The Voxco Answers Anything Blog

It takes an average of 10 seconds for consumers to form a perception of a brand.  

This is how quickly people judge brands. Every second counts when it comes to making the desired brand. But what is customer perception?  

Although the term “customer perception” is a fairly self-explanatory branding term on the face of it, which essentially means the perception of your brand in the market, only a few understand the art of shaping that perception to lead a brand to success.

In this article, we’ll discuss what customer perception is, its importance, the different ways to monitor and measure brand perception, and what you can do to improve or shift your brand’s perception in the mind of your audience.

What is brand perception?

The brand image of Nike is different from other apparel brands. It is considered a premium, cult brand that deals only in sportswear. Nike consumers perceive the brand as one that deals only in sportswear.

This is an example of what a customer perception is.

However, a lot of times, brand owners feel they understand exactly what their brand represents but utterly fail at figuring out the actual perception of the audience.

Brand perception is owned by consumers, not brands. Regardless of what image you aim to represent for your brand, your brand perception will be defined by what people think your product or service represents.

What is a brand perception survey?

A brand perception survey is a strategic tool utilized by businesses to gauge how the target audience perceives their brand. It involves collecting valuable insights and opinions from customers, stakeholders, and the general public to understand the image, values, and reputation associated with the brand.

In essence, a brand perception survey delves into the minds of consumers, allowing companies to comprehend how their products, services, and messaging are resonating with the intended market. This survey typically encompasses a series of well-crafted questions that touch upon various aspects of the brand, aiming to capture both quantitative and qualitative data.

Why does brand perception matter?

Do you know people are far more likely to share negative brand experiences with their friends and family than positive brand experiences? Over 50% more likely, according to a report published by MarketingCharts.

What is customer perception if it doesn’t impact your customers’ decision to choose your product or service, do business with you, or recommend your brand to others, or dissuade them entirely?

Brand perception can impact your bottom line to a great extent.

In today’s hyper-competitive marketplace, brand perceptions or customer opinions matter a lot just like your customers do.

Tracking and understanding customer perceptions gives you an opportunity to shift and elevate these perceptions. It’s an essential part of brand reputation management.

Believing your brand perception to be more positive than it actually is won’t help yield results. An honest look at the factors driving or killing your brand loyalty will. This is why brand perception is so important in building your business and leading it to success.

What is the Importance of a brand perception survey?

Brands often assume they know how their customers feel about them. Based on customer interactions alone, brands tend to magnify brand perception. But this is not what brand perception is.

Understanding the significance of a brand perception survey is paramount in today’s competitive business landscape.

1. Insight into customer sentiment:

A brand perception survey provides a direct avenue to grasp how customers truly feel about your brand. It helps uncover sentiments, preferences, and pain points, enabling you to tailor your strategies accordingly.

2. Strategic decision making:

Informed decisions are the cornerstone of success. By gathering data through a perception survey, you equip yourself with the necessary information to make strategic moves that align with the market’s perceptions and expectations.

3. Adaptation and improvement:

Markets evolve, and so should brands. By regularly conducting perception surveys, you stay attuned to changing trends, allowing you to adapt your brand image and offerings proactively.

4. Competitive edge:

In a world inundated with options, standing out is imperative. A brand perception survey lets you identify what sets you apart from competitors, aiding in refining your unique value proposition.

5. Reputation management:

Your brand’s reputation is fragile and immensely valuable. A perception survey helps you identify potential reputation risks early on, enabling effective damage control and fostering trust.

A brand perception survey is a powerful tool that helps you align your brand with the perception, belief, and emotion of your target audience by fueling data-driven decisions.

Brand perception survey questions to ask your customers

Surveys can get you very detailed and honest insights into what your brand’s customer perception is, provided you frame the right kind of questions.

It all comes down to the kind of questions that can best get you to the insights you need to shape and enhance your brand perception.

Here are four factors that your questions should focus on to understand how customers perceive your brand.

1. Cognitive: The concept that customers associate

• When you think of our brand, what comes to your mind first?
• What words would you use to describe our brand? Choose from the list.
• How do you feel about each word you have chosen on a scale of 0 to 7?

2. Emotional: The feelings they associate

• What emotions/feelings do you experience when you think about our brand?
• (Positive/negative scale) How would you define the emotional attachment you feel towards our brand?
• Describe a memorable experience you’ve had with our brand.

3. Language: How customers understand & describe your brand

• What words would you use to describe our brand to a friend?
• Do you find our brand’s messaging easy to grasp?
• Which words would you use to describe the personality of our brand?

4. Action: The experience customers have with your brand

• How would you describe your recent experience with our brand?
• (Scale of 0 to 10) How likely are you to buy the newest product from our brand?
• Rate your overall satisfaction with your last buying experience.

If you’re running short on ideas, consider using these types of questions in your brand perception survey:

Net Promoter Score (NPS ) questions:

1. How likely are they to recommend your brand to a friend, colleague, or family member?
2. Why or why not? (Take a quick look at this recent blog post that includes detailed NPS questions in your customer satisfaction surveys)
3. What’s the one thing we could do to make your experience better?

Ability to meet expectations:

1. Did your most recent experience or interaction with our brand meet or exceed your expectations?
2. What did you like least about the product or service?

Brand Image:

1. Has your most recent experience enhanced your view of our brand? In what way and why?
2. Do you rate our brand highly in specific attributes and poorly in others, and how?

Be sure to include follow-up questions to dig further into your customer feedback and power your research

‍How to measure brand perception?

A study on brand recognition statistics by JAMA Network discovered that 91.3% of 6-year-olds could match Old Joe’s image with a picture of a cigarette in 1991.

This demonstrates just how early brand name recognition and perception starts.

Although brand perception can be very difficult to quantify without the right tools in hand, it is, after all, what affects your brand visibility and marketing success.

Here are some of the most efficient ways of monitoring and measuring your brand perception:

1. Send out customer surveys

Surveys prove to be one of the most efficient ways to gather targeted and detailed insights from your consumers to better understand how they perceive your brand and the key drivers of these perceptions. These valuable first-party insights will help you dig deeper into what your brand’s consumers' perceptions are and boost them by making strategic CX decisions.

2. Monitor social media

Today, social media is the most popular place where people share their opinions and experiences.

Keep a close watch on the different social media platforms out there, including Facebook, Instagram, Twitter, Linkedin, Pinterest, and Reddit, among others. Your aim should be to catch the latest trends and see how people’s opinions shift around certain keywords or topics.

Consider performing a sentiment analysis for this. Track the mentions of your brand.

3. Keep an eye on review sites

Customer and employee reviews will help you stay on top of what your customer perception is and how your brand is being mentioned and discussed online rather than on social media. Keep an eye on public online forums, customer review sites (like Amazon customer reviews and Google Places), blogs, and news sites for mentions of your brand.

4. Maintain an active email list

Reach out to your target audience regularly via emails to know what they think about your brand. This is probably the most straightforward way and often not very effective to get the lowdown from the horse’s mouth.

Who should you send the brand perception survey to?

Many companies think they should only try to understand the brand perception of their existing customers. While it is important and proper to gather valuable insights, you should also focus on customers who decided they don’t enjoy using your brand and stopped any interaction.

The churned customers can provide unfiltered, genuine, and valuable insights because something about their experience has pushed them to stop doing business with your brand. Most importantly, if these people are in the range of your target audience, this can give you clear insights into what you must improve and resolve.

How to improve brand perception?

Once you’ve developed what you believe is a great product or service and unleashed it to your audience, you will start receiving reviews on your social media channels. Reviews that are both positive and negative.

But there’s no point in feeling baffled about them.

The real verdict lies with your customers and their perception of your brand. That will ultimately decide whether your business will take off or break down.

Here are some tips to put you on the right track to improve your customer perception and increase brand loyalty:

1. Identify your audience and understand their perception of your brand

This goes beyond having a dry notion of surface-level customer details such as age, gender, demographics, and language. To get the answer of what your customer perception is, you need to analyze your customer behaviors, such as how they consume information and what drives them to choose you over your competitors.

You do not need a huge marketing budget to understand what your customers’ perception is. Omnichannel survey software proves to be the quickest and most efficient tool for capturing their perception.

2. Develop and maintain a positive connection with your customers

Tons of studies have shown how customers who have had the best and most personalized experiences with a brand spend over 100% more money compared to those who have had the poorest interactions. Apart from improved profitability, positive customer-business relationships can directly influence how your consumers perceive your brand.

