Incorporating AI technologies into business operations optimizes performance and pushes organizations toward success and sustainability. For long-term success, companies may face challenges when implementing this technology due to a lack of understanding and organizational experience with AI.

In this article, we’ll explore what AI insights are exactly, how they work and how they’re applied practically to progress different industries.

• What are AI insights?
• Advantages of using AI for generating data insights
• How AI insights help businesses
• Understanding how AI Insights are generated

What are AI insights?

AI insights are the knowledge and understanding gained by analyzing complex datasets using AI. This process involves a combination of machine learning (ML), natural language processing (NLP) and AI data visualization techniques (charts, graphs, dashboards, heat maps, etc) to make the data more accessible.

The visualization helps strategists find hidden patterns, trends and correlations. Organizations use AI tools to filter big data into actionable intelligence to support better decision-making and strategies.

Advantages of using AI for generating data insights

AI analytics offers many advantages, such as seeing hidden trends in large data sets, forecasting future market behaviors, analyzing customer sentiment, making decisions faster and creating personalized experiences.

Easier decision-making

AI insights arm decision-makers with comprehensive, real-time data analysis, reducing reliance on guesswork and intuition. The AI processes and analyzes data from various sources simultaneously at a speed and scale unattainable by human effort alone. As such, the insights can give you an in-depth view of the market, customers and competitors.

Predicting future trends

AI insights provide the power to predict future trends and customer behaviors through pattern recognition in data. By analyzing historical information, AI tools can forecast outcomes, offering a clear view of customer preferences and potential market shifts. This capability enables you to adjust your strategies proactively and remain competitive. Notably, 45% of business leaders recognize predictive analytics as AI’s most valuable marketing tool, allowing for precise forecasting of future customer behavior.

Creating better customer experiences

The secret to captivating your customers is in understanding their desires, expectations and perceptions of your brand, then turning those feelings into experiences people love. For example, you can anticipate customer needs and gauge opinions through social media listening to monitor chatter around your brand or competition. This empowers you to proactively tailor your offerings and marketing communication. A study by Boston Consulting Group found that companies using AI insights for personalization saw sales gains of 6-10%, which is two to three times greater than those not using AI.

How AI insights help businesses

Let’s explore how AI insights help industries strengthen business strategies, meet market needs and boost brand loyalty.

AI insights for automotive

AI insights improve automotive manufacturers’ understanding of consumer sentiment, market trends and product feedback. Through social listening and text mining, companies can tailor their designs, features and marketing strategies to meet consumer demands, enhancing customer satisfaction and loyalty.

For example, a car manufacturer can use review and AI-driven sentiment analysis to gauge global consumer reactions to product recalls. This in-depth investigation can reveal significant regional differences in perception and enable targeted, culturally sensitive crisis management strategies.

AI insights for banking

In the banking sector, AI insights are vital for fraud detection. But they’re also commonly used in customer service and the personalization of banking solutions. By analyzing transactional data and customer feedback, banks can improve their security and offer services that truly help their customers.

As an example, let’s look at a bank that wants to improve its customer service. The bank can use AI-driven sentiment analysis to dive deep into customer feedback, collected through social media listening campaigns. This comprehensive analysis, which can be conducted in multiple languages, helps the bank identify essential improvement areas, such as mobile banking, fees and branch services. The insights can help the bank initiate targeted reforms, such as overhauling the website experience or improving branch operations to boost customer satisfaction and loyalty.

AI insights for call centers

Using AI insights in call center operations can boost efficiency and pinpoint problem areas. For example, a mobile carrier can utilize AI-driven sentiment analysis to tackle customer churn by integrating text analytics with their call center software. This approach converts call voice data into text for real-time sentiment analysis, allowing proactive identification of customers at risk of leaving. By offering timely resolutions and incentives, the carrier can reduce its churn rate, improve agent effectiveness and overall customer satisfaction.

AI insights for finance

AI insights play a role in understanding market dynamics and enhancing strategic planning in the financial industry. For instance, a hedge fund can enhance its trading strategy by employing real-time sentiment analysis and entity extraction to analyze international market sentiment. This involves processing extensive data from varied sources, including news in multiple languages relevant to its global operations. The hedge fund can integrate market sentiment directly into its trading models by developing a sophisticated dashboard to compare market sentiment with share prices, optimizing its decision-making process.

AI insights for government

Governments can use AI insights to improve public services and policies and engage with communities. For example, predictive analytics can help the government anticipate public service bottlenecks, allocate resources efficiently and minimize service downtimes. At the same time, text analytics can monitor public concerns on social media. Helping to monitor, analyze and extract insights from public sentiment. This approach can help officials identify similar complaints or praises, and find areas that require immediate attention.

AI insights for health and pharma

AI provides valuable insights that significantly improve patient care and drug development in the healthcare and pharmaceutical sectors by efficiently structuring complex medical data. An example could be a hospital network leveraging NLP-based text analytics to transform unstructured EMR progress notes into searchable and organized data. This approach helps the hospital extract actionable insights on medication effectiveness and patient outcomes. By applying named entity recognition, the hospital could analyze detailed information about medications, dosages and patient responses, enhancing patient care precision.

AI insights for hospitality

AI insights have the potential to revolutionize the hospitality industry, empowering businesses to cater to guests’ preferences in a more personalized way. One of the key innovations in this field is the semantic analysis of hotel reviews that offers highly tailored recommendations to travelers. While traditional five-star ratings are widely recognized, they often fail to capture the unique needs of individual guests.

By semantically analyzing text-based reviews, a hotel review aggregator could develop a “smart” search feature that allows hotels to be filtered based on specific attributes such as breakfast quality, internet speed or proximity to nightlife, aligning directly with the traveler’s preferences. This approach goes beyond generic ratings to provide customized hotel recommendations, improving the guest selection process and enabling people to have more personalized travel experiences.

AI insights for quantitative trading

AI insights are transforming quantitative trading by leveraging unique data sources, such as employee feedback, to predict company performance. For instance, a hedge fund might analyze employee sentiments on platforms like Indeed and Glassdoor, theorizing that internal sentiments predict market trends. Sophisticated sentiment analysis allows the fund to categorize employee reviews, unveiling trends in company health and potential. This approach uses unconventional data to give traders an edge, offering a novel angle on investment strategies.

AI insights for market research

AI insights revolutionized market research, enabling marketers to extract valuable competitive insights from a large consumer base quickly. Consider a new healthy snack brand, analyzing thousands of consumer surveys and open-ended responses about snack preferences and brand recognition. Through AI-driven data extraction, the brand can quickly categorize responses, pinpoint key themes and identify mentioned brands. This analysis offers the newcomer precise market positioning insights, including identifying indirect competitors like essential vegetables. With this knowledge, the company can strategically focus its marketing and product positioning efforts to maximize success in its core markets.

