Practitioner Marketing How to Measure Share of Voice for Practitioner Content Marketing
Share of voice (SOV), when calculated correctly, measures the impact of your brand’s content marketing compared to that of your competitors’, over time. All your marketing creativity has to be harnessed for one thing—revenue. In saturated markets, that’s not easy. You have to build a relationship with your market with practices like practitioner marketing. It takes a lot of effort, and it can be hard to connect that effort to success. Measuring the SOV captured by your practitioner content marketing campaigns provides practical insight that typical metrics do not.
Reconciling something as subjective as a company’s relationship with the market is very difficult. Marketers tend to stick to metrics because of their familiarity, not their relevance to strategy.
Clickthrough rate (CTR), Unique Visits, and Market Qualified Leads give us numbers that are easily conveyed to others. However, these metrics are not enough. Focusing on those metrics alone doesn’t support healthy or effective marketing efforts. The goal of practitioner content marketing is to extend and deepen customer lifetime value by creating a unique relationship. Quantifiable metrics don’t measure the quality of relationships.
What should you measure?
The two best metrics for practitioner marketing are share of voice measurement and engagement. Unlike definitions of share of voice in public relations and advertising, practitioner SOV measures the percentage of content that originates from references, or refers visitors to your product/content in any given relevant industry topic. It’s how much of a say you have in a specific conversation within your industry and among your customers.
Engagement measures how attractive your practitioner-generated content is, or how many people interact or share your content without a prompt. In other words, they found it, and it spoke to them.
Key Performance Indicators (KPIs), such as net promoter score and brand awareness, are other potential metrics that could be right for your company. But, they are for the brand only, not your success in individual topic areas.
Because of the way digital content and digital advertising is evolving, the definition of share of voice has fundamentally changed. It depends on whether you are calculating marketing or advertising share of voice,. This means the definition is also dynamic. You have to constantly measure share of voice based on the “conversation” and time frame in a shifting environment.
How is share of voice measured?
The definition of SOV is not complex, but how you calculate it is. Think of share of voice as a stock portfolio. You want to know what portion of each stock you own, and each stock represents topic areas you want to dominate. For example, let’s take a DevOps vendor who sells release automation to developers. Their portfolio would likely consist of four DevOps terms: DevOps, release automation, software delivery, and software delivery chain. In that term set, you hope to have some portion of each of these conversations. However, the term “DevOps” is very broad, so you will target a smaller portion due to size and priority. Release automation is one term you particularly care about because it is exactly what you do. Most of the other vendors in this conversation will be competitors.
Your goal is to increase your position in all the target terms. You’ll know you have done well according to the delta from one period to the next—usually monthly.
To calculate SOV, isolate every instance when and where the term comes up in social media and content. When it does, if you participated or were mentioned (good or bad), then you get a share. The number of shares you have—divided by the total number of instances of the conversation—is your share of voice measurement.
The problem: it’s impossible to get EVERY instance of the conversation—mostly because the venues where they appear do not allow you to do so, and the number moves. Fortunately, the law of big numbers helps greatly. A reasonable sample size yields high accuracy, as long as the sample size is diverse enough.
This post was originally published in March 2019 and updated in April 2021.