3. Be open to feedback and strive to improve

It just can’t be stressed enough how important it is not to ignore the negative criticism from your clients. Be open to receiving both positive and negative customer feedback. When it comes to collecting customer feedback, some of the best options include surveys and polls.

Like any other perception, brand perception, too, doesn’t change overnight. Be prepared to research and take the necessary steps to improve how your consumers perceive you.

Conclusion

Do you think you know what your brand’s customer perception is or what it represents?

Before you nod, remember that brand perception is not what you say in your advertisements.

It is what consumers think and say you are.

Regardless of the message you transmit, it’s important that your audience receives it the way you intended them to. Otherwise, it’s null and void.

Navigating the realm of brand perception is a dynamic and multi-faceted journey that demands careful consideration. Once you have insights into your customer needs and motivations, you can devise strategies to shift and improve your brand perception among them.

Harnessing the power of artificial intelligence (AI) in market research has become critical in the digital age. The combination of artificial intelligence and market research has transformed the way firms obtain insights, make educated decisions, and remain ahead of the competition. This article goes into the world of artificial intelligence-powered market research tools, their significance, and the top must-have tools that any wise marketer should consider including in their plan.

Definition of AI-Powered Market Research Tools

The term "AI-Powered Market Research Tools" refers to a group of software programs and platforms that use artificial intelligence (AI) and machine learning techniques to improve various areas of the market research process. These applications automate data gathering, processing, and interpretation, allowing organizations to obtain a better understanding of consumer behavior, market trends, and competitive landscapes.

These technologies can process enormous volumes of data, discover patterns, anticipate future trends, and deliver actionable insights by leveraging AI capabilities, thereby supporting businesses in making better educated and strategic decisions based on data-driven evidence. Partnering with an AI software development company can further enhance market research capabilities, creating custom tools to analyze complex data and produce meaningful, strategic insights.

Importance of AI-Powered Market Research Tools

Traditional market research methodologies frequently fall short in dealing with the volume and complexity of data accessible in the digital age. AI solutions, developed by the Best AI developers, provide automation and precision to the process, allowing firms to make data-driven decisions. These tools not only reduce time but also improve accuracy, resulting in better strategic decisions.

1. Unprecedented Efficiency and Speed:
   Traditional market research approaches sometimes entailed time-consuming and resource-intensive manual processes. This landscape is being transformed by AI-powered systems that automate data gathering, processing, and interpretation. What used to take weeks or even months with traditional procedures can now be completed in a fraction of the time. This rapid rate of analysis provides businesses with real-time insights, enabling them to make quick and educated decisions.
2. Accurate Data Analysis:
   The accuracy of market research insights can be game changers for businesses. AI tools are skilled at analyzing large datasets, finding subtle patterns, and deriving meaningful relationships. The potential for errors is considerably decreased by minimizing human participation, resulting in more dependable and trustworthy study outputs.
3. Enhanced Customer Understanding:
   Understanding client behavior, preferences, and attitudes is critical for effectively customizing products and services to market demands. To evaluate sentiment and acquire subtle insights, AI-powered solutions excel at analyzing customer comments, social media interactions, and online reviews. These tools can also leverage AI Translation for multilingual feedback, ensuring businesses capture sentiments accurately across different languages. This in-depth awareness of client feelings enables organizations to develop items that are appealing to their target audience. With an AI video translation tool, businesses can easily translate video content into multiple languages, making it possible to interpret and respond to feedback from customers around the world. This opens up new opportunities to connect with diverse audiences and gather valuable insights. Businesses can also enhance accessibility and engagement by integrating text-to-speech solutions. These tools allow the swift creation of realistic voiceovers, all while enabling content localization across global markets. This not only broadens audience reach but also enriches the user experience.
   
4. Predictive Insights:
   Predictive analytics, a critical component of AI-powered solutions, enables organizations to forecast future market trends and behaviors. These technologies can discover emerging patterns and forecast prospective adjustments in consumer preferences by analyzing past data. This proactive approach allows businesses to stay ahead of the curve and alter strategies as needed.
5. Personalized Marketing Strategies:
   AI-powered market research solutions make audience segmentation based on multiple qualities and behaviors easier. Based on data-driven insights, this segmentation helps firms to create personalized marketing strategies that resonate with certain customer categories. As a result, there is increased engagement, conversion rates, and customer happiness.
6. Cost-Efficiency:
   While the initial cost of AI-powered products may appear high, the long-term advantages much outweigh the expenditures. The increased efficiency in resource utilization, time savings, and correct decision-making can result in significant cost savings over time. Making informed decisions is critical for success in today's fast-paced business market. Market research is critical in this process, and with the development of AI technology, it is more powerful and insightful than ever. This blog will go into the realm of AI-powered market research tools, examining their significance, how to utilize them, and the top five tools that can alter your market research efforts.

Read how Voxco helped HRI conduct complex market research and accelerate insight generation.

Comparison of Traditional vs. Modern Tools

Traditional market research approaches frequently entail manual data gathering and processing, which is time-consuming and susceptible to human bias. AI-powered technologies, on the other hand, automate procedures, analyze large datasets fast, and give actionable insights with greater accuracy.

Feature

Traditional Market Research Tools

Modern AI-Powerd Market Research Tools

Data Collection and Analysis

• Surveys, focus groups, and interviews are examples of manual data collection approaches.
• The time-consuming procedure is prone to human mistakes.
• Limited capacity to adequately handle huge datasets.
• Analysis frequently necessitates manual coding and categorizing of replies.

• Data collection from numerous sources, such as social media, online reviews, and site scraping, is automated.
• AI algorithms enable the rapid processing of large datasets.
• Real-time data analysis enables the rapid detection of trends and patterns.
• Capabilities for advanced natural language processing for sentiment analysis and subject categorization.

Speed and Efficiency

• Long timelines for data collection, compilation, and analysis.
• Delays in decision-making owing to time-consuming processes.
• Limited ability to keep up with the ever-changing market scenario.

• Rapid data collection and processing allow for real-time insights.
• Rapid detection of developing trends and changes in consumer behavior.
• Decision-making procedures that are faster, offer organizations a competitive advantage.

Accuracy and Reliability

• The presence of humans increases the possibility of errors and biases.
• There is limited scope for detecting minor patterns or connections.
• Data and response interpretation are subjective.

• Errors and biases are reduced when there is less human participation.
• Intricate patterns and relationships in data are detected using advanced algorithms.
• The objective analysis is based on data-driven insights, which increases the dependability of the conclusions.

Predictive Analytics

• Predicting future market trends or consumer behavior is difficult.
• Speculative conclusions are frequently drawn from historical data analysis.

• Machine learning algorithms are used to forecast future trends and behaviors.
• Improved forecasting accuracy based on historical and real-time data.
• Allows for proactive decision-making and strategy changes.

Personalization and Segmentation

• A broad approach to market segmentation.
• The ability to cater to particular customer preferences is limited.
• Targeting is based on broad demographic data.

• Artificial intelligence-powered solutions allow for exact segmentation based on behavioral, demographic, and psychographic characteristics.
• Marketing strategies that are geared to certain customer categories.
• Because of customized marketing, engagement, and conversion rates have increased.

Cost Considerations

• Costs for manual data gathering, labor, and analysis.
• Resource-intensive processes can result in greater costs.

• The initial investment in AI technologies may appear to be larger, but it yields long-term cost savings.
• Because of automated processes and speedy analysis, resource allocation has been reduced.

In essence, the change from old to modern market research instruments reflects a journey from manual and time-consuming practices to data-driven, automated processes. While traditional approaches have their advantages, the capabilities of AI-powered solutions in terms of speed, accuracy, predictive insights, and personalized strategies make them important assets for organizations attempting to navigate today's market scenario.

Top 5 must-have AI-Powered Market Research Tools

Here are five must-have AI tools for effective market research:

1. Voxco: Omnichannel Market Research Tools

• Provides adaptable multi-channel survey software for a variety of data-gathering methods such as phone surveys, personal interviews, web surveys, and IVR systems.
• Provides both on-premise and hosted alternatives, providing deployment flexibility based on organizational needs.
• Centralized Database: All data collected through various channels is housed in a single centralized database, allowing for more efficient data management and analysis.
• Allows for a more comprehensive perspective of customer information obtained from various acquisition channels, resulting in more informed decision-making.