Understanding how AI insights are generated

As you can see, many industries use AI for sentiment analysis to provide an in-depth understanding of their customer behavior. As an example, this section will walk you through how AI processes numerical and textual data to give you better customer insights.

Step 1: Data collection

The first step involves collecting the data for analysis. This can be social media posts, customer reviews, surveys, customer care logs, NPS scores and emails. The aim is to gather a comprehensive dataset that reflects the sentiments and opinions of the target audience about your brand or product. The data can be uploaded directly through APIs or manually entered as CSV files.

For example, let’s say we wanted to understand people’s sentiments around Sprout Social across social media and review platforms.

Step 2: Data processing

Once collected, the AI tool processes the text or numerical data using AI and ML algorithms tailored to interpret and analyze the specific data type. For text, the tool uses subtasks like NLP and text analysis to understand the language, converting sentences into structured formats that machines can work with. It also understands emojis. This enables the AI tool to perform tasks like sentiment mining, language translation or text generation by finding patterns and relationships within the data. Numerical data is processed using statistical and ML models that can identify trends, classify data into categories or predict future values.

The neural networks (NNs) in these tools help them learn from the data they analyze and adjust their parameters to accommodate new information. This continuous learning mode improves the accuracy over time.

In the Sprout Social example, AI would use NLP and text analysis to decipher complex language nuances, emojis and sentiment within social media posts and reviews – transforming the open-ended feedback into structured data.

Step 3: Data analysis

The tool analyzes the processed data by picking out important parts or patterns it is trained to recognize from pre-processed, labeled datasets. For text, this could be things like the tone of a message or the main topics discussed. With numbers, it might look for trends or unusual patterns. The AI uses special algorithms to sift through this data and learn from it, improving its tasks over time by adjusting the internal rules to reduce mistakes.

For the Sprout Social case, the structured data gets analyzed to detect prevalent themes, such as sentiment tones and customer concerns. This step is important for understanding the broader sentiment landscape identifying strengths and potential areas for service enhancement based on the collective feedback.

Step 4: Visualizing the data

Data visualization is the last step that involves the tool transforming the data into intuitive graphs and charts, making it easier to digest and understand. Visualization helps you identify trends and outliers in the data, offering a granular view that can influence decision-making. For Sprout Social, this could mean a graph that tracks sentiment trends or compares service perceptions across different demographics. These visualizations provide a clear, at-a-glance understanding of how the brand is perceived, enabling Sprout Social to make informed decisions on service improvements or marketing strategies.

The future of AI adoption

Despite AI’s capacity to sift through and make sense of big datasets and produce actionable insights, only 20% of strategists harness AI-related tools, such as ML or NLP, within their strategies. This highlights a wealth of untapped potential, where AI’s capabilities can improve business strategies, giving adopters the opportunity to innovate and create great customer experiences.

To incorporate AI insights into your own marketing strategy, social media data is an excellent starting point. As one of the world’s biggest readily available datasets, using tactics like social media listening will provide you with real-time insights into your customers and market.

Next, learn how marketers are leveraging AI in marketing to generate content, automate operations and create powerful campaigns.

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Social media engagement is an essential piece of the Dreamforce experience and integral to Salesforce’s customer relationships year-round. So, in 2022, Salesforce made the enterprise-wide transition to a new social media management platform. The move to Sprout Social has paid off, according to Mikaely Quaranta, Senior Manager, Social Media Strategy, for the customer relationship management (CRM) software company.

“Sprout is such an intuitive platform,” Quaranta said. “Our social media practitioners were excited to jump onto Sprout and start using the Reports and Listening features right away. We were also confident going into Dreamforce because we knew that Sprout’s automation and workflow features would allow us to move at the speed of social during our biggest event of the year.”

Tracking trends and securing real-time approvals on the go

Marissa Kraines, Vice President and Global Head of Social Media at Salesforce, said social media plays an integral role in helping the company build excitement for Dreamforce.

“We want to bring the magic and conversation of Dreamforce to all our audiences on social media,” she explained. “First, we’re looking for ways to amplify the in-person experience through content and interactions across our social media channels. Second, we’re determining how to translate the nuances of the on-site event to the virtual and on-demand experiences.”

With Sprout, Kraines and her team can easily track mentions from conference attendees and other interested parties, including the media, as well as keep tabs on announcements happening online and offline during the event.

Social listening is especially important during events like Dreamforce. Sprout allows us to understand when our audience is online, when they want us to engage with them and how we can assist them throughout their conference experience. Sprout also helps us stay on top of trends and be more strategic with our planning.

Mikaely Quaranta
Senior Manager, Social Media Strategy

Sprout’s mobile app also quickly emerged as a crucial time-saving tool for the social team, according to Kraines. She said it helps them to get approvals from stakeholders for content and messaging “in real time—easily and concisely,” whether they’re running around at events or on the Salesforce campus.

Forging and fortifying one-to-one relationships with Salesforce’s greatest champions

Sprout’s reporting helps Salesforce stay close to what drives their audience engagement. “It’s so important to measure engagement to ensure we continue creating content our audience loves,” said Max Benesi, Salesforce’s Associate Manager, Social Media and Community. “With Sprout, we can do that quickly—reporting out at any time so we always know where things stand.”

Through social listening, analytics and other features in the Sprout platform, like Sprout’s Smart Inbox, Saleforce’s social media team is learning even more about the Trailblazer community, and how to engage with them effectively via the company’s 150+ social channels.

“We’ve learned that the best way to build relationships with our Trailblazers is through one-on-one engagements on social—and Sprout’s Smart Inbox helps us to accomplish that,” said Benesi.

Salesforce’s Trailblazers and other highly engaged social audience members are open to having Benesi and his team test-drive new content with them. “They’re very honest—they will tell us what they like and don’t like,” Benesi said. “We use their feedback to inform our content creation.”

With other social media management platforms that we’ve used, the reporting was not intuitive. We often had to pull reports natively and work with spreadsheets. When our team started using Sprout, all that manual work went away.

Max Benesi
Associate Manager, Social Media and Community

Accelerating speed to insights—and eliminating thousands of hours of manual work

The business intelligence Salesforce gains from using Sprout helps them evolve their marketing strategies far beyond specific events. “We can’t just make decisions on a hunch. We need accurate data to understand where we’re finding success,” said Kraines. “The insights we get from Sprout allow us to have confidence in our decision-making.”

Quaranta underscored further just how game-changing Sprout’s reporting capabilities have been for the social media practitioners at Salesforce. “We saw immediate value following our implementation,” she said. “We’re reporting faster, and in real time, and sharing information continuously with our stakeholders.”

She added, “We’re also moving 10 times faster per day with community management by using Sprout’s automation and workflows. That gives our team more time to focus on strategy and bring our creative vision to life—the things that we do best.”

