Understanding Online Hotel Reviews Through Automated Text Analysis
When nosotros conduct research, need to explain changes in metrics or understand people'southward opinions, we always turn to qualitative information.
Qualitative information is typically generated through:
- Interview transcripts
- Surveys with open-ended questions
- Contact center transcripts
- Texts and documents
- Sound and video recordings
- Observational notes
Compared to quantitative information, which captures structured information, qualitative information is unstructured and has more than depth. It tin respond our questions, can help codify hypotheses and build understanding.
Merely unfortunately, analyzing qualitative information is hard. While tools like Excel, Tableau and PowerBI crunch and visualize quantitative information with ease, there are no such mainstream tools for qualitative information. The majority of qualitative data analysis however happens manually.
That said, there are ii new trends that are changing this. Offset, there are advances in tongue processing (NLP) which is focused on understanding homo linguistic communication. 2nd, at that place is an explosion of user-friendly software designed for both researchers and businesses. Both assist automate qualitative data analysis.
In this post nosotros want to teach you how to deport a successful qualitative data analysis. We will teach yous how to conduct the analysis manually, and also, automatically using software solutions powered past NLP.
Nosotros'll guide you lot through the steps to bear a manual analysis, and look at what is involved and the role engineering science tin can play in automating this process.
More businesses are switching to fully-automated analysis of qualitative data because it is cheaper, faster, and simply every bit accurate. Primarily, businesses purchase subscriptions to feedback analytics platforms so that they tin understand customer pain points and sentiment.
We'll have you through 5 steps to deport a successful qualitative data assay. Within each step we will highlight the key difference between the manual, and automatic approach. Here's an overview of the steps:
The 5 steps to doing qualitative information analysis
- Gathering and collecting your qualitative data
- Organizing and connecting into your qualitative data
- Coding your qualitative data
- Analyzing the qualitative data for insights
- Reporting on the insights derived from your analysis
What is Qualitative Data Analysis?
Qualitative data assay is a process of gathering, structuring and interpreting qualitative data to understand what it represents.
Qualitative information is not-numerical and unstructured. Qualitative data generally refers to text, such as open up-ended responses to survey questions or user interviews, just besides includes sound, photos and video.
Businesses ofttimes perform qualitative data analysis on customer feedback. And within this context, qualitative information generally refers to verbatim text information from sources such as reviews, complaints, chat messages, support eye interactions, customer interviews, case notes or social media comments.
Qualitative Information Analysis methods
Once the data has been captured, there are a multifariousness of analysis techniques available and the choice is determined by your specific enquiry objectives and the kind of data you've gathered. Common approaches include:
Content Analysis
This is a popular arroyo to qualitative data analysis. Other analysis techniques may fit within the broad scope of content analysis. Thematic analysis is a part of the content analysis. Content assay is used to identify the patterns that emerge from text, by grouping content into words, concepts, and themes. Content assay is useful to quantify the relationship betwixt all of the grouped content. The Columbia Schoolhouse of Public Health has a detailed breakdown of content analysis.
Narrative Analysis
Narrative analysis focuses on the stories people tell and the language they utilise to brand sense of them. It is especially useful for getting a deep agreement of customers' perspectives on a specific issue. A narrative analysis might enable u.s.a. to summarize the outcomes of a focused case study.
Discourse Assay
Discourse analysis is used to go a thorough agreement of the political, cultural and power dynamics that exist in specific situations. The focus hither is on the way people express themselves in dissimilar social contexts. Discourse assay is commonly used by brand strategists who hope to empathise why a group of people experience the style they exercise near a brand or production.
Thematic Assay
Thematic analysis is used to deduce the meaning behind the words people apply. This is accomplished by discovering repeating themes in text. These meaningful themes reveal key insights into data and can be quantified, particularly when paired with sentiment analysis. Oft, the outcome of thematic analysis is a lawmaking frame that captures themes in terms of codes, also chosen categories. So the process of thematic analysis is too referred to as "coding". A common use-case for thematic assay in companies is analysis of client feedback.
Grounded Theory
Grounded theory is a useful approach when little is known most a subject. Grounded theory starts by formulating a theory around a single data case. This means that the theory is "grounded". It's based on actual information, and not entirely speculative. Then additional cases can be examined to see if they are relevant and can add to the original theory.
