With a Social Listening Post, You Need to Analyze the Data

social media listening

Last week I gave you 5 reasons you need to add a listening post to your marketing strategy. In that post, I made the case that, even if you DON’T have social media as part of your marketing strategy, you need a social listening post to “hear” what others are saying about you online and on social networks, as well as monitoring mentions of your competitors and trends in your industry. You must also assess the evolution of consumer culture, especially among those comprising your target market, so you can adapt your messaging to continue resonating with your market. I hope you now understand how important it is to listen to what customers and prospects say. NOW, your problem is you have a TON of unstructured (non-numeric) data and you can’t make heads or tails out of it. That’s our topic for today.

social media listeningHaving a social listening post is great, but only if you can analyze data QUICKLY, get it to the RIGHT person for follow-up, and FIX problems identified through online conversations. Gone are the days when you could simply send an email acknowledging the issue and promising someone would resolve it SOON. With the internet age, consumers learned they can demand nearly immediate feedback and complaint handling. Many larger companies how host customer service through social platforms like Twitter. During designated hours, the company assigns staff to monitor complaints and other customer service issues to address them in real-time. Chatbots, fueled by artificial intelligence and machine learning now sit perpetually on your website ready to interact with customers and prospects any time of the day or night without taking a break or calling in sick.

This requires a system that captures data (blog posts, Tweets, Updates, YouTube and Flickr, etc) in REAL-TIME based on keywords critical for your business, automatically forwards the data to someone empowered to fix it, then tracks the data to ensure a someone actually fixes the problem (or reassign the data to someone who WILL fix it immediately).

Analyzing data from your social listening post

It’s a relatively simple task to analyze structured data, which is primarily numeric. You run the data through an algorithm developed based on past data and the “answer” comes out. For instance, the Google Search Algorithm categorizes and ranks thousands of pieces of content every day so that users get useful results from their queries such that the most likely answers are at the top of the results list. This saves users a huge amount of time in getting their answers quickly. It also explains why Google search represents 90+% of all queries online. Their algorithm provides a competitive advantage.

Analyzing unstructured data isn’t nearly as simple. First, written words, images, spoken words (such as from podcasts), and video lack the non-verbal elements of communication needed to accurately interpret meaning. For instance, gestures, inflection, and even the relationship between communicators contribute to interpreting the conversation. The accuracy of interpretation varies with language such that some languages rely less on content (such as German and Swiss) and some rely more heavily on context (such as Japanese). English is in the middle of the continuum. Thus, analyzing data from social listening isn’t straightforward and tools that attempt to automate the analysis aren’t all that accurate.

But, before you throw up your hands and give up, there are some tools that, with human help, can aid in the analysis of data from your social listening. At the end of this post, you’ll see more about these tools. Below are the steps you must use in analyzing your social listening data.

Keyword selection

This is a critical first step in effective social listening.  Keywords should reflect issues critical to the organization. For instance, you should include words like “bad service”, “unhappy”, “dissatisfied”,  and similar terms as words that trigger data collection. Likewise, the firm should “listen” to compliments from customers as these can help you improve your marketing strategy. For instance, you might use these compliments in other marketing communications, to reward employees who provide superior service, and even monitor customer satisfaction levels. You’ll find several tools for monitoring sentiment so you can track the favorability or unfavorability of your brand over time.

Selecting the right keywords ensures the company has the most important information to assess the success of its current marketing strategy, identify failures in that marketing strategy, and suggest changes to the marketing strategy likely to improve performance.


Sure, you can read through the data generated by your listening post by pulling out sentences or even larger chunks of text surrounding your keywords and, if you’re a small business, you might find this is all you need. But, if you get lots of results from your social listening, you can’t expect to evaluate the data, assign it, and track the handling of problems identified in a timely fashion. You’ll need a hand.

Here are the steps you need to accomplish:

1. Categorize the data

You can do this based on keywords, such that complaints go together and for categorization using some criterion such as product line, store location, etc. You categorize compliments together and additional keywords generate additional categories. To effectively categorize your data, you may have to work extensively with the data to identify relevant categories in the beginning, but later you can feed these categories for analysis automatically (using some of the software solutions listed below). Periodically, you need to reevaluate these categories to ensure you categorize data properly.

You probably need to set up a system for handling data you miscategorized on the first pass to assign it to the right category.

