accurate indicators of success.

It’s crucial that we separate the two for the purpose of this article as the measurement on flows and campaigns can be significantly different, depending on the objective of the email(s).

Tracking Email Automation Metrics

Certain Email Automation is absolutely designed to maximise conversions. When the goal is that simple, we can definitely measure the success of our email marketing program quite clearly.

More specifically, it is obvious that the following flows are mostly exclusively utilised to drive conversions from customers:

Abandoned Cart Flow
Welcome Flow
Browse Abandonment Flow
Customer Winback Flow

Given that the first 2 flows in particular are the main drivers of revenue for most eCommerce stores from an email automation perspective, it makes sense to test aggressively on these components and meticulously track KPIs.

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www.magnetmonster.co.uk/blog/which-kpis-should-you-track-over-email-part-2 | Magnet Monster

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Here is what you should be tracking in each flow:

Placed Order Rate (Conversion Rate) per email (either through Klaviyo or Google Analytics)
Click Rate per email
Open Rate per email

And I would go as far to say that I would follow the above in that specific order as well.

In part 1 of this series, I said that tracking aggregated percentages often doesn’t make sense when judging the success of your email marketing program.

The example I gave to illustrate this was sending to a hyper-targeted segment of VIP customers that drove a high conversion rate as opposed to a broad-scale segmentation campaign that had a much lower conversion rate but higher net gain.

For behavioural automation, the rules are different and it makes sense to scrutinise percentages.

This is because the audience selection has no bias and is triggered by onsite behaviour that anybody can enter.

You can’t choose who abandons a shopping cart or subscribes to a pop-up, but you can choose the audience to send to in an email campaign.

The elephant in the room when discussing the above is quality of traffic, and this can skew your reporting significantly.

For example, you may run a large sweepstake campaign across social media which generates thousands of leads entering your Welcome Flow.

Given that these leads are likely to be less interested in buying your products, you’ll likely see a noticeable downward trend when reporting on your metrics (assuming they all entered a Welcome Flow).

This is another clear example of why as an email marketer, you need to be aware of the wider business activities and not silo your role solely to the channel in your possession.

Without an understanding of what is happening externally in the business, your reports can lack context and value to stakeholders.

Tracking Negative Metric Signals in Flows

A common question I get asked is whether you should monitor bounce rate, unsubscribes and spam rates in your flows.

Yes, absolutely keep a close eye on them. However, in my experience, they stay relatively stable across the board irrespective of your strategy in each flow.

That’s not to say you shouldn’t pay attention to them; it’s more about understanding that they’re unlikely to be influenced too much by your approach in these emails.

An exception to this rule I’ve noticed is when people try misleading tactics such as sending emails saying “Thank you for your order” in order to get somebody to open an Abandoned Cart email. This will inevitably ramp up spam complaints and unsubscribes and should be avoided as a tactic.

Final note on this: if you’re running a quiz (as as through Octane AI) into a Welcome Flow, pay very close attention to your Bounce Rate as this may harm your deliverability.

A common theme I’ve noticed is that customers will pass through to the end of quizzes and then enter a fake email address at the end, privy to the fact that the results are often on the page beyond the opt-in regardless.

This can erode your sender reputation as it will trigger a high proportion of bounces when these users enter your Quiz/Welcome Flow.

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Optimise Testing based on the above metrics

Based on the above, I would recommend optimising the aforementioned metrics with an aggressive testing protocol.

I wrote a guide here on how to structure A/B tests for all of these flows which should help you fast track results.

Tracking Email Campaign Metrics

Tracking email campaign metrics can either be more complex or less depending on your overall strategy and nature of the business.

For example, if you run a regular Newsletter, then it’s possible you focus on value delivery within the body of the email itself (and I’ve been a proponent of this in the past).

This will possibly lead to a low click rate and high open rate over time.

With the recent iOS 15 changes to email open tracking, we’ve now seen a shift in how emails are being designed to optimise for clicks.

I’m fine with this and I think it makes sense (most of the time) in order to help create more engaged segments.

However, do be aware that this will require dev support on the front-end website, as it’s likely you’ll need to start hosting content on your web pages.

This may sound simplistic, but you’d be surprised how cumbersome it can be working with some brands and asking them to host pages multiple times per week.

Thus, optimising for clicks can be nuanced and not always straightforward.

Opens are also now more unreliable than ever due to the recent iOS changes.

