For many companies social media monitoring still represents a number of challenges. There are just so many conversations, so many social media channels that it can be hard to separate actionable consumer signal from noise. But what is noise and what is consumer signal?
For many companies social media monitoring still represents a number of challenges. There are just so many conversations, so many social media channels that it can be hard to separate actionable consumer signal from noise. But what is noise and what is consumer signal? Noise can mean a variety of different things, it can mean data that is full of spam and duplicates, it can mean that your data is full of news, press releases. Pure consumer signals are the subjective reactions – the intentions – of your consumer to an ad, a new product, or other outreach effort. Getting to pure data – consumer intentions – requires shifting your business focus to one that reports on mentions to one that tracks:
- how social conversations are driving key business metrics
- the influence of advertising, pricing discounts or new product releases on consumer conversations
- consumer intentions and preferences related to specific business efforts
It’s not that the more basic monitoring metrics are of no value but they should be used as building blocks towards a monitoring strategy that provides your organization with actionable insights.
Building a Monitoring Approach
Of course, one of the first points of interest is to track the volume of conversation going on around your brand. It’s a great way to detect emerging issues by tracking the spikes in conversation and correlate that activity to other events, like a campaign or a show’s premier. Other basic metrics can include content sources, which can represent the conversation breakdown by social media channel or gender.
This is a good start because we can observe trending information over time as it relates to demographic behavior or if the source of the conversation volume shifts. But we don’t really know what the customer is saying or if there are particulars topics driving the conversation. Let’s drill down and see the details from verbatims (social media conversation) that can be extracted from a post. I am going to show two different examples: one that just displays the text, and another with additional data related to a specific post.
The image above display the conversations related specifically to a group of viewers, who have used terms like ‘favorite’ or ‘love’ in their conversation.
The image above represents how conversations can be captured to reveal meta information about both the post and the author. Source detail, including 1st person narrative, influencer rank, links to the full post and other details can provide additional insights into your consumer and inform a more comprehensive outreach strategy. But what these important metrics fail to explain is ‘the why”.
- Why is the consumer posting?
- What is driving the conversation?
- How are marketing efforts or new product releases influencing consumer intention?
Building Social Into Your Business
Getting to ‘the why’ behind consumer conversations is harder to identify if your organization focuses exclusively on volume-based activity monitoring. Instead your organization may need to extend its focus beyond how much is being said to analyzing consumer expression of intentions and preferences. This more precise analytics may require that you identify the key business metrics for your organization, which might be sales health or ratings or customer service-related metrics. Next steps are to configure your text analytics system to categorize and analyze social media conversation that track to those to business metrics. For example, if your organization is developing an ad campaign to improve brand awareness and sales, you’ll want to create indicators that allow you to track those consumers using purchasing language as it relates directly to your campaign. It’s not enough to know that a consumer expresses an intent to purchase, you want to know the impact of your marketing effort to help understand and drive ROI.
Depending on the sophistication of your text analytics tool you can begin to configure what you track by focusing on traits of consumer language to provide insight into their intentions and preferences. This is how you begin to transform your social media metrics efforts from a siloed effort that tracks “likes” to fully-integrated analytics that helps to drive key business metrics. Of course, the critical metrics of success will vary for your company, including important author attributes, like geographic or psycho graphic details. For example, publicly available online author information may be an important data point for your organization to collect as you build out your social CRM system. For another organization, it may be the impact of trailers on consumer behavior related to referral or viewing intention. The point is to move beyond simply monitoring the volume of mentions, which although is important represents only a first step in integrating social intelligence into your business.