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Practitioner Marketing Topic Facets: Log analytics

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Fixate Team

March 22, 2018

When we evaluate our customers’ greatest needs, we always come back to the challenges they face when developing topics for their blogs and assets. While some of our customers may choose to create their own topic strategy, normally, part of our partnership with our customers involves helping them generate a topic for each piece of practitioner-written content we deliver to them. So how do we get at topics? Let’s look at one conversation important to many DevOps tool vendors out there—log analytics.

Log analysis, a part of the DevOps workflow, is employed to make sense out of computer-generated records to understand and improve application, device or server performance. The process of creating such records is called data logging. Log analytics is a big data use case that allows you to visualize and analyze log data from websites, mobile devices, servers, sensors and more for applications as diverse as app monitoring to IoT.

Our approach to determining topics within this conversation begins and ends with a share of voice (SoV) calculation, which ultimately is giving 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 (or Conversation Share of Voice) is the percentage of any specific conversation you own. Conversation Share of Voice 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 topic areas.

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 this post will give you an idea of how we do it.

Core Calculations

  1. 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.
  2. 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:
    • Market
    • Demand Gen
    • Mindshare/Thought Leadership
  1. Find your competition: Competition is derived by identifying the top 4-9 vendors in each conversation based on their SoV in those conversations.
  2. 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.

Domain Expertise

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.

Results Influence Topic Selection for Log Analytics (Feb 2018)


Biggest Twitter reach: (or top Twitter users for log analytics for Feb 2018)

Microsoft 600k

Amazon 150k

Kevin L. Jackson


Highest-Impact Industry News

Logz.io—You would not generally expect them to lead in an SoV calculation, simply because they are not a market leader, and have not been in the past—which is why they are so fascinating. Logz.io open-sourced their two log analytics tools, Apollo and Sawmill. The technical community loves the open source movement. It encourages collaboration and long-term viability. Open source projects (like Kubernetes) often gain significant traction in viral media and praise from technical audiences. Interestingly, the article mentioned below opens with: “Splunk competitor Logz.io…” bringing Splunk (another log tool) into the fold. Check it out:


Blogs of Interest

Company blogs and Twitter accounts show up the most when we calculate SoV for a given conversation, apart from major industry news like that from Logz.io above. In the month of January, Sumo Logic and SolarWinds gained traction due to acquisitions. In February, no major acquisitions were announced, so the month was dominated by company blogs. For example, both Microsoft and Amazon published blogs on their sites that reached the front page of Google when searching the keywords log analytics and log analytics tools.

Example Viral Blog on Sumo Logic

An example of practitioner marketing gone right comes from Fixate.io’s very own Twain Taylor—and shows that consistent technical blogging will increase your SoV and establish you as an industry expert in the field. Don’t miss https://www.sumologic.com/blog/monitoring/kubernetes-logging/.

Practitioner Profile

What do these results say about the practitioner profile for log analytics? The trick with this particular conversation is that the use cases for infrastructure logs have evolved over time. Prior to DevOps, IT Operations were the target users and practitioners. While this has not changed completely, IT Ops are not the only user, and perhaps not even the most active. IT Ops still greatly benefits from log analysis solutions, but generally only use them when something breaks, and in enterprise infrastructure not necessarily meant for DevOps teams. In DevOps environments, things happen much more frequently, which means more things happen in production infrastructure.

In addition, log analysis is now being used in pre-production infrastructure environments, so now you have three categories of practitioners: IT Operations, DevOps/Site Reliability Engineers (SREs), and Developers. Out of these three, DevOps/SREs are going to be the loudest, and easiest to engage with in practitioner content marketing. However, all three are important, which means when picking a practitioner, you should find someone who is cross-functional from DevOps to IT Ops, and a developer practitioner to cover log analysis topics specific to all three use cases.