Using Data At Startups: MailTrack’s Latest Survey As A Case Study

Startups have light and small structures, but what makes them different from any other small business is their ambition to escalate and grow rapidly. Data based analysis then becomes essential, but how to have it without much resource? MailTrack‘s users helped us complete a long survey a couple of months ago that taught us a few important lessons.

Data Startup

To add planning and data analysis to a startup is undoubtedly one of its major challenges.

On one hand, you have the lack of time and resources that is a characteristic trait of the sector. The routine at a startup is not to have all the resources assigned to plan and design the business the way any general manual in the field would teach you to do so.

On the other hand, almost contradictorily, a startup is also characterized for its scalability — the capacity of finding and creating a business model that brings growing returns. And this must happen without compromising the organization’s limited budget and lean structure. When those constraints are gone, one wonders if that company is still a “startup”.

At MailTrack’s Marketing Department, the survey we ran with with our August’s newsletter meant a turning point at exploring that quasi-contradiction. Our company is definitely still a startup, which means we have to do a lot with very little resources. But the growing need for more data-driven decisions pushes us to research, no matter how busy we are with everything else.

Once in the sector, you learn fast that this is the way things happen in startups. Something is impossible until it becomes inevitable.

 

Our Survey’s Incredible Level of Participation

The first insights our survey revealed to us is how interested our users are in our product and in helping us grow. In fact, we’ve learned by A/B testing through our newsletters that we receive a larger commitment from our users by using the word “help”, instead of focusing on what they got from lending us a hand. This is not a common pattern in email marketing.

The high response to our request for input from our users was, of course, influenced by what we were offered: exclusive access to our unreleased iOS and Android app for smartphones. Although we were aware this offer adds an important bias to the evaluation process (most answers could be coming in by tech-savvy users, interested in this kind of release), we considered it to be one of the methods with the lowest impact on results.

Later, the profile defined by the respondents’ answers showed us that, maybe, that bias wouldn’t affect the results that much. Nevertheless, we did keep an eye on false positives risks, especially after seeing our users’ proneness to recommending our product.

 

Don’t take this concern as humble-bragging. Of course, we are pleased to learn that survey respondents seem to be very happy with our double-checks. Actually, those results match other surveys and interviews we’ve done with our users before — which happens to be a good way of validating conclusions. But one of our priorities now is to evaluate what is the perception of users that maybe don’t have such a positive view of our product, which will be undoubtedly priceless feedback for us.

Counterintuitive Conclusions: Being Data-Driven Means Going Beyond Hypotheses

Good guessing is at the core of the scientific rationale. Though essential, hypotheses are always a first step, and should be confronted as soon as possible (when possible) with reality, whatever that definition of reality is.

Especially at the beginning of startups, one might have to count more on hypotheses than desired. Even though the entrepreneurship literature has been already flooded by great contributions on the importance of data-informed decision-making, all the constraints mentioned before, plus the need to move fast and take timely decisions, might oblige startups to rely on careful guessing for a good amount of time.

Some of the most important hypotheses that we were glad to question at MailTrack are related to our users’ traits: what is their professional and personal profile, the use they make of our tool, and how they expect us to develop it.

I’d like to share with you 3 principles we’ve used to assume for a while, and that we now know were wrong. Thanks to your participation, we’re able to have a better understanding of where we come from, and where we should go.

#1 Hypothesis: Most of Our Users Are Salespeople

MailTrack was idealized when part of our team was in a previous project and needed to get the information on which of our sales emails had been opened. Very naturally, we thought that most of our users, especially the most active ones, would be salespeople.

August’s survey questioned that principle, together with some evidence we were getting from other channels, such as the Support Department and social media. Across languages and countries, we found out that MailTrack’s user profile was really varied, and that also impacted the way people would use our tool.

Salespeople

#2 Hypothesis: MailTrack Is Used Mostly As a Professional Tool

Possibly a direct variation of hypothesis #1, we thought MailTrack would be especially interesting for business profiles.

Our users confirmed to us that MailTrack is indeed one of those features nobody understands how it doesn’t come by default with email. This is a concept we’ve cherished since the beginning of our enterprise, but we didn’t believe that would mean our users would take MailTrack everywhere, including their homes.

Use of MailTrack

For us, this brings an important opportunity to MailTrack as a business. The simplicity of the concept behind double-checks for Gmail makes our users take our Chrome extension to their personal sphere, but the opposite process is also true.

People learn about our email tracking app from colleagues, family and friends, and they also take us to their work places. This characteristic of our product potentializes our virality and word of mouth, which is always good news for any technology startup.

#3 Hypothesis: We Shouldn’t Focus Much on SEO

MailTrack was born at the end of 2013, when the whole talk on the “death of Search Engine Optimization” started getting traction. In the digital marketing field, most of us heard that Google was getting better and better in ranking websites by the relevance of their content. Those old, skewed keyword tricks would not be as effective anymore – finally!

In one way, this insight helped us better conceptualize what MailTrack was, focusing less on how Google would rank us, and more on how our product could connect to our users, both in practical and emotional grounds.

This invited us to learn how to tell the story of MailTrack well. Our “elevator pitch”, as it is often called, was so short and direct that we were able to fit it easily within a tweet with all the hashtags we wanted. We are the ✓✓ for Gmail. Ever wondered why you get those everywhere, but email? So did we.

Having said that, why are so many of our users finding us through Google?

How Did You Find Out About MailTrack

The explanation for us is clear: by making the “mistake” of not thinking too much about old “keyword tricks” that were always the negative side of the Search Engine Optimization. In the end, we were making good SEO!

We focused on telling our story right, and got mentions on blogs and media everywhere. Now, when people look for an email tracking software like ours, they find mentions on our startups no matter what continent they live on.

What Surprises You About MailTrack?

One of the most important results of this survey you helped us complete was consolidating the view in our team that research is important, and that our users are willing to give us a hand in order to improve our product and business.

Once more, thanks for your help! And let us know what type of ideas, suggestions or criticisms you have on our product.

Any other hypothesis we should be confronting? Back to you!