Understanding consumer behavior is essential for companies aiming to stay ahead of the curve. Consumer insights research serves as the compass that guides strategic decision-making, helping organizations uncover valuable insights into their target audience's preferences, motivations, and behaviors. 

In this comprehensive guide, we delve into the intricacies of consumer insights research, exploring its significance, methodologies, and practical applications. By harnessing the power of consumer insights, businesses can unlock new avenues for growth, innovation, and customer-centricity.

 

 

What Are Consumer Insights? 

Consumer insights are the data-driven observations you make from analyzing consumer behavior, attitudes, and preferences. These insights can provide an in-depth look at how people interact with your product, engage with your marketing, or perceive your brand.

 

 

What's the Difference Between Consumer Insights and Market Research? 

Consumer insights focus on understanding individual consumer motivations and behaviors, offering a deeper understanding of needs and preferences, while market research provides a broader view of market trends and competition.

Together, they inform strategic decision-making and enable businesses to better meet consumer expectations. 

It can help to think of it like this; market research answers the "what" – like market size, demographics, and trends – while consumer insights explores the "why" behind consumer actions. By deciphering the underlying motivations, aspirations, and pain points of consumers, businesses can better understand how to grow customer loyalty, expand their product’s reach, and build brand trust. 

 

 

Why Do Consumer Insights Matter? 

Consumer insights serve as a compass for businesses navigating the complex terrain of consumer preferences and behaviors, guiding them in developing products, services, and marketing strategies that resonate with their target audience. Moreover, consumer insights enable businesses to forge deeper connections with their audience, fostering brand loyalty and advocacy in an increasingly crowded marketplace. 

Here are the ways your business can benefit from gathering and using consumer insights:

 

Understand Consumer Behavior

Consumer behavior is complex and constantly evolving, shaped by a multitude of factors including demographics, psychographics, and socio-economic trends. Consumer insights provide businesses with invaluable knowledge that allows them to evaluate and track their target audience's ever-changing motivations, behaviors, and preferences. 

 

Anticipate Trends

By collecting consumer insights, businesses can gain a deeper understanding of emerging trends and shifts in consumer behavior. This enables them to anticipate market changes, identify new opportunities, and adapt their strategies accordingly, ensuring they stay ahead of the curve.

 

Identify Untapped Opportunities

Consumer insights shed light on unmet needs, pain points, and opportunities within a given market. Businesses can leverage this knowledge to innovate and develop products or services that address specific consumer demands, gaining a competitive edge and capturing market share.

 

Tailor Your Marketing Strategies

Consumer insights empower businesses to create targeted marketing campaigns that resonate with their audience on a personal level. By understanding the motivations and preferences of their target demographic, businesses can deliver tailored messages and experiences that drive engagement, loyalty, and conversions.

 

Improve Product Development

Consumer insights play a crucial role in product development, guiding businesses in refining existing offerings and developing new ones that meet consumer needs and preferences. By gathering feedback from consumers, businesses can identify areas for improvement, optimize features, and ensure their products resonate with their target audience.

 

Foster Brand Loyalty

Building strong, lasting relationships with consumers is essential for long-term success. Consumer insights enable businesses to connect with their audience on a deeper level, fostering (much sought-after) brand loyalty and advocacy. By delivering personalized experiences, addressing consumer concerns, and consistently exceeding expectations, businesses can cultivate loyal customers who become brand evangelists.

 

Drive Business Growth

Ultimately, consumer insights are instrumental in driving business growth and profitability. By aligning products, services, and marketing efforts with consumer preferences and behaviors, businesses can increase customer satisfaction, drive repeat purchases, and attract new customers. This leads to sustainable growth, increased market share, and a stronger competitive position in the marketplace.

 

 

How Do I Gather Consumer Insights?

 

Surveys & Questionnaires

Surveys and questionnaires remain a staple tool for collecting quantitative data from consumers. Whether conducted online, via email, or in-person, surveys allow businesses to gather feedback on specific products, services, or marketing initiatives. By asking targeted questions, businesses can gain valuable insights into consumer preferences, opinions, and purchasing behaviors.

 

Focus Groups

Focus groups offer a qualitative approach to understanding consumer perceptions and attitudes. By convening a small group of individuals for in-depth discussions, businesses can delve into the underlying emotions and deeper motivations that drive consumer behavior. Focus groups provide rich insights that go beyond numerical data, offering nuanced perspectives and valuable anecdotes.

 

Use AI to Analyze the Data

Advancements in artificial intelligence and machine learning have revolutionized the field of consumer insights research. By leveraging SightX's Generative AI Consultant, businesses can analyze vast amounts of consumer data with unprecedented speed and accuracy. SightX's AI-powered algorithms can uncover hidden patterns, trends, and correlations that human analysts might overlook, providing deeper insights into consumer behavior.

 

 

Consumer Insight Use Cases and Examples 

Consumer insights serve as a goldmine of valuable data for businesses looking to understand their customers better and improve their overall experience. Here we’ll look at some practical ways businesses can leverage consumer insights to enhance their product offerings, optimize support processes, and develop self-service resources, all aimed at delivering exceptional customer experiences.

 

Example: Understanding Customer Needs and Preferences

A software company analyzes customer feedback and usage data to identify common pain points and areas for improvement in their product. Based on the insights, they release regular updates and enhancements, addressing customer concerns and adding new features that align with their users' needs and preferences. As a result, customer satisfaction increases, leading to higher retention rates and customer loyalty.

 

Example: Spotting Trends and Optimizing the Customer Journey 

An e-commerce retailer analyzes customer purchase history and browsing behavior to identify trends in product preferences and shopping habits. Based on the insights, they optimize their website layout and navigation, making it easier for customers to find and purchase products they're interested in. Additionally, they personalize marketing campaigns and promotions based on individual customer preferences, increasing engagement and conversion rates throughout the customer journey.