“When my team is able to automate simple tasks, it enables them to take that bandwidth and focus it toward innovation,” said Kraines. “We’ve saved over 12,000 hours this year by using Sprout, and our team is having a lot more fun with their work.”

Making plans to leverage a 360-degree view of Salesforce’s social media audience

Kraines underscored that while Sprout is a valuable tool for tracking social media around events like Dreamforce, her team uses Sprout’s capabilities to help them deliver on their year-round mission to increase brand awareness. That means helping customers truly understand what the global CRM software and applications provider does, and how it impacts their businesses—as well as their customers.

“Salesforce is focused on helping businesses become ‘customer companies,’” said Kraines. “We provide them with a 360-degree view of their customers so they can bring those ‘wow’ moments to life. I believe that social is the bread and butter of delivering that 360-degree view of the customer.”

Looking ahead, Kraines said her team is eager to see what impact Sprout Social’s integration with Salesforce will have on how they craft social strategies and create new campaigns. “Sprout has already made it easier for the Salesforce social team to share insights with our leadership about what our customers are talking about, and what their needs and issues are,” she said. “This information helps to shape our company’s marketing, sales and operational strategies. And now, with Sprout’s integration across our platform, we can bring social insights and data everywhere across Salesforce.”

Kraines said her team is confident that Sprout is the right partner to help support Salesforce’s growing social media practice. “Sprout has great products,” she said. “But more importantly, they’re evolving and scaling along with our team. By partnering with us and listening to our input, Sprout allows us to be at the forefront of social media.”

I recommend Sprout to other social leaders because they have been such an amazing partner. When you’re onboarding a critical component of your Martech stack, it’s so important to have people alongside you who want to see you succeed.

Marissa Kraines
Vice President, Global Head of Social Media

To find out how your social media team can be more productive, free up time for innovation and never miss a moment to engage meaningfully with customers, request your free demo of Sprout Social today.

The post Salesforce’s social media team saves 12,000 hours in first year using Sprout Social appeared first on Sprout Social.

In this session, we will cover:

• How brands can best utilize social media for quick, impactful, omni-present care 
• How to create competitive care strategies using AI to scale efficiency and data analysis 
• How personalization is fueling the future of exceptional care experiences

This session will be led by Sprout Social’s President, Ryan Barretto, and Vice President, Solutions Engineering, Colleen Geiselhart. They will also discuss how Sprout Social’s global partnership and integrations with Salesforce provide a unique perspective on how the world’s leading brands are harnessing the power of social data and insights to build successful Customer 360 strategies.

The post Social Media, AI, and the Future of Omni-Channel Care appeared first on Sprout Social.

NLP powers AI tools through topic clustering and sentiment analysis, enabling marketers to extract brand insights from social listening, reviews, surveys and other customer data for strategic decision-making. These insights give marketers an in-depth view of how to delight audiences and enhance brand loyalty, resulting in repeat business and ultimately, market growth.

Read on to get a better understanding of how NLP works behind the scenes to surface actionable brand insights. Plus, see examples of how brands use NLP to optimize their social data to improve audience engagement and customer experience.

What is natural language processing?

NLP is an AI methodology that combines techniques from machine learning, data science and linguistics to process human language. It is used to derive intelligence from unstructured data for purposes such as customer experience analysis, brand intelligence and social sentiment analysis.

NLP uses rule-based approaches and statistical models to perform complex language-related tasks in various industry applications. Predictive text on your smartphone or email, text summaries from ChatGPT and smart assistants like Alexa are all examples of NLP-powered applications.

Deep learning techniques with multi-layered neural networks (NNs) that enable algorithms to automatically learn complex patterns and representations from large amounts of data have enabled significantly advanced NLP capabilities. This has resulted in powerful AI based business applications such as real-time machine translations and voice-enabled mobile applications for accessibility.

What are the types of NLP categories?

Using generative AI tools like ChatGPT has become commonplace today. So have business intelligence tools that enable marketers to personalize marketing efforts based on customer sentiment. All these capabilities are powered by different categories of NLP as mentioned below.

Natural language understanding

Natural language understanding (NLU) enables unstructured data to be restructured in a way that enables a machine to understand and analyze it for meaning. Deep learning enables NLU to categorize information at a granular level from terabytes of data to discover key facts and deduce characteristics of entities such as brands, famous people and locations found within the text. Learn how to write AI prompts to support NLU and get best results from AI generative tools.

Natural language generation

Natural language generation (NLG) is a technique that analyzes thousands of documents to produce descriptions, summaries and explanations. It analyzes and generates both audio and text data. The most common application of NLG is machine-generated text for content creation.

NLP in optical character recognition

NLP algorithms detect and process data in scanned documents that have been converted to text by optical character recognition (OCR). This capability is prominently used in financial services for transaction approvals.

How does NLP work?

According to The State of Social Media Report ™ 2023, 96% of leaders believe AI and ML tools significantly improve decision-making processes. NLP is what powers these tools.

To understand how, here is a breakdown of key steps involved in the process.

• Tokenization: Text is broken into smaller units such as words or phrases called tokens.
• Text cleaning and preprocessing: The text is standardized by removing irrelevant details such as special characters, punctuations and upper cases.
• Part-of-Speech (PoS tagging): NLP algorithms identify grammatical parts of speech such as nouns and verbs for each token to understand the syntactic structure of the text.
• Text parsing: The grammatical structure in sentences are analyzed to understand the relationships between words.
• Text classification: Text is classified into various categories using statistical models. Text classification powers various capabilities such as sentiment analysis and spam filtering.

Which are the top NLP techniques?

There are several NLP techniques that enable AI tools and devices to interact with and process human language in meaningful ways. These may include tasks such as analyzing voice of customer (VoC) data to find targeted insights, filtering social listening data to reduce noise or automatic translations of product reviews that help you gain a better understanding of global audiences.

The following techniques are commonly used to accomplish these tasks and more:

Entity recognition

Named entity recognition (NER) identifies and classifies named entities (words or phrases) in text data. These named entities refer to people, brands, locations, dates, quantities and other predefined categories. NER is essential to all types of data analysis for intelligence gathering.

Semantic search

Semantic search enables a computer to contextually interpret the intention of the user without depending on keywords. These algorithms work together with NER, NNs and knowledge graphs to provide remarkably accurate results. Semantic search powers applications such as search engines, smartphones and social intelligence tools like Sprout Social.

Machine learning (ML)

NLP is used to train machine learning algorithms to predict entity labels based on features like word embeddings, part-of-speech tags and contextual information. Neural networks in ML models depend on this labeled data to learn patterns in unstructured text and apply it to new information to continue learning.

Content suggestions

Natural language processing powers content suggestions by enabling ML models to contextually understand and generate human language. NLP uses NLU to analyze and interpret data while NLG generates personalized and relevant content recommendations to users.

