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anomaly detection conversation

Practitioner Content Marketing Playbook: Topic Selection Lessons

May 29, 2018 - influencer content marketing, Influencer marketing, Practitioner Marketing - ,
By: Turner Sblendorio, Chris Riley, Yolanda Fintschenko

We specialize in producing expert-written content for highly technical markets, but perhaps the greatest service we provide our customers is developing topics for their blogs and longer-form content marketing assets. While we encourage all content marketers to create their own topic strategy, a critical part of our partnership with our customers is topic selection advice for each piece of practitioner-written content we deliver to them.

We help our audience by offering a blog series called “Topic Facets” where we focus on a specific conversation topic from the previous month and illustrate how to use data and the share of conversation/share of voice metrics within a topic to discover influential news, social media sites, and blogs within that topic. The process of researching topic facets has helped surface some lessons for topic selection that can help you interpret data wisely and narrow down the best topic to attack.

Begin, but don’t stop, with your SOV and SOC metric

How can you figure this out? Sometimes it is more obvious, but using SoV and SoC metrics and then digging into the sources that defined those results can really help. Recently, we approached the topic of anomaly detection. Anomaly detection refers to the identification of outliers from expected patterns in a dataset. Depending on the data set, deviations in events, patterns, or items can be indicative of anything from bank fraud to text errors. In the case of system security, it is often the detection of an unexpected pattern of events, such as a burst of activity, rather than an unusual or unexpected event. Because so much data is now stored in the cloud, anomaly detection has enormous implications across many platforms, architectures, and industries.

We looked at search trends via Google Trends and used our own platform, Re:each, to calculate share of conversation (SoC, or conversation share of voice). Share of voice 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 topic areas.

The results we saw pointed to big companies (IBM Proventia, AWS Streaming Data, InfluxData, Cloudability, and Progress Data RPM) as the five main companies contributing to the conversation of anomaly detection. So, for a company trying to enter and grow their voice in this conversation, what would we advise?

April 2018 Anomaly Detection – Conversation Topic Interest Over Time by Google Trends

The Re:each Share of Conversation Calculation for Anomaly Detection for April 2018

A look at key individual sources that are driving the conversation

A deeper dive into the organizations and content that were driving the anomaly detection conversation revealed that the topic was driven not just by these companies, but by non-industry sources hard to compete with—Forbes, Inc.com, and Arxiv.com. See below:

  • Forbes Article highlighting CEO and President Yogesh Gupta and his journey from his beginnings in India to a U.S. Big Data powerhouse. Progress claims to be the “largest vendor in the world whose primary customers are other software vendors.”
  • https://arxiv.org/. Universities have an unfair advantage when it comes to content and conversations. Arxiv is not really a blog, but is as powerful as one, particularly in the conversation space. For instance, a Cornell University website gives open access to 1,377,332 e-prints in Physics, Mathematics, Computer Science, Quantitative Biology, Quantitative Finance, Statistics, Electrical Engineering and Systems Science, and Economics.
  • https://www.inc.com/ The impact of data security across every industry means that this general media outlet covers anomaly detection as it applies to multiple sectors of business.

Find your unfair advantage

In this case, Arxiv has the unfair advantage, as it is a blog-like site that houses information that technically contains the topic, but is not really relevant. It can be hard to compete with this. The big picture and the data point to narrowing the topic. (For example, anomaly detection in Big Data or anomaly detection for app dev would take your content out of general content.)

The takeaway

Starting with your SoV and SoC measurement to look at your share of a conversation that is a large part of your content marketing strategy is important. But don’t stop there. Uncover the key content sources that seem to really be driving the conversation and make sure your content for that topic has a chance. Some of that is distribution channels and content quality, but some of this strategy relies on picking conversations where you minimize the content competition. Taking your core topic and narrowing it to something of importance where you can establish leadership is smarter than trying to overcome a flood of content from a source that you can’t out-content.


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