Building a positive reputation for a business necessitates years of diligent effort. However, it can be diminished in an instant by a single error, such as an inadequately phrased response to customer feedback or a lapse in customer service. Nevertheless, this does not imply that brands are powerless in the face of negative publicity and furious reviews.
Online reputation management (ORM) is a two-pronged strategy that involves the administration of the way in which consumers perceive your brand. ORM enables the early identification of potential issues and the mitigation of damage caused by crises and unforeseen events. It is both proactive and reactive.
Data is essential in all facets of ORM, from the development of strategies to the evaluation of their effectiveness and comprehension of the current consumer perception of your brand. By utilizing concrete facts and numbers rather than estimates and guesswork, you can make data-driven decisions that optimize your ORM efforts and maximize their impact on your brand reputation.
Nevertheless, there is still some ambiguity regarding the type of data that is employed in ORM. In this article, we examine the two primary categories of data: structured and unstructured data. Comprehending the reasons for the importance of both in the context of your ORM endeavors and the manner in which they interact will enable you to create a more comprehensive and effective reputation management strategy.
Understanding Structured Data vs Unstructured Data
What Is Structured Data?
Structured data is information that adheres to a predetermined format for the organization. It is typically compiled and stored in relational databases or data warehouses. A relational database is a collection of data elements that are organized into rows and columns based on predefined relationships.
The schema, which serves as a blueprint for the organization of structured data within the relational database, defines the structure and organization of the data.
Examples of Structured Data
A consumer list spreadsheet is a common example of structured data.
Individual records are represented by the sections. Each record pertains to a particular item in the data set. For example, a row may include addresses, phone numbers, and names.
In the interim, columns denote the attributes or characteristics of those items. They provide a definition or description of the information contained within each record. For example, columns may consist of “Customer Name,” “Phone Number,” and “Mailing Address.” Each cell in this spreadsheet contains a single type of data, which simplifies the process of locating the information you require.
The following are examples of structured data that are pertinent to ORM:
- Surveys with close-ended questions
- Website analytics
- Marketing performance reports
- Engagement metrics and follower counts are examples of social media data.
- Number of customer reviews
- Star ratings
- Net promoter score (NPS)
Characteristics of Structured Data
Easily Searchable
Structured data is readily interpretable by both humans and machines due to its organization into a schema. This simplifies the process of accessing, searching through, and analyzing structured data.
Consistent and Predictable
Databases and other data storage systems can be effortlessly incorporated with structured data. Structured data is less susceptible to inconsistencies and can be accessed and analyzed more efficiently due to its adherence to a predefined blueprint that standardizes its collection, storage, and presentation.
Has Specific Data Types
The consistent application of data types is another attribute of structured data. Data types guarantee that each item of data is consistent with a particular format, including numerical, textual, and date-based types. These enable systems to execute data-driven operations, analyses, and searches at a rapid pace.
What Is Unstructured Data?
It is raw and complex, and it can be compared to the more free-spirited relative of structured data, which is straight-laced and organized. Unstructured data is not stored in a conventional manner and does not neatly fall into predefined categories.
Although it may have some internal organization, this structure is not standardized, rendering it irreconcilable with conventional databases. Despite the fact that improvements in machine learning have enabled some analysis, unstructured data frequently necessitates specialized tools and techniques in contrast to the fundamental software and pivot tables that are employed for structured data.
Examples of Unstructured Data
Unstructured data comprises 80% to 90% of all data, according to analyst projections. Although structured data, such as website analytics and customer evaluations, establishes a robust foundation for ORM, the vast quantity of unstructured data has the potential to provide even more valuable and profound insights.
In the context of ORM, unstructured data is exemplified by:
- User-generated content, including images and videos
- Brand mentions in websites and news articles
- Online reviews and testimonials
- Social media remarks and posts
- Customer service interactions, including conversation logs, emails, and phone call transcripts
- Forum discussions
Characteristics of Unstructured Data
No Consistent Structure
In contrast to structured data, which is typically organized in rows and columns, unstructured data is present in a variety of formats. The complexity of categorizing, sorting, and analyzing this inconsistent structure surpasses that of structured data.
