La economía del correo electrónico frío
Stevens
Reyes-Simpe*
Daria
Kubantseva*
ABSTRACT
This study provides a
mathematical model that analyze the factors that determines response rates in
bulk cold emails. To test the model, we conducted an empirical analysis based
on data from our experiment that included 5,187 cold emails, having an average
response rate of 15%. Three key determinants were found: email length combined
with the sender's presentation timing, personalization for non-patient buyers,
and the time between each product’s features presentation. Specifically, the
findings claim that an email under 150 words with a timing introduction of the
sender (up to 3 seconds after the start) increases the response rate from 17%
to 44%. Personalization, defined as name, company, and location, for
non-patient buyers increases the rate to 58%. In contrast, response rate
decreases by 17% each increment of 1 second on the average time taken between
presenting each product’s features. Unexpectedly, some variables like price
discrimination turned out to not have statistical significance on response
rates.
Keywords:
Cold emails, email
lengthiness, personalization, sales strategy
RESUMEN
Este estudio proporciona un
modelo matemático que analiza los factores que determinan las tasas de
respuesta en los correos electrónicos masivos no solicitados. Para probar el
modelo, realizamos un análisis empírico basado en datos de nuestro experimento,
que incluyó 5187 correos electrónicos no solicitados, con una tasa de respuesta
promedio del 15 %. Se identificaron tres factores determinantes: la longitud
del correo electrónico combinada con el momento en que se presenta el
remitente, la personalización para compradores que no son pacientes y el tiempo
transcurrido entre la presentación de las características de cada producto. En
concreto, los resultados indican que un correo electrónico de menos de 150
palabras con una presentación del remitente en el momento adecuado (hasta 3
segundos después del inicio) aumenta la tasa de respuesta del 17 % al 44 %. La
personalización, definida como el nombre, la empresa y la ubicación, para los
compradores que no son pacientes aumenta la tasa al 58 %. Por el contrario, la
tasa de respuesta disminuye un 17 % por cada segundo adicional en el tiempo
medio transcurrido entre la presentación de las características de cada
producto. Inesperadamente, algunas variables como la discriminación de precios
resultaron no tener significación estadística en las tasas de respuesta.
Palabras clave: Correos electrónicos en frío, longitud de los
correos electrónicos, personalización, estrategia de ventas
INTRODUCTION
Emailing
is one of the most important channels to acquire potential buyers. Aufreiter et al. (2014) stated in 2014 that email channel
will keep being useful since it serves as a primary and effective mean of
communication between sales representatives and potential customers, which can
be observed nowadays, due to salespeople dedicate approximately 21% of their
working hours to the task of just writing and sending emails (Suresh, 2023). In addition, some marketing studies strongly
suggest that 80% of buyers keep indicating their preference for being contacted
through cold emails, which makes sense in the big picture when comparing a leading
43% of salespeople that rate it as their most effective sales channel (Suresh,
2023). However, aside the potential suggested, cold emailing faces significant
challenges when it comes to response rates. Gartner (2019) found that only
23.9% of cold emails are opened, with just an 8.5% of recipients eventually
replying to the messages (Dean, 2019).
These
data suggest a gap between the perceived effectiveness of cold emailing as a
sales channel and its per-email performance. This gap is largely driven by the
many variables that influence the success rate of cold emails, including
factors such as personalization, subject lines, and list segmentation,
specifically, research suggests that personalization increase the response rate
of cold emails up to 32.7% compared to non-personalized emails (Dean, 2019).
Moreover, subject lines that state a question have been shown to increase
response rates by 21%, according to Keohane (2021). Similarly, Siewierska (2024) demonstrated that reducing the number of emails
recipients in each email bulk by fivefold can lead to a 60% increase in average
response rates. These researches suggest the need of following some techniques
in order to have higher response rates, providing some sort of recipe behind
the logic of cold emails.
However,
the cold email field remains under-researched under an academic scope, as Tucker
(2016) argued in the Harvard Business Review. Among the few researches that
used experiments, we can find Le Plaisir (2024) experiment that suggests that a
lack of personalization decrease the open rate from 62.2% to 17%, and reduce
the response rate from 8.9% to 0.4%. The lack of substantial academic research
into cold emailing should raise some concerns, since there is a lot of
strategies suggested on the internet that can be detrimental to the
optimization of cold emailing. For example, during the internship of one of the
authors at a Chinese company, it was witnessed how some salesmen incorporated
emojis into their cold emails, inspired by internet blogs mentioning the
“science” behind using emojis to enhance response rates (Collis, 2020).
However, instead of increasing the response rate, it provoked many serious
buyers to reply negatively, complaining that the use of emojis was
inappropriate in a business context.
In
this research, we aim to address this gap by developing a mathematical model
for cold emailing, built upon insights from both qualitative studies, specially
of Rodrigues (2024) and Tucker (2016), and real data from an experiment conducted.
This experiment involved sending 5,187 emails across 32 bulk emails, each with different
characteristics. By analyzing the results, we aim to empirically test our model
and provide evidence-based conclusions on how to improve cold email
effectiveness.
MATERIALS AND METHODS
Incentives of sending
bulk cold emails
A
product