Customer Service Tells: SaaS Onboarding Through Nuances

We’ve written before about the critical role of customer service in the onboarding process. Customer service is a tricky, elusive factor in your customer success plan – on one hand, the customer service usually revolves around responsive interaction (responding to needs). On the other hand – it has been scientifically proven that companies sending long feedback forms to their customers, thereby creating work for them instead of alleviating the pressure – are annoying.

It’s time to delve into what we at Iridize like to call “customer service tells”, that will give you a better indication of where you stand with your customers, in terms of customer service, and where that is on the via dolorosa that leads to conversion.

customer service tells



The Tone

Now, before you roll your eyes at the seemingly obvious, understand that we are referring to the nuances that differentiate different kinds of feedback. As we’ve mentioned previously, feedback tone is an indication of the level of a user’s commitment to your service/ product. A whiny, passive aggressive complaint of “your software doesn’t do what I want it to” is not the same as insightful, thoughtful recommendations for new features. The former relays impatience, the latter communicates a sense of identification, desire to be part of the service’s success and the willingness to contribute to that success.


Both customer types are important to consider and respond to: The Complainer is most likely a user in very early stages of onboarding. She is testing your software, your customer service, your work flow – everything.


What to do: respond as efficiently and as proactively as possible. Avoid friction at all cost, respond to the complaint, not the tone.


The second customer type (let us call her – The Enthusiast) is probably at a later stage of the onboarding process. She is more comfortable with both service and software, and generally more forgiving about issues or bugs in the process.


What to do: respond as precisely as you can to her suggestions. The more to the point – the better. She may be a power user and is familiar with the technical lingo. Create a conversation, if possible – follow up with her at a later point and tell her what the company decided to do with her suggestion.


Take your analytics seriously, part 1

Assuming you use some kind of CRM or customer service syem to track customer requests – try segmenting the raw data as follows:


  1. Customer
  2. Customer request/complaint type
  3. Date


The results for this query can be fascinating. If you can, try to convert the report into graphic form (a basic excel Line or Scatter chart will be fine). What you can learn about your users’ customer service needs from this is priceless.


Analyzing the Data

What you are looking for is users who contact the helpdesk frequently (depending on the service’s inherent use frequency); about the same issues or else basic technical issues. You can tell from someone’s wording whether they are technically self-sufficient, or not (or even if they simply tried querying the knowledgebase).


These users are less tech savvy, possibly bordering on the technophobic – and less motivated to learn how to use your software. They may be super-psyched about your service or product – it’s just that the technical bits are giving them a rash and slowing down their progress.


What to do: These users need personalized help getting onboard. So instead of sending them feedback forms, send a personalized email and offer additional training (relating to a topic they are having particular trouble with), get them to save the knowledgebase solution in their Favorites or tailor a personalized automated-guide for them, to run on their browser whenever they need it.


More examples of issue types that should raise this red flag: repeated logging in issues, repeatedly not finding buttons and menus, misplacing integration features with other systems, repeated issues with short, multiple step processes (3-5 steps).


Take your analytics seriously, part 2

Then again, you could let your Predictive Analytics take off entirely. Predictive Analytics allow you to not only see where your customers have been, but where they are going – and more importantly, where they are NOT going. It’s not just the end goal that matters – it is every step of the way, no matter how small.


Predictive Analytics can break down major conversion goals to minor ones and report to you which user has made it past each conversion goal. This way you can monitor your users’ progress on the road to conversion and reach out when they get stuck or don’t complete one of the goals.

Noa is Iridize's Head of Content. With a background in digital strategy planning and database management, Noa translates Iridize's vision, stories and data into words. Digital learning and user experience are a particular passion of hers.