It goes without saying that to deliver on customer success, you have to know what your customers actually want to accomplish.
Being able to meet customer expectations and provide an excellent customer experience tends to correlate with greater customer satisfaction, retention, and customer lifetime value.
In other words, meeting customer expectations brings real business value. It seems simple, but understanding customer expectations is anything but easy.
Understanding what your customers really want, not just what you think -- or even what they say -- they want, is a longer process of trial and error.
It’s often a messy process of collecting and analyzing customer feedback, implementing new experiences, and iterating based on the reaction of users. This is true of multiple business disciplines -- from marketing to sales and, of course, customer success.
Thankfully, tools from behavioral science and marketing experimentation can help us find customer expectations to deliver a better customer experience. It’s now easier than ever to validate our findings with real-time data.
An Experimental Approach to Customer Expectations
Everyone has their preferred research methodology, and different methods work at different stages and for different purposes.
At the beginning stages of a startup, for instance, you may not have an abundance of quantitative user data. So instead of running dozens of A/B tests, you could simply get on the phone and talk to customers.
Large-scale companies, on the other hand, can get away with quicker research cycles and can get insights back very quickly from simply setting tons of experiments live.
Whatever the differences in methodologies, it usually comes down to a simple and common way of looking at things: Customer needs should be treated like a research project.
Instead of guessing what customers want, we’ll gather data, formulate a hypothesis, and then test it. In this way, we can be much more certain of customer expectations as well as the quantitative results achieved by changing aspects of the customer experience.
How to Gauge Customer Expectations
1. Gather Customer Data Points
The first step is to gather data points. We need information with which we can make preliminary hypotheses on user behavior and customer expectations.
What data is available that hints at what your customers are looking for from the product? As I mentioned, it will depend on the stage of your company as well as the type of product you offer. But all companies can collect some sort of qualitative data as well as some sort of quantitative behavioral data. And every company should combine the two to get better insights on user intentions.
First, look for qualitative data from your users. On the surface, this question of what users want seems straightforward: just ask them.
There are many ways to do this, ranging from the time consuming in-person interview to the anonymous feedback form. Here are just a few ways to collect customer feedback today:
In-app or on-site survey tool
Third party review sites
Social media monitoring
Another thing you can do is talk to other teams who may be collecting Voice of the Customer data, even if they may not call it that, like marketing or sales. It’s rare that organizations work across departments to share this information, but those companies that do have an informational superpower.
Basically, take advantage of anywhere you can collect direct feedback from existing customers or prospects, or where you can get it indirectly from other departments or passive collection sources like Capterra, Finances Online, or G2 Crowd.
While qualitative feedback is valuable, behavioral science teaches us that behavior often differs from stated preferences or intentions. To learn what that behavior is, you’ll need to pull some quantitative data, too.
Ask yourself: What are your customers, or people like them, actually doing?
Start with analytics, both in your product itself and your digital marketing assets, and ask the following questions:
What features are people gravitating to?
What are your top users utilizing the most?
What have A/B tests shown?
Which CTAs and value propositions drive the most activity?
What type of questions are prospects asking on Google?
What similar products are they using to solve their problems?
Collect as many data points as you can to get an idea of the landscape of prospect and customer behavior.
2. Form Hypotheses
With the available qualitative and quantitative data in hand, you can combine the two and start formulating some hypotheses.
Given what users are telling you -- and what they are doing in your product and elsewhere -- what seems to be resonating and leading them to success with your product? Which communications are they engaging with? Which features do happy users tend to utilize most? What level of activity, like frequency of log-ins or engagement with features, seems to correlate with the best customers?
Use those takeaways to form some hypotheses on what’s driving success for your customers. You have enough input to formulate a hypothesis of what’s working that you can put to the test. Form as many as you’d like given what your research has found, but be sure to prioritize them by their estimated impact on the business, as well as the ease of implementing new solutions.
3. Test and Verify Your Hypotheses
Now, it’s time to put those hypotheses to the test.
Ask yourself: What’s the most efficient and significant way you can evaluate your customer success options?
Whenever possible, you should run an A/B test, as it will give the clearest indicator of what works and doesn’t.
If it’s something new, like a product feature or method of communication, you can simply see if it works at all. If you don’t have the time or resources to run a formal test, get prototypes in front of actual people to get real feedback. The goal here is to validate or invalidate your hypothesis using the least expensive methods.
In the case of customer experience and onboarding, perhaps you discover that users simply don’t understand the value of your product when they sign up via a free trial CTA on your site. They need more education, and it needs to be in a format that is scalable to your business.
Now, with this insight in mind, you have many options for solving this problem.
For high-value accounts, you could assign a dedicated customer success manager to personalize their onboarding and teach them how to get value out of the product. You could set up an automated onboarding sequence using dummy data to get them to see the value of your product immediately. You could build out a knowledge base that includes an academy feature to train them in a new methodology (for example, both HubSpot and Optimizely do this very well).
There’s never a quick and easy answer to the possible solution, but if you have the data as to what’s potentially wrong as well as some insights on how to fix it, you can test out solutions until you reach an optimal solution.
Whatever you choose, get real-world input on whether your hypothesis is valid or not.
We often think about A/B testing and data analysis in terms of marketing -- landing pages, emails, and the like -- but they’re highly applicable and valuable to customer success at all. It’s really just a process of listening to your customers through qualitative and quantitative feedback, coming up with hypotheses, and testing them.
While I wish I could provide a definitive and final step, in reality, the process is continuous.
As you run more tests, get more data, and receive more feedback, you’ll continue learning new things and find shifting priorities of your customers. It’s iterative.
To adapt and optimize, you must always be researching, testing, and iterating.
Originally published Dec 28, 2017 8:00:00 AM, updated October 02 2018