One of the greatest tools available to me as an interaction designer is the ability to see real metrics. I’m guessing that’s surprising to some people. After all, many people still think that design all happens before a product ever gets into the hands of users, so how could I possibly benefit from finding out what users are actually doing with my products?
Well, for one thing, I believe that design should continue for as long as a product is being used by or sold to customers. It’s an iterative process, and there’s nothing that gives me quicker, more accurate insight into how a new product version or feature is performing than looking at user metrics.
But there’s something that I, as a user advocate, care about quite a lot that is really very hard to measure accurately. I care about User Happiness. Now, I don’t necessarily care about it for some vague, good karma reason. I care because I think that happy users are retained users and, often, paying users. I believe that happy users tell their friends about my product and reduce my acquisition costs. I truly believe that happy users can earn money for my product.
So, how can I tell whether my users are happy? You know, without talking to every single one of them?
Although I think that happy users can equal more registrations, more revenue, and more retention, I don’t actually believe that this implies the opposite. In other words, there are all sorts of things I can do to retain customers or get more money out of them that don’t actually make them happy. Here are a few of the important business metrics you might be tempted to use as shorthand for customer happiness – but it’s not always the case:
An increase in retention numbers seems like a good indication that your customers are happy. After all, happier customers stay longer, right?
But, do you mean retention or forced retention? For example, I can artificially increase my retention numbers by locking new users into a long contract, and that’s going to keep them with me for awhile. Once that contract’s up, they are free to move wherever they like, and then I need to acquire a customer to replace them. And, if my contract is longer than my competitors’, it can scare off new users.
Also, the retention metric is easy to affect with switching barriers, which may increase the number of months I have a customer while making them less happy. Of course, if those switching barriers are removed for any reason – for example, cell phone number portability – I can lose my hold over long time customers.
While retention can be an indicator of happy customers, increasing retention by any means necessary doesn’t necessarily make your customers happier.
Revenue’s another metric that seems like it would point to happy customers. Increased revenue means people are spending more, which means they like your service!
There are all sorts of ways I can increase my revenue without making my customers happier. For example, I can rope them into paying for things they didn’t ask for or use deceptive strategies to get them to sign up for expensive subscriptions. This can work in the short term, but it’s likely to make some customers very unhappy, and maybe make them ex-customers in the long run.
Revenue is also tricky to judge for free or ad-supported products. Again, you can boost ad revenue on a site simply by piling more ads onto a page, but that doesn’t necessarily enhance your users’ experience or happiness.
While increased revenue may indicate that people are spending more because they find your product more appealing, it can also be caused by sacrificing long term revenue for short term gains.
NPS – Net Promoter Score
The net promoter score is a measure of how many of your users would recommend your product to a friend. It’s actually a pretty good measure of customer happiness, but the problem is that it can be tricky to gauge accurately. It generally needs to be obtained through surveys and customer contact rather than simple analytics, so it suffers from relying on self-reported data and small sample sizes. Also, it tends to be skewed in favor of the type of people who answer surveys and polls, which may or may not be representative of your customer base.
While NPS may be the best indicator of customer happiness, it can be difficult to collect accurately. Unless your sample size is quite large, the variability from week to week can make it tough to see smaller changes that may warn of a coming trend.
Conversion to Paying
For products using the freemium or browsing model, this can be a useful metric, since it lets you know that people like your free offering enough to pay for it. However, it can take awhile to collect the data after you make a change to your product because you have to wait for enough new users to convert to payers.
Also, it doesn’t work well on ad-supported products or products that require payment upfront.
Most importantly, it doesn’t let you know how happy your paying customers are, since they’ve already converted.
Conversion to Paying can be useful, but it is limited to freemium or browsing models, and it tends to skew toward measuring the free part of the product rather than the paid product.
Engagement is an interesting metric to study, since it tells me how soon and often users are electing to come back to interact with my product and how long they’re spending. This can definitely be one of the indicators of customer happiness for ecommerce, social networking, or gaming products that want to maximize the amount of time spent by each user. However, increasing engagement for a utility product like processing payroll or managing personal information might actually be an indicator that users are being forced to do more work than they’d like.
Also, engagement is one of the easiest metrics to manipulate in the short run. One time efforts, like marketing campaigns, special offers, or prize giveaways can temporarily increase engagement, but unless they’re sustainable and cost effective, they’re not going to contribute to the long term happiness of your customers.
For example, one company I worked with tried inflating their engagement numbers by offering prizes for coming back repeatedly for the first few days. While this did get people to return after their first visit, it didn’t actually have any effect on long term user happiness or adoption rates.
Engagement can be one factor in determining customer happiness, but this may not apply if you don’t have an entertainment or shopping product. Also, make sure your engagement numbers are being driven by actual customer enjoyment of your product and not by artificial tricks.
While registration can be the fastest metric to see changes in, it’s basically worthless for figuring out how happy your users are, since they’re not interacting with the product until after they’ve registered. The obvious exception is products with delayed (i.e. lazy) registration, in which case it can act like a lower barrier-to-entry version of Conversion to Paying. When you allow users to use your product for awhile before committing, an increase in registration can mean that users are finding your product compelling enough to take the next step and register.
Registration is only an indicator of happy customers when it’s lazy, and even then it’s only a piece of the puzzle, albeit an important one.
Customer Service Contacts
You’d think that decreasing the number of calls and emails to your customer service team would give you a pretty good idea of how happy your customers are. Unfortunately, this one can be manipulated aggressively by nasty tactics like making it harder to get to a representative or find a phone number. A sudden decrease in the number of support calls might mean that people are having far fewer problems. Or, it might mean that people have given up trying to contact you and gone somewhere else.
Decreased Customer Service Contacts may be caused by happier customers, but that’s not always the case.
So which is it?
While all of these metrics can be extremely important to your business, no single one can tell you if you are making your customers happy. However, looking at trends in all of them can certainly help you determine whether a recent change to your product has made your customers happier.
For example, imagine that you introduce a new element to your social networking site that reminds users of their friends’ birthdays and then helps them choose and buy the perfect gifts. Before you release the feature, you decide that it is likely to positively affect:
- Engagement – every time you send a reminder of a birthday, it gives the user a reason to come back to the product and reengage.
- Revenue – assuming you are taking a cut of the gift revenue, you should see an increase when people find and buy presents.
- Conversion to Paying – you’re giving your users a new reason to spend money.
- (Lazy) Registration – if you only allow registered users to take advantage of the new feature, this can give people a reason to register.
- Retention – you’re giving users a reason to stay with you and keep coming back year after year, since people keep having birthdays.
Once the feature is released, you look at those numbers and see a statistically significant positive movement in all or most of those metrics. As long as the numbers aren’t being inflated by tricks or unsustainable methods (for example, you’re selling the gifts at a huge loss, or you’re giving people extra birthdays), you can assume that your customers are being made happy by your new feature and that the feature will have a positive impact on your business.
Of course, while you’re looking at all of your numbers and metrics and analysis, some good old fashioned customer outreach, where you actually get out and talk directly with users, can also do wonders for your understanding of WHY they’re feeling the way they’re feeling. But that’s another post.
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