 

Example: Streamlining Support Processes 

A telecommunications company leverages customer insights to identify common questions and issues that customers face when setting up their internet service. Based on this insight, they developed a comprehensive self-service resource center, including FAQs, troubleshooting guides, and instructional videos, empowering customers to find answers and solutions independently. This reduces the volume of support inquiries and allows support agents to focus on addressing more complex issues, ultimately improving the overall customer support experience.

 

Example: Enhancing Product Development

A consumer electronics company collects feedback from customers through surveys and product reviews to identify areas for improvement in its latest smartphone model. Based on this insight, they make adjustments to the design and functionality of the phone, addressing common complaints and adding new features that align with customer preferences. As a result, the new smartphone receives positive reviews and generates high demand among consumers.

 

Example: Optimizing Marketing & Advertising Campaigns 

Marketers utilize consumer insights to refine their digital marketing and advertising strategies. Through analysis of customer demographics and engagement metrics, they uncover that their target audience responds particularly well to influencer partnerships on social media platforms. In response, they reallocate a portion of their marketing budget to collaborate with relevant influencers who align with their brand values and resonate with their audience. As a result, they experience a significant increase in brand awareness, website traffic, and ultimately, sales.

 

 

AI Driven Consumer Insights with SightX

SightX is an AI-driven market research platform that offers you a single unified solution for product, brand, marketing, and pricing research. While powerful enough for insights teams at Fortune 500 companies, our user-friendly interface makes it simple for anyone to start, optimize, and scale their research. 

And with our new Generative AI consultant, Ada, you can harness the power of OpenAI’s GPT to transform your marketing research and insights. Collaborating with Ada is like having an expert researcher, brilliant statistician, and ace marketer on your team, helping you ask the right questions, choose the best experiments, pick out key insights, and seamlessly apply them to your business. 

 

 

 

Generative AI is a type of artificial intelligence designed to generate new content, like text, images, audio, or video. Unlike traditional AI systems that rely on explicit programming and rule-based logic, generative AI leverages advanced machine learning techniques to understand and mimic patterns in the data it's trained on.

In the context of language models like GPT-3 (Generative Pre-trained Transformer 3), generative AI is capable of creating coherent and contextually relevant text based on the input it receives. It can understand context, follow the structure of language, and produce human-like responses. 

This technology has great potential to automate tasks, enhance creativity, and assist in complex problem-solving scenarios. Especially within the market research and insights space. 

Here are 6 ways Generative AI is already being used in market research today: 

 

1. Strategic Research Recommendations 

When you’re first beginning your research journey, generative AI can give you initial guidance. Working with an AI consultant (like our very own Ada) you can share some context around your business and product, like objectives, challenges, and target audience (if known). And in turn, personalized receive research recommendations on the right tests and experiments to hep you achieve your goals. 

For example, let’s say a startup founder is looking to refine and de-risk their product before launch. They have a strong product design but need to dial in on the pricing, features, and messaging. Working with generative AI, you would learn: 

That a Van Westendorp Test will help you determine an optimal price for your product. 
Feature testing will help you gather feedback on your potential features, allowing you to choose the most needed/preferred by your target audience. This data would also be able to inform your messaging strategy, allowing you to focus on the most popular features of your product. 

 

Once matched, the tests can be automatically generated for you, meaning you only need to fill in the details specific to your product, industry, or brand. 

 

 

2. Streamlined Survey Creation & Test Building

When there isn’t a test or experiment to match your needs, AI can custom-tailor one to fit! By simply sharing some background about your business and the goals for your research, generative AI can craft tests that give you the answers you need. 

Let’s look at another example: let's say a customer success professional at a large SaaS company notices an uptick in user churn. A generative AI research consultant can turn that context into an in-depth survey that aims to understand: 

Overall satisfaction with the product
Why a user is leaving the platform 
And ways they might be able to retain them

 

 

3. Sample Size Calculations

No matter what type of research you are conducting, selecting the correct sample size is crucial to the accuracy of your data. If your sample is too small, it will undermine the accuracy of your results. But, on the other hand, if your sample is too large, small differences can quickly morph into (seemingly) significant insights. 

Using generative AI tools like Ada, all you need to know is the population size (estimate), your preferred confidence level (we suggest 95%), and the acceptable margin of error (we suggest 5%). From there, AI can calculate the number of respondents your test would need to have significant findings. 

 

4. Deep Analysis 

Generative AI can help you dig into your data, without getting lost in the details. With AI’s fast and powerful analysis capabilities, you can instantly get answers to commands like: 

“Determine the likelihood of repeat purchases”
“Investigate the impact of pricing on customer behavior”
“Explore the gender distribution of respondents” 
“Run sentiment analysis on text data in a question”

 

This functionality will get you the answers you need quickly, whether you're building a report or sharing insights on a team call. 

 

5. Executive Summaries & Key Insights 

Don’t miss the forest for the trees. Generative AI tools are excellent at analyzing and condensing large data sets into digestible information. So, it only makes sense that this skill would be ideal for market research!

You can use generative AI tools to create high-level executive summaries that get straight to the insights that matter. These can be handy to use yourself or share with department heads at your organization to equip them with the information they need to make better product, marketing, pricing, and brand decisions. 

 

 

 

6. Product & Marketing Asset Creation

Another one of generative AI’s talents is the ability to generate human-like text based on data, prompts, and context given by the user. 

This means that AI tools can scan your test data to generate: 

Product descriptions that highlight features your target audience cares about most
Ad copy that appeals to your ideal buyers
Blog posts that deep dive into your data and explore key insights and themes through your study

 

 

AI-Powered Customer Research with SightX

SightX is an AI-driven market research platform that offers you a single unified solution for product, brand, marketing, and pricing research. While powerful enough for insights teams at Fortune 500 companies, our user-friendly interface makes it simple for anyone to start, optimize, and scale their research. 