2. Lexalytics: Text and Sentiment Analysis Tool

• Analyses textual data from a variety of sources, including social media, customer evaluations, and surveys.
• Extracted feelings, opinions, and trends using powerful natural language processing (NLP) methods.
• Assists firms in understanding customer perceptions and sentiment shifts, which guides marketing tactics and product enhancements.
• Allows for real-time tracking of brand reputation and customer input.

3. RapidMiner: Predictive Analytics Platform

• Machine learning algorithms are used to forecast future market trends and behaviors.
• Identifies patterns, correlations, and prospective alterations in customer preferences by analyzing historical data.
• Provides organizations with actionable insights for making informed decisions.
• Ideal for demand forecasting, pricing strategy optimization, and finding upcoming market opportunities.

4. Clarifai: Image and Video Analysis Tool

• Analyses visual content such as photos and videos.
• Deep learning algorithms are used to discern objects, feelings, and demographics in visual data.
• Insights into how a brand or product is perceived visually by the public.
• Use a robust video analysis tool to assess how your brand is perceived visually and optimize visual marketing strategies accordingly.
• Allows organizations to evaluate the success of visual marketing initiatives and client responses.

5. Qualtrics: AI-Powered Survey and Feedback Analysis

• By adding AI-powered insights, it goes beyond standard surveys.
• Open-ended responses are categorized and analyzed in order to discover patterns and feelings.
• Respondents are segmented depending on their interests and behaviors, providing deeper insights into client segments.
• Allows companies to make data-driven decisions based on extensive survey data.
  

These artificial intelligence-powered market research tools enable firms to turn raw data into meaningful insights, allowing for informed decision-making and strategic planning. Whether it's advanced survey capabilities, sentiment analysis, predictive modeling, or image identification, each tool offers a distinct set of features to meet a variety of market research requirements.

Conclusion

AI-powered market research tools are the guiding compass that navigates through the sea of information, transforming it into usable insights, in a data-driven business landscape. You'll be well-equipped to comprehend the market's intricate patterns, make informed judgments, and stay ahead in the competitive race if you understand the power of these tools and choose the correct ones for your purposes.

Keep in mind that the world of AI tools for market research is continuously changing, As the field of AI tools for market research continues to evolve, staying up-to-date with the latest advancements and case studies will be crucial for enhancing strategic capabilities. By embracing AI-powered market research tools, businesses can navigate the data-driven terrain, transforming raw information into actionable insights that drive success and innovation.

FAQs/People Also Ask:

Is there an AI for market research?

Yes, there are AI-powered solutions created expressly for market research. Artificial intelligence and machine learning techniques are used in these technologies to automate and improve many areas of the market research process, such as data collecting, processing, and interpretation.

What are the AI tools for market study?

AI tools for market research include text analysis, predictive analytics, picture analysis, and survey feedback analysis. There are several AI tools available for conducting market studies. Some popular options include

1. Voxco
2. Google Cloud AutoML
3. Lexalytics
4. Crimson Hexagon
5. Tableau

Which AI is best for research?

The "best" AI tool is decided by your specific research needs. Voxco, Lexalytics, RapidMiner, and Qualtrics are examples of tools that can be used to suit a range of research objectives.

Can AI tools analyze social media data for market research?

Yes, artificial intelligence (AI) tools such as Voxco, Crimson Hexagon, and Lexalytics are developed to analyze social media conversations and sentiments. They assist organizations in gaining a better understanding of consumer opinions, preferences, and brand impressions on social media platforms.

Are there any ethical considerations with using AI in market research?

Yes, ethical considerations for the use of artificial intelligence in research operations include data privacy, unbiased analysis, and open information. It is vital to observe ethical laws and legislation while using AI approaches in market research.

All businesses today are flooded with unstructured, open end consumer comments. From internet reviews to social media posts and responses to survey questions, a mountain of unstructured text begs for analysis. And the information in these open ends is extremely valuable because these comments are your customers and prospects telling you what is most important to them.

Unfortunately, open-end analysis is difficult, time-consuming and labor intense. Unstructured comments can be chaotic and filled with misspellings, symbols (e.g., "$!#??!"), and just plain nonsense. Cleaning, coding, sorting, and analyzing unstructured text responses can take hours and days that most marketing and research professionals simply do not have.

CX Inspector, now with the power of Theme Extractor and Generative AI, is a significant leap forward in text analysis technology, extracting descriptive actionable insights from open end responses instantly. Load a dataset of any size and in one click charts and tables reveal insights that uncover a comprehensive understanding of customers’ comments. The benefits of CX Inspector with Theme Extractor and Generative AI include:

• Clear and Descriptive Insights Instantly. CX Inspector identifies key themes in the responses, allowing you to grasp the underlying patterns and trends in a matter of minutes. The intuitive interface immediately delivers charts and tables highlighting the themes and visualizing the results. Exporting data tables and charts is a breeze, allowing you to incorporate insights into your reports and presentations seamlessly.
• Clustering Ideas into Groups. CX Inspector can group themes together. This intelligent feature analyzes the data and provides cohesive clusters of insights, enabling you to uncover deep, interconnected patterns that might have otherwise gone unnoticed.
• Dive Deeper Into the Data. Explore the results further by applying filters and sentiment, examining co-occurrences, and generating trend reports and crosstabs with t-tests. And at any point, you can click through a theme to see the individual respondent comments. The platform empowers you to navigate the results quickly, ensuring you understand both the comprehensive insights and a granular view of customer comments.
• Save Time and Labor. With CX Inspector's Theme Extractor with Generative AI, you can extract insights from datasets with unparalleled speed. No longer will you need to spend countless hours manually sifting through data and struggling to identify meaningful patterns. Instead, with a load-to-insights timeframe of minutes, not hours or days, you can quickly load your file and let the powerful CX Inspector process the information, freeing up your valuable time to interpret the results and formulate strategic recommendations.
• Scalable to Any Size Dataset. Whether you're dealing with a handful of responses or a larger dataset, CX Inspector can analyze the data quickly. No need to carve out a sub-set of the data file for analysis hoping it is representative. CX Inspector can analyze it all while maintaining a high level of accuracy and efficiency.
• Multilingual Capabilities. Furthermore, CX Inspector is language-agnostic and handles multiple languages effortlessly. This flexibility allows you to analyze datasets with responses in numerous languages, unlocking insights from diverse markets and customer segments.
• X-Score for Customer Satisfaction Measurement. CX Inspector includes X-Score, Ascribe's proprietary measure which identifies the key drivers that will increase customer satisfaction and loyalty right from the open-end responses. This invaluable information lets you quickly and confidently make data-driven decisions to enhance customer experiences and improve business outcomes.

Simply put, Theme Extractor with Generative AI enables CX Inspector to deliver better insights from open ends faster.

Some companies today have concerns about the security of data processed using tools such as ChatGPT, Open AI, and Generative AI. At Ascribe, customers' privacy and security concerns come first, and customers choose the level of automation and Generative AI for each analysis. Ascribe uses the Microsoft Azure OpenAI environment where your data is not available to other customers and is not used to enhance the OpenAI models, nor any Microsoft or third-party products or services. Additional information can be found at https://learn.microsoft.com/en-us/legal/cognitive-services/openai/data-privacy.

For over 20 years, Ascribe has been innovating with the most advanced AI technologies to make open end analysis easy. Researchers and marketers around the globe have collaborated with us to develop our innovations and they trust Ascribe’s solutions to meet their business needs.

Experience the power of CX Inspector with Theme Extractor and Generative AI today and revolutionize how you uncover insights from open ends. With its user-friendly interface which instantly extracts descriptive theme-based insights from a dataset and visualizes them in charts and tables, CX Inspector’s latest release is a game-changer in text analysis. Your organization's success depends on staying ahead of the curve, and Theme Extractor with Generative AI gives you the key to unlocking untapped insights faster than ever before.

Market research has always been about reducing uncertainty—helping businesses understand their customers, anticipate market shifts, and make informed decisions. But as the volume and speed of data grow, traditional research methods—reliant on manual surveys, slow data processing, and static reports—are struggling to keep up. Researchers need faster, more adaptive approaches to extract meaningful insights without sacrificing accuracy.

