This piece is the second in a three-part series exploring the ways AI makes it difficult to lose touch with consumers in go-to-market and product launch strategies. Specifically, we are investigating the excuses often used when organizations forget to center their customers, and how AI is making those excuses obsolete. To read part one, click here. 

Excuse 2: “Adding sophisticated market research techniques throughout our go-to-market motion will be too slow and expensive.”

Another key reason the consumer often gets sidelined in product launches is that it has historically been very expensive and slow to leverage market research capabilities (beyond simple surveys) to fill blind spots. And if done cheaply and fast, the quality precipitously declines in inverse proportion. 

But with generative AI, it’s no longer a matter of “speed, quality, cost … pick one”. It’s now “speed, quality, cost … pick three”.

Why? Because increasingly, users are able to use prompts to create  studies in-platform within minutes.  That same study, just a year ago, would only have been able to be created, deployed, and summarized by teams of researchers, with months-long timelines, with dozens of email exchanges and status update calls, at budget-busting costs. 

If you’ve ever opened a >70 slide deck, a 100-page research report, or joined a weekly hour-long research project update call for the 13th week in a row, you may know what I mean. It’s not unlike eating a soup sandwich.

Let’s consider a few of these study designs that go beyond simple customer surveys,, and the implications of how AI is changing the game by improving speed, quality, and cost:

 

Pre-Launch De-Risking: Validate Ideas Before Significant Investment

 

Concept Testing

Traditionally, testing product concepts involved static mockups or physical prototypes. In the digital age, users were able to upload images or crudely sketched design concepts. Today, AI-powered platforms increasingly allow users to create ultra-realistic product simulations in seconds, for things that have never existed in the real world. 

With simple prompts, users can gather immediate feedback on aesthetics, features, and messaging, refining a concept before significant investments are made in development. Results are summarized in seconds, and immediate next actions are clarified to improve concepts on behalf of prospective customers.

 

MaxDiff

Gone are the days of tedious surveys asking consumers to rank a long list of features. AI platforms are increasingly able to create sophisticated MaxDiff studies, where participants choose their most preferred and least preferred options from sets of product features, randomized intentionally based on participant demographics or on prompt-informed hypotheses about consumer differences. 

This helps identify which features hold the most weight with a target audience, allowing product development efforts to be efficiently prioritized, and for product fit to be maximized relative to a wide range of possible options.

 

Conjoint Analysis

Imagine simulating real-world purchasing scenarios with countless product variations and pricing combinations, even when the concept has never existed. 

AI platforms make this a reality through conjoint analysis and increasingly, AI-generated conjoint simulations. By understanding how price interacts with various features and configurations, both real and imagined, the optimal combination that drives conversions and maximizes customer satisfaction can be determined prior to incurring significant inventory expenditures.

 

Post-Launch Growth Acceleration: Refining Your Product on Behalf of Customers

 

Buyer Persona Development Through Segmentation

Consider for a moment how often your preferences as a customer are identical to everyone else in the market. 

Not often, right? 

Audience segmentation brings to life non-obvious buyer personas who have different price points, product preferences, or unique keywords and language they use to search. 

AI-powered platforms can analyze vast datasets to segment your audience into distinct groups with shared characteristics, often using unsupervised clustering algorithms. This allows for detailed buyer personas to be created, providing invaluable insights into their motivations, pain points, and preferred communication channels. Marketing efforts and product roadmaps can in turn be tailored  to cater to specific customer needs, ensuring long-term product adoption and maximal reach.

 

TURF Analysis:

Understanding how different customer segments interact with your product and competitors is crucial for post-launch optimization. 

AI allows for efficient TURF analysis, such as going beyond an approximate approach. Employing linear programming to assess every possible combination, enables users to find optimal solutions without approximations. It also can allow the identification of segments that are most susceptible to competitor offerings. This empowers users to tailor marketing messages and strategies for each segment, maximizing customer retention and acquisition, even as the market may shift as a result of variables such as competitor actions and macroeconomic conditions. 

 

In conclusion, AI is increasingly helping consumer insights researchers leapfrog the speed and cost barriers traditionally faced in common GTM motions. And it’s allowing for a range of new product concepts to be comprehensively studied quickly, even when they’ve never existed. As prompt-driven imagery and video become more widely available in consumer insights platforms, things will only become faster and more cost effective, leaving GTM teams very little excuses for sidelining customers when bringing a new product or service to consumer markets. It’s an exciting time to be a researcher! 

 

Stay tuned for part 3!

In our new series, we’re exploring the ways AI is making it harder than ever to lose touch with consumers in go-to-market and product launch strategies. Specifically, we will look at the excuses often used when organizations forget to center their customers, and how AI is making those excuses obsolete. 

Excuse 1: “We were so busy executing our GTM process that we didn’t have time to talk to customers.”

How often does your organization embark on a product launch plan or a go-to-market strategy with the intent of staying deeply connected with the end customer, only to realize months later that the only thing happening is the churn of a large bureaucratic engine? Where your future customers have become an afterthought?

It’s not hard for even well-intentioned businesses to lose their way. Nobody sets out to execute a go-to-market motion that devolves into never-ending conference calls with a crowd of 50-plus people talking past each other, referencing acronym-filled status updates. 

Where words like “deadlines” don’t mean that a “line” has been drawn, and the product launch is “dead” if not achieved by the end date. In such cases, “deadlines” are more appropriately deemed, “estimates that my boss requires for the executive team, that we all know we can extend with a laundry list of acceptable reasons we keep in our back pocket.” 

