It takes years of hard work for a business to build a positive reputation. Still, it can be tarnished in a moment by a single misstep, such as a lapse in customer service or a poorly worded response to customer feedback. However, that doesn’t mean brands are helpless in the face of angry reviews and poor publicity.
Online reputation management (ORM) is a two-pronged approach to managing how your brand is perceived by consumers. Both proactive and reactive, ORM allows you to identify potential problems early on as well as mitigate damage caused by crises and unforeseen events.
Data plays a crucial role in every aspect of ORM, from understanding current consumer perception of your brand to developing strategies and measuring their success. Leveraging hard facts and numbers instead of estimates and guesswork means you can make data-driven decisions that optimize your ORM efforts and maximize their impact on your brand reputation.
However, there is still some confusion on what kind of data is used in ORM. In this article, we discuss the two major types of data: structured vs unstructured data. Understanding why both are crucial to the success of your ORM efforts and how they work together will empower you to develop a more comprehensive and effective reputation management strategy.
Table of Contents
• Understanding Structured Data vs Unstructured Data
• What Is Structured Data?
• What Is Unstructured Data?
• How Structured Data Helps With Online Reputation Management
• The Importance of Unstructured Data for Online Reputation Management
• Structured vs Unstructured Data: Why You Need Both for ORM
Understanding Structured Data vs Unstructured Data
What Is Structured Data?
Structured data is information that follows a predefined format for organization. It is typically collected and stored in data warehouses or relational databases. A relational database is a collection of data points with predefined relationships organized into rows and columns.
Within the relational database, the structure and organization of structured data are defined by a schema, which acts as a blueprint for how the data should be organized.
Examples of Structured Data
A typical example of structured data is a customer list spreadsheet.
The rows represent individual records. Each record refers to a specific item within the data set. For instance, a row might contain names, phone numbers and addresses.
Meanwhile, columns represent attributes or characteristics of those items. They define or describe the pieces of information about each record. For instance, columns might include “Customer Name,” “Phone Number” and “Mailing Address.” All the cells in this spreadsheet have one type of information, making it easy to find what you need.
Examples of structured data relevant to ORM include the following:
• Surveys with close-ended questions
• Website analytics
• Marketing performance reports
• Social media data, such as engagement metrics and follower count
• Number of customer reviews
• Star ratings
• Net promoter score (NPS)
Characteristics of Structured Data
Easily Searchable
Because structured data is organized into a schema, it can easily be interpreted by both humans and machines. This makes structured data simple to access, search through and analyze.
Consistent and Predictable
Structured data can easily be integrated with databases and other data storage systems. Because structured data follows a predefined blueprint that standardizes how it is collected, stored and displayed, it is less prone to inconsistencies and can be accessed and analyzed more efficiently.
Has Specific Data Types
Another characteristic of structured data is the consistent use of data types. Data types ensure that each piece of data adheres to a specific format, such as numerical, textual and date-based types. These allow systems to perform data-driven operations, analysis and search quickly.
What Is Unstructured Data?
You can think of unstructured data as straight-laced and organized structured data’s more free-spirited cousin; it is raw and complex. Unstructured data doesn’t fit neatly into predefined categories, and it isn’t stored in a traditional way.
While it might possess some internal organization, this structure isn’t standardized, making it incompatible with traditional databases. Although advancements in machine learning allow for some analysis, unstructured data often requires specialized tools and techniques compared to the pivot tables and basic software used for structured data.
Examples of Unstructured Data
According to analyst estimates, unstructured data makes up 80% to 90% of all data. While structured data like website analytics and customer reviews provide a strong foundation for ORM, the massive volume of unstructured data holds incredible potential for even deeper and more valuable insights.
Examples of unstructured data in the context of ORM include:
• Online reviews and testimonials
• Social media comments and posts
• User-generated content, such as images and videos
• Brand mentions in news articles and blogs
• Interactions with customer service (chat logs, emails and phone call transcripts)
• Forum discussions
Characteristics of Unstructured Data
No Consistent Structure
Unlike structured data, which is typically organized in rows and columns, unstructured data appears in various forms. This inconsistent structure makes it harder to categorize, sort and analyze than structured data.
Diverse Formats
Unstructured data can exist in multiple formats, such as text, images, videos and audio files. This makes handling and making sense of this type of data even more complex.
Constant Growth
As unstructured data comes from various sources, such as social media or customer interactions, it tends to grow exponentially. For this reason, businesses may find it challenging to scale their data analysis and storage infrastructure.
Semi Structured Data
In between structured and unstructured data lies semi structured data, which combines elements of both. However, this type of data still falls under structured data. This is because it is not entirely raw, and while it does not follow a conventional structure, elements like tags make it easier to organize and analyze.
A common example is email, which has defined fields such as sender email and recipient email. However, it also contains unstructured data in the form of its body content.
Data representations like JSON and XML are also classified as semi structured data. These allow businesses to gain additional insights into customer behavior, preferences and trends.
How Structured Data Helps With Online Reputation Management
Increased Online Visibility
It’s essential to note that structured data does not directly impact your online reputation. However, it supports your ORM efforts by increasing your search engine visibility.
Structured data helps search engines better understand your business information, making it easier for them to display accurate and relevant information in search results, such as rich snippets and Knowledge Graph cards.
By boosting your search engine visibility, structured data makes users more likely to find and engage with your business, giving you opportunities to make a positive first impression. Additionally, the enhanced results help users have a better idea of what your brand is about and allow you to distinguish yourself from your competitors.