Diverse Formats
Text, images, videos, and audio files are among the numerous formats in which unstructured data can be found. This further complicates the process of managing and comprehending this type of data.
Constant Growth
The exponential growth of unstructured data is a common phenomenon, as it originates from a variety of sources, including social media and consumer interactions. This is why businesses may encounter difficulties in scaling their data analysis and storage infrastructure.
Semi-Structured Data
Semi-structured data is a hybrid of structured and unstructured data, incorporating elements of both. Nevertheless, this category of data is still classified as structured data. The reason for this is that it is not wholly raw, and while it does not adhere to a traditional structure, elements such as tags facilitate organization and analysis.
Email is a prevalent illustration, which includes predetermined fields such as the sender and recipient email addresses. Nevertheless, it also includes unstructured data in the form of its body content.
Semi-structured data encompasses data representations such as XML and JSON. These enable businesses to acquire supplementary information regarding consumer preferences, behavior, and trends.
How Structured Data Helps With Online Reputation Management
Increased Online Visibility
It is crucial to recognize that your online reputation is not directly influenced by structured data. Nevertheless, it augments your ORM endeavors by enhancing your search engine visibility.
Structured data facilitates the comprehension of your business information by search engines, thereby facilitating the display of relevant and precise information in search results, including Knowledge Graph cards and rich excerpts.
Structured data, by increasing your search engine visibility, increases the likelihood that users will discover and interact with your business, providing you with the opportunity to establish a positive first impression. Furthermore, the improved outcomes facilitate users’ comprehension of your brand’s objectives and enable you to differentiate yourself from your competitors.
Increased Traffic
These findings assist users in comprehending the essence of your brand and distinguishing it from its competitors. Furthermore, enhanced search results are more appealing to users and frequently lead to increased click-through rates (CTR). This not only augments web traffic but also indirectly supports your ORM endeavors by directing more qualified traffic to your website.
Showcase Your Awards
Additionally, Google structured data encoding can be employed to emphasize the positive and factual aspects of your brand, such as certifications or awards. This can be accomplished by employing specific schema types, such as “qualifications” or “awards.”
Quantify Customer Feedback
By gathering structured data regarding your consumers, including star ratings for post-purchase reviews and NPS scores, you can quantify feedback and monitor trends over time. This information can assist you in identifying areas where your business may benefit from development and areas where you excel.
Measure Marketing Success
Structured data enables you to evaluate and quantify the effectiveness of your marketing initiatives. For instance, by monitoring metrics such as click-through rates (CTR), conversion rates, and web traffic, you can evaluate the effectiveness of your campaigns and make data-informed decisions regarding future marketing and ORM strategies.
Leverage the Google Rich Results Test
The Google Rich Results test (previously known as the Google Structured Data Testing Tool) is an additional method for bolstering your ORM endeavors through structured data. It enables you to validate and evaluate Google structured data markup on your website, thereby guaranteeing that search engines comprehend its content accurately. It accommodates a variety of markup formats, including Microdata and JSON-LD.
The Google Structured Data Testing Tool/Google Rich Results test identifies and displays any errors or warnings in your structured data, enabling you to address potential issues and ensure accurate search engine representation. This structured data testing tool also provides a preview of how your structured data may be displayed in search results (rich excerpts), thereby assisting you in comprehending the manner in which users will interact with and perceive your brand online.
By employing the Structured Data Testing Tool/Google Rich Results Test, it is possible to:
Guarantee that your business information is consistently and accurately displayed in search results, as this contributes to the development of trust among potential customers. The Local Business Discovery Trust Report indicates that over 60% of consumers will refuse to patronize a business if they encounter inaccurate information about it online, and 7% will cease their search for a business altogether if they encounter inaccurate address details.