And with our new Generative AI consultant, Ada, you can harness the power of OpenAI’s GPT to transform your marketing research and insights. Collaborating with Ada is like having an expert researcher, brilliant statistician, and ace marketer on your team, helping you ask the right questions, choose the best experiments, pick out key insights, and seamlessly apply them to your business. 

 

 

Market research is the process of gathering, analyzing, and interpreting consumer data to make informed business decisions.

It is an indispensable tool that empowers companies to understand their audience, refine their strategies, and stay ahead in an ever-evolving market. In this piece, we'll explore the eight essential types of market research that form the backbone of strategic decision-making. 

 

Primary market research 

Primary research is the research data that you collect yourself by going directly to your target audience. This can be done a number of ways, but the most popular are often through focus groups, interviews, and surveys. 

 

Focus Groups

A focus group is a research method that brings together a small sample of people to answer questions about your product, brand, or category in a moderated setting. This group is often selected based on predefined demographic or behavioral traits, and the questions are designed to shed light on a specific topic of interest. 

While this approach is flexible and can provide deep insights, it does come with drawbacks. A group setting can cause a number of bias's to emerge, like social desirability bias (the desire to answer questions “correctly” instead of honestly) or acquiescence bias (a desire to please the interviewer). 

 

Surveys

A survey is simply a collection of closed and open ended questions that are sent to a targeted group of respondents, usually digitally.

Surveys are a great way to carry out your own primary research, allowing you to gather responses digitally instead of having to physically source participants. They also allow you to gather consumer data at a larger scale. But, much like anything, it does come with some drawbacks. Long monotonous surveys can cause respondent fatigue, which degrades the overall data quality. 

 

 

Secondary Market Research 

Secondary research involves using data that has already been collected, analyzed, and published; usually by an agency or reporting group. One of the key advantages to this type of research is that it allows you to get insights without having to collect the data yourself. It can save you time, budget, and allows you to build upon existing knowledge. 

But secondary research has its disadvantages. One major drawback is the fact that the data is not specific to your exact market, brand, or product, so making assumptions based upon it can be dubious. There is also no guarantee that the data you need is credible, authentic, or even available. 

 

 

Quantitative Market Research 

Quantitative market research is the collection of data that is quantifiable in nature. This can include responses to surveys, polls, or questionnaires. Researchers collect this type of data because it provides hard facts on a brand, product, or market. 

 

 

Qualitative Market Research 

Qualitative Research is the collection of data that is non-numerical in nature, making it difficult to measure. It often comes from focus groups or one-on-one interviews, where insights are derived from free-flowing discussions. Researchers will often collect this type of data to add more depth and perspective to their foundational quantitative market research. 

 

 

Branding Research 

Brand research is the process of gathering feedback from past, present, and prospective customers to better understand how your brand is perceived in the market. This type of research allows you to measure the ROI of your -often intangible- brand building efforts. 

Using brand trackers and surveys, you can measure and analyze key performance indicators like: 

 

Brand awareness 

Understand how familiar the market is with your brand.

Brand perception

Find out what consumers really think about your brand.

Brand usage

Determine how often customers purchase and use your products.

Brand preference

Measure the degree to which consumers choose your brand over competitors.

Brand loyalty

Understand the likelihood of customers continuing to engage with your brand.

Net Promoter score (NPS) 

Quantify the probability that a customer will recommend your brand.

 

You can use a mix of qualitative and quantitative methods, like focus groups and surveys, to gather the insights you need. 

Product Research 

Product research is the process of applying market research techniques during your product development cycle. It is used to make sure your products are ready for launch and perform as well as possible. 

Product research can be used at nearly every stage of your product development for a number of reasons: 

Find gaps in the market

Screen product ideas and prototypes 

Test product designs and pricing

Better understand the wants, needs, and preferences of your ideal customers

Find the best messaging to describe your product

 

Primary research methods will be the most useful here, with surveys and experiments ranking among the most popular ways to gather quantitative data. Some specific tools you can use include:

Concept testing for screening your product ideas, prototypes, and designs. 

Conjoint analysis or MaxDiff analysis for finding the right set of features for your product. 

Gabor-Granger or Van Westendorp to  help you find your optimal price. 

 

 

Customer Research 

Customer research explores the preferences, behaviors, influences, and needs of your target customers. The goal is to better understand the people who buy your products, and find ways to keep them happy. Some common themes of customer research include: 

Creating customer segments / buyer personas

Measuring metrics like customer satisfaction, loyalty, or a Net Promoter Score (NPS)

Checking in on the customer experience at various touchpoints

 

Surveys are often the most popular way to gather customer research at specific points along the customer journey.

 

Marketing Research 

Marketing research uses tools and techniques from market research to improve marketing impact and better understand audiences. It is used to collect feedback from current and target customers that can be used to inform your messaging, advertising, and overall marketing strategy. 

Marketing research is often used to: 

Understand what your audience wants, where they are, and what messaging resonates with them to ensure marketing ROI. 

Learn the topics, formats, and tone your audience prefers to deliver blog, video, and social content that wins. 

Better align your messaging to speak the language of your customers. 

 

A mix of surveys, ad testing, messaging testing, and tracking studies be useful for this type of research. 

Market Research with SightX

SightX is an AI-driven market research platform that offers you a single unified solution for product, brand, marketing, and pricing research. While powerful enough for insights teams at Fortune 500 companies, our user-friendly interface makes it simple for anyone to start, optimize, and scale their research. 

And with our new Generative AI consultant, Ada, you can harness the power of OpenAI’s GPT to transform your marketing research and insights. Collaborating with Ada is like having an expert researcher, brilliant statistician, and ace marketer on your team, helping you ask the right questions, choose the best experiments, pick out key insights, and seamlessly apply them to your business. 

If you're ready to dive in click the button below to get started for free!