How to do Qualitative Data Analysis: 5 steps
Now we are going to prove how you lot tin do your own qualitative data analysis. We will guide yous through this process step past step. As mentioned earlier, you lot will learn how to do qualitative data analysis manually, and as well automatically using modern qualitative data and thematic analysis software.
To go best value from the assay procedure, it'south important to be super clear about the nature and scope of the question that's existence researched. This will help yous select the enquiry drove channels that are most likely to help yous answer your question.
Depending on if you are a business looking to empathize customer sentiment, or an bookish surveying a schoolhouse, your approach to qualitative data analysis volition exist unique.
Once yous're clear, there's a sequence to follow. And, though in that location are differences in the manual and automatic approaches, the process steps are mostly the aforementioned.
The use case for our step-by-stride guide is a visitor looking to analyze customer feedback - in order to improve customer experience. You can follow these aforementioned steps regardless of the nature of your research. Allow'south go started.
Step 1: Gather your qualitative information and carry research
The first step of qualitative inquiry is to do information collection. Put simply, information collection is gathering all of your data for analysis. A common state of affairs is when qualitative data is spread across diverse sources.
Classic methods of gathering qualitative data
Near companies use traditional methods for gathering qualitative data: conducting interviews, running surveys, and running focus groups. This information is typically stored in documents, CRMs, databases and knowledge bases. It's important to examine which information is available and needs to exist included in your inquiry, based on its scope.
Using your existing qualitative feedback
As it becomes easier for customers to engage across a range of different channels, companies are gathering increasingly large amounts of both solicited and unsolicited qualitative feedback.
Most organizations accept now invested in Vocalism of Customer programs, support ticketing systems, chatbot and back up conversations, emails and even customer Slack chats.
These new channels provide companies with new ways of getting feedback, and also let the collection of unstructured feedback information at scale.
The great thing about this data is that it contains a wealth of insights and that information technology's already there! When y'all have a new question about your customers, you don't need to create a new enquiry written report or gear up a focus group. Y'all can find virtually answers in the data yous already accept.
Typically, this information is stored in third-political party solutions or a fundamental database, just there are means to export information technology or connect to a feedback analysis solution through integrations or an API.
Utilize untapped qualitative information channels
There are many online qualitative data sources you lot may non have considered. For example, you lot tin can notice useful qualitative data in social media channels like Twitter or Facebook. Online forums, review sites, and online communities such equally Soapbox or Reddit also incorporate valuable data nigh your customers, or research questions.
If you are considering performing a qualitative benchmark assay against competitors - the net is your best friend. Gathering feedback in competitor reviews on sites like Trustpilot, G2, Capterra, Better Business Bureau or on app stores is a peachy way to perform a competitor criterion analysis.
Customer feedback analysis software often has integrations into social media and review sites, or you could use a solution like DataMiner to scrape the reviews.
Stride 2: Connect & organize all your qualitative information
Now you all take this qualitative data but there's a problem, the data is unstructured. Before feedback can exist analyzed and assigned whatever value, it needs to be organized in a unmarried place. Why is this important? Consistency!
If all data is hands attainable in i place and analyzed in a consequent style, you volition have an easier time summarizing and making decisions based on this data.
The transmission arroyo to organizing your data
The classic method of structuring qualitative data is to plot all the data you've gathered into a spreadsheet.
Typically, research and support teams would share large Excel sheets and different business concern units would make sense of the qualitative feedback data on their ain. Each team collects and organizes the data in a manner that best suits them, which ways the feedback tends to be kept in separate silos.
An culling and a more robust solution is to store feedback in a cardinal database, like Snowflake or Amazon Redshift.
Go along in mind that when you organize your data in this way, you are ofttimes preparing information technology to be imported into another software. If you go the route of a database, you would need to utilise an API to push the feedback into a tertiary-party software.
Computer-assisted qualitative data analysis software (CAQDAS)
Traditionally within the manual analysis approach (simply not always), qualitative information is imported into CAQDAS software for coding.
In the early 2000s, CAQDAS software was popularised by developers such every bit ATLAS.ti, NVivo and MAXQDA and eagerly adopted by researchers to assist with the organizing and coding of data.
The benefits of using computer-assisted qualitative data analysis software:
- Assists in the organizing of your data
- Opens you upwards to exploring dissimilar interpretations of your data assay
- Allows yous to share your dataset easier and allows group collaboration (allows for secondary analysis)
Nevertheless y'all still demand to code the data, uncover the themes and practice the assay yourself. Therefore information technology is however a manual arroyo.