2. Assign

You then assign data to employees based on categories (this is why categorizing the data is so critical). Thus, you might assign complaints to customer service employees; while compliments go to marketing. You might assign tasks from different categories to different employees based on product line or store region, depending on your needs.

3. Develop a marketing strategy for complaint handling

It’s not enough to assign communications to the right employee, you must have policies and procedures for how employees handle complaints assigned to them. For instance, at Marriott, assigned employees receive an email as soon as the system detects a customer complaint (commonly on a post-visit survey) and have 24 hours to satisfy the customer. The system then takes ANY complaints that exceed the 24-hour cut-off and forwards them to the assigned employee’s manager, who now has 24 hours to resolve the issue. The procedure repeats at the next level if this time limit is exceeded.  Employees are empowered to fix problems (this is a critical aspect of any customer satisfaction program) and their success in reaching a resolution within the allotted time is tracked to use for employee feedback and to ensure the system is working efficiently. Although this was set up as an offline process, you can apply the same principles in developing a procedure for responding to issues identified in your social listening.

4. Track success

You must produce daily reports to track the success of your social listening program. Reports should contain a count of posts entered into the listening post from trolling the internet, a count by category, a count of the number of posts assigned to each employee, and the disposition of each post. You might include information on how long elapsed between the employee notification of the problem and its resolution.

Preferably, you should track this information in a marketing dashboard to aid in improving your marketing strategy. A marketing dashboard contains data over time (preferably graphically) and has relevant divisions such as product lines or regions. An interactive dashboard allows managers to adjust their view of the data, such as by comparing results across time periods or by breaking down or collapsing the data to get an overview or do a deep dive. A dashboard makes it easy to see what’s going on (for instance, if several posts have NOT been handled yet), especially whether things are improving or getting worse.

5. Make changes to your marketing strategy

Your marketing dashboard guides you in making changes to improve your marketing strategy. For instance, if the same type of complaint arises with some frequency, a change in marketing strategy is needed to reduce the incidence. A product performance complaint may mean enforcing better quality control or ensuring the product meets promises made in marketing communication, for example.

A complaint about long lines that occurs in your data frequently may mean the company needs to hire new employees, reduce the amount of time employees spend with each customer, or should consider adding self-service technologies to speed customers through the lines. Complaints may also identify poor-performing employees that you should consider for action, such as retraining, reassigning, or removing them. Complaints might also reflect bad processes (Deming argues that 90% of all failures are related to having the wrong process) that require attention or change. However, without the listening post and related analysis of posted data, the company has no clue as to the existence of a problem or guidance toward its solution.

Software solutions

As mentioned, this process is likely too complicated to accomplish by hand, and while I’m normally a strong advocate for not using automation in social media, this is one place where it is appropriate (as it scheduling posts to ensure consistency).

Here are some software solutions I found effective in working with clients over the years. They vary dramatically in terms of ability and cost, so the benefits of each one should be evaluated relative to the needs of the organization.***** I am NOT compensated for these opinions *****

HyperRESEARCH is a product from ResearchWare. I used this extensively for qualitative research and find it very intuitive, easy to use, and powerful. While I never used this for keyword-based categorization, it should be able to handle this just fine. One advantage of the software is it can handle audio and visual data (ie. YouTube and TikTok), however, you must first categorize sections of the video before using it for analysis (at least in the version I own).

nVIVO is another excellent qualitative analysis software program. I used an earlier version of this software with excellent results. Again, it’s intuitive and easy to use.  It’s not as powerful as HyperRESEARCH (in my opinion), but still a good option.

SPSS text analysis software is a new addition to the SPSS family. Since it shares an interface with the SPSS software you likely used in the past, it’s easy to use and subsequent data analysis can be accomplished using SPSS statistics after converting data to a numeric representation. A drawback is that this software doesn’t handle visual or auditory data. It’s also an add-on to SPSS so it comes at a cost

A common problem with these software solutions is they are NOT built with social listening in mind, they are more commonly employed in the analysis of research data such as open-ended questions from surveys and interviews. Hence, I’m not sure how successfully or automatically they would work in this context. Certainly, their power for analysis and integration into a dashboard is excellent, especially for SPSS, which could run regression or cluster, or other statistics on data once converted to numbers. (The other software packages are limited to frequencies and means, at least in the version I have). How effectively they integrate with your listening post (which does the data collection and MAY do the assignment) is unclear.

If you know of specialized software built to handle a more nuanced analysis of a listening post, please let me know.

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