 

 

 

 

When designing a survey, many factors come into play. Of course, you’ll need to include questions surrounding your core metrics. However, delivering a seamless respondent experience can make all the difference in data quality and survey completion rates. 

Page randomization and looping are two fantastic ways to optimize your survey’s flow for better respondent engagement! In this piece, we'll be exploring these techniques and their benefits. 

 

What are Survey Pages? 

Survey pages go far beyond mere question organization. They are your storytellers, grouping questions by topic, audience, or conditional logic to create a narrative that keeps respondents engaged. Now, brace yourself as we introduce the dynamic duo of Randomization and Looping, turning your surveys into an interactive masterpiece.

 

Randomization

Page randomization allows you to inject spontaneity into your surveys by shuffling the questions within a page. Giving each respondent a unique experience and reducing the likelihood of potential bias by ensuring that the position of a question doesn't influence the way respondents answer.

 

Looping 

If you need to dive deeper into specific topics without overwhelming your respondents, looping is very helpful. Generally, this tool is used when you want to ask a set of questions about each answer in a previous question. 

For example, you may want to ask respondents which brands they have bought from a given list. Then for each brand they’ve purchased from, respondents would receive a series of questions about their experience. 

 

When to Use Survey Page Randomization & Looping 

Consider using Randomization and Looping within a Page if your study needs the following:

 

Minimizing Predictability

Randomizing questions keeps respondents on their toes, reducing predictability and encouraging more thoughtful responses.

 

Reducing Bias

Randomization minimizes order bias, ensuring that the position of a question doesn't influence the way respondents answer.

 

Increasing Engagement

The element of surprise introduced by randomization makes the survey more engaging, keeping respondents interested and focused.

 

Versatile Data Collection

Looping allows for versatile data collection by repeating questions, enabling in-depth exploration of specific topics or tracking changes over time.

 

Balancing Attention

By presenting questions in a random order, all questions receive balanced attention, preventing some from being consistently overemphasized.

 

Use Cases for Survey Page Randomization & Looping 

 

Use Case: Product Feedback Surveys

When conducting a feedback survey for a product with a diverse set of features, survey pages allow you to group questions by feature. This creates thematic consistency and guides respondents through a coherent exploration of each aspect of your product. 

 

Use Case: Audience-Specific Questionnaires 

If your survey targets different customer segments with unique preferences, use survey pages to organize questions specific to each audience, ensuring a personalized and relevant survey experience that resonates with every respondent.

 

Use Case: Adaptive Surveys  

If you  want to capture nuanced feedback based on a respondents' previous answers, you can use survey pages with conditional logic to enable dynamic questioning. This adapts the survey path based on individual responses, and provides a customized journey that acknowledges and responds to each respondent's input.

 

Tips for Using Survey Page Randomization & Looping

 

Consider the Respondent's Experience

Put yourself in the shoes of the respondents. Consider how the survey structure, including pages, randomization, and looping, will impact their experience. Aim for clarity and simplicity.

 

Organize Questions Thoughtfully

Group questions into pages based on common themes, topics, or target audiences. This ensures a logical flow and makes the survey more respondent-friendly.

 

Clearly Communicate Instructions

Provide clear and concise instructions to respondents, especially when using features like looping. Explain why certain questions are repeated or presented in a specific order to maintain respondent engagement.

 

Strategically Apply Randomization

Be strategic in applying randomization. Randomize questions when the order could introduce bias or influence responses. For instance, randomizing your question order can minimize sequence effects.

 

Test Randomization and Looping

Thoroughly test the randomization and looping logic in different scenarios. Ensure that questions are randomized correctly, and looping conditions are well-defined without creating unintended repetitions.

 

Monitor Survey Length

Be mindful of survey length. While randomization and looping offer flexibility, an excessively long survey can lead to respondent fatigue. Strike a balance between comprehensive data collection and respondent engagement.

 

 

Surveys with SightX

The SightX platform is the only tool you'll ever need for market research: a single, unified solution for consumer engagement, data collection, advanced analysis, and reporting. While powerful enough for insights teams at Fortune 500 companies, the user-friendly interface makes it simple for anyone to start, optimize, and scale their research. 

And with our Generative AI consultant, Ada, you can harness the power of OpenAI’s GPT to transform your marketing research and insights. Collaborating with Ada is like having an expert researcher, brilliant statistician, and ace marketer on your team, helping you ask the right questions, choose the best experiments, pick out key insights, and seamlessly apply them to your business. 

If you're ready to dive in click the button below to get started for free!

 

 

 

 

In the world of survey design, embedded variables (EV) are a powerful tool that can significantly enhance the depth and precision of your data collection. 

However, like any powerful tool, it requires careful handling and an understanding of its nuances to ensure optimal functionality. In this guide, we'll delve into key considerations and best practices to harness the full potential of embedded variables.

 

What are Embedded Variables and Why Are They Important? 

Embedded variables (EV),  also sometimes known as a “typing tool” or “segmentation tool”, allow you to set parameters that will automatically segment (or type) research respondents as they take your survey. You can think of EVs as a type of coding that groups your respondents based on their responses. 

This is very useful for multiple reasons. 

First, it saves you time on the backend by pre-segmenting your audience before the analysis process even begins. Making it easy to filter and compare different personas instantly. 

It can also reduce potential bias and serve as a routing tool for respondents. For example, let’s say you need to screen a few ad concepts for an upcoming campaign. This campaign will be reaching a few target demos for you, you’d like to ensure that respondents only see the ad concept that is relevant to them. This means you’ll need to segment (or “type”) them in real-time.

Taking it a step further, let’s say one ad concept is geared towards a wellness-focused audience while the other is for an audience that regularly enjoys fast food. 

While you could simply ask respondents to rate how healthy their eating habits are on a scale from 1-10, this is very likely to trigger social desirability bias. 

What does this mean? It means that people will often over-report good behaviors and under-report the bad ones to look favorable. In this specific case, people may choose to rate their eating habits as much healthier than they actually are. 

