How to Translate Social CRM Media Data into Metrics

When many people first think of Social CRM data and analytics, they think of quantitative results suited for spreadsheets, and they can yield powerful insights by analyzing quantitative factors. The number of RTs on Twitter can tell you how many people found content interesting, and the number of likes on a Facebook Page provides a good indicator of people who want to engage or associate with your company.

Define text analysis

The process of deriving high-quality information from text is often referred to as text analysis or text data mining. Patterns and trends are applied to text to identify a value. Spam filters exemplify a widely recognized use of pattern recognition and text mining. Your e-mail service provider taps into linguistic features that typically indicate an unwanted bulk message.

In social CRM, sentiment analysis looks at the text of a customer’s message to determine if it's positive, negative, or neutral. Like spam filters, sentiment analysis isn't 100percent accurate, but it can provide a strong indicator of trends. A sharp spike in negative messages is worth looking into, even if the exact number of messages is off slightly.

Text mining is a complicated process that goes on behind the scenes of most social CRM solutions. It starts with a classification system using statistical, linguistic, and algorithmic techniques to analyze text. The information gathered from that text is then arranged in a format that's easy to read and provide metrics on.

Here are the typical ways text is consumed or identified for text mining:

  • Text categorization: This is putting textual terms into similar groups or classes.

  • Text clustering: Here you outline a set of rules or characteristics for grouped (clustered) words.

  • Sentiment analysis: This is similar to text clustering. With sentiment analysis, you presume a feeling or emotion within the text.

  • Summarization: This derives a condensed statement or indicator from the entire document or block of text.

  • Named entities: Here, text mining considers a relationship between a name and accompanying text in the source.

Use data to enhance customer interaction

Knowing how and why customers interact with your brand is vital to fostering long-term relationships. You need to stay up to date on the trends among your customers in order to maintain engaging conversations with them. So, if you have 2,000 Facebook fans, you’ll want to stay on top of what they’re saying, whether they’re commenting, and whether they’re sharing you content.

Analyzing the content that’s produced through social media channels can help you answer these questions accurately. Instead of acting on a hunch or on what you think is happening, you can use analytics to get real data from numbers and text. Then you can prepare for more-targeted interactions with your customers.

Determine what metrics matter for social CRM

The central theme for social CRM is customer loyalty, but identifying it can present a challenge for many marketers. Just about everyone can agree to what customer loyalty is: a strong affinity for a brand that results in a desired customer behavior. Where the challenge exists for marketers is defining that desired end result, which is customer loyalty.

You’ll first need to identify what you want to know about customer loyalty before you can start to extract data to measure. Behaviors beyond transactions and purchases can indicate customer loyalty. Perhaps a loyal customer will share your Facebook post or mention your brand in a post. That can indicate loyalty as well and be rewarded as such.

Your data can help you reward customers for loyalty if you segment your customers based on level of affinity. Here are a few ideas on segmentation using social CRM:

  • Likely to recommend your services: Look for fans or followers who frequently post positively about you, or share your messages.

  • Likely to repeat purchases: Combine traditional CRM sales information with social information about their habits and lifestyle.

  • Likely to purchase additional products or services: Gather information on life events, such as marriage and new babies, as well as demographics, to predict future behavior.

  • View your brand to be superior: Use text mining to track positive posts, as well as sharing behavior. You can also look at other brands a customer is connected to (or not) in social media.

  • Wouldn’t ever purchase a similar product from a different brand: Identify so-called superfans through text mining and volume of posts about you. Sometimes these customers even mention your company in their bio information or handle, such as MacFan4Life.

  • Communicates actively with the brand: Tally posts that mention your brand, and social media conversations with your social media representatives, to find your most vocal customers.

How you measure and segment your customer loyalty can be based on short- or long-term goals. Just keep in mind that outside factors may impact some of the situations outlined previously, and they don’t always correlate to customer loyalty.

Net Promoter Score

Net Promoter Score is a metric for customer loyalty, specifically evaluating the likelihood of a customer to recommend your brand. It's widely adopted by many Fortune 1000 businesses but can be a powerful measuring tool for small- to medium-sized businesses as well. The score identifies customers in these three categories based on asking a simple survey question: How likely are you to recommend this brand?

  • Promoters

  • Passives

  • Detractors

Businesses use the results of the Net Promoter Score to direct employee and company interactions with customers. With insights into this metric, you can identify customer service issues and see where you aren’t delivering your desired experience. You can then adjust your communications to meet the needs of your customers. For example, if a detractor shares negative comments about your brand, you would address that person very differently.

An official NPS score can be measured only using the NPS proprietary system, but that doesn’t mean you can’t approximate by looking at social CRM data or designing your own survey.

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