Increase the number of views on your website. According to a study conducted by Milestone Research, consumers clicked on rich results 58% of the time, while non-rich results had a click-through rate (CTR) of 41%. If your website provides a positive user experience, it is possible that a higher click-through rate (CTR) will result in increased web traffic and a potentially stronger online presence.
The Importance of Unstructured Data for Online Reputation Management
Sentiment Analysis
Unstructured data frequently contains a multitude of information regarding the opinions of individuals regarding your business. Consumer perception of your brand can be assessed by examining the emotional tone or sentiment of reviews, remarks, or social media posts.
Rich Media Insights
Unstructured data, including images and videos, offers a distinctive viewpoint on how others perceive your brand. It is possible to monitor brand mentions, evaluate the effectiveness of your visual marketing initiatives, and identify any potential damage to your reputation by analyzing rich media content.
Builds Trust
Tim Clarke, Senior Reputation Manager at SVBY Local, recommends that businesses utilize this type of data to demonstrate trust signals and capitalize on the influence of social proof.
“Companies should also leverage unstructured data, such as testimonials on their website and marketing collateral, to enhance their sales and lead generation endeavors.”
You can employ analytics tools and software to identify positive sentiments about your brand from your reviews, social media mentions, and other unstructured data sources. Subsequently, you can incorporate these sentiments into your marketing and ORM strategies.
85% of consumers place the same level of trust in online evaluations as they do in personal recommendations from their loved ones, according to a survey conducted by BrightLocal. Your target audience can gain a deeper understanding of your business and its offerings by reading positive feedback from actual consumers on your website. This fosters the development of trust, which in turn generates leads and motivates conversions.
Identifies Risks and Opportunities
Social listening, which involves the surveillance and analysis of social media conversations regarding your brand, is contingent upon unstructured data from social networks. Social listening insights can assist you in the identification of potential risks and opportunities, the management of crises, and the more effective engagement with consumers.
Structured vs Unstructured Data: Why You Need Both for ORM
If you are uncertain about which variety to employ, the answer is both. In reality, the issue is not one of structured versus unstructured data. Both are extremely beneficial for informing your ORM strategy.
Businesses can acquire comprehensive insights into their online reputation and make data-driven decisions to enhance it by effectively utilizing structured and unstructured data. The following are several significant advantages of combining these two categories of data:
Holistic View of Customer Sentiment
Valuable quantitative insights into consumer satisfaction can be obtained by analyzing structured information, such as star ratings. Nevertheless, this information is insufficient to provide a thorough comprehension of the sentiments and opinions of your consumers.
The “why” behind the “what” presented by structured data is provided by unstructured data. By utilizing unstructured data from social media, blogs, and forums, you can develop a more comprehensive comprehension of how your customers perceive your brand. This will allow you to identify areas that require improvement and implement the necessary changes.
Early Detection of Online Reputation Issues
Structured data analysis can assist in the identification of trends, such as a decrease in positive reviews or website traffic. Nevertheless, this information may not provide an obvious and immediate explanation of the root cause of the problem.
The fundamental cause of the issue can be rapidly identified by monitoring unstructured data sources, which may be a negative social media post or a damaging review that has gone viral. This enables you to promptly resolve the issue and implement the requisite measures to safeguard your online reputation.
Enhanced Content Strategy
Rather than selecting between structured and unstructured data to inform your content strategy, incorporate both. The utilization of structured and unstructured data enables the acquisition of more comprehensive insights into the content that resonates with your target audience.
For instance, website analytics (structured data) can indicate which blog posts generate the most traffic and engagement. This, when combined with social media analysis (unstructured data), enables you to identify the topics that your audience is discussing, thereby facilitating the development of more relevant and engaging content.
A business’s success and expansion are contingent upon its favorable online reputation. Utilize both structured and unstructured data to construct and sustain it. This process provides you with a more profound comprehension of your brand’s online presence and enables you to make data-driven decisions regarding your online reputation management strategy.
At SVBY Local, we provide reputation management solutions that are supported by a team of experts and advanced analytics tools. We develop an ORM strategy for your business that generates results by extracting valuable insights from both structured and unstructured sources.
