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How SLA response times are impacted by the way your customer support team uses Slack

Your customer support team lives in Slack. It’s practically their second home. They thrive on its powerful communication and collaboration functionality. But it’s convenience also has drawbacks, and it may be impacting your SLA times negatively ⏱.

Slack is not a wiki

It seems logical, but Slack is not designed or optimized to be used as a wiki. So why do customer support agents use it this way? Because Slack is where questions are asked and answers are given, so they know the answers must be in there somewhere, right?

enable self serve it support confluence create ticket

There are a number of reasons why Slack conversations are not a reliable source of knowledge and consequently increase SLA response times for customer success agents. They are:

  • Data structure 🏗
  • Verification ✅
  • Volume 😳

Slack search is not based on structured data

When a support agent is using the Slack search bar to access knowledge stored in conversations and messages, the results that are returned are not ranked based on any other criteria other than the query’s text input (and possibly date and/or channel). That limitation seems insignificant, but it isn’t. It is simply not enough information to return results that require additional data structure for accessing knowledge.

Obie Confluence Intelligent Previews in Slack
Intelligent previews of Confluence knowledge in Slack

There are tools within Slack that enable wiki-like functionality (eg. Slack Posts), but its the noise created by months and years of conversations that create ambiguity as to the results returned in search queries.

Knowledge Verification

Another big challenge with relying to Slack to distribute trusted knowledge as a repository is the lack of verification. Slack is very powerful in that anyone can share their own knowledge – and that most certainly has value to the company and customer support agents. But there is no way to communicate the knowledge’s accuracy in a scalable way. For example, there are no upvotes or downvotes that help push more accurate information to the top of searches. Emoji are helpful to signaling quality, but they don’t contribute to any ranking algorithm either. But most glaringly of all, there is also no easy way to remove/edit inaccurate knowledge from Slack on the fly. This particular issue is one that can create more confusion for customer support agents than assist them to delivering on an SLA.

Content Verification in Obie
Content verification in Obie

Lots of volume but there is no single source of truth

The sheer volume conversational data makes Slack a truly inappropriate repository for knowledge. There is absolutely no way to separate the proverbial wheat from the chaff in a Slack workspace. Even with dedicated channels for knowledge retention, they can get filled with contradicting information that will just lead Customer Success agents down the wrong path and prolong resolution of customer issues.

The solution for how to eliminate reliance un unreliable knowledge in Slack

If your customer success team insists on looking for knowledge in Slack because it is the most reliable tool they have, maybe you should explore ways to integrate your wiki or knowledge silos in Slack. There a number of compelling options available, depending on your specific use case.

Slack wiki alternatives – Obie can convert Slack messages into knowledge

Shoulder Taps cost productivity

With Slack becoming both the operating system for work as well as the virtual office of remote and distributed teams, customer support agents rely on it for access to knowledge and support. Quite often, customer support agents forget that their colleagues are quite busy as well and DM or “ask a channel” for help. Even when they check the wiki for an answer to their issue, if one isn’t found, they might not flag a knowledge gap and default to the shoulder tap behavior.

Shoulder taps have a compounding effect on SLA response times, particularly in the Slack environment.

  • They prevent the support agent (who is seeking help) from closing the support issue because they are waiting for assistance.
  • They create unnecessary noise in Slack that invariably will be read by many other agents, taking them away from their work.
  • One or more subject-matter experts may chime in with a response, distracting them from their ongoing duties.

How to eliminate shoulder taps in Slack?

While solving distractions caused by shoulder taps arguably requires a culture-based solution (eg. one that prioritizes a “documentation-first” culture), there are a few tactical things that you can do to eliminate frequent shoulder taps:

  • Dedicated support channels
  • Pinned messages
  • Slack Posts
  • Readme files

While these are strategies that can be employed to reduce shoulder taps, they simply do not replace both an org-wide cultural priority on documentation and a fully robust, Slack-integrated knowledge management solution.

Optimize for Slack-based workflows

The reason that Customer Support agents SLA response times are impacted by their Slack behavior is because of a workflow issue. By acknowledging that Slack is a foundational technology in the customer support agent’s productivity stack, you can optimize for common workflows that inevitably happen.

  • Integrate knowledge search, capture and sharing of knowledge in Slack, from right within conversations
  • Seek out diagnostic tools that identify knowledge gaps (typically using AI) to surface opportunities to add to the knowledge base
  • Identify knowledge formats that are optimized for both Slack and frequently asked questions.
  • Deeply integrate existing silo search tools (eg. Confluence, Dropbox, Google Drive) into Slack to create a single source of truth.

Obie helps Customer Support and RevOps teams get the answers they need so that they can deliver on SLAs and ensure that they have more satisfied customers.