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Secret to measuring and benchmarks Slack channel customer service
One of the biggest challenges with Slack support is reporting. Because you can only improve that which you measure, the more you can measure from support, the more you can improve. In general, here are some metrics you should be looking at:
✅ Time to first response or time to triage — This is a great metric to optimize. The faster you respond and understand the issue, the more customers feel like they are being heard and addressed. Especially if this is a high severity issue. This does not mean you have a solution to their problem right away, this simply means you understand the problem quickly.
❌ Time to resolution/close (MTTR, FCR) — Though it’s popular, this is not a great metric to optimize. It encourages agents to do their job faster, often at the cost of quality and precision. This metric encourages agents to jump to conclusions, drop conversations, and overall make the conversation feel rushed. Even if this metric is only exposed to managers or as a way to predict staffing demand, it creates internal bias. Don’t forget, we use Slack to create a more personal relationship with our customers — encouraging agents to end the conversation faster does not encourage a great working, long-term relationship.
✅ Time to update — Instead of time to resolution, it’s better to optimize adherence of time to update. What this means is creating a promise that you will update the customer about their issue on a certain cadence, then following that cadence. For example, if the customer needs a new piece of hardware and you know that on average it will take a week to resolve 1) set the expectation up front, 2) tell them you will update them every 2 to 3 days, 3) update them on the given schedule with a quick status of the request. The metric you want to measure is adherence to this policy. The 2 ways to quantify that are by creating an SLA and measuring percentages of adherence to this SLA or by measuring the average number of minutes between messages.
✅ Types of questions — Use tags or other methods to group questions into buckets, then optimize those buckets. One approach is to start adding tags to all conversations. Each week or two, look at which tags appear most often. If the tag is too broad to outline a specific issue, look through the tickets created for 2 weeks and try to find the more specific groupings. Based on these groupings, update your list of available tags and wait another 2 weeks. Once you can run a report that will tell you how a specific tag defines a specific issue, you have your area of focus. Create automation, add features, or create educational content and distribute it to your customers. Try to find ways to prevent these questions from coming into support and use tags as a way to prioritize.
✅ CSAT/NPS/CES — These feedback types will tell you how customers are feeling about your experience. The feedback type you choose should be selected based on your team and your goals. For example, if you want your customers to feel like you are highly accessible and support is easy, measure Customer Effort Score (CES). If you want customers to feel satisfied with the overall support experience, use NPS or CES. If you want to optimize both, simply sample both at random.
✅ Number Questions per day — This metric is great to understand and predict staffing. By factoring in the number of questions per day, average messages per question, and some measurement of effort per question. Looking for changes in questions per day/hour will help predict staffing needs.
✅ Average messages per question (by type) — The number of messages per question is a way to understand the average effort per question for both the agent and customer. However, because different types of questions will require different levels of effort, it is best to further break this down by priority, severity, or type based on tags.
✅ Customer Churn —Once a quarter (or more or less based on your customers), map churned customers to support cases. Look for patterns that could indicate a non-stellar support experience. Group churned customer requests by tag and try to identify patterns. Find average SLA adherence based on churned groups.
How to do this in Slack?
After digging around, unfortunately, we did not find a great, already built solution to getting this information out of Slack. (If you find something, please let us know!)
One approach is to use to use Google Sheets or Google Data Studio to consume the Slack APIs directly. With enterprise grid, you can use the admin.analytics.getFile API to pull channel and member analytics. Without Enterprise Grid, you can also use the conversations.history API to consume the last1000 messages of conversation history and use Google Sheets or Google Data Studio to try and calculate these metrics. There are a few resources that can help consume APIs in Google Sheets or Google Data Studio.
The Foqal Agent bot automatically calculates most of these metrics for you. You can drill down by date range, agent, or customer to understand the total number of questions, the average number of questions per week or working hour, the average time to first (human) response, time to close/resolution, and messages per question. You can also see leaderboards to understand who is asking most questions, is answering most questions, and the tags which are most discussed. Because Foqal Agent integrates with Google Data Studio, you can get pretty granular and complex if you like. The dashboard below comes out of the box, but you can drill down further and build your own reports and tables using the Agent Data Studio Connector. When all else fails, you can export a CSV file and massage it how you need.
What metrics do you care about?
What metrics are most important to you? We want to hear - message us at email@example.com. Also, this is part 2 of our discussion of internal Slack support. You can check out Part 1 titled “Why Slack support and Getting Started”. Of course, we would love to know what you think or hear about your own experience — message us at firstname.lastname@example.org.