Automation isn’t just about saving time; it’s reshaping how research is conducted. By streamlining data collection, analysis, and reporting, automation allows researchers to focus less on logistics and more on interpretation. But what does this shift mean for the quality of insights, and how can organizations use automation to their advantage?

What is Automation in Market Research?

Market research automation refers to the use of technology to minimize manual tasks across the research process. This includes everything from survey design and data collection to analysis and reporting. By leveraging automation, businesses can conduct research more efficiently, reducing time-to-insight while maintaining data integrity.

Rather than outsourcing to a research agency, automation empowers organizations with self-serve platforms, enabling them to design surveys, collect and analyze data, and generate reports—without excessive costs or delays.

A well-designed automated research platform integrates:

• Survey creation with logic-based routing
• Automated sampling and data collection
• AI-powered analysis and visualization
• Interactive, real-time reporting

This all-in-one approach allows researchers to focus on high-level strategy rather than getting bogged down by administrative tasks.

How Automation is Reshaping Market Research

Automation addresses the limitations of traditional market research—such as high costs, time delays, and sample size constraints—by streamlining workflows and accelerating insights. Here’s how:

1. Intelligent Survey Design and Routing

Survey routing (or skip logic) dynamically adapts question paths based on a respondent’s answers, ensuring they only see relevant questions. This not only improves the survey experience but also enhances data quality by eliminating irrelevant responses.

2. Automated Data Collection

Modern research platforms can collect data from multiple sources—online surveys, mobile apps, social media, and customer databases—without manual intervention. This speeds up research while maintaining accuracy through built-in validation mechanisms.

Additionally, automated data cleaning helps filter out inconsistencies, ensuring high-quality data inputs.

3. AI-Driven Data Analysis

With machine learning and predictive analytics, automated tools can process vast amounts of data in real time. Unlike traditional methods that require manual uploads and batch processing, AI-powered analytics continuously update as new data flows in, reducing lag time in decision-making.

4. Interactive Dashboards and Automated Reporting

Insights are most valuable when they are easily interpretable. Automated reporting tools create real-time dashboards, allowing stakeholders to filter and visualize key metrics instantly. This eliminates the need for static spreadsheets and ensures that insights remain current and accessible.

Key Benefits of Market Research Automation

Beyond operational efficiency, automation offers significant advantages that reshape how research is conducted:

1. Increased Productivity

By automating repetitive tasks, researchers can focus on interpreting data, identifying trends, and making strategic decisions rather than spending time on manual processes.

2. Improved Accuracy and Reliability

Automation reduces human error in data entry, analysis, and reporting. AI-driven analysis also ensures consistency, providing more precise and actionable insights.

3. Real-Time Data Access

Instead of waiting weeks for data processing, automation enables real-time monitoring, allowing businesses to make decisions based on up-to-date information.

4. Greater Agility

Self-serve platforms allow researchers to quickly adjust survey questions, refine target audiences, and iterate on data collection—all without needing external assistance.

5. Cost Efficiency

Automating data collection and analysis significantly reduces the need for manual labor and eliminates the cost of multiple tools by centralizing research operations into a single platform.

Common Misconceptions About Market Research Automation

Despite its benefits, some myths persist about automated market research. Let’s address a few common concerns:

1. "Automated research tools are too complex to use."

Today’s market research platforms are designed for ease of use, enabling both experienced researchers and non-experts to conduct sophisticated studies without specialized technical knowledge.

2. "You need multiple tools to conduct research."

End-to-end platforms, like Voxco, consolidate survey design, data collection, analysis, and reporting into a single system, eliminating the need for multiple disconnected tools.

3. "Automation limits control over sample selection."

Advanced automation tools allow researchers to define their target audience, apply screening criteria, and integrate online panels, ensuring complete control over sample quality.

4. "Automated research lacks customization."

While automation standardizes workflows, it also provides extensive customization—allowing brands to tailor surveys, reporting, and dashboards to align with research objectives and branding guidelines.

When to Leverage Market Research Automation

Automation is especially valuable in:

• Large-scale studies: Handling extensive data collection and analysis efficiently
• Time-sensitive research: Generating quick insights for agile decision-making
• Longitudinal studies: Automating tracking surveys for real-time updates
• Self-directed research: Enabling in-house teams to conduct studies independently

The Future of Automated Market Research

As technology advances, automation will continue to play an even greater role in market research. AI-driven insights, predictive analytics, and real-time dashboards will empower researchers to uncover patterns, optimize strategies, and drive smarter business decisions.

From survey creation to reporting, automation enhances the speed, accuracy, and scalability of market research—ensuring that organizations can answer critical questions with confidence.

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Leading retailers often use price to position their brands against the competition in the market. They reduce the price as they believe this will help them boost the customers’ perceived value of the product. However, they often forget that - Will the customers respond as the brands expect them to?

Customers consider price as a driving factor in their purchase decision. It is as essential as the quality of the product. Price communicates to customers about the value the product brings. A dollar-store cosmetic brand shouldn’t sell its perfume at the same price as a luxury brand. The price would signal a premium product which would lead to the brand losing existing customers while also being unable to compete with high-end brands. How can you determine the best price of your product that customers would be willing to pay and also see value in? Our blog on market research is an answer to this question; Pricing Surveys.

What is a pricing survey?

Pricing surveys help brands, and their product researchers use research methods to determine the best price to sell their products and services. In a pricing survey methodology, you can ask questions to your target customers and gather their insights on the following:

• What do they believe the price should be for the product,
• How much are they willing to pay for it,
• The customers’ perceived value of the product,
• Which features are likely to increase the sale.

A product pricing survey helps you understand your target customers and the optimal price and identify the impactful features of the product. It provides you with the data you need to make informed decisions.  

The data helps you make pricing strategy decisions for the new products and also on any price changes required for the existing product. And when used correctly, it can help you predict the impact of the price on your revenue by gauging the customer’s perception of the price.

What are the four types of pricing surveys?

Customers will never want to pay more if getting the product at a lower price is feasible. So, it’s complicated to decide the right price for the product.

There are four commonly used pricing survey methodologies that help you gain insight into your pricing strategy. Let’s look at these four pricing surveys in detail.

01. Willingness to pay:

Question: “How much would you be willing to pay for [product name]?”

WIP, or willingness to pay, informs you of the price or price range that your target customers would be willing to pay for a particular product/service.

In this pricing survey, you need to offer the product details to the respondent and then ask the WIP question. The question is clear and straightforward and gathers precise data, which is easy to evaluate.

Depending on the research needs, you can ask an open-ended or closed-ended question.

• Use an open-ended question when the customers are generally familiar with the product.
• Use a closed-ended question when your product is unique in the market, and nothing similar exists.

While this question can help you understand how much a customer is willing to pay, it is best not to use it alone. The question doesn’t offer context on how high or low you can set the price before you lose customers.

The following three pricing survey methodology can help determine customers’ willingness to pay.

02. Van Westendorp price sensitivity meter:

Peter Van Westendorp, a Dutch economist, developed a pricing survey methodology, which is known as Van Westendorp Price Sensitivity Meter. This pricing model uses four product pricing survey questions to help you gauge customers’ perception of the product’s value.

Brands use these four pricing survey questions to identify a range of price points that are crucial to the target customers.

Here are the four questions you can use in your pricing survey:

1. At what price would you think [product/service name] is so inexpensive that you would question its quality and now buy it?
2. What price would you consider the [product name] a good value for money?
3. What price would you consider the [product name] too expensive but still buy it?
4. At what price would you consider [product name] too expensive to buy?

When you use the data collected in context to these questions, you can determine the optimal price points. You can also uncover the position price, the acceptable price, and the premium price.

The advantage of using this pricing strategy survey questions is that the questions are easy to respond to, and the data is easy to analyze. Moreover, the model offers the flexibility of identifying a series of price points to help you decide the optimal price that brings the highest profit.

03. Gabor-Granger pricing survey methodology:

The Gabor-Granger pricing method helps you establish the best acceptable price for the product by determining its price flexibility. British economists Andre Gabor and Clive Granger ask the likelihood of purchasing questions at different prices.

Using the data collected from a series of the likelihood of purchasing survey questions you can plot demand and revenue curves to determine the best price that brings maximum revenue.

Let’s see how you can use this type of question.

Question: How likely are you to purchase our subscription package at $350/month?