A practical example of this NLP application is Sprout’s Suggestions by AI Assist feature. The capability enables social teams to create impactful responses and captions in seconds with AI-suggested copy and adjust response length and tone to best match the situation.

Sentiment analysis

Sentiment analysis is one of the top NLP techniques used to analyze sentiment expressed in text. AI marketing tools like Sprout use sentiment analysis to power several business applications such as market research, customer feedback analysis and social media monitoring to help brands understand how customers feel about their products, services and brand.

Text summarizations

Text summarization is an advanced NLP technique used to automatically condense information from large documents. NLP algorithms generate summaries by paraphrasing the content so it differs from the original text but contains all essential information. It involves sentence scoring, clustering, and content and sentence position analysis.

Question answering

NLP enables question-answering (QA) models in a computer to understand and respond to questions in natural language using a conversational style. QA systems process data to locate relevant information and provide accurate answers. The most common example of this application is chatbots.

Machine translations

NLP drives automatic machine translations of text or speech data from one language to another. NLP uses many ML tasks such as word embeddings and tokenization to capture the semantic relationships between words and help translation algorithms understand the meaning of words. An example close to home is Sprout’s multilingual sentiment analysis capability that enables customers to get brand insights from social listening in multiple languages.

How brands use NLP in social listening to level up

Social listening provides a wealth of data you can harness to get up close and personal with your target audience. However, qualitative data can be difficult to quantify and discern contextually. NLP overcomes this hurdle by digging into social media conversations and feedback loops to quantify audience opinions and give you data-driven insights that can have a huge impact on your business strategies.

Here are five examples of how brands transformed their brand strategy using NLP-driven insights from social listening data.

Social listening

NLP powers social listening by enabling machine learning algorithms to track and identify key topics defined by marketers based on their goals. Grocery chain Casey’s used this feature in Sprout to capture their audience’s voice and use the insights to create social content that resonated with their diverse community.

As a result, they were able to stay nimble and pivot their content strategy based on real-time trends derived from Sprout. This increased their content performance significantly, which resulted in higher organic reach.

https://www.instagram.com/p/CtwhId1NOa8/

Topic clustering

Topic clustering through NLP aids AI tools in identifying semantically similar words and contextually understanding them so they can be clustered into topics. This capability provides marketers with key insights to influence product strategies and elevate brand satisfaction through AI customer service.

Grammerly used this capability to gain industry and competitive insights from their social listening data. They were able to pull specific customer feedback from the Sprout Smart Inbox to get an in-depth view of their product, brand health and competitors.

These insights were also used to coach conversations across the social support team for stronger customer service. Plus, they were critical for the broader marketing and product teams to improve the product based on what customers wanted.

Content filtering

Sprout Social’s Tagging feature is another prime example of how NLP enables AI marketing. Tags enable brands to manage tons of social posts and comments by filtering content. They are used to group and categorize social posts and audience messages based on workflows, business objectives and marketing strategies.

Purdue University used the feature to filter their Smart Inbox and apply campaign tags to categorize outgoing posts and messages based on social campaigns. This helped them keep a pulse on campus conversations to maintain brand health and ensure they never missed an opportunity to interact with their audience.

Deriving qualitative metrics

NLP capabilities helped the Atlanta Hawks monitor qualitative metrics from social listening and get a comprehensive view of their campaigns.

The basketball team realized numerical social metrics were not enough to gauge audience behavior and brand sentiment. They wanted a more nuanced understanding of their brand presence to build a more compelling social media strategy. For that, they needed to tap into the conversations happening around their brand.

NLP algorithms within Sprout scanned thousands of social comments and posts related to the Atlanta Hawks simultaneously across social platforms to extract the brand insights they were looking for. These insights enabled them to conduct more strategic A/B testing to compare what content worked best across social platforms. This strategy lead them to increase team productivity, boost audience engagement and grow positive brand sentiment.

https://www.instagram.com/p/Cwf0kngphsI/

Monitor social engagement

NLP helps uncover critical insights from social conversations brands have with customers, as well as chatter around their brand, through conversational AI techniques and sentiment analysis. Goally used this capability to monitor social engagement across their social channels to gain a better understanding of their customers’ complex needs.

Using Sprout’s listening tool, they extracted actionable insights from social conversations across different channels. These insights helped them evolve their social strategy to build greater brand awareness, connect more effectively with their target audience and enhance customer care. The insights also helped them connect with the right influencers who helped drive conversions.

https://www.instagram.com/p/CwTNQbCBCXG/

Harness NLP in social listening

In a dynamic digital age where conversations about brands and products unfold in real-time, understanding and engaging with your audience is key to remaining relevant. It’s no longer enough to just have a social presence—you have to actively track and analyze what people are saying about you.

Social listening powered by AI tasks like NLP enables you to analyze thousands of social conversations in seconds to get the business intelligence you need. It gives you tangible, data-driven insights to build a brand strategy that outsmarts competitors, forges a stronger brand identity and builds meaningful audience connections to grow and flourish.

Learn how social media listening can impact your business.

The post A marketer’s guide to natural language processing (NLP) appeared first on Sprout Social.

Edgio provides powerful solutions across web apps, content delivery and video streaming—servicing approximately 4% of global internet traffic and clients in 38 countries worldwide. While the company isn’t new, its brand name is. Previously known as Limelight Networks, Inc., the business rebranded in June 2022 following its acquisitions of Layer0 and of Yahoo’s Edgecast. What followed was a year of significant change for the company as they pivoted from a CDN vendor to an edge company providing applications and solutions that take advantage of their global edge network.

When Lindsay Moran, Senior Manager, Content and Brand Strategy, joined Edgio in the summer of 2022, her challenge was to amplify the company’s new brand on social media with a strategy that could deliver results quickly and cost-effectively. Moran led the charge to implement Sprout Social’s Employee Advocacy platform, a decision that not only drove employee engagement, but helped the company generate $126,000+ in earned media value (EMV) in just three months after launching its advocacy program company-wide.

The extra reach from our employees—not paid advertising—is helping us grow our audiences on social. Our earned media value was over $126,000 in the first three months of our program

Lindsay Moran
Senior Manager, Content and Brand Strategy

Reinvigorating employee advocacy during a time of significant transformation

Before its rebranding, Edgio had tried to launch an employee advocacy program, but it did not gain much traction internally. Moran said she recognized that the time was right to try again after helping Edgio develop a host of new assets, from blogs to web content, to help the business “rebuild” its position as an industry thought leader. Delivering consistent messaging about the company’s products, solutions and brand across social media channels was also a priority.

“Social media is a no-brainer for sharing this quality content,” said Moran. “We have small audiences on our key corporate social media channels, like LinkedIn, Twitter and Facebook. But we knew we could amplify our reach by inspiring employees to use their personal networks to help promote our brand.”

