The Power of Measuring Customer Engagement

Mark Kofman-300mg

Talking about Customer Engagement, there are many metrics that could show a user engagement level, i.e. user are considered to be higher engaged if they refer your app to other potential user. Another example would be for returning users – users are more committed to your app if they keep coming back and use your app.

Talking to Mark Kofman, CEO and Co-Founder of 300.mg, these 2 metrics are what’s on his company’s schedule these days and using tools like Totango, help them calculate them correctly and exempt them from developing their own solution in-house.

Sometimes, understanding your users is not as simple as it looks, as it involves human behavior which is not always expected. Also, there are many metrics in the puzzle which complete a whole picture and any SaaS company should be measuring its most converting metrics and properly analyze them in order to grow customer lifetime value and accelerate revenues.

To measure YOUR customer engagement and increase your revenueSignup here for free!

To read the full transcription of the video, click here

   

Video Transcription:

My name is Mark, and I am CEO. and cofounder of 300.mg.
We’ve built an education center for all your collaboration activities. so we like consolidate all the indications which comes from Dropbox and Google Docs and other things at work with the team. I’d say a couple of months we are in beta right now and doing lots of tweaks and lots of experiments with the application right now.
Since we are coming from 500 start-ups. So, as you might have guessed if you know about Dave McClure’s activation acquisition of AARRR metrics. So, this is probably one of the core things what we do and in addition to the beta we also use a lot of metrics called Virility to understand how our current users actually referring the product to other users.
So, these are the two core things which we do. I think right now one of the most important ones is returning users. Basically, in this we track it simply, if user has been signed up last week like before last week, and if he is still coming back to the applications, so, this what we call a returning user in our system.
Just basically the only thing we are doing, and yeah its like lots of experiments we try out different positioning, ideas, different products, feature improvements. I would say everything what we do is to actually to increase that part, to measure the things which we are trying to achieve, have less metrics probably, not more, and focus on two or three things which we are trying to once and not more.
We try to use the tools and Totango is one of them but clearly something with other people who has built in that, and ideally we would not like to do any calculations or full metrics for this part and overall but there are some components which neither of the tools do, so we to need to do that ourselves.

Understand Customer Behavior in Your Trial Period

Tracy Kaufman Cloud9

Today I’m going to share another interview with Tracey Kaufman, VP of Customer Experience at cloud9.

Tracey was watching the Totango demo and then answered some questions about what she thinks of the application from a Customer Experience point of view.

Tracey believes that anyone who has trial period in the SaaS business or has the need, like herself, to understand users activities and prevent churn, would want to actively know what customers are doing in their application during that trial period.

It is very important to understand users behavior – especially at the trial period in order to proactively prioritize customers to refer to during that time. This way you could know who is potentially at risk because they’re not using the product or – the opposite – you can spot customers who are using the product well and you can use them as a reference or a case study.

 

Tracey especially likes the integration Totango has with salesforce and the fact that sales people are exposed to information that shows the level of customer engagement. This will allow them to determine the priority of their calls – for example they can first contact the users who are actually evaluating the service and try and move them up in the sales cycle.

To read the full transcription of the video, click here

 
 

Video Transcription:

Q: Hello, who am i speaking with here today?

A: Hi, my name is Tracey Kaufman, I’m the a VP of customer experience for cloud 9.

Q: Since we just went through the demo of Totango, can you give me your first reaction and who do you think should look at the Totango product and what’s cool about it?

A: So i think, actually, you know, certainly any one in the SaaS business who has recurring revenue or has trials, whereas anything to do where they need to understand the activities of their users and really try to chart from the time that you say OK, we’re going and we’re deploying and I’ve launched you, then what happens?

If you have a trial it’s absolutely critical to understand what is your leave conversion? Right who using this trial. Because what are you going to do? You’re going to call. You’re going to say, here you go, have this 30-day trial. And then you sit and you call them 30 days later so if you are a sales VP or a marketing VP and you actually want to know what happens, and you want to get a proactive look at some key trick so that you know when to call them or when to send off an email.

You know, if you’re like me and your running customer experience and you want to understand churn, and you want to understand who is potentially at risk because they’re not using the product or on the other side, obviously, people who are really using the product and gang busters and you can use them as a reference or case study think, you know, marketing, sales, customer experience, i mean, product, what part of the product you’re using. So i think it’s widespread usage, actually.

Q: That’s great. What do you think is the most sticky feature of the product?

A: So, i really love the fact that, of course you’ve got these great analytics and you’ve got people who are individuals in executive positions who love numbers. But what I think is the best thing you talked to me about, was how you’re actually going to put the information back in sales force and that way your sales reps can go in and figure out “Oh wow, I have this trial customer and I don’t know whether they’re using it, but wow now I can find out who’s actually using the product. So now I know I’m going to call them, because they’re going to be hot. Because I’ve got these ten people who’ve started and tried using my product and I can call them and now maybe move them down the sales cycle. I just think that’s great.

3 Ways to do Cohort Analysis on SaaS Churn

Ways to do Cohort Analysis on SaaS Churn

Last week, Jason Cohen wrote a very comprehensive blog on software-as-a-service churn: Deep Dive – Cancellation Rate in SaaS Business Models. I required everybody at Totango to read this blog and recommend that you do the same. Jason looks at many different definitions for the SaaS Cancellation Rate metric.

Eventually, Jason recommends performing cohort analysis when looking at cancellation rates. He suggests to divide customers in segments based on their “time to cancel” (i.e. cancelled after 30 days vs. cancelled after more than 30 days) and, for all intends and purposes, he recommends focusing in the long-term users who have greater business revenue potential and cancellation reasons which can be addressed and resolved more easily.

This is indeed an interesting way to look at it, and very analogous to the importance of the “time to convert” metric when it comes to inbound marketing and trial conversion. However, I argue that this is not the only, and maybe not always the best, way to do cohort analysis on SaaS churn.

Let’s take for example an email service application. If 2 users have signed up at the same time:

  • One of them is using the service more frequently, creating many accounts, visits almost all application features and cancels after 10 days
  • The other accesses the service 3 times a week but just checking very limited features and cancels after 31 days

Who should be given more weight?

If I’d measure by Jason, I would focus my efforts on the second user, but if I weigh my analysis with user behavior altogether, then my most valuable customer to understand is the first one.

So this leaves us with three promising ways to segment customers for cohort analysis:

  1. Traditional way: create cohorts based on the week or month in which they signed up for the service. This will allow you to analyze the effect of changes you made to your product or service over time.
  2. Jason’s way: to create cohorts based on the “time to cancel” (or the “time to convert” for that matter). This will allow you to focus on long-time users of your product and sift out those who signed up in error.
  3. The customer engagement way: to create cohorts based on the “engagement level” with the product or service. This will allow you to focus on frequent users of your products, independent on how long it took them to cancel, but still sift out those who signed up in error (and never started to use the product).

Of course, in all cases, measurement is just the first phase of the process and the complementary phase must be to prioritize the changes needed in the service which would ultimately lead to increase customer satisfaction and customer engagement.

What about you? What is your definition for cohort analysis?