• Extremely likely

• Very likely

• Somewhat likely

• Not so likely
• Not at all likely

This pricing model requires you to ask the same question at multiple price points. So, use online survey tools that offer the functionality skip logic to ask follow-up questions based on the answer you receive.

For example, start with a price at which you would like to position the product. Now, if the respondent selects any of the bottom three responses, you can ask the same question at a lower price. However, if the respondent selects the top two responses, you can use the top-2-box score analysis.

04. Conjoint analysis:

In this pricing survey methodology, you can effectively evaluate customers’ perception of value for certain features or attributes of the products/services. The conjoint analysis brings the element of real-life shopping experiences in online surveys. It helps you identify which feature has the most impact on customers’ purchase decisions.

This product pricing survey question offers respondents a number of combinations with product features or attributes (including price). The question then asks respondents to choose between the different combinations.

A conjoint analysis pricing survey question may look like this:

Question: If you were shopping for a new smartphone, which would be most appealing to you?

Why is it important to conduct a pricing survey?

The most effective way to determine the appropriate price is to gather feedback from target customers using pricing surveys. Conducting pricing surveys is important in market research for several reasons, including:

1. Optimize pricing strategy:

Gather data on how much customers are willing to pay for your products and services to understand their sensitivity toward the price range. You can use the knowledge to inform and optimize pricing strategies to maximize revenue.

2. Identify opportunities for pricing differentiation:

Using the various pricing survey methodologies mentioned above, you can identify the features or attributes for which customers are willing to pay more. This insight can help you adjust the price, demonstrate value, and remain competitive in the saturated market. You can introduce pricing tiers that offer customers additional features.

What questions to ask in a pricing survey?

Say a survey software vendor uses the pricing survey to determine the proper survey software pricing before launching it as a mobile app.

So, let’s look at these examples of product pricing survey questions:

1. Reading the product details, how much are you willing to pay for [survey software’s name]?

- Understand the perceived value of your product and the price point customers are willing to pay. The question helps you identify the acceptable price point for the product.

2. How do you think the price of [survey software’s name] compares to similar offerings?

- Understand how your competitors position their products in the market and compare the difference. Use the insight to thoroughly analyze what drives the price difference and adjust it accordingly to remain competitive.

3. At what price range would you be more likely to purchase [survey software’s name]?

- Use this question to gain insight into how price-sensitive your target audience is. You can adjust the price to increase sales.

4. What features are you willing to pay more for?

- This question can help you identify features and attributes that would make customers pay more.

5. At what price point would [survey software’s name] be so inexpensive that you would question its quality?

- This question will help identify the lowest price limit or range below which you will lose customers.

Conclusion

Pricing surveys are valuable for businesses looking to make informed pricing strategies for their products/services. Gather data on customers’ perception of the product’s value, their willingness to pay, and the acceptable range of price to remain competitive in the industry.

Pricing surveys can help you understand customers’ purchase behavior and the driving factors impacting purchasing decisions. The insight enables you to build price tiers or decide on positioning prices that will maximize the business profit.

The review website also awarded Voxco badges for “Momentum Leader” and “High Performer Enterprise.”

The Spring 2023 Reports by G2 (the world’s largest and most trusted software marketplace) have been released, and we are proud to announce that Voxco has been recognized as a Leader in the Survey Software category.This recognition is based on the responses of real users for each of the report-related questions featured in the G2 review form. In the report, Voxco has earned the following badges based on reviews gathered from G2's user community and data aggregated from online sources and social networks.

Grid® Report for Survey | Spring 2023

Momentum Grid® Report for Survey | Spring 2023

Enterprise Grid® Report for Survey | Spring 2023

Asia Pacific Regional Grid® Report for Survey | Spring 2023

Asia Regional Grid® Report for Survey | Spring 2023

India Regional Grid® Report for Survey | Spring 2023The Momentum Grid® Report shows the growth trajectory that products have had in their respective spaces over the last year based on user satisfaction scores, employee growth, and digital presence.High scores across multiple categories helped secure our overall performance.Being named a leader by the user-generated review site, which highlights the best-reviewed technology companies every quarter, is a big achievement for Voxco.

"Our recognition by G2 as a market leader is a truly humbling honor and reflection of the trust and outstanding partnerships we have with our customers. We owe this achievement to our dedicated team and thank all our clients and users who generously shared their valuable feedback to help us improve and grow consistently.” Sumit AnejaCEO of Voxco Insights

G2 leaders are measured and determined based on significant customer satisfaction scores and dominating market presence through reviews. Learn more about what real users have to say (or leave your own review) about Voxco on G2’s Voxco review page!

About G2

G2 is the world's largest and most trusted software marketplace. More than 60 million people annually — including employees at all of the FORTUNE 500 — use G2 to make smarter software decisions based on authentic peer reviews. Thousands of software and services companies of all sizes partner with G2 to build their reputation, manage their software spend, and grow their business – including Salesforce, HubSpot, Zoom, and Adobe. To learn more about where you go for software, visit www.g2.com and follow us on Twitter and LinkedIn.

Obtaining crucial information through text analysis about customers and other stakeholders is a requirement for any business to survive in the modern age.

Structured and unstructured data about a company are plentiful, but they are useless unless you can analyze them to gain knowledge for making decisions.

Best text analysis software is useful in this situation. Software for text analysis can extract statistical patterns from raw text data by identifying patterns and trends.

Let's first discover what this software is before getting too far into it

How to define the best text analysis software?

A subset of natural language processing called natural language understanding is used by text analysis programs to help companies evaluate their text data. These analytics solutions utilize text as an input and provide some form of labels, tags, or insights as input due to the unstructured nature of text data.

Businesses are embracing the need to comprehend company data like never before in the age of digital transformation.  Over the past ten years, text analysis software, also known as software for text mining or text analytics software, has become a vital instrument for almost every industry. The requirement to comprehend both organized and unstructured data is a more recent development in analytics and business intelligence.

Text data, for example, can be mined for meaning to guide business choices. Businesses can eventually benefit from a better understanding of textual data sets thanks to text mining initiatives.

Benefits of best text analysis software

Users need to analyze text, which is a fairly obvious reason for using text analysis software. However, there are many reasons why a company might want to perform text mining and analysis. To have an effect on business operations and the bottom line, it all comes down to better understanding and utilizing company data. It should be used to boost output and efficiency and to streamline procedures that could be more effective.

1. Sentiment analysis

Companies constantly seek to measure client satisfaction, and text analytics makes this task simple. Customer sentiments can be found in a variety of text data sources, including social media, emails from customers, phone transcripts, customer evaluations, and more. A business can better support and manage those customers if they are aware of their weaknesses or areas in which they excel. This may ultimately result in higher income.

2. Employee satisfaction

Best text analysis software, like better-knowing customers, can help businesses increase employee engagement and satisfaction. While businesses should not exactly spy on employees, they can use surveys, emails, or phone transcripts to determine employee sentiment and satisfaction. This can assist businesses in ensuring that they are promoting the correct company culture and offering a healthy and happy working environment.

3. Document classification

Classifying documents is a simple application for text analysis software. Businesses frequently need to organize their current documentation; by identifying themes and sentiments, it can be much simpler to group documents like invoices and contracts.

Top attributes to search for in best text analysis software

To discover quantifiable insights and enhance business decisions, organizations should consider the following attributes when choosing the best software for text analysis.

• Interprets unstructured data, including information from emails, chats, social media streams, messages, and reviews.
• Info is sorted based on a pre-set filter. For instance, the classification of immediate support requests.
• Identifies themes in open-ended writings and quantifies comments to gain understanding.
• Analyzes customer feedback to find phrases that are more effective with the intended group.

You can protect data by ensuring that your text analysis program has these capabilities.