Organizing your qualitative data in a feedback repository
Another solution to organizing your qualitative data is to upload it into a feedback repository where it tin can exist unified with your other data, and hands searchable and taggable. There are a number of software solutions that act as a central repository for your qualitative enquiry data. Hither are a couple solutions that you lot could investigate:
- Dovetail: Dovetail is a research repository with a focus on video and sound transcriptions. You can tag your transcriptions within the platform for theme assay. Y'all tin likewise upload your other qualitative data such every bit inquiry reports, survey responses, back up conversations, and client interviews. Dovetail acts every bit a single, searchable repository. And makes it easier to interact with other people around your qualitative research.
- EnjoyHQ: EnjoyHQ is another research repository with similar functionality to Dovetail. It boasts a more than sophisticated search engine, just it has a higher starting subscription cost.
Organizing your qualitative data in a feedback analytics platform
If y'all have a lot of qualitative data and information technology is customer or employee feedback, you lot will benefit from a feedback analytics platform. A feedback analytics platform is a software that automates the process of both sentiment assay and thematic analysis.
Companies utilise the integrations offered by these platforms to straight tap into their qualitative data sources (review sites, social media, survey responses, etc.). The data is so organized and analyzed consistently inside the platform.
If you lot have data prepared in a spreadsheet, it can also exist imported into feedback analytics platforms.
Once all this data has been organized inside the feedback analytics platform, information technology is set to exist coded and themed, inside the same platform.
Thematic is a feedback analytics platform that offers ane of the largest libraries of integrations with qualitative data sources.
Step 3: Coding your qualitative data
Your feedback data is now organized in one place. Either inside your spreadsheet, CAQDAS, feedback repository or within your feedback analytics platform. The adjacent step is to lawmaking your feedback information and so we can extract meaningful insights in the adjacent footstep.
Coding is the process of labelling and organizing your data in such a way that you can then identify themes in the information, and the relationships betwixt these themes.
To simplify the coding process, you lot will accept small samples of your customer feedback data, come upwardly with a set up of codes, or categories capturing themes, and characterization each piece of feedback, systematically, for patterns and significant. Then you will take a larger sample of information, revising and refining the codes for greater accuracy and consistency as you become.
If you choose to use a feedback analytics platform, much of this process will be automated and achieved for you.
The terms to describe dissimilar categories of meaning ('theme', 'code', 'tag', 'category' etc) can exist confusing every bit they are often used interchangeably. For clarity, this commodity will use the term 'code'.
To code means to identify cardinal words or phrases and assign them to a category of pregnant. "I really hate the customer service of this software company" would exist coded as "poor client service".
How to manually code your qualitative data
- Determine whether y'all will use deductive or inductive coding. Deductive coding is when you create a listing of predefined codes, and so assign them to the qualitative data. Inductive coding is the opposite of this, you create codes based on the information itself. Codes arise directly from the information and y'all label them as you go. Y'all need to weigh upwardly the pros and cons of each coding method and select the near appropriate.
- Read through the feedback data to become a wide sense of what it reveals. Now it'due south fourth dimension to starting time assigning your first ready of codes to statements and sections of text.
- Keep repeating pace 2, calculation new codes and revising the code description every bit frequently as necessary. Once it has all been coded, go through everything again, to exist sure at that place are no inconsistencies and that nothing has been overlooked.
- Create a code frame to group your codes. The coding frame is the organizational construction of all your codes. And in that location are two ordinarily used types of coding frames, apartment, or hierarchical. A hierarchical code frame will get in easier for you to derive insights from your analysis.
- Based on the number of times a particular code occurs, you can now meet the mutual themes in your feedback data. This is insightful! If 'bad customer service' is a mutual code, it's time to take action.
We have a detailed guide defended to manually coding your qualitative information.
Using software to speed upwards manual coding of qualitative data
An Excel spreadsheet is still a popular method for coding. But various software solutions tin can assistance speed up this procedure. Here are some examples.
- CAQDAS / NVivo - CAQDAS software has born functionality that allows you lot to lawmaking text within their software. You lot may discover the interface the software offers easier for managing codes than a spreadsheet.
- Dovetail/EnjoyHQ - You can tag transcripts and other textual information within these solutions. Every bit they are also repositories you may find information technology simpler to keep the coding in 1 platform.
- IBM SPSS - SPSS is a statistical analysis software that may make coding easier than in a spreadsheet.