The focus gets placed on the process, but the process isn’t the thing! It’s the customer, and the unmet consumer demand that the new product or service will fulfill. Sounds hackneyed, but my bet is that the example above will resonate with more than a few of you. 

Or consider this… in the last go-to-market product launch that you were involved with, how many members of the team were engaged in routine conversations with prospective customers, and deeply knowledgeable about the consumer markets that the product or service was being launched into?

In the “Resist Proxies” section of Jeff Bezos’ 2016 Letter to Amazon’s Shareholders (later referred to as Shareowners), he states: 

 “As companies get larger and more complex, there’s a tendency to manage to proxies… A common example is process as proxy. Good process serves you so you can serve customers. But if you’re not watchful, the process can become the thing. This can happen very easily in large organizations. The process becomes the proxy for the result you want. You stop looking at outcomes and just make sure you’re doing the process right. Gulp. It’s not that rare to hear a junior leader defend a bad outcome with something like, ‘Well, we followed the process.’ A more experienced leader will use it as an opportunity to investigate and improve the process. The process is not the thing. It’s always worth asking, do we own the process or does the process own us?”

When it comes to following a go-to-market process, getting the prospective customer off of the sidelines and placed on a pedestal as the central focus for every decision is the #1 way to de-risk product launches and rapidly accelerate post-launch sales. And in the age of AI, it has never been easier to do this. 

 

Using AI for Customer-Centric Go-to-Market Strategies

Before we talk about how AI is making this easier and more cost effective, I’d be remiss to not share the very next paragraph from this Letter to Shareowners, which ironically is perhaps one of the most damning statements about market research I’ve read from a business leader: 

“Another example: market research and customer surveys can become proxies for customers – something that’s especially dangerous when you’re inventing and designing products. ‘Fifty-five percent of beta testers report being satisfied with this feature. That is up from 47% in the first survey.’ That’s hard to interpret and could unintentionally mislead. Good inventors and designers deeply understand their customers. They spend tremendous energy developing that intuition. They study and understand many anecdotes rather than only the averages you’ll find on surveys. They live with the design. I’m not against beta testing or surveys. But you, the product or service owner, must understand the customer, have a vision, and love the offering. Then, beta testing and research can help you find your blind spots. A remarkable customer experience starts with heart, intuition, curiosity, play, guts, taste. You won’t find any of it in a survey.”

**Not cringing** as the Vice President of Strategy at SightX, an AI-Powered End-to-End Consumer Insights platform.

But I agree. And I acknowledge that Amazon has a bit of experience bringing new products and services to market. This statement helps put market research and consumer insights into its appropriate place, in the “...Then, beta testing and research can help you find your blind spots” sections of the go-to-market motion. It’s also a call to action to employ the use of more sophisticated and effective study types to gather consumer input than simple surveys and straightforward averages can provide.

Unlike even a year ago, with today’s access to “AI-powered” consumer insights platforms, there’s no excuse for keeping your future buyers on the sidelines of a go-to-market motion. 

Many platforms will increasingly be constructed to empower users to build entire studies, not just simple surveys, with a simple prompt. These include pricing studies, concept tests, MaxDiff Analysis, KDA, TURF Analysis, unsupervised buyer persona clustering, and a wide range of conjoint studies, to name a few. 

These studies go far beyond simple surveys with the misleading averages that Jeff refers to above, and will  be increasingly deployed, analyzed, and summarized with simple AI prompts. The most difficult part will be stating exactly what consumer insights you want to gather.

In summary, don’t get caught up in worrying about process execution at the expense of hearing directly from the customer. Don’t over-index on simple surveys, and don’t only rely on them to inform you and your GTM team about the product or service.

Leverage consumer insights research to inform you of blind spots to de-risk a launch and to rapidly accelerate post-launch sales growth. Use a software platform that is powered by AI to build, deploy, analyze, and summarize consumer market findings with a prompt - saving your budget many thousands of dollars, dramatically speeding up the time-to-insight ratio, and delivering unprecedented quality. Avoiding these pitfalls and eliminating these common GTM excuses for sidelining the consumer will ensure that you deliver a product or service in a customer-centric way, and as a result, deliver something they love.

Stay tuned for part 2!

Concept tests are one of the most valuable insights tools available to you, which explains why it's the most popular experiment on the SightX platform!

When you make concept testing a regular part of your product development strategy, not only will you be more confident in your launches. But you'll also avoid costly mistakes. 

Find out how to use concept testing throughout your product development life cycle: 

 

What is Concept Testing? 

Concept testing is a market research methodology that provides insights into the viability of your ideas, products, or services before you invest significant resources.

Concept testing involves presenting your product designs or prototypes to a carefully selected sample of your target audience and collecting feedback on key measurements like appeal, relevance, and purchase intent. 

For product development, you can use concept testing at nearly every stage of your workflow.

By engaging with consumers early on, you can identify your product's strengths, weaknesses, and areas for improvement, finding data-backed ways to differentiate your new product. Later in your development cycle, you can use concept testing to refine your product's packaging, messaging, or marketing collateral. 

 

Types of Concept Testing

 

Comparison Testing

Comparison testing is exactly what you'd expect. Respondents are shown two or more concepts and compare them by simply selecting their favorite or using ranking or rating-scales questions. 