The top 7 best text analysis softwares

According to us these are the top 7 softwares for text analysis in 2023;

1. Voxco Insights Platform
2. Monkey Learn
3. Qualtrics XM Discover
4. Chattermill
5. Lexalytics
6. IBM Watson
7. Google Cloud NLP

Softwares

Features

1. Voxco Insights Platform

• An omnichannel insights tool
• Makes use of password management, robust security measures
• You can instantly evaluate millions of texts
• It helps in automatic language recognition
• Interprets text from the data and converts it to machine language

2. Monkey Learn

• Automatically examines text and draws useful conclusions from data
• You can conduct sentiment analysis, keyword extraction, intent categorization, and language detection
• Tags your content using pre-built text analysis models

3. Qualtrics XM Discover

• Uses more than 130 pre-built industry templates  to evaluate unstructured data
• Your customer service chatbots can be enhanced
• Customers' motivations, emotions, and next steps are revealed

4. Chattermill

• Chattermill is a platform for unified customer feedback analytics
• Utilizes sentiment and subject AI to assist in observing how customers feel

• It accepts input from a variety of sources
• It has several pre-configured interfaces and an API for building bespoke ones

5. Lexalytics

• It converts complicated text documents into data and insights
• With NLP intentions, themes, entities, categories, subjects, and multi-layered sentiment scores are all extracted
• The user has the option to customize these models
• Lexalytics can handle text in more than 20 different languages
• Supports a broad range of data sources

6. IBM Watson

• Provides a set of AI tools that extract and categorize data from organized or unstructured text data
• Natural Language Understanding extracts ideas, entities, keywords, and categories
• Developers can build a custom machine-learning model

7. Google Cloud NLP

• Google Cloud NLP as a response to the expanding text analysis trend
• Aids companies in extracting insights from unstructured text and giving it meaning
• It includes entity extraction, syntax analysis, sentiment analysis, and content classification
• You can combine your cloud storage with Google's AI tools to provide a smooth text analysis experience

1. Voxco

Voxco is an omnichannel insights tool. Our robust text analytics software can instantly analyze millions of lines of open text to help you understand the actual attitudes of your customers. The user-friendly layout of our text analytics software makes it simple to use and navigate, giving you a hassle-free and seamless experience.

The best text analysis software from Voxco makes use of password management, robust security measures, and granular access permissions to guarantee that the data of your customers is managed with the utmost care and kept private.

With the help of Voxco's AI-enabled text analytics software, you can instantly evaluate millions of texts to better understand your customers' needs.

Automatic Language Recognition

To analyze customer feedback quicker, interpret text from the data and convert it to machine language.

For quicker consumer feedback analysis, software for text mining interprets text from the data and converts it to machine language.

2. Monkey Learn

A machine learning tool called MonkeyLearn automatically examines text and draws useful conclusions from data. For greater levels of accuracy, you can use pre-trained best text analysis software or make your own by customizing them to your requirements.

You can conduct sentiment analysis, keyword extraction, intent categorization, and language detection using text analysis models, which include text classifiers and text extractors.

With their own WordCloudGenerator, MonkeyLearn welcomes word clouds.

MonkeyLearn automatically tags your content using pre-built text analysis models. You have the option of choosing pre-built models or creating your unique classifications and extractors.

Any data can be used to immediately create a word cloud. It relies on how many categories you are after when it comes to categorizing data.

You can get direct access to your data through important integrations with common applications like Excel, Google Sheets, Zapier, RapidMiner, and Zendesk. It is necessary to pre-clean data before uploading it.

Also read : sentiment analysis tool

3. Qualtrics XM Discover

To evaluate unstructured text data, XM Discover uses more than 130 pre-built industry templates and categorization models. Customers' motivations, emotions, and next steps are revealed by XM Discover.

Your customer service chatbots can be enhanced using XM Discover analytics to better comprehend people. It integrates with other Qualtrics products to provide an all-in-one system that covers data collection, analysis, and implementation.

A wide range of preset models for various industries is available in XM Discover, which employs both rule-based and machine-learning techniques. Specific components can be customized by users to better represent their businesses.

How much time does it take to launch?

It will take a few weeks to get going if your dataset closely fits one of the taxonomies that XM Discover provides. However, a highly customized execution could take up to six months to complete. This usually entails an XM Discover expert working closely with the business stakeholders to develop complex rules to capture topics of interest.

With more than 100 pre-built connectors for different data integrations, XM Discover connects to a variety of feedback and interaction sources. These include chat, social media, surveys, and contact center records.

4.  Chattermill

Chattermill is a platform for unified customer feedback analytics that unifies, measures, and examines your customer feedback. The platform uses theme and sentiment AI to help you see how customers feel, stay on top of new topics, and understand what keeps them coming back.

To find the hidden layers of meaning in your data and determine what influences user experience, use Chattermill's filters. To correctly find and forecast opportunities to increase revenue, brand loyalty, and customer retention across customer touchpoints, analyze the signals in your customers' feedback.

The predefined taxonomies used by Chattermill are centered on typical customer success problems. If necessary, their data staff will adapt the model to your data.

Chattermill accepts input from a variety of sources. It has several pre-configured interfaces and an API for building bespoke ones.

How much time does it take to launch?

It takes at least two weeks to develop the original theme structure, followed by another two weeks to review and improve it.

5. Lexalytics

A conventional text analysis program called Lexalytics converts complicated text documents into data and insights. Intentions, themes, entities, categories, subjects, and multi-layered sentiment scores are all extracted by Lexalytics using NLP. You can see what's being discussed, what individuals are feeling, and why.

The product consists of three major components:

• Salience, their main text analytics and NLP software library designed for data scientists.
• Salience's powers are integrated into the current data analytics infrastructure by Semantria.
• Built on the strength of the Semantria API is Spotlight, the comprehensive Lexalytics solution. Interactive dashboards allow you to share your findings and visualize your outcomes.

For composing summaries, extracting keyphrases (which they refer to as themes), entities (proper nouns), and classifying documents by subjects, Lexalytics has pre-built models available. (e.g. Sports). The user has the option to customize these models, which are available in packs for particular sectors.

Lexalytics can handle text in more than 20 different languages and supports a broad range of data sources.

How much time does it take to launch?

Initial results are available right away, but customization may take weeks if your business is not included in one of the available packs.

6. IBM Watson

Technology behemoth IBM provides a set of AI tools that extract and categorize data from organized or unstructured text data. The IBM Watson Tone Analyzer, Watson Personality Insights, and IBM Watson Natural Language Understanding & Classifier are all helpful tools.

In addition to other things, IBM Watson Natural Language Understanding extracts ideas, entities, keywords, and categories. When used for sentiment analysis, it not only divides the text into the three general categories of positive, negative, and neutral sentiments but also divides these sentiments into specific emotional categories like perplexed, excited, sad, confident, etc.

Developers can assign a classification to text using the IBM Watson Natural Language Classifier without having to be experts in statistical or machine learning algorithms. By uploading their data, developers can build a custom machine-learning model that will classify texts, extract insights, and spot patterns.

7. Google Cloud NLP

Not one to fall behind, Google offers Google Cloud NLP as a response to the expanding text analysis trend. This tool aids companies in extracting insights from unstructured text and giving it meaning.

The text analysis applications that Google Cloud NLP works on include entity extraction, syntax analysis, sentiment analysis, and content classification. All you need to fine-tune your models to your domain-specific keywords, sentiments, subjects, etc., are some training data if you're eager to train your machine-learning models.

One more benefit! You can combine your Cloud Storage with Google's AI tools to provide a smooth text analysis experience.

Conclusion

It can be challenging to find the best text analysis software for your business. Hopefully, our guide will assist you in limiting your options. In the end, the finest software is the one that meets all of your business needs and offers reliable, precise insights.

Frequently Asked Questions (FAQs)

1. Which is the statistical analysis tool used for text content?

An online best text analysis software called Textalyser produces data about your text. With this analysis tool, you can quickly determine the word count, word prominence, phrase density, and word groupings.

2. What program is used for text analysis?

For importing text data and converting it to numeric form for use in machine learning, deep learning, sentiment analysis, and categorization tasks, MATLAB provides a Text Analytics Toolbox. One method of record for the survey, social, text, written, and online feedback is provided by Medallia.

3. Is NLP the same as text analysis?

Text mining (also called text analytics) is an artificial intelligence (AI) technique that uses natural language processing (NLP) to transform  free (unstructured) text in documents and databases into normalized, structured data suitable for analysis or  machine control learning algorithms (ML).

4. Can you do text analysis in Excel?

If you have a lot of data, text mining in Excel can take a lot of time and become very challenging to manage. Thank goodness, there are AI text analytics tools that can detect patterns in your text for you!

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Call center agents often rely on cold calling for market research, lead nurturing, or upselling. The agents have to look for customer contact and dial the numbers manually. This results in agents spending most of their time dialing and waiting for customers to receive the call. As a result, it decreases their productivity and declines company growth.