Creating a segmentation strategy that targets Apple users separately from your main segments on other devices may help, but if we’re being honest, these changes are likely to be rolled out across other companies in the near future as well.

With these factors in mind, let’s take a step back and define our overall goals with email campaigns to support our stores:

We want to maximise engagement to stay top of mind with our customers
We want to maximise site traffic to improve the potential for conversions, and:
We want to optimise for conversions (occasionally) if we’re running a specific offer or promotion

Often, these goals will be interchangeable depending on the strategy deployed by the brand.

During Black Friday Cyber Monday, it’s likely that most brands will want to optimise for sales and site traffic and less for engaging content in order to stay competitive in the market.

At other times of the year, discounting and offers may be less prevalent, which means engagement and a content-led approach becomes more important.

How do you find a happy medium? It’s a tricky question to answer.

My opinion is to optimise for all 3 over a monthly, quarterly and yearly basis, and look at the success of each individual campaign each month and try to pick up clues as to what’s resonating with your audience.

Therefore, I’d look for trends that maximise the total volume of:

Total Clicks/site traffic MoM from email
Total Revenue generated through email
Total Opens (least important now given iOS changes)

Notice that I didn’t use averages in the above (i.e ‘Click Rate’, ‘Open Rate’, ‘RPR’). While some may argue that increasing the average of each of these metrics would mean you’re sending more relevant content to your users, I’d fire back by saying that this requires an increase in resource allocation from the brand, resulting in less profitability.

It only makes sense to do extremely granular segmentation based on zero-party data and transactional data when you’re working with extremely big stores and the ROI and store catalogue warrant such a thoughtful approach. If you follow this approach, it’s better to track the aforementioned metrics on these specific segments over time, as opposed to total numbers across the whole database.

For the overwhelming majority of stores, increasing site traffic and engagement each month is going to correlate with an increase in revenue. It’s just logical.

Design an email campaign strategy that aims to move the needle on all 3 of these metrics and track it MoM to ensure you’re moving in the right direction.

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Tracking Negative Metric Signals in Campaigns

Unlike automation, I track negative intent signals diligently in campaigns as it provides vital insights into where I’m going wrong and what content doesn’t resonate with my audience.

For example, if I see a sharp increase in unsubscribes and spam complaints, I know I need to reexamine my segmentation and content strategy, as well as potentially my email frequency.

It can be tricky to draw parallels as to why these metrics may be increasing, as there is often an element of subjectivity involved in us guessing and trying to draw correlations.

We can ask customers to tell us why they’re unsubscribing (see below), as well as respecting their preferences in regards to email frequency and the type of content they’d like to receive on a monthly basis.

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Final note on this: I’d also highly recommend tracking the Total Volume of Email Sends each month to draw correlations as to how your frequency affects all of these metrics

Sample Report on Email Campaign Metrics

Here’s an example of a stores email marketing performance on a monthly basis as well as the metrics we’re tracking using our custom Tableau dashboard at Magnet Monster:

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Looking at the above, it’s relatively easy to conclude that while an increase in sending volume during November correlated with an increase in clicks and placed orders, it also had a detrimental effect on unsubscribe rates and spam complaints (the bounce rate appears abnormally high for this store - which suggests to me that they were using a wider net during Black Friday in their segmentation strategy).

What I would be curious to see is whether a continued increase in sending volume could be sustained MoM for this store and whether the revenue level stayed consistent. If so, you could make a case for continuing at that sending volume, but it’s unlikely.

The best reporting system in the world will only provide you with raw data. It is the marketers responsibility to interpret that data and break it down into actionable insights for the stakeholders and share learnings from their strategy.

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Putting it all together

Let’s have a quick recap on what to track on a monthly, quarterly and yearly basis with email marketing.

Email Automation

Track the following metrics across your core revenue-generating flows:

Placed Order Rate (Conversion Rate) per email (either through Klaviyo or Google Analytics)
Click Rate per email
Open Rate per email

Add A/B testing for incremental improvements to all of these metrics over time in the above order.

Email Campaigns

Track the following metrics across your email campaigns:

Total Clicks/site traffic MoM from email
Total Revenue generated through email
Total Opens (least important now given iOS changes)

Don’t look for average percentages - they lack context and can easily be manipulated. Aim to maximise total traffic and overall engagement with your sends as this is the most economical from a resource perspective and most importantly, results.

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