Helping employees feel connected to our brand and aware of our news was also a top factor in the decision to reinvigorate employee advocacy at Edgio. “When merging companies and pivoting to new value-added capabilities for our customers, there’s a lot to share,” she said. “Employee advocacy is helping us stay abreast of all the good news and stay focused on the future by inspiring our employees to play a front-line role in telling our brand story.”

Employee advocacy needs to feel natural and authentic. What’s great about Sprout is that we can equip our employees with the content we want them to share, but they still have the opportunity to put their individual spin on it and make it their own.

Lindsay Moran
Senior Manager, Content and Brand Strategy

Impressive post-launch results that inspired a “happy dance”

To get the new employee advocacy program off to a successful start, Moran said she leaned on the team from Sprout to provide training to a select group of handpicked brand enthusiasts at Edgio who took part in a 30-day pilot program in February 2023. “It was a great experience,” Moran said. “Sprout let us record the pilot training session, and we’ve since turned it into an internal training tool for our new hires.”

She added, “The pilot program helped us to ensure we had plenty of content prepared going into our company-wide rollout of Sprout’s Employee Advocacy platform. And through a post-pilot survey, we gathered more valuable insights on how to make our program successful from the outset.”

Edgio officially introduced the new employee advocacy program to its workforce through a monthly sales awareness call and a companywide “EdgeTalks” presentation (Edgio’s version of a TED Talk). Moran said these forums were a vital way to communicate the “why” for the program clearly, and to offer a solid overview of program basics plus “tips and tricks” for users to make the most of employee advocacy.

Moran also emphasized that the “tremendous support” of Edgio’s Chief Marketing Officer, Nancy Maluso, who played no small part in helping to build momentum around the employee advocacy effort. “She continues to promote and encourage the program among our leadership teams,” Moran said.

With Sprout’s Reports, Moran tracks the impact of Edgio’s employee advocacy program and shares the results with employees to help them stay motivated “cheerleaders” for the brand.

There’s been new business activity percolating at Edgio, too, which can be attributed to the company’s use of Employee Advocacy by Sprout. “During a recent team meeting, our sales leader in EMEA told us she’d received a lead from content shared through Advocacy,” said Moran. “I did a happy dance! It’s so exciting that we’re already starting to see fruit from our labor. It validates what we’re trying to do as a company, and why we’re working so hard to promote this program.”

Sprout makes it easy to jumpstart and grow employee advocacy

Moran said the employee advocacy program at Edgio is blooming, and the sales, marketing and customer success teams are particularly active in sharing content. But several product specialists and other subject matter experts have also become frequent users, and Moran said their outreach is particularly valuable for helping Edgio grow its reputation as an innovator.

“The ease of adopting a tool like Sprout helped our team embrace the new employee advocacy program quickly. After just the first month, the adoption rate was 34%,” Moran said. “Today, 20% of employees are active users, sharing content with their audiences more than three times per month, on average. Over time, I’d love to see our adoption rate hit 50%. That’s my aim.”

To learn more about how Employee Advocacy by Sprout can help amplify your brand’s social presence and engage your team—without spending more on paid promotion—request a demo today.

The post Edgio sees $126,000+ in earned media value with Employee Advocacy in just 3 months appeared first on Sprout Social.

Staying agile seems daunting and finding meaningful insights from non-stop social and online chatter feels akin to finding a needle in a haystack. Add to that, tight budgets and limited manpower.

Fortunately, AI marketing techniques like sentiment analysis and machine learning (ML) enable marketers to overcome shrinking bandwidths and harness social listening for business intelligence. AI tools extract key data points from thousands of social conversations across multiple networks within minutes, giving you actionable insights that impact your market growth and revenue.

But how do these tools identify relevant information from the barrage of conflicting data online? How do they identify brand mentions for competitive analysis? And how do they distinguish between individuals, businesses or currencies in data?

Enter: named entity recognition (NER). This core AI technology works behind the scenes to power AI marketing tools, so you get critical, data-driven metrics from social and online data for strategic business decisions.

In this guide, we break down what NER is and how it benefits businesses. Plus, share a list of five tools with the best NER capability.

What is named entity recognition?

Named entity recognition is a subtask of artificial intelligence. It’s used in natural language processing (NLP) to identify and extract important information or “entities” in text. An entity can be a word or a series of words such as names of famous celebrities or cities as well as numerical data such as currencies, dates and percentages.

NER is used in AI marketing tools to automatically spot and categorize important information in data to conduct tasks like social listening, sentiment mining or brand analysis. NER is also crucial in search engines, enabling them to understand and recognize key elements in queries and then search and provide relevant results.

How does named entity recognition work?

Named entity recognition, or entity chunking, is an AI task that enables text analysis and assists in natural language generation (NLG)—a capability commonly used in chatbots, virtual agents and search engines.

NER is manually coded into a machine-learning model with annotated data to train the model into recognizing important entities from unstructured data. Manual tags are created so all similar NER entities are classified into a pre-determined category such as “people”, “locations” or “currencies”.

Misspellings and abbreviations are also encoded to assist in getting more accurate results. For example, the United States may be annotated as The United States of America, The US and U.S.

On average, an AI tool has upwards of 7 million NER entities. The more robust a tool’s NER, the more precise the results. It allows the tool to scan millions of data points in comments, social posts, reviews, news stories, etc. and immediately identify keywords for data analysis to reveal brand health or customer experience insights.

For example, in the sentence “Sprout Social, Inc. is ranked #2 on the Fortune Best Workplaces in Chicago™ 2023 SM List”, NER identifies and categorizes Sprout Social as a business, Fortune Best Workplaces as an award category, Chicago as a location in the US and 2023 as a calendar year.

In this way, tools powered by NER identify highly relevant entities from tons of scattered data to provide insights on competitors, customer demographics and emerging industry trends. These enable you to create data-driven, customer-centric marketing strategies that can improve your return on investment.

What are the business benefits of NER?

Many businesses are already using AI and ML for business intelligence. According to The 2023 State of Social Media Report, 96% of leaders agree AI and ML technologies are significantly improving business decisions, and 87% expect to increase AI and ML technology investments in the next three years.

Here is a breakdown of how NER is enabling this transformation.

Better customer support

Per the same report, 93% of business leaders plan on increasing investments in AI tools to elevate customer support functions in the coming three years.

NER is pivotal in supercharging customer care functions. It helps an AI tool automatically categorize queries and complaints by identifying keywords (such as brand names or branch locations), so they’re queued and routed to relevant customer care teams for smoother support.

NER also enables marketing automation and assists in tailoring and optimizing customer care responses for maximum impact. For example, Sprout’s Suggested Replies helps support teams respond faster to commonly asked questions on Twitter. NER powers semantic analysis algorithms in the tool to understand messages contextually, identify topics and themes through keywords and then suggest the best-suited responses.

Improved customer experience

Named entity recognition also helps you find critical details in customer experience data to elevate customer delight throughout the purchase journey.

