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 serverless computing. Serverless computing is essentially a resource use and business model for offering machine resources based on the actual amount of resources used by an application, instead of pre-purchased or prior designated units of capacity. This relies on a dynamic cloud computing execution model that manages the allocation of machine resources for each application.
April 2018 Serverless Computing — Conversation Topic Interest Over Time by Google Trends
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 topic areas.
The Re:each Share of Conversation Calculation for Serverless Computing for April 2018
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.
- 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.
Results from April 2018 that Influenced Topic Selection for Serverless Computing
Function-as-a-Service (FaaS) systems were featured recently in Forbes, driving the conversation in serverless computing. The companies mentioned look like a who’s who in SoC/SoV for serverless computing in April. A press release from Amazon Aurora captured interest with its announcement of doubling active users. And a feature on new cloud services highlighting serverless computing in PC Mag got the industry chattering.
- Forbes article highlighting the benefit of FaaS systems. This article includes AWS Lambda, IBM Cloud, Azure, and Google Cloud.
- Amazon Aurora doubles the number of active users from last year.
- “New Cloud Services Let IT look Beyond Simple Infrastructure” from PCMag.
Technical Blog Posts
All three mentioned below are considered major publications in the tech world, and we usually focus on mid-level/smaller blogs to avoid any conflict of interest (such as sponsorship and bias). However, PCMag, TechCrunch, and DZone have written far more about the serverless computing topic.
It should not come as a surprise that the top social media voices are the serverless computing companies themselves, with best practices, updates, and news from the source.
- Wamda Twitter account: EMEA-based Twitter account catering to entrepreneurs, with frequent Tweets on serverless adoption. Nearly two and a half million followers.
- Microsoft Developer Facebook Page (India): It’s worth overriding the redirect to US (if that’s where you are) to see this content.
- AWS Cloud Twitter: promoting everything from content to events.
That being said, here are other publications and influencers to follow that do not have the same number of subscribers as those mentioned above, but still influence the conversation:
- Ars Technica Twitter — promoting the content from this tech media site.
- The Linux Foundation and their Twitter Account (to be honest, they should share the serverless computing accolades with CNCF, whose content appears on the account as well).
- Simon Porter Twitter has a lot to say about technology trends and their implications in market segments.
Don’t expect to find experts in serverless computing for practitioner content—but do find someone who has at minimum built cloud functions using AWS, Azure, or the like. The exciting thing about a topic area like serverless computing is that it is so new that there is a lot to say, and there is an audience hungry for guidance. Your target practitioner is going to be a developer. Topic areas they should be able to cover are not only how to build cloud functions (because that is limited), but also how they impact application architectures and delivery chains.