It’s commonly reported that automated emails typically exhibit significantly higher levels of engagement than non-automated campaigns like promotions and newsletters. Why wouldn’t they? After all, automated emails are responding at a 1-1 level to specific subscriber actions. Relevance is one of the key factors in email engagement and, when done well, automated emails are highly relevant. When it comes to delivering the right content to the right person at the right time – robots do it better!
Here are some statistics
From our own 2016 email marketing benchmark results, on average across all of the industry sectors that we track, automated emails receive around twice the number of opens (56%) and 7 times more clicks (31%) than the average performance seen across all email campaigns.
Looking in more detail, opens of automated emails range from over 70% in Community, Music/Entertainments and B2C Service sectors to 21% in Banking/Finance. Of all 29 sectors tracked the open rate of automated emails is higher than that of manually scheduled campaigns. At just 21% only one sector (Banking/Finance) shows an automated open rate less than the average across all industries for all campaigns (24.9%) and indeed less than the overall open rate for that individual sector (23%). All other sectors show an elevation of automated opens from 2 to 10 times that of standard campaigns.
Click through performance of automated campaigns shows similar, and in many cases even further improved performance. Again click-through rates vary from over 70% in the Community sector to around 2% in Banking/Finance and Property/Estate Agencies. 25 of the 29 tracked sectors show automated click-through performance higher than the average across all campaigns for all sectors (3.4%).
Let’s look at a few common campaign examples.
Welcomes and other simple automated campaigns
Welcome emails, confirmations and upsell campaigns are common examples of automated campaigns. High levels of engagement are anticipated since each is responding with a personal, timely and targeted response to a particular subscriber action.
The mechanics of each automated process is relatively simple. A single marketing automation (MA) rule is set up.
The trigger is a new subscriber being added to a list (for example completing a subscription form to receive the newsletter). The response is to send the associated email campaign, and the timing is generally as soon as possible, while the event is still fresh in the subscriber’s mind. A further automation rule could also be added to send an internal alert to any of these actions, providing useful information for future conversations or follow up.
Newsletter sign-up = open rate 56%, = click-through rate 12%
Event confirmation = open rate 71%, = click-through rate 21%
Product evaluation = open rate 74%, = click-through rate 43%
Content download = open rate 52%, = click-through rate 26%
As compared to averages open and click-through rates of 24.9% and 3.4% we recorded averages of 63.3% and 25.5% for these common types of automated campaign. Across all that’s approximately 2.5 times the open rate and 7.4 times the click-through as compared to a background value of normal performance.
Automated event sequences
With the three basic steps of a marketing automation rule in place, that’s the trigger, the response and the timing, it’s easy to combine or chain MA rules together to form more complex automated processes. As blogged before, an automated event sequence is a nice example.
In this case, 8 different MA rules are combined to invite, guide and follow up an event. In each case the observed open and click through rates are documented below – again each of these should be viewed in the context of overall open and click-through average rates of 24.8% and 3.42% respectively.
Initial invitation (non-automated) = open rate 33%, = click-through rate 12%
Automated resend (to non-openers) = open rate 12%, = click-through rate 1%
Automated confirmation (to registrants) = open rate 75%, = click-through rate 25%
Automated internal alert (internal) = n/a (100%)
Automated event news update (to registrants) = open rate 73%, = click-through rate 16%
Automated diary reminder (to registrants) = open rate 70%, = click-through rate 9%
Automated thank you (to attendees) = open rate 77%, = click-through rate 51%
Automated rebooking invitation (to non-attendees) = open rate 55%, = click-through rate 16%
A progressive promotional campaign
Another common example of automation is a progressive promotional campaign. In the following example an initial introduction offer was manually scheduled to a main subscriber audience. This was followed by 2 further automated repeats to (only) those who failed to open either of the previous campaigns. In each repeat case the message content was the same but the subject line and delivery time were varied. Those who did open any of the introductory offers were sent a series of 4 further automated follow up campaigns. Automatically scheduled at 1, 3, 5 and 7 days after the initial open each subsequent campaign contained additional content to further develop and incentivise the initial offering.
The marketing automation rules here are a little more complex. Those who open an introduction campaign are automatically added to a new list. This provides an exclusion list from the main audience when the initial offer is next repeated. Next, for the follow up campaigns those who open a campaign are redirected onto a series of marketing automations which trigger new campaigns at intervals of 1,3,5 and 7 days. Further MA rules are set up to internally alert of any activity and provide the opportunity for recording and personal follow up. Behind the scenes, marketing automation also takes care of any unsubscribe requests.
Not surprisingly, each repeat of the original campaign sees a progressively decreasing response, after all this both a smaller audience and one which has already failed to respond to a previous communication. Open rates for the 3 initial introductions were 21%, 12% and 8% respectively. This is largely in line with previous repeat send studies which show some onward benefit but a progressively decreasing return, with little else to be gained after 2 or 3 repeats.
As anticipated open rates of the automated responses were significantly higher than the initial scheduled outreach, with 53%, 48%, 49% and 49% for the 1, 3, 5 and 7 day follow ups. Interestingly, each follow up also received a largely similar open response, only reducing slightly over the 4 campaigns in the follow up sequence.
In conclusion, it’s generally clear that well designed automation can have significant benefits in terms of performance. For you as an email marketer, it reduces the effort repetitive tasks and ensures that your response is always delivered and consistent. For your customers it’s one of the best ways of delivering a timely and targeted response to their individual needs. This not only generates higher levels of engagement but also builds confidence and trust in your future communications – it’s effectively an upwards spiral.
However, automation needs thought. Here are just a few tips to consider when designing an automated campaign or sequence:
You’ll need to review your campaigns and decide what can/can’t and should/shouldn’t be automated. It will depend on things like the frequency of the requirement, the expected volume of actions and whether or not suitable responses can be automatically generated, or whether a personal intervention is needed. Taking this step will also help you to plan and define rules that carry out your tasks appropriately.
Although you can leave your Marketing Automation rules in place for as long as you like, don’t forget to come back from time to time to monitor the results and review that everything is still working correctly. And don’t forget to personalise. Just because it’s automated (in fact because it’s automated) personalisation is just as important as in your regular campaigns.
Finally think about building a non-automated response into your process too. As humans, there are some who’ll always opt or prefer to respond outside of your designed algorithm. Have a process that works for them too.