I tested out the Intercom integration, Statbot, to see what actionable customer service metrics I could harvest. And guess what? I plucked some pretty interesting results! If you’re searching for Intercom metrics that prove you’re improving, take a look!
Those quarterly reports
Say you’re a Customer Service Manager. Maybe you already are. Think about all of weekly/monthly/quarterly meetings you have. You know, the ones where you have to show your boss actual facts and figures. The stats. The metrics. The proof. Whatever you want to call it… You need to justify its existence with data all the whilst sourcing metrics that make your boss say “I can see what you’ve done, how it’s going and compare it to the stats from last quarter.”
After my little test, I discovered that customer service metrics can easily be sourced from Intercom and Statbot… (Hence the title guys!) But before we get ahead of ourselves, let’s take a look at the customer service and support metrics that we should be sourcing in the first place.
- Conversation metrics
- Response Time
- Customer Satisfaction
- Retention rates
For those of you who don’t know already, Statbot is a 3rd party Intercom plugin. Every large platform seems to have a company like Statbot that specialises in producing stats for the main platform e.g. Baremetrics for Stripe. They’re specialists in one area and take the time to display data in ways Intercom does not yet have time for.
Below we’ll show how both Intercom and Statbot display data.
A simple, possibly underestimated, yet powerful stat. By tracking this over time, you have a full overview of support trends. Of course, don’t go nuts and start basing everything around this one stat, as some conversations are not meant to be responded to.
Conversations per teammate + per day
Well this one is also pretty self explanatory. You are able to see how many conversations your customer service team are having. It is a great indicator as to whether or not, some members of your team may need some help with the quantity of conversations they are dealing with. It also acts as a guide as to how many conversations everyone should be having on average per day. That being said… There are other factors that can change these results. Sorting out complicated and lengthy customer issues can change this Intercom stat, so it’s important to use this metric as a KPI for team members, rather than a set standard of work. There’s no need to exhaust your lovely customer service team!!
As stated previously, conversations per teammate is unique to your team and how the live chat conversations are dealt with. If you’re looking to estimate a per-day benchmark for your customer service team, Geckoboard states that “dividing the total average number of conversations per week by the number of team members you have. Then divide it by either 5 or 7 depending on when you offer support (business days only or 7/days/week).” This average will at least provide a starting point for your team, that’s adjustable as the workload changes. It also gives a rough outline of the amount of work that the customer service team is doing.
Not so surprisingly enough, a lower response time is always better. Nothing worse than leaving your customers and potential customers hanging.
However, this metric does depend on the way that you respond to each group of people. For example, some companies, not all, respond to paying customers quicker than those who aren’t paying. Response time is also not resolution time and while it’s ideal to have a statistic for ‘first resolution’ time, that’s a lot trickier to measure purely by stats. However, response time does matter to customers and will impact sales.
According to Live Chat Inc’s 2017 Customer service report, the average live chat response time for a business is 56 seconds, down from the 59 second response rate in 2015. So, inevitably businesses that respond quicker to everyone, are going to attract more people on live chat.
For a useful metric, Intercom recommends looking at a 90th percentile value. This is the longest wait time for 90% of customers who get in touch.
It’s also important to take note of response time by weekday and hour. This allows you to see the times and days that you need to improve response time. That’s going to make organisation a breeze, as you can schedule shifts accordingly, and possibly grow your customer service team to fit the needs of your customers.
Overall Team Performance
I love this Intercom metric from Statbot. It is the easiest way to quickly glance and see the overall performance from everyone in your team. It includes the number of replies sent, conversations closed, Median time to first response in the last month, and median time to close. This shows each member of your team what they need to improve on, whether it be response time or the amount of replies sent. The best way to determine the improvement is by comparing last month’s overall team performance stats with the current month. A simple, yet effective Intercom metric to take note of!
Overall Team Performance — Statbot
Now, as we’ve just gone through the importance of speed with the response time metric. It’s no surprise that you’ve also got to consider the quality of the responses.
I recently read an interesting Marketo article that stated:
- “66% of B2B consumers want to advocate for brands that engage well.”
- “66% of B2B consumers fully expect that all communications with a brand to be personalised.”
With such a high numbers, you need to keep the quality up for everyone who messages you on live chat, whilst being speedy. I know, it’s always a combination thing. So, a low response rate and high conversation quality is key here! I mean, no matter how fast a company responded, if their message was absolute crap, you wouldn’t take the time out of your day to recommend the company to a friend, let alone purchase their product.
Well, the great thing is, as you’re able to measure the speed of the responses, you can also measure the customer satisfaction quality too.
“There are two primary basis for great customer relationships: communication and trust. Trust is earned, so leveraging communication to develop a customer’s trust is the best strategy for relationship-building. Use product usage data to help you communicate to the right users at the
right time for the right reason.” — Keri Keeling, VP of Customer Success and Operations at Bluenose Analytics.
Of course, there are many ways to measure customer satisfaction. One being surveys and customer feedback. Intercom plugins, such as Survicate, integrate advanced surveys that collect users’ feedback and data. You are also able to add a Net Promoter Score (NPS) survey to the bottom of emails, and identify cancellation reasons to reduce churn rate.
Retention Cohort Analysis
Of course, to some, a retention cohort analysis can seem fairly confusing. Oddly enough, it’s pretty much a fancier version of a google spreadsheet. That being said, if used properly, it’s the holy grail of SAAS businesses. Intercom’s Retention, cohorts and visualisations article explains it best.
In the Statbot application, there are two different types of retention cohort analysis: Users and Companies.
Retention Cohort Analysis For Users and Companies
‘First column is given month. Second column is amount of users/companies registered in that month. Next 12 columns is amount (absolute or percentage) of users/companies that were seen after 1st of given month. E.g., for May 2017 column labeled ‘1’ shows amount of users that were seen after 01 June 2017, and column labeled ‘5’ shows amount of users that were seen after 01 October 2017’ -Statbot
Benefits of reading the cohort table:
- Product Lifetime- (Vertically) Compare different cohorts, to see the percentage of customers that return to your app. The comparison and (hopefully) improvement can be a reflection of your onboarding experience and the performance of your amazing customer success team!
- **User Lifetime- **(Horizontally to the right) As you go along the chart, you can see how the retention develops over a the customer lifetime. This is linked to the quality of your product and customer support team.
But how do I break this down?
- Divide users from when they first joined/signed up for your product. You can break down your cohorts daily, weekly or monthly. This shows you the period of time that your customers continue to use the app.
- Another great way to analyse the information is charting out a retention curve. This shows the retention over time and makes it easy to identify the time that customers are leaving your product.
For example on the chart below, we can see a steep drop, where customers are leaving quite early. So, obviously, this means you need to improve onboarding. An article from Popcorn metrics states that ‘For website and web-apps, typically 60–80% of new users are lost within the first week of signup.’ The curve should flatten over time, if not, you would need to improve customer engagement.
I am not the most analytical person. So, I was pleasantly surprised with how easy it was to use and understand Intercom metrics from the Intercom platform, and Statbot. You really realise that the right data answers important questions, allows you to track progress and plan strategically. (Most importantly, if you work in customer success, you’ll look like a boss in the reports meetings) :).
The combination of Intercom and Statbot metrics works as a powerful tool, that when used right, will reduce churn, improve customer satisfaction and onboarding, and keep the customer support team heading in the right direction.
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