At SalesLoft, we love to look at data to help our customers succeed and to hone our own sales engagement processes. Mostly, we look at big data, such as our analysis of 200 million sales interactions to optimize cadence design.
However, we also look at ‘regular data,’ in this case our internal operational data, to understand what is working and what is not.
Recently, we got curious about email response rates by touch count.
To understand this concept, we pulled all email touches sent by our sales development representatives (SDRs) and new business sales executives (SEs) over the past 12 months. We counted an email as a 1st email touch if no emails were sent by a given SDR or SE in the prior 35 days. (At SalesLoft, you lose the account if you don’t engage an account at least once in a 30 day period. We also impose limits to the number of accounts each sales professional can own so that SDRs and SEs do not “camp” on accounts.)
Two of the questions we had:
- Might touch #2 have a higher response rate than touch #1 since the prospect has been warmed up by the 1st email and /or by other engagement?
- How rapidly do response rates fall off?
Here is what we found.
Email reply rates vary widely based on context – prospect personas, calls-to-action, etc. Therefore, the graph above shows response rates relative to the response rate of the 1st email touch.
To make this more concrete, consider the following example:
Email #1 response rate = 20%
Email # 2 response rate = 13.4% (67% of 20% = 13.4%)
Email #3 response rate = 10% (50% of 20% = 10%)
And so on…
In case you are worried about your own response rates, the 20% number we used in this example is an arbitrarily high number for outbound prospecting emails. After all, most studies show that open rates, inclusive of emails sent to skeptical prospects and warm clients, hover between 20% and 30. Response rates are, of course, much lower than open rates.
Question 1: Might email #2 have a higher response rate than email #1 since the prospect has been warmed up by the 1st email and /or by other engagement?
The answer to the first question we posed is that response rates drop by a third from email #1 to email #2. This does not mean that warming prospects up with non-email touches does not matter. It just means it is very unlikely that one can warm someone up so much that email #2 reply rates exceed those of email #1.
In the data, you’ll also notice ranges of touches with similar relative response rates. Take touches #3 and #4 which are both 50% of email #1. In our data, the rates for touches #3 and #4 were not statistically different from each other but were statistically different from touch #2 and from touches #5 and #6.
Question 2: How rapidly do response rates fall off?
On the second question, response rates, as expected, continue to fall with more and more touches. By the 28th touch, the reply rate drops to 14% of the reply rate experienced in touch #1. In our arbitrary example, the reply rate would decay from 20% for touch #1 to 2.8% for touch #28.
In case you are thinking, “Holy cow, 28 email touches! Really?” the answer is yes, we do sometimes execute long duration personalized nurture campaigns. If one sent 28 emails over a short duration, you’d have a lot of angry prospects opting out and bashing you on social media.
The ultimate value of this sort of data is in predictive modeling. Let’s say you did a 5-touch campaign to 100 clean email addresses with an initial (again arbitrarily high) response rate of 20%.
As Table 1 shows, the unique person response rate for the 5-touch email campaign is 48.6%.
Since that feels high (it is), let’s take this analysis one level further and model the unique person response rate for a 5-touch campaign. To do that, we simply recalculated the above table for various email #1 response rates.
Figure 2: Expected unique person response rate to 5-touch email campaign vs email 1 reply rate
Here is what the chart above says. As we learned before, if you run a 5-touch email campaign where the 1st email had a 20% response rate, then you would expect a 48.6% unique person rate based on unique people engaged. If email #1 instead had a 10% reply rate, then you’d expect 27 people to reply. Finally, if email #1 had a 1% reply rate, then you’d expect just 3 people to respond.
This article simply focuses on email campaign reply rates. With sales engagement platforms like SalesLoft, you’ll soon be able to get predictive reply rates for multi-touch, multi-channel campaigns based not only on your data but also on the anonymized data of other other customers.
Ultimately, sales engagement platforms will recommend modifications to your language and cadence structure to optimize your overall reply rates.
If this all seems a little rocket-sciency, then your SalesLoft sales, implementation, and customer success team are “standing by” to help you optimize your cadences.
Curious about email response rates by touch count? This analysis of reply rates provides insights.
Looking for more information about best practices and examples for email cadences? Don’t miss our new eBook, Best Practices & Benchmarks for Sales Cadences.