Predictive dialing software can help you boost productivity by reducing the call processing time. The software automates outgoing calls by predicting agents’ availability. The predictive dialer system adjusts the dialing rate based on the time taken by every agent and connects them to customers.

In this article, we will help you evaluate the 10 best predictive dialer software. But before that let’s discuss what a predictive dialing system is and how it works.

What is predictive dialer software?

A predictive dialing software intelligently places outbound calls based on an algorithm. It automatically queues up calls for agents while they are finishing a prior call.

The best feature of the dialer is that it connects your agents to available people, i.e., not busy signals or voicemails. This helps you eliminate contacts from your list that are not in use anymore. This helps increase efficiency as it filters out unanswered phones or voicemails, thus saving agents time.

An automated predictive dialer measures the amount of time an agent spends talking to a prospect to queue the next phone number. The software replaces manual work like physically dialing the number or scrolling through thousands of contacts, etc. By automating the entire process, the dialer helps increase efficiency and productivity.

Predictive dialer tools facilitate a sales campaign and enable sales reps to reach a wide population within a short span of time. However, more and more industries are migrating towards cloud-based dialers to scale their communication. Industries such as financial institutes, market research, debt collections, nonprofit fundraising, and political campaigns leverage predictive dialers to prioritize leads and volumes.

Now that we know what the dialer is, let’s see how it works.

How does the outbound predictive dialer work?

The software develops an algorithm by predicting agent availability. It takes the following factor into consideration to automatically adjust the dialing speed for each agent.  

• The number of agents available in the call center.
• Average call handle time.
• Average abandonment rate.
• The ratio of connected and unconnected calls.
• The number of calls it takes to connect an agent with a real human.
• Average downtime between calls.

While it is in the principle of predictive dialers to be aggressive in dialing, it should also be precise and efficient. The dialer uses these statistics to ensure that not many calls are placed before an agent is available. It often takes some time to connect a potential customer to a live agent, this means the customer hears a long silence, which can lead to an abandoned call. The predictive dialer app uses the data to learn and improve its algorithm to make the best use of an agent’s time.

1. The software dials a call and waits for a real person to respond.  It predicts how many numbers it needs to dial to boost the agent’s productivity.
2. The dialer filters any unanswered calls, busy signals, or answering machines and connects agents with a live audience. Any unanswered calls are automatically set up for redial.
3. The best predictive dialer uses the algorithm, and the collected data on previously attempted calls to predict when an agent is available for the next number. It uses the prediction to set up the dialing speed and dials the next number.

In case when your call center is overburdened, predictive dialer tools reduce the dialing rate or halt an outreach until it detects an agent completing the ongoing call.

List of top 10 predictive dialer software

1. Voxco

Voxco, one of the best predictive dialer software, implements an omnichannel approach to increase your market research efficiency. The dialer is a state-of-the-art telephony system that integrates with Voxco CATI survey software.

Voxco Dialer offers a 4-in-1 dialing solution – predictive, power, preview, and hybrid. Use the dialer dashboard to monitor interviewer productivity and create performance reports.

Voxco’s predictive dialing continually adjusts the dialing pace to match agents’ availability automatically. It works dynamically with the rhythms of your call center to boost productivity.

Voxco dialer is compliant with the highest protocols and uses standard high-performance telephony cards and VoIP licenses. With 25+ years of experience, Voxco offers cloud and on-premise predictive dialer systems.

Features:

• Automated dialing.
• Seamless integration with Voxco CATI.
• On-premise or cloud predictive dialer.
• Extended interview capabilities, such as conference, transfer out, recording, and playback.
• Server virtualization support.
• Live audio/call monitoring and whisper mode.
• Visual customization of interviewer activity status.
• Access to powerful Voxco Dialer Dashboards.

Pros:

• Easily integrate dialer with CATI & IVR phone survey software.
• Built for scale – big or small call centers.
• Set desired balance between productivity & drop rate.  
• Powerful dialer dashboard: Monitor and improve performance with quality assurance features.
• Join mode to allow supervisors to enter a conversation in conference with agent and respondent.
• Works across multi-sites for remote and home agents.
• Reach customers/prospects on landline and mobile devices with Voxco’s TCPA-compliant phone software.

Price: Available on request. Get a pricing quote!

2. Talldesk

Talldesk is a cloud-based call center software. It offers an omnichannel system to help your agents reach customers/prospects via text, social media, etc. Talldesk’s predictive dialer system also offers IVR and complies with DNC rules.

Features:

• Omnichannel contact options include both voice and SMS channels.
• Call recording, monitoring, and transferring.
• Activity dashboard.
• Automatic call distribution and IVR are offered in the lowest pricing model.
• Upload DNC updates via CSV files

Pros:

• The predictive dialer complies with DNC, aka Do Not Call rules
• Create analytics reports for call volume, abandonment rate, and more.
• Access 60+ integrations.

Cons:

• You get a 100% uptime service level agreement only at the Enterprise Level subscription.

Price: Available on request.

[Related read: Top 15 Auto-Dialer Software.]

3. NICE inContact

NICE inContact’s predictive dialer software also implements an omnichannel approach. The company refers to its predictive dialer as the “CXone Personal Connection”.  The CXone dialer eliminates awkward pauses and instead, greets customers/prospects at the first “hello” to keep them from hanging up.

The software allows you to customize campaigns and ensures that your call center complies with contact laws.

Features:

• Greets customers/prospects at first “hello” to eliminate awkward and lengthy pauses.
• Omnichannel contact - IVR, voice, chat, and social media.
• Intelligent call routing.
• Self-service portal.
• VoIP provider.

Pros:

• 99.99% uptime.
• Call recording helps with quality assurance purposes.
• Create custom reports.

Cons:

• Reporting has a steep learning curve.

Price: Available on request

4. Genesys

Genesys Cloud CX Dialer offers multiple dialers, i.e., preview, predictive, and manual dialing. It enables you to route calls through multiple channels, voice, outbound IVR, SMS, emails, etc. As one of the best predictive dialer software, it uses a pacing algorithm that predicts agent availability.

Features:

• Built-in integrated analytics for reporting and monitoring performance.
• Call routing via multiple channels - outbound IVR, SMS, emails, and web pages.
• Customizing pacing algorithms based on the campaigns.
• Preview and manual dialing are available in addition to the predictive dialer.

Pros:

• Skill-based intelligent call routing.
• Custom script for agents.

Cons:

• Call drops.
• AI features are available after the second-tier subscription.
• Difficult to set up call flows.

Price: Genesys Cloud CX1 starts at $75/user/month.

5. ChaseData

ChaseData offers cutting-edge technology for call centers. It offers an all-in-one dialing solution with an outbound predictive diaper system, queued progressive dialer, and a preview dialer.

Features:

• TCPA-compliant predictive dialing rules.
• Complies with PCI DSSS for credit and debit transactions.
• Campaign-specific caller ID.
• Call recording and monitoring.
• Call scripting.

Pros:

• Omnichannel call routing options.
• Complies with HIPAA and HITECH.
• Also, complies with PCI DSS (Payment Card Industry Data Security Standard).

Cons:

• Not user-friendly; it requires lots of training and onboarding help.
• You can only unlock the predictive dialer with a Professional Plan subscription.
• Expensive.

Price: Small Business subscription plan starts at $89/ user/ month

6. CallHub

CallHub offers an automated dialer, power dialer, and preview dialer along with a predictive dialer. It also includes voice broadcasting and text message marketing tools. The software also offers phone number verification, DNC rules, local caller ID, answering machine detection, and more features.

Features:

• VoIP provider.
• Call recording and monitoring.
• Reporting and analytics.
• CRM integrations.
• Built-in voice mail detection.

Pros:

• Clean and easy-to-use interface.
• Complies with DNC rules.
• It supports answering machine detection.

Cons:

• Best software for companies that don’t need predictive dialers.
• Hosting is limited to cloud, SaaS, or web-based.

Price: Starts at 6.9 cents/minute.

7. Convoso

Convoso’s cloud-based predictive dialer software gives you an advanced dialer solution for sales and lead generation teams. With an intuitive interface and customer support, you can easily get agents onboarded.

Convoso offers 5 dialing modes in one auto-dialing system. Choose from the predictive dialer, power dialer, progressive dialer, preview dialer, and voice broadcasting.