The results of a comparison test are often clear and simple to understand. Which makes it easy to determine which of your concepts is the winner. 

But comparison tests have drawbacks. One major issue is a lack of context. Comparison testing gives little insight into why one concept was selected over the others. 

 

Monadic Testing

With monadic testing, your sample (pool of respondents) is separated into groups. Each group sees only one concept, meaning there is no comparison, simply an in-depth evaluation of the concept shown. 

Because respondents are only shown a single concept, this method makes it possible to get in-depth insights without the drawbacks of a lengthy survey. So, instead of simply understanding which concept won, you can better understand how consumers feel about the elements of each. 

But, once again, there are some drawbacks. You'll need a larger sample size to break respondents into groups, which can drive up your cost and time-to-insights. 

 

Sequential Monadic Testing

Much like monadic testing, sequential monadic testing requires you to split your audience into groups. But instead of only showing one concept to each group, respondents evaluate all of your concepts in random order. Each group is asked the same follow-up questions at the end of the rotation. 

As each group evaluates all concepts, the required sample size for a sequential monadic test is smaller- reducing costs. It also allows you to test multiple ideas in a single round, making it quite efficient. 

But, as you guessed it, this methodology isn't foolproof. The survey length can be lengthy because respondents see all of your concepts. This ultimately affects the completion rate and can even cause respondent fatigue, leading to poor data quality.

 

Proto-Monadic Testing

As the name might suggest, proto-monadic testing combines sequential monadic and comparison testing. This method has respondents examine multiple concepts and then choose the one they prefer. 

Ultimately, proto-monadic testing allows you to confirm that the winner of your comparison test is compatible with the in-depth insights gained on individual concepts. 

 

 

How to Use Concept Testing in Your Product Development Cycle

Concept testing typically involves a few key steps, each crucial to successfully gathering, analyzing, and applying consumer feedback. 

We'll illustrate the process through an example. Let's say the team at a CPG company wants to test ten new flavors of ice cream to identify the top three flavors consumers will likely buy. 

The first step in the process is concept creation, where the CPG company will generate potential flavor concepts to test. 

Once flavor concepts have been developed, the next step is audience selection, where the team identifies a representative sample of their ideal target audience to participate in the concept testing process. 

After audience identification, it is time to develop the concept test study. Questions in this survey may cover topics like consumer attitudes, preferences, purchase intent, and demographic information, among others. 

The actual Stimuli within the concept test (aka what the respondents in your sample see/interact with) may include prototypes, mock-ups, descriptions, or visual representations of the concepts being tested.

Once the surveys and stimuli have been designed, it is time to collect data to gather insights. The team can set up the experiment to expose participants to all concepts (Sequential study) or randomly assign participants to one of the concepts to evaluate in-depth (Monadic test). Because they are testing a relatively large number of concepts, the team chooses a monadic test. 

With the data, the team can see which flavors had the highest appeal and purchase intent rating from their target market, allowing them to easily choose their new three flavors. 

 

 

Best Practices for Concept Testing in New Product Development

To ensure the reliability and validity of your concept testing data, you should adhere to a few best practices. These include:

 

1. Define clear research objectives

Before conducting any concept test ng, you should be able to clearly define your research objectives and know your criteria for success. This will help ensure the testing process is focused and aligned with the business's goals.

 

2. Select an appropriate sample size and composition

The sample size and composition of the test group are critical factors that can impact both the reliability and validity of your concept testing results. You can use resources, like our knowledge hub, to get recommendations on how many respond to your surveys or specific experiment needs. 

 

3. Design clear and concise tests and stimuli

Both your concept test and stimuli should be designed in a way that effectively communicates the concepts being tested and elicits meaningful feedback from participants. Questions should be clear, concise, and easy to understand. At the same time, the concepts themselves (stimuli) should accurately represent the product ideas you want to test.

 

4. Analyze data rigorously

Once data has been collected, you will want to look at the data overall. However, you should also filter/cut your data in a way that enables you to look at different segments. When seeking meaningful differences between the concepts, where relevant, make sure you run tests for statistical significance. Some platforms, like SightX, will run this automatically on your concept testing data. 

 

5. Integrate concept testing throughout the product development process

For real success, concept testing should be integrated throughout your product development process. Early and iterative concept testing allows you to optimize and perfect your product throughout its development.

 

The benefits of concept testing are wide-ranging and extend across your entire organization. In new product development, concept testing provides valuable insights into consumer preferences, enabling brands to bring better products to market. 

For insights managers, concept testing enables you to collaborate with other teams within your organization, like innovation or marketing, and develop well-thought-out strategies that resonate with your target market. 

 

Product Concept Testing with SightX

SightX is an AI-driven market research platform offering a 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. 

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. 

 

 

 

 

In pricing research, a Gabor-Ganger study offers a systemic approach to understanding price sensitivity and demand elasticity for a product. It helps organizations make more informed pricing decisions that drive revenue and maintain competitiveness. 

However, like any research methodology, Gabor-Granger studies come with their own set of best practices. In this article, we'll explore key best practices for conducting Gabor Granger pricing studies: 

 

Best Practices for Gabor Granger Pricing Studies

 

1.Define Clear Objectives

Before embarking on your Gabor-Granger pricing study, it's crucial to define clear and specific objectives that align with the goals of your business. Take the time to thoroughly analyze what you hope to achieve through the study. Are you seeking insights into consumer price sensitivity for a new product launch? Or perhaps you aim to optimize your pricing strategies for an existing product line? By defining your objectives upfront, you can tailor that the study's design and execution to address your specific needs.