In Sprout, NER identifies and tracks keywords you define, including hashtags and @mentions, in a wide range of social listening sources like Reddit, Glassdoor and YouTube. Capture what customers are talking about and what their preferences are to identify how you can improve your brand.

These brand insights are also beneficial across the organization, informing targeted advertising, product enhancements and more engaging social content.

Precise competitive intelligence

NER algorithms identify and track competitors for competitive benchmarks and key performance indicators (KPIs) from customer and market data. For example, in Sprout, you’re able to track and analyze competing brands and their content simultaneously based on several KPIs like volume, type, frequency or hashtag usage with competitor reports and listening tools.

These insights provide a strategic guide to creating better brand experiences, from maintaining market share to tailoring your messaging for better audience engagement.

Brand sentiment insights from social listening

Forty-four percent of leaders agree one of the most important uses of AI and ML tools is understanding customer feedback in real time through sentiment analysis.

NER algorithms enable sentiment analysis in social listening data by extracting important entities from direct comments, brand mentions and other user-generated content. This enables you to measure what customers love about your brand and where to improve.

NER is also critical in tracking brand reputation. It helps AI tools identify negative brand mentions as and when they occur in social comments and DMs. This enables your team to be proactive and concentrate on taking relevant actions to resolve issues rather than spend time manually monitoring your brand health.

Impactful summaries from text

NER is widely used across industries to identify important entities in keywords, topics, aspects and themes in text sources to provide impactful summaries. These text sources include news articles, podcasts, legal documents, movie scripts, online books, financial statements, stock market data and even medical reports. NER plays an important role in how AI generative tools interpret queries or prompts. Click on the link to learn how to maximize the output from AI tools by using the best ways of writing AI prompts.

Summaries from these sources can serve strategic purposes such as brand reputation management, patient experience (PX) analysis or gauging a company’s financial performance over time.

How named entity recognition assists social listening

Social media listening can be overwhelming, especially if you have to manually search thousands of comments and posts for important brand and product insights on a regular basis.

AI-powered social listening tools, like Sprout, overcome this challenge by using technologies like NER. These algorithms automatically identify keywords in social chatter and discussions across social networks so AI tasks like sentiment analysis and machine learning can derive meaningful business insights from the listening data.

For example, Sprout’s Query Builder uses NER to keep a pulse on the social conversations happening around your brand. NER identifies and categorizes social listening data with keywords you‘ve pre-determined (brand names, product names, topics)—even misspelled names—behind the scenes.

Thus, it helps the Query Builder to sort through millions of data points and return only those messages that match your query. It also powers a spam filter to further refine the data.

Social listening can have many conflicting data points but entity chunking and semantic clustering overcome it by removing redundant data. This enables you to contextually see how often messages with a particular keyword are occurring. This is essential for customer support teams to identify common complaints in products and services.

Champion growth with NER-powered social listening

Marrying superior AI-driven brand intelligence capabilities with a user-friendly experience puts power directly in marketers’ hands. NER and social listening enable you to get insights in real time to stay ahead of competitors and deepen customer loyalty.

Use social listening to tap into the unfiltered thoughts of your audience and derive candid insights into your brand, products and services—and your competitors. Download this social listening cheatsheet to identify your listening goals and use social data to grow your entire business.

The post How named entity recognition (NER) helps marketers discover brand insights appeared first on Sprout Social.

But if you work in a highly regulated, “serious”, or niche industry, it might seem like you don’t belong on the trend-setting platform. That’s an understandable feeling. When lists of brands to watch and the majority of viral posts feature B2C brands, it’s easy to feel like your brand has nothing to say—but that couldn’t be further from the truth. No matter your industry, you can make waves on TikTok.

Check out our panel discussion with brands from unexpected brands and industries that are leveraging creative tactics to make the most out of their TikTok presence.

We’ll discuss:

• Challenges that “serious” or regulated brands face on TikTok and how brands can overcome these obstacles 
• Examples of TikToks from the nonprofit, SaaS, and technology sectors that made waves on the platform and what you can learn from them
• Top tips for how unexpected industries can leverage TikTok to build their brand and community

Your Speakers:

The post TikTok for “Serious” Industries & Brands appeared first on Sprout Social.

Boingo provides next-gen wireless solutions that enable smart connected environments and needed a seamless social media management tool to support its award-winning customer care team. Boingo, which uses Salesforce Service Cloud, was looking to automate the social media service ticketing process to properly tag items of priority and filter out unnecessary noise.

“When I came across the integration between Sprout Social and Salesforce, it was just what we were looking for—a solution we could easily transition to because it was compatible with Salesforce,” said Susan Nordquist, Senior Manager, Business Systems, at Boingo Wireless.

The integration between Sprout Social and Salesforce Service Cloud is one of the most flexible integration packages I’ve seen. They get a gold star for the smart design—and for using as much native functionality as possible.

Susan Nordquist
Senior Manager, Business Systems

An opportunity to “spring clean” keywords

Nordquist, who describes her role at Boingo as part data architect and part systems architect, was keen to find a solution that would complement the company’s current workflow without taxing internal resources. The transition to Sprout Social presented an opportunity for Nordquist to make adjustments that would help ease the administrative burden.

Nordquist said she made “spring cleaning” of keywords a high priority. The solution to cutting down the noise, Nordquist said, was to rethink how the team was using keywords and target handles instead. Through this refresh, its customer care service agents can focus on responding to messages from customers looking for assistance.

And because agents don’t have to spend time weeding through a never-ending stream of messages, they have more time to focus on high-quality customer care initiatives. We have reduced the number of cases that are created from social media channels by 89%.

Sprout Social is a fully mature social media management platform with way more functionality than I ever knew anybody needed.

Susan Nordquist
Senior Manager, Business Systems

Making the transition to Sprout

Boingo’s transition to Sprout Social was “extremely easy,” according to Nordquist. And while Nordquist said she was initially concerned about how challenging it might be to move the customer care agents to a new solution, the training process went smoothly. Nordquist said the Sprout team has been responsive to requests for guidance—like walking her through the process of keyword cleanup—and for customizations to suit Boingo’s specific needs. “Sprout has been quick to incorporate our feedback,” she said. “Our current version of the package is really solid. It’s one of the best integration experiences I’ve ever had.”

I was very happy with the level of detail that Sprout delivered. It’s clear that they care about setting up their customers for success.

Susan Nordquist
Senior Manager, Business Systems

If you’d like to see for yourself how quick and easy it is to switch to Sprout Social, request a personalized demo.

The post Good connection: How Boingo Wireless seamlessly switched to Sprout Social appeared first on Sprout Social.

The customers I speak to daily, especially those in the B2B space, are always concerned about proving their return on investment for social media. There are no impulse buys in the B2B SaaS industry and quantifying the impact of social media within the buyer’s journey is not always straightforward.