So how can you avoid this? And how would EVs help? 

You could instead ask questions about the types of foods and restaurants your respondents eat at regularly. Using EVs you could ensure that those who reported eating organically, visiting restaurants like Sweetgreen, or exercising daily would be grouped and shown a wellness-focused concept. While those who regularly eat at restaurants like McDonalds and did not report movement/exercise as a part of their daily or weekly life would be shown a different concept that better aligns with their preferences. 

 

Tips for Using Embedded Variables

 

Defining Your Variables

When defining options for embedded variables, it's crucial to ensure they are mutually exclusive. Overlapping definitions can compromise the effectiveness, leading to inaccurate data representation. A meticulous approach to option definitions sets the foundation for a well-functioning embedded variable.

 

Be Selective About the Variables You Include

Understand that not every option needs to be included in one of your variables. Selective inclusion is acceptable, and for options not covered, be diligent in setting the correct value to avoid discrepancies in data interpretation.

 

Avoid Deleting Options Post-Data Collection

If you have existing data, refrain from deleting options in an embedded variable or any multi-select. The system processes them based on order/position, not content. Rename unused options instead of deleting them to maintain data integrity.

 

Don’t Alter Your EVs After Data Collected Has Begun 

Beware that altering conditions of embedded variables after data collection has begun won't retroactively update the embedded variable for previously collected data. Plan ahead and ensure your setup aligns with your analytical goals from the start.

 

Survey Sequence Matters

You can set up embedded variables anywhere in the survey as long as the necessary precursor questions are posed beforehand. This flexibility empowers you to structure your survey in a way that aligns with the logic of your research. 

 

Consider Naming Your Leftover Options 

When using an EV, the platform requires you to input an “Other Value” for those that may not fall into the categories you’ve laid out that you needed. Thinking about a fast food example, if you’re segmenting respondents based on their some people fit into the “Burgers and Fries” category and a “Chicken” category, there could be many more respondents who too fit in an “Other”. You have a choice to label those other options (ex: Coffee) or simply put “Other”.

 

 

No matter the type of study you run, the ultimate goal of your research is to better understand how a population thinks and behaves. That population could be your customer base, shoppers in your category, or customers loyal to your competitors. 

To get accurate data about that population, you have to round up a sample of respondents who accurately reflect them. 

Sometimes, whether due to sampling error or other factors, there is a misalignment between your target population and your study’s sample. When this happens, you can use weighting to correct the course. Find out how: 

 

What is Weighting? And Why Should I Use it? 

In market and consumer research, we gather data from a sample of a given population to make judgments about the whole. But sometimes, that data becomes skewed when certain segments are over- or under-represented within the sample. 

Weighting survey or experiment data is a technique used to bring your sample in line with the population you are trying to study. You can do this by giving more or less “weight” to specific segments within your sample. 

For example, let’s say your target audience is the general population of the United States. While you set your audience quotas to match the latest census data, you notice that the number of men who have responded to the survey is low. If you’re unable to get more men to complete your survey, you can weight the data from those who did complete your survey, making their data more evenly represented. 

Ideally, the market and consumer research data you collect will be from a sample that accurately reflects your target audience. But when there are discrepancies between the two, weighting is an incredibly useful alternative to re-running a project or losing valuable data. 

 

 

Types of Weighting

When it comes to weighting, you have a few options depending on your needs: 

 

Cell-Based Weights

With this weighting methodology, you can specify the weight/multiplier for each specific group in your sample. You can calculate your multiplier by dividing the population [desired state] by your sample. 

For example, let’s say you survey 100 respondents, 80% are plant-based eaters and 20% are omnivores. You know that only 30% of your target population is plant-based. So, it’s fair to assume that the overall results will be biased due to the inaccurately large representation of plant-based eaters. 

With cell-based weighting, it’s a simple fix! If you know that only 30% of your target population is plant-based, and 70% are meat-eaters then the weights you would apply to your data set would be:

Plant-based: 30/80 = 0.375

Meat-eaters: 70/20= 3.5

 

RIM/ Raking Weights (Random Iterative Method)

This technique is used when you need to weight multiple variables that overlap. Using this technique, you define the audience variables and their distribution, letting the software iteratively adjust the weights for each case until the sample distribution matches the audience parameters you set. 

For example, let’s say you know that your target audience should be 48% male and 52% female, 40% should have a high school education or less, 31% should have completed some college, and 29% should be college graduates.

If your sample was misaligned to this audience, raking weights would be applied to correct the gender distribution of the sample. Next, the weights would be re-adjusted so that the education groups are in the correct proportion. If the adjustment for education pushes the gender distribution out of alignment, then the weights are adjusted again to correct it. The process is repeated until the weighted distribution of all variables matches their specified targets.

 

Weighting with SightX

The SightX platform is the only tool you'll ever need for market research: a single, unified solution for consumer engagement, data collection, advanced analysis, and reporting. While powerful enough for insights teams at Fortune 500 companies, the user-friendly interface makes it simple for anyone to start, optimize, and scale their research. 

And with our new Generative AI consultant, Ada, you can harness the power of OpenAI’s GPT to transform your marketing research and insights. Collaborating with Ada is like having an expert researcher, brilliant statistician, and ace marketer on your team, helping you ask the right questions, choose the best experiments, pick out key insights, and seamlessly apply them to your business. 

If you're ready to dive in click the button below to get started for free!

 

 

Technology drives innovation and change. 

The printing press drove the democratization of knowledge. The light bulb led to the first domestic electrical wiring, paving the way for countless in-home appliances. And now, we are watching as Artificial Intelligence (AI) transforms the way we live and work. 

Within AI there are many branches, each suited for a specific use case. But one particularly interesting branch is Natural Language Processing (NLP), which has revolutionized the way we understand the written word. This is especially true within the market research field, where NLP has had a transformative impact on deciphering the wealth of textual data. 