Increased Traffic
These results help users better understand what your brand is about and distinguish you from competitors. Additionally, enhanced search results are more enticing to users and often result in higher click-through rates (CTR). This not only increases web traffic but also indirectly supports your ORM efforts by driving more qualified traffic to your website.
Showcase Your Awards
Additionally, Google structured data markup can be used to highlight positive and factual aspects of your brand, such as awards or certifications. This can be achieved through specific schema types like “awards” or “qualifications.”
Quantify Customer Feedback
Collecting structured data about your customers, such as NPS scores and star ratings for post-purchase reviews, allows you to quantify feedback and track trends over time. This information can help you identify areas where your business may need improvement and areas where you excel.
Measure Marketing Success
Structured data allows you to assess and measure the success of your marketing efforts. For example, by tracking metrics such as CTR, conversion rates and web traffic, you can understand the impact of your campaigns and make data-driven decisions on future marketing and ORM strategies.
Leverage the Google Rich Results Test
Another way to support your ORM efforts through structured data is using the Google Rich Results test (formerly the Google Structured Data Testing Tool). It allows you to validate and preview Google structured data markup on your website, ensuring that search engines correctly understand its content. It supports various markup formats, such as Microdata and JSON-LD.
The Google Rich Results test/Google Structured Data Testing Tool identifies and displays any errors or warnings in your structured data, allowing you to fix potential issues and ensure accurate search engine representation. This structured data testing tool also previews how your structured data may be displayed in search results (rich snippets), helping you understand how users will see and interact with your brand online.
Using the Google Rich Results test/ Structured Data Testing Tool, you can:
Ensure your business information is accurate and consistently represented in search results, which helps build trust among potential customers. According to the Local Business Discovery Trust Report, over 60% of consumers will not use a business if they find incorrect information about it online, while 7% will stop searching for a business completely if they find incorrect address details.
Get more clicks to your website. A study by Milestone Research found that users clicked on rich results 58% of the time, while non-rich results had a CTR of 41%. Higher CTR can lead to increased web traffic and potentially a stronger online presence if your website delivers a positive user experience.
The Importance of Unstructured Data for Online Reputation Management
Sentiment Analysis
Unstructured data often contains a wealth of information about how people feel about your business. By analyzing the emotional tone or sentiment behind reviews, comments or social media posts, you can gauge consumer perception of your brand.
Rich Media Insights
Unstructured data, such as images and videos, provides a unique perspective on how others perceive your brand. Analyzing rich media content can help you track brand mentions, gauge the impact of your visual marketing efforts and identify anything that may be causing damage to your reputation.
Builds Trust
Tim Clarke, Senior Reputation Manager at Thrive Local, advises businesses to leverage this type of data to showcase trust signals and leverage the power of social proof.
“Companies should also use unstructured data like testimonials on their website and marketing collateral to help their sales and lead generation efforts.”
You can use analytics tools and software to identify positive sentiments about your brand from your reviews, social media mentions and other unstructured data sources, and then use these in your marketing and ORM strategies.
According to a survey by BrightLocal, 85% of consumers trust online reviews as much as they do personal recommendations from their loved ones. Having positive feedback from real customers on your website allows your target audience to learn more about your business and offerings from others who have actually interacted with your brand. This helps build trust, which in turn generates leads and inspires conversions.
Identifies Risks and Opportunities
Unstructured data from social networks is vital for social listening, which entails monitoring and analyzing social media conversations about your brand. Insights you gather from social listening can help you identify potential risks and opportunities, manage crises and engage with customers more effectively.
Structured vs Unstructured Data: Why You Need Both for ORM
If you’re wondering which type to use, the answer is both. In fact, It’s not a question of structured data vs unstructured data. Both are incredibly useful for informing your ORM strategy.
By using structured and unstructured data effectively, businesses can gain comprehensive insights into their online reputation and make data-driven decisions to improve it. Here are some key benefits of combining these two types of data:
Holistic View of Customer Sentiment
Analyzing structured information, such as star ratings, can provide valuable quantitative insights into customer satisfaction. However, this data alone does not give you a comprehensive understanding of your customers’ opinions and feelings.
Unstructured data gives you the “why” behind the “what” shown by structured data. Utilizing unstructured data from social media, blogs and forums can help you gain a broader understanding of how your customers perceive your brand, enabling you to identify weak points and make necessary improvements.
Early Detection of Online Reputation Issues
Structured data analysis can help you spot trends, such as a drop in website traffic or a decrease in positive reviews. However, this information may not clearly and immediately explain what’s causing the issue.
By monitoring unstructured data sources, you can quickly identify the root cause of the problem, which may be a negative social media post or a damaging review that has gone viral. This allows you to address the issue swiftly and take the necessary actions to protect your online reputation.
Enhanced Content Strategy
Instead of choosing between structured vs unstructured data to inform your content strategy, use both. Utilizing structured and unstructured data allows you to gain better insights into the type of content that resonates with your target audience.
For example, website analytics (structured data) can reveal which blog posts generate the most traffic and engagement. Combining this with social media analysis (unstructured data) tells you what topics your audience is discussing, helping you create more relevant and engaging content.
A positive online reputation is crucial to a business’s growth and success. To build and maintain it, leverage structured and unstructured data. Doing so gives you a deeper understanding of your brand’s online presence and empowers you to make data-driven decisions about your online reputation management strategy.
At Thrive Local, we offer reputation management solutions backed by advanced analytics tools and a team of experts. We extract valuable insights from structured and unstructured sources to develop an ORM strategy for your business that delivers results.
Contact us today to get started!
