Producing expert-written content for highly technical markets is our strength. However, something our customers have come to depend on as part of that service is help with developing topics for their blogs and longer-form content marketing assets. We certainly encourage you to develop your own topic strategy. However, part of our partnership with our customers involves helping them generate a topic for each piece of practitioner-written content we deliver to them.
We write this series to help our customers and marketing managers look under the hood to discover how we develop topics using SoV and SoC as metrics. We always begin with SoV calculations for specific conversations, and then use that data to inform our topic selection.
Today, we take a look at which tech companies are moving the needle in ChatOps. Like DevOps, ChatOps is a methodology (rather than a technology) that allows developers to have a conversation-driven development environment in a chatroom in order to work more efficiently. Like DevOps, the process of ChatOps has spawned the creation of tools to support it and render it more effective, with less friction.
A Metrics-First Approach
Our approach to determining topics within this conversation begins and ends with a share of voice (SoV) calculation, which ultimately gives us an idea of a vendor’s share of this conversation (SoC). Our share of voice methodology is described in some detail in a variety of places, but here is a quick summary:
Share of conversation is the percentage of any specific conversation you own. SoC is more precise because it looks at specific conversations within a market versus focusing only on global SoV compared to competitors. While it’s interesting to know how your brand or product is doing in the world of all products, you can make the greatest impact by going local with specific conversations.
May 2018 ChatOps — Conversation Topic Interest Over Time by Google Trends
Below, we will dig deeper into why the results were the way they were.
Fixate’s Re:each platform has algorithms which derive conversation share of voice across traditional and social media. The phases of calculation are data collection, normalization, and interpretation. We can’t give you the secret sauce, but we can give you an idea of how we do it.
Re:each Conversation SoV (SoC) Results from May 2018 for ChatOps
- Identify your place: Identify specific keywords and concepts associated with your brand and product based on those concepts that appear the most in all conversations you participate in.
- Determine your conversations: From there, the concepts are applied across a body of sources in order to identify the three conversations which are most relevant to you. For each vendor, there are three types of conversations identified:
- Demand Gen
- Mindshare/Thought Leadership
- Find your competition: Competition is derived by identifying the top 4-9 vendors in each conversation based on their SoV in those conversations.
- Determining relevant topics: Topic suggestions are derived from entity/concept extraction of content that was most prevalent in each conversation selected over the set period of time. Those concepts that had the greatest reach in that conversation are weighted and end up as the core elements of a suggestion.
Data is collected from traditional social media sources as well as trusted media sources for each broad market. Weight is put on content based on the source it came from using a proprietary algorithm. Currently, calculations are done at the end of each month for the entire month’s worth of data.
The machine learning used in SoV is human-supervised (Human-in-the-Loop). SoV calculations can be fully automated; however, topic suggestions are subject to language challenges, and domain expertise based on raw data collection. Domain experts validate SoV calculations, and reformulate raw entity extraction on top-performing content in each conversation to build coherent topic suggestions.
May 2018 Sources that Influenced Topic Selection and SoC for ChatOps
- “Micro Focus Transforms IT Operations with Industry’s First Containerized ITOM Platform”
- This includes ChatOps collaboration as well as orchestration and analytics.
- The platform helps to speed up service between large-scale Hybrid IT environments.
- “MayaData Releases Litmus — Open Source Chaos Engineering for Kubernetes and Free Tier of MayaOnline”
- DZone.com included Alertsite and PagerDuty in articles that highlight best practices for reducing downtime on the way to production, and using alerts to become more proactive with security.
Industry Blogs to Follow
- DZone.com writes several articles a month around ChatOps. Check out the two that managed to crack our high-impact industry blog list:
- TechBeacon.com: check out this article on how to get your enterprise ChatOps-ready
- PagerDuty Community Blog: At the top of a “ChatOps” search, you will find this blog, which is simply a short introduction on ChatOps and how to get started.
Top Social Influencers to Follow
- Eric Vanderburg Twitter
- Eric has made our influencer media list several times, but his active tweeting about the current market makes him a must-follow.
- Nicolas Babin Twitter
- This French #growthhacker has over 50k followers. You can read his personal blog here: https://nicolas-babin.blogspot.com/
- Rich Simmonds Twitter
- The self-proclaimed top 20 influencer and disruptor makes the list with a substantial following of over 500k users on the Twitter platform.
Everyone in the application development lifecycle can benefit from and use ChatOps to be more efficient through the integration of alerting and analytics, as well as collaboration. These elements create strong practitioners. However, DevOps engineers and SREs are both heavy users and implementers. Favor these personas—They can talk about both the implementation of ChatOps (which is important due to its being so new) as well as use cases. Content from developers and quality engineers will be beneficial as well.