In this blog, we'll dive into the world of natural language processing, exploring its essence, applications, and the profound impact it has had on our ability to analyze text at scale. 

 

What is Natural Language Processing? 

Before we get too deep, let’s cover the basics. 

Natural Language Processing is a subset of AI focused on the interaction between computers and human language. It enables machines to comprehend, interpret, and analyze text. 

In market and consumer research specifically, NLP is used for text analysis on open-ended survey questions, product reviews, social media comments, or other sources of text-based data. 

 

How to Use NLP: Use Cases in Market Research

 

1. Understand Consumer Sentiments

While multiple-choice or ranking-style questions can give you some insight into consumers' perceptions of your brand or product, it's difficult to get the full picture. 

NLP allows you to take raw customer feedback and analyze it to identify how people feel about your brand, product, or messaging. It does this by examining the text-based data it’s given, categorizing the emotional tone or attitude expressed in each piece of text. For those curious, this involves analyzing individual words and phrases to discern whether the overall sentiment is positive, negative, or neutral

The data you receive not only aids in assessing customer satisfaction but also enables proactive decision-making and targeted improvements based on the identified sentiments. Ultimately contributing to a more customer-centric and responsive approach. 

 

2. Uncover Trends & Patterns

Because NLP algorithms can scour vast amounts of unstructured textual data, it can easily uncover emerging trends and patterns in your customers' feedback. For example, maybe you notice that customers keep mentioning support wait times in a negative context. Or, maybe an analysis of your product reviews yields insight into a new competitor encroaching on your market share. 

These insights allow you to see patterns emerge, long before they affect your bottom line. Enabling your organization to develop strategies that align with the evolving market landscape. 

 

3. Find the Language that Customers Use

How confident are you in the language you use to describe your brand or offerings? 

While your marketing, sales, or product teams may use certain words or phrases to describe and promote your products, that doesn’t necessarily mean it’s the same language used by your customers or target market. 

By using NLP on raw text data, you can find commonalities in the descriptive words used by your customers and target audience. These insights will allow you to craft messaging that better aligns with the natural language surrounding your brand, products, or industry, helping to set you apart from the rest of the pack. 

 

 

The Benefits of NLP for Text Analysis in Market Research

 

Make Data-Driven Product Decisions

By analyzing product feedback with NLP, you can pick out common issues, uncover barriers to adoption, and discover unique ways customers may be using your product. The insights will help you drive your product development in a customer-centric way, tailoring your new launches and updates to the needs and use cases you know to be true. 

 

Improve Your Messaging

Once you understand the words and phrases your customers use when discussing your product, you can insert them directly into your messaging and marketing collateral. Doing this will not only show your customers that you understand their pain points, but it will also allow you to speak their language in a way your competitors just can’t. 

 

Enhanced Accuracy

When combing through hundreds, if not thousands, of text-based data snippets, human error is bound to occur. NLP on the other hand operates with remarkable precision, minimizing the risk of misinterpretation or oversight. Ensuring the insights derived are reliable and reflective of actual sentiments and trends. 

 

Scalability

As your business grows, the volume of textual data will also grow exponentially. This is where scalability becomes a critical factor. NLP systems can effortlessly handle large datasets, making it feasible to analyze extensive amounts of information swiftly. This scalability is particularly advantageous for businesses aiming to expand their market research efforts as they grow. 

 

 

Text Analysis with SightX

The SightX platform is the only tool you'll ever need for market research: a single, unified solution for consumer engagement, data collection, advanced analysis, and reporting. While powerful enough for insights teams at Fortune 500 companies, the user-friendly interface makes it simple for anyone to start, optimize, and scale their research. 

With our Generative AI consultant, Ada, you can harness the power of OpenAI’s GPT to transform your marketing research and insights. Collaborating with Ada is like having an expert researcher, brilliant statistician, and ace marketer on your team, helping you ask the right questions, choose the best experiments, pick out key insights, and seamlessly apply them to your business. 

If you're ready to dive in click the button below to get started for free!

 

 

 

Your pricing is nothing to take lightly. Like it or not, the number you choose sends signals about your product quality and brand's status to potential customers. 

If you want to find that pricing sweet spot for your product, you'll have to better understand what the market is willing to pay and how consumers perceive different price points when attached to your product.  

In this blog, we're diving into pricing research, unraveling its significance and introducing you to the indispensable tools that can make or break your pricing strategy: 

 

What is Pricing Research? 

Pricing research is a type of market research that allows you to find out what people are willing to pay for your offering. While there are a few different types of pricing studies to choose from, the goal is to understand the price sensitivity in your market, explore new pricing strategies, and investigate the relationship between price and demand for your offering. 

 

Pricing Research Methods

 

Van Westendorp

If you are looking for a simple and quick study that will give you a lower threshold, upper threshold, and optimal price point for your offering, Van Westendorp is your test. 

It accomplishes this by asking respondents four core questions: 

At what price would you begin to consider the product so inexpensive that you would question the quality and not purchase it? 
At what point would you think the product to be a bargain? 
At what price would you say this product is starting to become expensive- to the point that you’d have to give some thought to buying it? 
At what price point would you consider the product to be so expensive that you wouldn’t consider buying it? 

 

While the questions themselves are quite simple, the output is powerful and will reveal a range of acceptable prices for your product.

This range is end-capped by the point of marginal cheapness (PMC) on the left and the point of marginal expensiveness (PME) on the right. If you price your offering any lower than the PMC, shoppers would likely find your product too cheap and question the quality. Similarly, any price above the PME would be considered too expensive for most of your market. 

But that’s not all it will show you. 

Directly in the center, you will see the intersection of the “too cheap” and “too expensive” lines. This is your optimal price point. What makes it optimal? It minimizes the number of people who are dissatisfied with your price one way or the other. 

 

If you want to learn more about this pricing methodology, check out this explainer. 