 

2. Select Representative Samples

Selecting a representative sample is paramount to the success and validity of your Gabor Granger pricing study. It's essential to ensure that your sample size and composition accurately reflect your target market's demographics, preferences, and behaviors. Start by identifying the critical characteristics of your target audience, like age, gender, income level, geographic location, or specific shopping behaviors. Then, you can use appropriate sampling techniques recruit participants who mirror these attributes. By selecting a representative sample, you can ensure that the insights gained from the study are applicable and actionable for your target market.

 

3. Randomize Price Points

To minimize bias and ensure the accuracy of the study results, it's crucial to randomize the order of the price points shown to respondents. Randomization helps to mitigate the influence of order effects, where respondents' reactions may be influenced by the sequence in which prices are presented. By presenting prices in a randomized order, you can ensure that responses are based solely on the prices presented rather than any preconceived expectations or biases. This approach helps to enhance the validity and reliability of the study findings, providing a more accurate representation of consumer behavior.

 

4. Include Price Sensitivity Metrics

Incorporate price sensitivity metrics into your Gabor-Granger pricing study to better understand consumer behavior and preferences. Price sensitivity metrics provide quantitative measures of how changes in price impact consumer purchasing decisions. By analyzing these metrics, businesses can identify the price points most likely to resonate with their target audience and optimize their pricing strategies accordingly. As a bonus, price sensitivity metrics can also help you assess the potential impact of price changes on company revenue and profitability. 

 

5. Analyze Segmentation Differences

Consumer preferences and behaviors can vary significantly across different market segments. Therefore, conducting a segmentation analysis as part of your Gabor-Granger pricing study is important. Segmentation analysis involves dividing your target market into groups based on demographic, psychographic, or behavioral characteristics. By analyzing how price sensitivity and demand differ among these segments, you can tailor their pricing strategies to better meet the needs and preferences of each group. This approach allows for more targeted and effective pricing decisions, ultimately increasing customer satisfaction and loyalty.

 

6. Account for External Factors

When conducting a Gabor-Granger pricing study, you should also account for external factors influencing consumer perceptions of price. These factors include competitor pricing strategies, economic conditions, industry trends, and seasonal fluctuations. Considering these external factors in your analysis, you can ensure that your study's results accurately reflect real-world market dynamics. Failure to account for external factors can lead to unrealistic pricing recommendations that do not align with market realities, ultimately hindering business performance. 

 

7. Iterate and Refine

Gabor Granger pricing studies are iterative processes that require ongoing refinement and optimization. After conducting an initial study, take the time to analyze the results and identify areas for improvement. Consider factors like sample size, survey design, and pricing scenarios, and iterate on your approach to enhance the accuracy and reliability of future studies. You can develop more robust pricing strategies that drive long-term success by continuously refining your methodology based on feedback and insights gained.

 

8. Validate Findings Through Market Testing

While Gabor-Granger studies provide valuable insights into consumer purchasing behavior and pricing preferences, validating your findings through real-world market testing is essential. Implement pricing strategies based on the study results and monitor consumer response to assess their effectiveness. Market testing allows businesses to verify the applicability of study findings in real-world scenarios and make adjustments as needed. By validating study findings through market testing, organization can ensure that their pricing strategies are aligned with consumer preferences and market dynamics, ultimately leading to greater success.

 

Gabor-Granger Pricing Studies with SightX

SightX is an AI-driven market research platform offering 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 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. 

 

 

Whether developing a product or crafting a social media ad, it's important to recognize that your target market is not a monolith. Each individual has distinct characteristics, behaviors, and motivations.

Today, we'll explore how market segmentation enables you to better understand the diversity and differences within your audience and improve customer satisfaction while doing so. 

 

What is Market Segmentation?

Market segmentation is the dividing of a broad market into smaller, more manageable groups based on specific criteria, like demographic, location, psychographic, or behavioral data. 

By dividing a market or customer base into smaller segments, organizations can better understand the different types of people who buy from them and why. With this information, organizations can better tailor their product, marketing, and branding efforts to the most profitable segments, maximizing their ROI. 

 

The Types of Market Segmentation

 

Demographic Segmentation

Demographic segmentation divides a market based on age, gender, income, education, occupation, and family size. Getting started with demographic segmentation is relatively straightforward, as the data is easy to collect and analyze.

However, there are some drawbacks. We primarily live in a "post-demographic" world. Consumers continually construct and reconstruct their own identities, rebelling against top-down driven "norms" handed to them by the advertisements of old. The clear delineations between consumers based on gender, age, income, education, or ethnicity are not as helpful as they once seemed. To learn more about this, check out our piece: Segmentation: Maybe You Could Be Doing It Better. 

 

Geographic Segmentation

Geographic segmentation divides a market based on factors like region, country, city size, climate, and population density. This approach helps businesses target specific locations for summer promotions and others for winter. 

This type of segmentation can be a subset of demographic segmentation or a unique strategy. Understanding the geographical differences in customer needs and preferences helps determine where to sell, advertise, and expand a business. You can collect geographic segmentation data from customer surveys, third-party research, or operational data like IP addresses. 