- Ascribe - Ascribe's 'Coder' is a coding management system. Its user interface will make it easier for you to manage your codes.
Automating the qualitative coding process using thematic analysis software
In solutions which speed upwardly the manual coding process, you still have to come upwardly with valid codes and oft apply codes manually to pieces of feedback. But in that location are as well solutions that automate both the discovery and the application of codes.
Advances in machine learning accept now made it possible to read, code and structure qualitative data automatically. This type of automatic coding is offered by thematic assay software.
Automation makes it far simpler and faster to code the feedback and group it into themes. By incorporating natural linguistic communication processing (NLP) into the software, the AI looks across sentences and phrases to place meaningful statements.
Some automated solutions detect repeating patterns and assign codes to them, others make you train the AI past providing examples. You could say that the AI learns the meaning of the feedback on its own.
Thematic automates the coding of qualitative feedback regardless of source. At that place's no need to prepare upward themes or categories in accelerate. Simply upload your data and wait a few minutes. Yous can also manually edit the codes to farther refine their accuracy. Experiments conducted point that Thematic's automated coding is just as authentic as manual coding.
Paired with sentiment analysis and avant-garde text analytics - these automated solutions get powerful for deriving quality business or research insights.
You could besides build your ain, if yous have the resources!
The key benefits of using an automated coding solution
Automated analysis can often be fix fast and at that place's the potential to uncover things that would never have been revealed if you had given the software a prescribed listing of themes to wait for.
Because the model applies a consistent rule to the data, it captures phrases or statements that a human being eye might have missed.
Consummate and consistent analysis of client feedback enables more meaningful findings. Leading us into stride 4.
Footstep four: Analyze your data: Find meaningful insights
At present nosotros are going to analyze our data to detect insights. This is where we first to respond our research questions. Keep in mind that pace iv and stride v (tell the story) have some overlap. This is considering creating visualizations is both part of analysis and reporting.
The job of uncovering insights is to scour through the codes that emerge from the information and draw meaningful correlations from them. It is also about making sure each insight is singled-out and has enough data to support information technology.
Function of the assay is to plant how much each code relates to dissimilar demographics and customer profiles, and identify whether there'due south whatever relationship between these information points.
Manually create sub-codes to better the quality of insights
If your code frame simply has one level, you may notice that your codes are too broad to be able to extract meaningful insights. This is where it is valuable to create sub-codes to your primary codes. This process is sometimes referred to as meta coding.
Note: If you take an inductive coding approach, you can create sub-codes as you are reading through your feedback data and coding it.
While time-consuming, this exercise will improve the quality of your assay. Here is an example of what sub-codes could look like.
Y'all demand to carefully read your qualitative data to create quality sub-codes. Merely as you tin can meet, the depth of analysis is greatly improved. By calculating the frequency of these sub-codes you lot can go insight into which customer service bug you can immediately address.
Correlate the frequency of codes to customer segments
Many businesses use customer segmentation. And you may have your ain respondent segments that you tin can apply to your qualitative assay. Segmentation is the practise of dividing customers or research respondents into subgroups.
Segments can exist based on:
- Demographic
- Age
- Interests
- Behavior
- And whatever other data type that you care to segment by
It is particularly useful to see the occurrence of codes within your segments. If one of your client segments is considered unimportant to your business concern, but they are the cause of virtually all customer service complaints, it may be in your best interest to focus attention elsewhere. This is a useful insight!
Manually visualizing coded qualitative data
At that place are formulas you can utilize to visualize key insights in your information. The formulas we will suggest are imperative if you are measuring a score aslope your feedback.
Touch
If you lot are collecting a metric aslope your qualitative data this is a fundamental visualization. Impact answers the question: "What'due south the impact of a code on my overall score?".
Using Net Promoter Score (NPS) as an example, first you need to:
- Calculate overall NPS
- Calculate NPS in the subset of responses that practice non contain that theme
- Subtract B from A
So y'all can use this uncomplicated formula to calculate code touch on NPS.
You tin can then visualize this data using a bar nautical chart.
Y'all can download our CX toolkit - information technology includes a template to recreate this.
Trends over time
This analysis can help you reply questions like: "Which codes are linked to decreases or increases in my score over time?"
We need to compare two sequences of numbers: NPS over time and code frequency over time. Using Excel, summate the correlation between the two sequences, which can be either positive (the more codes the higher the NPS, see picture below), or negative (the more codes the lower the NPS).
Now you need to plot code frequency against the absolute value of code correlation with NPS.
Hither is the formula:
The visualization could look similar this:
These are two examples, but there are more than. For a third transmission formula, and to larn why discussion clouds are non an insightful form of assay, read our visualizations commodity.
Using a text analytics solution to automate analysis
Automated text analytics solutions enable codes and sub-codes to exist pulled out of the data automatically. This makes information technology far faster and easier to identify what'south driving negative or positive results. And to pick up emerging trends and find all manner of rich insights in the data.
Another benefit of AI-driven text analytics software is its congenital-in capability for sentiment analysis, which provides the emotive context behind your feedback and other qualitative data.
Thematic provides text analytics that goes farther by allowing users to utilize their expertise on business concern context to edit or augment the AI-generated outputs.
Since the move abroad from manual research is generally about reducing the human element, adding human input to the technology might sound counter-intuitive. However, this is more often than not to make certain important business nuances in the feedback aren't missed during coding. The effect is a higher accuracy of analysis. This is sometimes referred to as augmented intelligence.
Footstep 5: Report on your information: Tell the story
The final step of analyzing your qualitative information is to report on it, to tell the story. At this signal, the codes are fully developed and the focus is on communicating the narrative to the audience.
A coherent outline of the qualitative enquiry, the findings and the insights is vital for stakeholders to discuss and debate before they tin devise a meaningful form of activeness.
Creating graphs and reporting in Powerpoint
Typically, qualitative researchers take the tried and tested approach of distilling their report into a series of charts, tables and other visuals which are woven into a narrative for presentation in Powerpoint.
Using visualization software for reporting
With information transformation and APIs, the analyzed data can be shared with information visualisation software, such every bit Power BI or Tableau, Google Studio or Looker. Power BI and Tableau are among the virtually preferred options.
Visualizing your insights within a feedback analytics platform
Feedback analytics platforms, like Thematic, incorporate visualisation tools that intuitively turn key data and insights into graphs. This removes the time consuming work of amalgam charts to visually identify patterns and creates more than fourth dimension to focus on edifice a compelling narrative that highlights the insights, in bite-size chunks, for executive teams to review.
Using a feedback analytics platform with visualization tools means you don't accept to apply a separate production for visualizations. You can consign graphs into Powerpoints straight from the platforms.
Conclusion - Transmission or Automatic?
There are those who remain deeply invested in the manual approach - because it'south familiar, considering they're reluctant to spend money and time learning new software, or because they've been burned past the overpromises of AI.
For projects that involve small-scale datasets, manual analysis makes sense. For case, if the objective is simply to quantify a simple question similar "Do customers prefer X concepts to Y?". If the findings are being extracted from a small set of focus groups and interviews, sometimes it's easier to but read them
Nonetheless, as new generations come into the workplace, it's technology-driven solutions that feel more comfy and practical. And the merits are undeniable. Especially if the objective is to go deeper and understand the 'why' behind customers' preference for X or Y. And even more especially if fourth dimension and coin are considerations.
The ability to collect a gratis flow of qualitative feedback data at the aforementioned time as the metric means AI can cost-effectively scan, crunch, score and clarify a ton of feedback from one organisation in i go. And fourth dimension-intensive processes like focus groups, or coding, that used to take weeks, can now be completed in a thing of hours or days.
Merely aside from the ever-present business case to speed things upwardly and go on costs down, at that place are likewise powerful research imperatives for automatic analysis of qualitative data: namely, accuracy and consistency.
Finding insights hidden in feedback requires consistency, especially in coding. Not to mention catching all the 'unknown unknowns' that can skew inquiry findings and steering articulate of cerebral bias.
Some say without manual data analysis researchers won't get an accurate "feel" for the insights. Even so, the larger information sets are, the harder it is to sort through the feedback and organize feedback that has been pulled from different places. And, the more than difficult it is to stay on course, the greater the risk of drawing incorrect, or incomplete, conclusions grows.
Though the procedure steps for qualitative data analysis accept remained pretty much unchanged since psychologist Paul Felix Lazarsfeld paved the path a hundred years agone, the bear upon digital applied science has had on types of qualitative feedback data and the approach to the assay are profound.
If you want to try an automated feedback analysis solution on your own qualitative data, you tin can get started with Thematic.
Source: https://getthematic.com/insights/qualitative-data-analysis/
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