 

Gabor-Granger

Gabor-Granger is a great choice if you already have a defined price range, but need to pinpoint an exact price that will maximize your revenue without compromising consumer demand. 

It accomplishes this by giving respondents a series of nearly identical questions, the base of which being: “Would you buy [PRODUCT] for $[PRICE]?” 

Once the first question is posed to respondents, the subsequent questions are adapted to their answers. Presenting respondents with higher prices, until they indicate a lack of purchase intent. Ultimately, these questions are designed to determine the highest price a consumer is willing to pay. 

Based on the responses, a demand curve is generated, showing the relationship between price and demand and highlighting an optimal price point that maximizes revenue. Showing you exactly how fluctuations in your pricing will affect demand for your product. 

 

To learn more about Gabor-Granger, see the full guide here. 

 

Automated Conjoint Analysis

If you are interested in learning not only about price but also the optimal combination of product attributes to pair with it, we would recommend a conjoint analysis.

Conjoint analysis helps to identify the rules consumers explicitly (and implicitly) use to make their purchasing decisions. The premise of this technique is fairly simple. Consumers conduct mental trade-offs between pricing and other factors like quality, functionality, style, etc. 

For this type of research, you expose consumers to multiple product components shown in various combinations, each with different pricing. Once the data is collected, the subsequent analysis will show you what features consumers value the most and the price(s) they are willing to pay for them.  Ultimately allowing you to not only choose the right price, but the right features to pair with it. 

 

What are the Benefits of Conducting Pricing Studies? 

When done correctly, the ROI of pricing research is massive. The insights can help you in any number of ways, by enabling you to: 

Find a price that maximizes your revenue without compromising demand. 

Uncover the upper and lower threshold prices that consumers are willing to pay. 

Use your ideal price point and number of expected customers to predict revenue. 

 

Ultimately, the insights you take away from pricing studies will let you to better understand your market's willingness to purchase and the prices they consider acceptable, guiding your strategy. 

 

 

Pricing Research with SightX

If you're ready to maximize your revenue, we've got the tools to make it happen! The SightX platform is the only tool you'll ever need for pricing research:: a single, unified solution for consumer engagement, data collection, advanced analysis, and reporting. While powerful enough for insights teams at Fortune 500 companies, the user-friendly interface makes it simple for anyone to start, optimize, and scale their research. 

And with our Generative AI consultant, Ada, you can harness the power of OpenAI’s GPT to transform your marketing research and insights. Collaborating with Ada is like having an expert researcher, brilliant statistician, and ace marketer on your team, helping you ask the right questions, choose the best experiments, pick out key insights, and seamlessly apply them to your business. 

If you're ready to dive in click the button below to get started for free!

 

 

Starting a new business can be a lot like starting an exciting adventure. But much like any significant journey, it requires some preparation.

Maybe you have an innovative way to fill a market gap or are building a product that will change the landscape of your entire industry. Whatever your story may be, the same question applies: will people buy it?

Sure, you can choose to take your chances with the market. But, you should know that there are tangible ways to set yourself up for success early in your development. 

By taking the time to understand your industry, ideal customers, and the competitors in your space, you'll find that breaking into your market becomes that much easier. Read on to learn more about market research for startups, and the 8 studies every startup should run. 

 

What is Market Research?

Before we dive into the nitty-gritty, let’s cover some basics. 

Market research is the gathering of information about your target audience, competitors, and industry trends. With the intention to use that data to guide your decision-making. 

Market research is NOT something that you do just to validate your product ideas or screen your ads. 

Yes, you certainly can do those things. However, good market research allows you to evaluate what is already available to consumers and how it is (or isn’t) meeting their needs. It affords you a window into consumers' minds, allowing you to discover the real challenges they face and what you can do to ameliorate them. 

 

Why is Market Research Important for Startups?

The goal of any business should be to delight its customers. But if you’re not asking them what they want; how will you know?

When you’re entering an established industry as a startup, you’ve got to understand the current landscape so you can hold your own against the larger players in your space. Market research allows you to do that by giving you access to the preferences, perceptions, and opinions of your audience. 

Ultimately, the insights from market research will inform your product or service development, help you discover the messaging and branding that resonates, and enable you to create better customer experiences. 

 

8 Studies Every Startup Should Conduct 

While there are many types of market research studies and experiments you can run, here are the 8 that you should consider first for your startup: 

 

1. General Market Analysis Survey

This first study is a simple one. A market analysis survey will give you data on your market’s size, trends in your space, growth potential, and key customer segments (or buyer personas) in your market. The overall goal of this study should be to identify opportunities and potential gaps within your market to better differentiate your brand. 

 

2. Customer Research

The second study we’d recommend is a survey (or series of surveys) that examines your ideal customer. This type of research should focus on your customers' needs, preferences, behaviors, and pain points. This data will help you to build customer-centric products, ad campaigns, and experiences as you scale. 

 

3. Competitor Analysis

If you want your business to thrive, you've got to understand the competitive landscape. But if you're on the outside looking in, it might be difficult to tell what's working for your competitors and where they fall short. 

A competitor analysis survey is primarily focused on understanding the strengths, weaknesses, and market positioning of the players in your industry. It will help you get a better grasp on their product/service offerings, pricing strategies, and marketing tactics, enabling you to find and fill gaps in the market. 

 

4. Brand Perception and Positioning

While it is important to understand where your competitors stand, it’s equally important to understand how your brand fares with the public. Brand perception and positioning studies will help you measure how your brand is perceived by your market, along with the values and positioning that your audience associates with your brand. 

 

5. Product/Feature Testing

It’s been said that around 95% of all new product ideas fail. While the validity of that exact figure remains to be seen, the point remains true- product development is riddled with trial and error, with very few products rising to the top. 

Product research and feature testing will allow you to gather feedback on your prototypes during all stages of development. This can include using methodologies like MaxDiff or Conjoint Analysis to identify features to prioritize or identify an ideal combination of features. 