Features:

• Caller ID reputation management tools.
• Answering machine detection.
• Smart triggers.
• Workflow dialing.
• Local caller ID.
• Omnichannel communication outreach.
• Customizable dashboard.
• Support TCPA compliance.

Pros:

• Cut wait times for agents between calls.
• Bypass answering machines, busy signals, and unanswered and disconnected calls.
• Manage drop rates and save management time.
• Dial through lists at speed.

Cons:

• Expensive software.
• Not user-friendly.

Price: Starts at $90.00/month.

8. Nextiva

Nextiva is a cloud-based solution that syncs with your CRM to ensure the process is more streamlined. It also offers integration to its VoIP communication solutions. The platform combines multiple communication channels like SMS, chats, surveys, voice, and video,

Features:

• Unified communication with VoIP, video, and chat.
• Access call analytics for historical and live data.
• Alerts and notifications.
• Call recording.
• Call logging.

Pros:

• Executive level telephone service.
• Track agent/team performance and productivity with an analytics platform.

Cons:

• System glitches.
• VoIP platform does not include predictive dialer software.
• Expensive.

Price: Essential plan starts at $14.95/user/month.

9. Five9

Five9, as one the best predictive dialer software, offers predictive dialing and CRM integration. The software enables you to skip busy signals to improve agent performance. It also analyzes calls and improves scripts to ensure better customer engagement. In addition to the predictive dialer, it offers power and preview dialing modes.

Features:

• Intelligent omnichannel routing.
• Unified communication across the enterprise.
• Track and monitor performance with intuitive reporting and analytics.
• Supervisor tools.
• Accounts for time zones.

Pros:

• It is scalable and enables you to add agents immediately.
• User-friendly interface.

Cons:

• Often users find the software crashes in the middle of a call.

Price: Available on request.

10. PhoneBurner

PhoneBurner is a cloud-based Power Dialer and Sales Acceleration platform. The software is easy to set up; connect it to a browser, and you can start managing your account. The predictive dialer tools work with VoIP services or traditional telephone lines. You can use the administrative dashboard to track agent performance and boost productivity.

Features:

• Cloud-based call center.
• Call recording and monitoring.
• Call scheduling.
• List management.
• Reporting and analytics.
• Administrative dashboard.

Pros:

• Automate post-call workflows.
• Real-time call KPI reports and analytics.
• Send emails and SMS based on call results.

Cons:

• Hosting is available only for cloud, SaaS, and web-based.
• Expensive compared to other products with similar features.

Price: $149.00 /mo.

How can you use a predictive dialer system?

Call center predictive dialer is a common tool for most businesses nowadays. They assist in conducting surveys or market research, helping customers solve their problems, upselling, cross-selling, or lead nurturing. So we have put together diverse use cases for predictive dialer systems.

• Customer service: One of the most common uses of dialer is the customer service department. Use the outbound predictive dialer to gather CX or CSAT feedback, and share payment reminders and appointment confirmations.
• Healthcare: Engage patients using a dialer that helps you send reminders or schedule appointments, gather patient information, send medication updates/reminders, and more.
• Sales: Companies can use an outbound predictive dialer to leverage upselling and cross-selling opportunities.
• Banks: Keep the customers alert with bill payment reminders, fraud detection alerts, account balance updates, and more.
• IT: Use the dialer to inform customers of product updates and contract renewals.
• Hospitality: Use the dialer to gather guest satisfaction feedback after their stay in your hotel to understand their expectations and preferences.
• Political campaigns: Use predictive dialers to reach voters, gather their options on various matters, and identify potential supporters.

Conclusion

This concludes our list of best predictive dialer software. For outbound call centers, predictive dialers are the best choice. These dialers automate outbound calls and increase agent efficiency.

For businesses where leads and call volume are the priority, predictive dialer tools offer the best advantage with their plethora of cutting-edge features, speed, scalability, reliability, and accuracy.

This article should help you evaluate your choice. If you haven’t made a choice, it should guide you to understand what to look for in your predictive dialer. If you want to know what more Voxco offers, connect with our expert today.

FAQs on predictive dialer software

Q1. What does a predictive dialer system do?

The dialer uses an algorithm to automatically place outbound calls while the agent is wrapping up the last call. The software removes manual dialing and filters out busy signals and voicemails to reduce downtime and increase efficiency.

Q2. What is a power dialer Vs. a predictive dialer?

Here are some ways to differentiate between power dialer Vs. predictive dialer:

1. Power dialer does not use algorithms to dial numbers. It dials one number after another in sequence for each available agent.
   A predictive dialer uses an algorithm to dial the next no. based on several factors, like avg. handle time, agent availability, etc.
2. A power dialer dials the next number only after an agent completes their call.
   Whereas, a predictive dialer queues the next number before the agent finishes the call thus reducing downtime.
3. Power dialer connects the agent before the customer answers the call. This means the customer hears your agent right when they answer it.
   Predictive dialer software connects an agent when a caller answers the call. This means the customer may hear an awkward silence or a ringing tone.

Q3. How can you host a predictive dialer system?

Predictive dialers can be hosted on the cloud or on-site. Large corporations often host predictive dialers on their company server.

Q4. What kind of calls do predictive dialers make?

Predictive dialer software automatically makes outbound calls. So the software has no functionality for receiving calls.

Q5.What are the benefits of predictive dialer tools?

Outbound predictive dialer accelerates phone calling and facilitates the process of engaging the target audience. The best predictive dialer helps you gear up with scaling communication in today’s hyper-connected world. Here are some benefits of a predictive dialer.

1. Increasing productivity with higher call connectivity:

Using the algorithms, the predictive dialer determines the call duration and screens answering machines to assign connected calls to specific agents. It also detects the ratio of connected and unconnected calls and works around it to ensure agents only connect with a live audience.

2. Boosting agent efficiency:

One of the benefits of predictive dialer tools is that it automatically dials the next number and controls the dialing speed, which contributes to boosting an agent’s efficiency.  

3. Higher survey completion rate:

The outbound predictive dialer ensures that you don’t have to wait for busy signals, an answering machine, or incorrect numbers. Intelligence call routing and the lack of manual work means you can focus on engaging the audience for their feedback.

4. Lower operational costs:

The dialer eliminates the idle time and lowers call abandonment rates which reduces the cost of the voice channel. Less manual work, lower human error, and less downtime mean you need fewer agents to handle the call volume.

5. Scaling workforce according to business needs:

You can’t list down the benefits of the best predictive dialers without talking about scalability. More and more companies are migrating to cloud-based predictive dialing software as it allows them the flexibility and agility of adjusting the software to their business needs.

6. Improving compliance:

The benefit of predictive dialers is that it lets you cross-reference do-not-call (DNC) lists and filter them to help you comply with complex and changing regulations.

How do you set up a predictive dialer app?

Here’s a brief instruction on how you can set up predictive dialer tools.

Select a predictive dialer app provider: Find the best predictive dialer that offers cutting-edge technology, is scalable, offers round-the-clock support, and fits your budget.

Set up your app: Now that you have software that meets your needs, here are simple ways to set up your predictive dialer app.

1. Import contact lists or integrate the predictive dialer tool with your CRM.
2. Add a survey or call script to the software.
3. Set up your campaign.
4. Adjust the dialing setting based on the campaign needs.

Train your agents: Once you have set up your dialer, train your agents on how to use it.

Monitor campaigns: Don’t forget to keep on monitoring the progress of the campaign and the call quality.

How does the predictive dialers app boost sales?

There are many ways that a predictive dialer app boosts your sales outcome. Here are some of those ways:

Increased call connectivity: The dialer dials multiple numbers simultaneously, resulting in higher call connectivity. This leads to faster campaign completion and higher sales volume.

Increased productivity: Agents can focus on the campaign and offer products/services customers need instead of having to focus on dialing the next number and waiting for it to connect.

ACD: Automatic call distribution routes calls to available agents and thus reduces wait times. This helps improve customer satisfaction and agent efficiency.

What are the limitations of predictive dialers software?

With so many benefits of predictive dialer tools, there are a few limitations as well. Some of these limitations include the following:

Call drops: Predictive dialer dials a customer before an agent is available, this leads to awkward pauses. This often results in call drops and frustrated customers.

Compliance issues: Predictive dialer providers must comply with TCPA and GDPR. It’s important to ensure that your provider complies with these regulations.