At Sprout, we’re dedicated to shaping solutions that amplify sophisticated social strategies, creating a direct link from social insights to return on investment. The Total Economic Impact™ of Sprout Social, a commissioned study conducted by Forrester Consulting on behalf of Sprout, found that a composite organization, based on real interviewed customers, realized a 233% return on investment (ROI) and $1.3M in savings over three years.

I spoke with Loren Siegel Atlassian’s Community and Social Engagement Senior Team Lead to learn how Sprout helps her team execute premier social customer care, boost team productivity and understand their audience to craft better community connections.

Atlassian started using Sprout Listening in 2019. In 2021, the company incorporated the Smart Inbox and Publishing add-ons. They currently have 55 users across various teams including product marketing, corporate communications, product communications and community.

Loren says there are many moving parts in B2B software companies, but our platform helps nurture a more connected experience.

“Sprout removes the curtain of mystery between all teams that touch social media. It really helps bring visibility between the marketing, customer care, communications and brand teams,” she says.

• Using Sprout reporting to define and measure success
• Supercharging customer care
• Saving time with collaborative tools
• Championing community management
• Unearthing bolder paths to ROI

Using Sprout reporting to define and measure success

Before using Sprout, measuring performance was a convoluted task for Siegel’s team.

“I really struggled with pinpointing key performance indicators (KPIs) and health metrics for the team. Before Sprout, I didn’t have access to easy-to-use reporting or the ability to share reports with internal stakeholders,” she says.

Siegel says proving ROI is a primary focus, and she uses our platform to help her set goals and craft social strategy. Sprout makes reporting and understanding performance more efficient and digestible.

“Sprout helps me set KPIs and discover goals that are attainable and data-supported,” she says.

She uses Sprout to guide and define her team’s three main KPIs: average first reply time, response rate and increasing “Brand Love,” which refers to the engagement rate on positive brand interactions. Brand Love is also the social team’s primary OKR.

Siegel selected these KPIs based on data available in Sprout through reporting.

She uses Atlassian team collaboration software like Jira and Confluence in conjunction with Sprout’s reporting features to share her team’s successes. On a weekly basis, Loren provides updates on her Jira tickets to keep leadership and stakeholders informed on how the team is performing toward its goals. Along with weekly updates, Siegel uses Confluence to collaborate with her team for their monthly business review. Finally, each quarter Loren publishes a quarterly health report in Confluence using data from Sprout to share across the organization.

“The quarterly health report goes into more detail about the tactics that we used throughout the quarter to reach our goals. In Sprout, I find examples of successful moments that helped us achieve our goals. It’s been a nice way to show what my team has accomplished and what we’re thinking about for the future,” she says.

Supercharging customer care

Siegel says her team was originally—and still is at its core—a social customer care team. She explains one of Atlassian’s values is “Don’t f*** the customer,” and that’s always at the heart of everything they do, especially when it comes to customer care.

To keep response times low, Siegel has a framework for her team that allows them to work by network and product line across various channels. But she notes that Sprout’s user-friendly reporting provided the insights her team needed to become faster.

“In the first eight to nine months of using Sprout, before I was tracking the average first reply KPI, we were averaging about seven and a half hours for a first reply,” she says.

“When I recently checked, the team had an average reply time of two and a half hours. We shaved so much time off our response time. This was amidst other major projects, including a conference, so there was a lot of activity on social media,” she says.

Insights from Sprout also help Loren dig deeper into Atlassian’s social customer care performance.

“I discovered in Sprout that of all the incoming messages, it takes my team the longest to provide a response to technical questions,” she says, “We identified this by exporting data from the Smart Inbox, using tags and average first reply time to filter down and better understand our opportunities to improve,” she says.

The team’s first reply goal is four hours, but the data showed it was taking between 16 and 19 hours to provide a first response for technical questions.

“We were even able to drill down into how long it takes us to respond to technical Jira questions versus technical Confluence questions,” she says.

This data allowed us to look at alternative solutions to shorten that response time, from ensuring we have more detailed technical FAQs to having closer links to support engineers.

“[I want to] create a way for my team to get users seeking help on social into our support portal as effortlessly as possible,” she says, “If it takes us 19 hours to respond to a technical question on Twitter, that’s effing the customer. The data helps me make that business case to bring our trained support engineers into the process,” says Loren.

“My team acts as more of a concierge or front door rather than technical support. It would be impossible to train my team on all of the technical intricacies of our products, so I would rather they provide a really great customer experience,” she adds.

Saving time with collaborative tools

Sprout’s user-friendliness also saves time onboarding new teammates and streamlines collaboration, especially for Atlassian’s marketing managers. Here are just a few of the tools Loren and her team rely on to save time and increase productivity.

Maintaining transparency in the Smart Inbox

“My team lives out of the Smart Inbox,” she says.

Instead of using another platform to communicate internally or with audiences on social, everything is available within one centralized channel.

Her team also uses various custom Inbox Views to organize incoming messages.

“In some cases, we’re looking at the people who are @-mentioning all of our handles. We have other views set up with brand keywords, automated tags or VIP lists. [Seperation] allows us to segment the people who are talking directly at us versus those who are talking about us,” she says.

“We have a host of marketing managers for each [Atlassian] product and they each have their own group within Sprout. Everyone has their own swim lane, but at the same time there’s a lot of visibility,” she says.

Supporting a global team with Conversations

Sprout Conversations also streamline asynchronous communication for Siegel’s global team. They can quickly see who published a post in case there is feedback later on, such as a broken link or typo, and coordinate updates.

She explained that if a teammate in another country looks into an audience message, they may not get a response in real time, but they can leave a comment in the team Conversation and @-mention the next team member who’s coming online.

“[They] might link the Slack message in the Conversation so there’s a historical record of what everyone has done, everyone who has touched the message, and the status of where they’re getting help or where they’re trying to find the answer,” she says.

She also expressed that if one person on the team is out of office, someone else on the team can swiftly jump in to support them. Her example is just one testament of the collaborative workspace tools that customers tell me they enjoy when using Sprout.

Taking ownership with Tagging

Siegel also notes how using Tags in the Smart Inbox allows her to influence strategy and internal change management.

“We’re able to hand over tasks that are better suited for another team or department, and I have the data to show why,” she says.

For example, over the past few months, her team noticed a lot of Brand Love for Trello that the team wasn’t able to easily see and might miss. She had her team go into the Smart Inbox and tag all of the opportunities for easy visibility. In one month, there were over 200 messages of Brand Love which would have been hard to track, respond to and could easily have been missed.

“Using Sprout enabled me to show strong examples of opportunities that empowered and up-leveled the team to help boost the brand,” she says.

“I was able to make that case because of Sprout. I was able to pull strong examples of opportunities that could have helped boost the brand,” she says.

Championing community management

Siegel’s team also uses Sprout to reinforce their community-first mindset and provide a memorable customer experience. Siegel explained that her team is in the early stages of using Social Listening to power community management, but they have plans to incorporate it more in the future.

“We jump in on conversations that are happening about teamwork, work management or agile development—not necessarily our products,” she says, “We have three listening reports looking at the markets that we’re in. We will eventually use these reports to identify [specific] people talking about the market or the industries we want to break into.”

For instance, they have a query set up to listen for specific game developers on Twitter.

“We want to listen to what they’re talking about and provide solutions when they’re talking about Jira, Confluence and [other Atlassian] products to build their relationship with the brand. It’s been a fun new path for my team to focus on building community with a particular segment of professionals,” she says.

Siegel explains the Atlassian community started organically, but it’s up to her team to act as a lever of support to help it flourish.

“It’s really important to nurture [our community] and make sure they know that you’re listening and they can connect with you. That’s the biggest aspect for me—I want to make sure my team is creating connections and identifies the next step in the journey for them,” she says.

For instance, when customers complete an Atlassian University training course and post about it on LinkedIn, her team congratulates them and links them to next steps in the skill building journey, whether it’s another course, joining a community chapter or attending a virtual community event. They use UTM links to track the customer’s path beyond the LinkedIn post.

“We’re here to celebrate and promote their accomplishments and give them the tools they need to be successful with our products internally,” she says.

Unearthing bolder paths to ROI

My conversation with Siegel is similar to many discussions I have with our customers about proving the ROI of social.

With a solution that increases team productivity, makes customer service teams more efficient and removes the need for manual data aggregation and reporting, Atlassian unearths clearer connections between social and the bottom line.

To learn more about how Sprout’s platform helps customers drive business outcomes with social, read the Total Economic Impact™ of Sprout Social.

The post How Atlassian uses Sprout to enhance social media ROI appeared first on Sprout Social.

Medallia Experience Cloud uses artificial intelligence and machine learning technology to track and analyze customer “signals.” Those signals can include direct and indirect feedback from surveys, reviews, contact center interactions, comments on social media, and digital behaviors on websites and apps—across the customer journey. From the insights gained, organizations can understand their audiences better and develop more engaging, personalized experiences faster.

Medallia’s technology is powerful. But until recently, the company’s efforts to use content to build brand awareness and inform and inspire audiences around the globe about the platform weren’t reaching their full potential, including on social media. One factor: The company struggled to speak to all its target audiences and verticals from one brand account, unable to offer tailored content they’d find relevant and compelling.

When Justin Herrick, Senior Manager, Content Marketing, was tapped to lead Medallia’s content strategy in early 2022, he was more than ready for the challenge. And he knew Sprout Social would be a key tool for helping them increase the impact of their content, uplevel their social media management, and amplify employee advocacy. “It was like getting the keys to the car,” Herrick said.

Sprout quickly clarifies what content resonates with Medallia’s customers—and doesn’t

After analyzing the historical performance of Medallia’s content and creating benchmarks to measure the impact of future efforts, Herrick and the Communications & Content team got to work using Sprout to execute their new strategy. They leveraged features like Tagging to track content performance and Premium Analytics for accelerating data collection and focusing on relevant metrics to prove the return on investment (ROI).

We’re heavy users of Premium Analytics. Getting to build custom reports and distributing them uniquely to specific stakeholders is important because not everyone needs to know everything — instead, we want to give stakeholders what they’re likely to find useful.

Justin Herrick
Senior Manager, Content Marketing

These features, along with Sprout’s enterprise-ready social listening solution, are helping Herrick and the team learn how to use content more effectively to engage with Medallia’s customers in different industries and across the five key “horizontals” where the company operates—customer experience, employee experience, contact center, digital experience and market research.

The platform has also helped them gain a clear understanding of what and how often Medallia should be posting content on social, particularly on LinkedIn, where the company has more than 100,000 followers.
“We were posting way too often,” said Herrick. “Now we only post twice a day, at most.

They also learned that call-to-action (CTA) links on LinkedIn don’t deliver high-impact results when used regularly. Essentially, they’re a wasted effort without a diverse mix of other content types being shared. What does grab the eyeballs of Medallia’s customers on social media, according to Herrick, is content featuring video and imagery, which they learned by using Sprout.

Since Medallia has focused on using video and imagery more often, and more strategically, it’s seen some impressive results. That includes a year-over-year increase in video views of 174%, a nearly 50% boost in engagements, and a 39.5% uptick in engagement rate on LinkedIn. And video views on Twitter have skyrocketed—up nearly 1,884%, year over year.

“With the insights we get from Sprout, we can say with confidence to our stakeholders, ‘If we use links, we can expect only this average number of impressions with a clear ceiling. But if we use video and imagery, we can almost guarantee X amount of impressions.’

Sprout empowers Medallia employees to post meaningful content for their connections

Employee advocacy is also helping Medallia to improve content performance and drive customer engagement in its markets worldwide. In 2022, it completely revamped its employee advocacy program, with guidance from Sprout’s professional services team. And after relaunching the program in August, Medallia increased its employee advocacy user base by nearly 45% in just two weeks.

As part of the relaunch, Medallia also re-introduced the weekly email digest that it uses to alert employees to relevant, localized content they can easily share. It didn’t have to wait long to see results. “Employee shares of content essentially doubled starting on day one,” said Herrick.

Herrick attributes this growth to the Sprout platform increasing his team’s ability to deliver more relevant content to employees in both US and international markets, and to automate the distribution of content. And he said he’s feeling confident about future growth: “This year, we’re even more focused on mobilizing our people to use their connections to get the right content in front of the right audiences. Nearly two dozen posts are shared from Advocacy by our employees each weekday on average, and we’re trying to increase that by getting more of our employees active through gamifying Advocacy with the leaderboard.”

Using Sprout to improve our employee advocacy efforts has been a game-changer because we’re now satisfying the content needs of so many groups at Medallia. Sprout also provides our regional teams with a distribution mechanism to mobilize our salespeople all around the world.

Justin Herrick
Senior Manager, Content Marketing

Working faster with Sprout Social

Herrick said he doesn’t know what his lean team would do without the timesaving features in Sprout, like Sprout’s Smart Inbox to manage messaging and identify new engagement opportunities. They’re also using Sprout to schedule and publish reports automatically in a highly readable PDF format.

We don’t have a dedicated social media manager at Medallia—and everyone on our content team needs to be a five-tool player. We need the Sprout platform to help us do more with less, from analyzing data to automating tasks like scheduling and publishing our social media content.

Justin Herrick
Senior Manager, Content Marketing

Learn how Sprout Social can help your organization understand what content will resonate best with your users—and how you can maximize employee advocacy. Request your free demo today.

The post Medallia reignites employee advocacy and sees exponential growth with Sprout appeared first on Sprout Social.