 

Psychographic Segmentation

Psychographic segmentation considers psychological and lifestyle factors such as values, beliefs, interests, attitudes, and personality traits. This approach helps businesses understand and target specific consumer groups, such as environmentally conscious consumers or those interested in healthy living and exercise.

Surveys are an excellent way for organizations to get started with psychographic segmentation. Survey research makes gathering data points on beliefs, attitudes, and interests easy. This direct insight into people's beliefs and preferences allows for more effective targeting and messaging strategies.

 

Behavioral Segmentation

Behavioral segmentation categorizes consumers based on their purchase history, usage patterns, and brand loyalty. 

For example, segmenting an audience based on purchase behavior enables marketers to take a precise approach, centering their messaging and ads around the factors most likely to drive sales. 

 

Firmographic Segmentation

B2B organizations often use firmographic segmentation to divide their audience based on industry, company size, job titles, annual revenue, or company structure. 

This approach helps businesses understand the nuances of different types of organizations so they can tailor their strategy accordingly.

 

Benefits of Market Segmentation

Segmentation isn't just a strategy; it's a game-changer for organizations looking to connect with their customers on a deeper level. 

Aberdeen Research Firm found that email conversion rates increased by 10% when using segmentation. But not only were the conversion rates higher. People receiving segmented emails actually spent more. Marketers saw a 760% increase in revenues thanks to a segmented approach. 

Some additional benefits include:

 

Lower Acquisition Costs

By knowing your segments, you can tailor marketing communications and use advanced targeting on digital platforms to drive leads at lower costs. 

 

Attracting the Right Customers

Clear messaging that speaks right to your target audience's pain points will draw your ideal customers to your brand.

 

Market Expansion

Segmentation can uncover new opportunities, giving you ideas on where to expand your customer base and increase market share. 

 

Cost Efficiency

Targeting specific high purchase-intent segments reduces marketing waste, leading to a more efficient use of budget and resources. 

 

Product Customization

Understanding the unique needs of different segments allows you to customize your products or services to drive higher customer satisfaction and loyalty rates.

 

 

When and How Often Can Market Segmentation Studies Be Run? 

Traditionally, companies would perform segmentation every three to four years due to its time-consuming and expensive nature.

Today, organizations conduct market segmentation studies whenever there is a significant change in the market or target audience, like shifts in consumer preferences, the emergence of new competitors, or changes in market trends. 

Outside of standalone segmentation studies, you can run segmentation analysis on data from other research studies. This is done to understand how different segments react to the ideas in a concept test or to identify high purchase-intent consumers during new product development research.  

While there is no hard and fast rule on timing, waiting more than a year to update your segmentation will make it challenging to work with consumer behavior and preferences shifts. Keeping your segmentation up-to-date ensures that your marketing strategies remain practical and relevant in a constantly changing market.

 

Market Segmentation Examples

Market segmentation is a versatile tool you can apply across various departments and activities within your business. Here are some use case scenarios to illustrate how you can use market segmentation: 

 

Market Entry Strategy

Use market segmentation to assess the potential of new markets and identify the most lucrative segments to target. This can help you tailor your market entry strategy and optimize resource allocation for maximum impact.

 

Customer Acquisition

Segmenting your market allows you to identify high-potential customer groups and tailor your acquisition strategies to their needs and preferences. This leads to more efficient customer acquisition spending and higher conversion rates.

 

Product Portfolio Management

Market segmentation can help you evaluate your portfolio and identify opportunities for product development or retirement. Understanding the needs of different segments ensures that your product offerings remain relevant and competitive.

 

Customer Retention

Segmenting your customer base allows you to identify loyal customers and develop targeted retention strategies to keep them engaged. This can include personalized offers, loyalty programs, and exclusive content.

 

Price Optimization

By segmenting your market based on price sensitivity, you can optimize your pricing strategy to maximize revenue and profitability. This can involve offering different price points or discounts to different segments based on their willingness to pay.

 

Competitive Analysis

Segmenting your market allows you to analyze competitor performance within specific segments. This can help you identify competitive threats and opportunities for differentiation.

 

Customer Satisfaction and Loyalty

Market segmentation can help you understand the drivers of customer satisfaction and loyalty within different segments. This can help you tailor your customer service and engagement strategies to improve loyalty and retention.

 

Brand Positioning

Market segmentation can help you identify the most relevant positioning for your brand within different segments. This can help you develop messaging and branding strategies that resonate with your target audience.

 

 

Market Segmentation 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. 

 

In a hyper-connected world, customers expect more from the brands they engage with. 

As such, personalization has become a cornerstone of effective marketing and CX strategies, allowing businesses to tailor their offerings and communications to individual preferences and needs. At the heart of successful personalization strategies lies market segmentation – a process that divides customers into distinct groups based on shared characteristics or behaviors. 

In this piece, we'll explore how market segmentation enhances customer experience by enabling personalized interactions at scale: 

 

What is Market Segmentation? 

Market segmentation involves dividing a broad target market into smaller, more manageable segments based on factors like demographics, psychographics (attitudes, values, etc), and behaviors. 

By recognizing that not all customers are the same, businesses can better understand and cater to the unique needs and preferences of each segment.

 

How Market Segmentation Enables Personalization

 

Perfectly-Tailored Offerings

One of the primary benefits of market segmentation is the ability to tailor product offerings and messaging to specific customer groups. By understanding the distinct preferences, pain points, and motivations of each segment, you can develop products and services that solve real problems for your market. This level of customization increases the relevance of interactions and enhances the overall customer experience.

 

Personalized Communications

Speaking the language of your customers is an essential building block for strong relationships. Market segmentation allows you to craft personalized messaging that speaks directly to the interests, pain points, and preferences of each segment. Whether through targeted email campaigns, social media ads, or personalized recommendations, businesses can engage customers with relevant content that captures their attention and fosters a sense of connection.

 

Optimized Customer Journeys

Market segmentation enables businesses to design tailored customer journeys that guide individuals seamlessly through a sales funnel. By mapping out the unique needs and touchpoints of each segment, you can deliver a personalized experience that addresses specific pain points and facilitates conversions. Be it through targeted content, email nudges, in-app guides, or proactive support, businesses can ensure that each customer receives the guidance they need to achieve their goals.

 

Market Segmentation Techniques 

 

Demographic Segmentation

Demographic segmentation divides an audience based on various demographic factors like age, gender, education, occupation, income, ethnicity, family size, or marital status. For example, a business selling luxury clothing might target high-income individuals between the ages of 35 and 55. 

 

Geographic Segmentation

With geographic segmentation, a market is separated based on geographic variables like region, country, city, climate, or urban/rural areas. This allows organizations to modify marketing efforts based on the specific customer needs and preferences in different geographic locations. For instance, a clothing retail chain might prioritize ads for sweaters in colder regions and shorts in warmer climates. 

 

Psychographic Segmentation

Psychographic segmentation divides a market based on variables like interests, personality traits, values, and opinions. This approach helps you better understand the emotional and psychological factors that influence consumer decision-making. For example, an outdoor apparel company might want to target consumers who consider themselves adventure seekers and environmentalists. 

 

Behavioral Segmentation

Behavioral segmentation categorizes customers based on their behaviors. This can include factors like product usage, shopping habits, the benefits they seek from products, and more. For instance, an airline might share different promotional offers to frequent flyers and first-time travelers. 

 

Firmographic

Firmographic segmentation is used within the B2B space to organize customers based on shared company or organization attributes. Factors often include industry, company size, job titles, annual revenue, or company structure. This practice will guide marketing, advertising, and sales strategies for organizations that target a B2B audience.  

 

 

Measuring Success: Tracking the Impact of Personalization

One of the key advantages of market segmentation is its measurability. 

By analyzing data and metrics specific to each segment, businesses can track the effectiveness of their personalization efforts and make data-driven decisions to optimize performance further. From conversion rates and engagement metrics to customer satisfaction scores, businesses can easily gain valuable insights into the impact of personalization on the overall customer experience.

 

Elevating Customer Experience Through Personalization

Market segmentation serves as the foundation for effective personalization, allowing you to understand and cater to the unique needs of individual customer segments.

By tailoring offerings, communications, and experiences to specific segments, businesses can create meaningful connections with customers, drive loyalty, and ultimately, achieve long-term success in today's competitive landscape.

 

Market Segmentation 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. 

 

 

Survey research is heavily relied upon by organizations to collect direct feedback from customers. And for good reason! Surveys are an effective way to connect with an audience. 

But when you are looking to answer more specific questions about your business, you are likely ready to explore market research methodologies, a useful extension of survey research.

Maybe you want to know: 

What is the optimal price of my offering? 
What are the most valuable features of my product? 
What factors are most likely to predict my NPS score?   

These are just a few of the critical business questions that can be answered using market research methodologies. Today, we'll review some of the most popular experiments: 

 

 

Product Feature Optimization 

 

Market Research Methodology: MaxDiff Analysis

MaxDiff Analysis, also known as Maximum Difference Scaling, is a methodology used in market research to determine the relative importance of different attributes or features of a product. Researchers can design MaxDiff surveys and analyze the data to identify which attributes are most preferred and which are least preferred by consumers. This enables businesses to prioritize features that matter most to their audience. 

And it isn’t just helpful for product development. This information can also inform your go-to-market strategy and marketing messaging, allowing businesses to focus their efforts where they matter most. 

For example, if the innovation team of an ice cream company has thirteen potential flavors to bring to the market, and the business is interested in identifying the top three flavors, a maxdiff experiment will immediately show them which flavors are most popular with their market. 

 

Market Research Methodology: Conjoint Analysis

Conjoint Analysis is a powerful methodology used to understand how consumers make decisions when faced with multiple options or attributes. It helps businesses determine the relative importance of different features of a product or service and how these attributes influence consumer preferences and choices.

In conjoint analysis, respondents are presented with a set of hypothetical product profiles or scenarios that vary in terms of specific attributes, such as price, design, specific features, or brand. These attributes are systematically varied across different scenarios to create a range of product combinations. Respondents are then asked to evaluate or rank these product profiles based on their preferences. 

By analyzing the choices made by respondents across different scenarios, researchers can estimate the relative importance of each attribute and how it influences overall product preference.

 

Product Pricing Optimization

 

Market Research Methodology: Gabor-Granger Pricing Analysis

Gabor-Granger Pricing Analysis is a methodology used to determine the optimal price for a product or service by assessing consumer willingness to pay at different price points. 

The results help businesses identify the price sensitivity and demand elasticity in their market. Which, in turn, helps businesses to set prices that maximize revenue and profitability while ensuring customer satisfaction and market competitiveness.

 

Market Research Methodology: Van Westendorp Price Sensitivity Meter

The Van Westendorp Price Sensitivity Meter (PSM) is a technique used to determine the optimal price range for a product or service by assessing consumer price sensitivity. 

It involves asking respondents a series of questions about their willingness to buy a product or service at different price points. Specifically, respondents are asked four key questions: Price Point at which the Product is Seen as cheap, too cheap, expensive, and too expensive. 

The results give insights into four key price points: 

1) Point of Marginal Cheapness: The price at which a significant proportion of respondents perceive the product as too cheap 

2) Point of Marginal Expensiveness: The price at which a significant proportion of respondents perceive the product as too expensive 

3) Optimal Price Point: The price range in which most respondents perceive the product as providing good value 

4) Indifference Price Point: The price range in which respondents are equally divided between considering the product too cheap and too expensive.

 

 

Understanding Consumer Behavior

 

Market Research Methodology: Key Driver Analysis

Key Driver Analysis is a methodology used to identify the key factors that influence customer satisfaction, loyalty, or other important business outcomes.

It starts with identifying a set of potential driver variables based on prior knowledge, literature review, or exploratory data analysis. These variables can include product features, customer service quality, pricing, brand reputation, and other factors that may be relevant to shoppers. 

Respondents are asked to provide ratings or feedback on various factors that may influence the outcome of interest (like customer satisfaction).

Using statistical techniques such as regression analysis or correlation analysis, you can analyze the relationship between the driver and the outcome. This allows you to quantify the strength and direction of the relationship between each driver variable and the outcome.

By understanding these key drivers, businesses can focus their efforts on addressing the most critical factors to drive positive outcomes.

 

Moving Beyond Survey Research 

As companies rely on consumer research to form go-to-market strategies and optimize their operations, a comprehensive suite of market research methodologies that go beyond traditional survey research is necessary to uncover deeper insights and make more informed decisions. 

By leveraging these experiments, businesses can drive innovation, enhance customer experiences, and gain a competitive edge in today's dynamic marketplace.

 

SightX Market Research Methodologies

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. 

 

It’s no secret that markets are flooded with similar offerings. Which means standing out and making a lasting impact is easier said than done. 

From establishing brand recognition to capturing the attention of potential customers, new products encounter many hurdles on their journey to success. But what can you do to mitigate your risks and set yourself up for a successful launch?

Market research. 

By understanding your target audience, market trends, and competitors' strengths or weaknesses, you can refine your strategy and better position your product to win.  

Today, we'll explore the crucial ways that market research is used for successful product launches.

 

Elements of a Successful Product Launch

 

Understanding the Pain Points

If you’re going to deliver a product that people want to buy, you’re going to have to understand their needs and why other products aren’t meeting them. 

Using consumer research and market segmentation techniques, you can identify unresolved pain points and tailor your product accordingly. This data can also help you better assess the market landscape to uncover whitespace growth opportunities and gain insights into consumer preferences, motivators, and purchase barriers before designing new offerings.

 

Screening Product Ideas

While you, and your internal team, might LOVE your product idea it’s important to know if consumers feel the same way. That’s why testing product concepts and prototypes is crucial for refining your product's viability

By conducting early product research, like surveys, concept tests, or focus groups, you can better understand how people perceive your product, their likelihood to purchase it, and ways you can improve to increase your chances of success in the market. 

 

Performing Product Testing

Once you have a minimum viable product (MVP) backed by consumer data, it’s time to dial in the details. 

Feature testing with conjoint or maxdiff analysis will allow you to quickly identify the features your ideal customer finds the most enticing. The data from feature testing can also inform your messaging strategy- giving you insights on which factors matter most to buyers, and why. 

Pricing studies, like Gabor-Granger or Van Westendorp- will show you exactly what people would be willing to pay, and how you can maximize your revenue without compromising your demand. And competitive analysis at this stage will give you a better idea of how your fully-fleshed product will compete with the behemoths in your market. 

Iterate and refine your product based on the insights gained from testing to ensure it meets customer expectations and market demands.

 

Knowing Your Target Audience 

Throughout the entire process, an understanding of who your ideal buyer should be your north star. 

By using market segmentation tools, you can uncover the target audience for your product. And not just their demographics, but where they shop, how they make their decisions, and what factors ultimately play a role in their purchase decisions. All of this data is critical not only for your product but also for your marketing and overall go-to-market strategy. 

 

How to Use Market Research for a Winning Product Launch

Using market research effectively will significantly increase the likelihood of a successful product launch. Here are a few steps to get you there: 

 

1. Learn Your Target Buyer

Conduct segmentation studies to identify and understand your ideal customer's age, gender, location, interests, lifestyles, and purchasing behaviors.
Gather feedback through surveys, focus groups, and interviews to gain insights into their needs, preferences, and pain points.

 

2. Analyze Market Trends

Stay updated on industry trends, technological advancements, and regulatory changes that may impact your product or target market.
Monitor consumer behavior, like shifting preferences, emerging trends, and buying patterns, to identify potential opportunities and threats.

3. Identify Competitors

Conduct a competitive analysis to identify direct and indirect competitors operating in your industry. While you may be aware of some of the competitors in your space, you might be surprised by the brands your ideal customers buy from. 
Evaluate competitors' product offerings, pricing strategies, marketing tactics, distribution channels, and customer feedback/reviews.

4. Test and Validate

Conduct concept testing and prototype evaluations to gather feedback from potential customers and stakeholders.
Evaluate potential price ranges for your product with Price Sensitivity Testing. 

5. Refine Product Features, Pricing, and Positioning

Use market research methodologies like conjoint analysis or MaxDiff, to assess the appeal of different product features and combinations of features. 
Determine your product's unique selling proposition (USP) and positioning in the market to differentiate it from competitors.
Develop compelling messaging and branding strategies that resonate with your target audience and communicate the value proposition effectively.

6. Develop Data-Driven Marketing Strategies

Develop a comprehensive marketing plan that leverages market research insights to target the right audience through the most effective channels.
Create engaging marketing collateral that aligns with your brand messaging and resonates with your target audience.

7. Monitor and Adapt

Continuously monitor market trends, competitor activities, and customer feedback to stay agile and responsive to changes in the market.
Iterate and adapt your strategies based on insights to optimize the long-term performance of your brand and product. 

Successful Product Launches 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. 

 

 

 

 

 

 

MaxDiff, short for Maximum Difference Scaling, is a technique used in consumer insights to measure preferences for products, services, features, or concepts. Respondents are typically presented with a series of paired attributes and are asked to select their preference. 

While MaxDiff can be a powerful tool for understanding preferences and priorities, it's essential to ensure proper design, execution, and analysis to derive meaningful insights.

Here are some tips for conducting more effective MaxDiff studies:
 

 

1. Define Your Attributes

Identify the attributes or items you want to evaluate. These could be product features, brand characteristics, service offerings, value propositions, etc. Ensure that these attributes are relevant and cover the scope of your research objectives.

 

2. Use Balanced Designs

Ensure that the maxdiff design is balanced, meaning each item appears an equal number of times and in different combinations with other items. This helps reduce potential bias introduced by the order or grouping of items.

 

3. Provide Clear Instructions

Offer clear and concise instructions to respondents. Explain the task they need to perform and how to choose the most and least preferred items within each set accurately.

 

4. Consider Sample Size

Determine the appropriate sample size based on the complexity of your study and the statistical power required for reliable results. Larger sample sizes generally yield more robust data.

 

5. Data Analysis

Analyze the collected data using specialized software, like SightX, that can handle MaxDiff analysis. This involves calculating scores for each item based on the frequency with which they are chosen as the most preferred or least preferred.

 

6. Pick Out Key Insights

Interpret the results to draw meaningful conclusions. Use the scores obtained to rank the items in terms of preference or importance. Visual aids like graphs or charts can help present the findings effectively.

 

7. Validate the Results

Validate your results and consider conducting follow-up studies or iterations to refine your findings or explore specific areas of interest further.

 

 

MaxDiff Analysis 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 Generative AI research consultant, Ada, you can harness the power of OpenAI’s GPT to transform the way you collect consumer insights!

What can Ada do? 
Provide personalized research recommendations
Create custom surveys and tests
Calculate your ideal sample size
Deliver key insights and executive summaries from your studies 
Analyze consumer sentiments and answer questions about your data
Develop product descriptions and marketing collateral based on customer feedback

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

 

 

 

 

In the digital age, data privacy and security are paramount, especially when utilizing powerful generative AI tools, like our research consultant Ada. 

 

Today we’ll explain the mechanisms that ensure your data remains private and secure while you benefit from Ada’s robust analytical capabilities: 

 

Ada's Foundations of Privacy and Security

Ada prioritizes data security when utilizing OpenAI's API for her work.  OpenAI's API provides a secure and privacy-focused solution, as it does not use customer data to train its models or retain any data within OpenAI's systems. 

This means that the sensitive information Ada works with remains confidential and is not incorporated into OpenAI's models or stored on their servers. Additionally, Ada cannot connect to the internet while working with the API, adding an additional layer of privacy. 

 

A Strong Privacy Framework

Ada’s design incorporates rigorous standards for data protection. Here are the steps we take to ensure your information is secure:

Data Anonymization

Ada anonymizes your data to eliminate personal identifiers, upholding anonymity.

Secure Data Transmission

We use encryption to protect your data as it travels to and from Ada's processing environment.

No Persistent Storage

Ada processes data in real-time and doesn’t store any information post-analysis, adhering to a strict policy of data transience.

Continuous Security Checks

Our systems undergo frequent security assessments to preempt and prevent any potential data breaches.

 

The Role of GPT in Ensuring Privacy

GPT, the technology behind Ada, is designed to process data without retaining it, respecting user privacy at every turn:

Pre-Trained Model

GPT-3.5 Turbo and GPT-4 have been pre-trained on a vast corpus of data, enabling it to generate insights without the need to learn from your data.

Independent Processing

Each user query is processed independently, safeguarding against the risk of data overlap or persistence.

 

User-Centric Security Practices

While Ada employs robust security measures, users also play a crucial role:

Sensitive Data Redaction

Always redact any personal or sensitive information from your data before consulting Ada.

 

Understand Data Permissions

Be clear on your organization's data sharing settings and adjust them as necessary to maintain control over your data.

 

Stay Informed

Regularly review OpenAI's privacy policy to stay updated on the privacy measures and how your data is handled.

 

Trusting Ada with Your Data

When using Ada for market research, you can trust that your data is handled with the utmost care. Privacy and security are not just complementary features—they are the bedrock upon which Ada operates.

With Ada, your insights are generated with a commitment to safeguarding your data, every step of the way.