This feedback can be crucial for refining and improving offerings before launching to a wider market, or refining after an initial launch. 

 

6. Price Sensitivity Research

Pricing is a crucial factor that often determines the success (or failure) of an offering. But without solid data on what consumers are willing to pay, you are left with best guesses and gut feelings. 

Pricing research allows you to test the price sensitivity in your market and investigate the pricing models that work best for your business. Tools like Van Westendorp’s price sensitivity meter and Gabor-Granger will show you the prices consumers are willing to pay for your product and give you the optimal price point that maximizes your revenue without compromising demand. 

 

7. Industry Trends and Insights

Research can extend far beyond product, marketing, competitors, and pricing. Regularly tracking industry advancements and trends is a great way to ensure your organization stays at the forefront of innovation (and on the top of customers' minds). 

You can use these types of studies to stay up-to-date on the latest market trends, technological advancements, regulatory changes, and shifts in consumer behaviors. 

 

8. Feasibility Studies

Whether you are just beginning the ideation process or making plans for a brand expansion, feasibility studies are invaluable. You can use this type of research to evaluate factors like market demand, cost structure, resource availability, and potential risk factors. 

 

Startups should approach market research as an ongoing process rather than a one-time activity. Regularly updating and refining your understanding of your market will help you make informed business decisions and stay competitive.

 

Running Market Research Studies for Your Startup with SightX

The SightX platform is where growth happens. Our software gives you the tools to ask the right questions, learn what people really want, and respond with the right actions. Every time. 

With our new Generative AI consultant, Ada, you can harness the power of OpenAI’s GPT to create custom surveys and goal-oriented studies instantly, analyze the results, and get  key insights in seconds. 

If you're ready to dive in click the button below to get started for free!

 

 

You might be familiar with the acronym GIGO. It stands for Garbage In, Garbage Out.

It dates back to the late 1950s, used by US Army Specialist William Mellin when explaining to the press that computers could not truly think for themselves. Which meant that "sloppily programmed" inputs would lead to sloppy and/or incorrect outputs.  

Despite our  advancements, this statement is as true today as it was back then. Especially when it comes to generative AI tools. 

While our Generative AI research consultant Ada is adept at creating custom projects and converting complex data into clear insights, effective communication is key for optimal results. In the following piece, we will explore the significance of prompts and provide valuable insights from our team of experts to help you become a prompt-writing pro: 

 

What are Prompts? And What is Their Role? 

Prompts are simply the instructions that you give generative AI tools (like Ada) to produce an output. But prompts aren't just about asking your question or sharing your needs. A well-written prompt will include contextual details about your business, goals, or the challenges you are facing. 

When packaged together, this information allows generative AI tools like Ada to create tests tailored to your unique situation, and pick our key insights in your analysis that she knows will be important to you. 

 

10 Tips for Writing Generative AI Prompts 

 

1. Be Specific with Your Objectives

When working with generative AI, general prompts will yield general results. This is why it’s so important to clearly state your goals in your prompt. For example, the prompt: "Identify the top factors influencing purchase decisions among our target demographic." is clear, focused, and direct with the objective.

 

2. Prioritize Your Objectives

If your request has multiple objectives, make sure that you rank them in order of importance within your prompt. For example, let's say your main objective is to screen new packaging concepts to learn which your customers liked best. But you'd also like to gather some data on purchase intent and potential upgrades for later product updates. A good prompt for this might be: "I need to screen my new packaging concepts to find out which is most popular with my target audience. Additionally, I would like to gather information on purchase intent and ways I can improve my product."

 

3. Consider Open-Ended vs. Closed-Ended Responses

Before you begin drafting your prompt, consider whether you want Ada to explore a topic broadly or, instead, answer a specific question. For example, asking Ada: “What are some of the key insights from my data?” would yield very different results from asking: "What market segment would be most likely to purchase my product?" 

 

4. Ask for Summaries and Key Takeaways

Ada is fantastic at summarizing your data and distilling it into bite-sized insights. Once you have data to analyze, try a prompt like: "Summarize the three most critical insights from this research for quick executive review."

 

5. Ask for Comparative Analysis 

Any prompts that encourage Ada to compare data segments can reveal interesting trends and outliers you may have otherwise missed. For this example, let's say you needed to better understand how different generations felt about your product. You could consider a prompt like: "Compare the satisfaction levels across different age groups for our latest product." 

 

6. Seek Cause and Effect

Ada is also fantastic at evaluating and investing potential relationships between variables. Consider trying prompts like, "What is the correlation between ad exposure frequency and brand recall?"

 

7. Request for Predictive Insights

A large facet of consumer research is uncovering trends early to give your organization time to act. To accomplish this, you can ask Ada: "What do the patterns in the current data suggest about upcoming market shifts?"

 

8. Explore Segment-Specific Data

If you are curious about any data that deviates from the norm, try using the prompt: "Highlight any unexpected responses in the data and provide possible reasons.

 

9. Inquire About Anomalies

If you are curious about any data that deviates from the norm, try using the prompt: "Highlight any unexpected responses in the data and provide possible reasons.

 

10. Focus on Actionable Insights

After you've gathered your insights, you can collaborate with Ada on the best ways to apply your findings. Prompts like: "Suggest actionable steps based on the most significant findings from the test" can turn data into decisions.

 

Remember, creating the perfect prompt is a skill that you can develop over time with practice. Great prompts, paired with Ada's analytical prowess can unlock a treasure trove of insights from your marketing research tests. 

 

Generative AI with SightX 

At SightX, we have always pushed the boundaries of market research. Our powerful tools, advanced methodologies, and automated systems have made our client’s lives easier for years. 

But with our new Generative AI research consultant Ada, we are changing the game. 

By harnessing the power of OpenAI’s GPT, Ada is ready to transform the way you gather insights. See for yourself: