Market Response Model: Predict Consumer Behavior

The discipline of marketing science utilizes the market response model as a quantitative tool, and its efficacy is continually refined through academic research at institutions like the Wharton School. Consumer behavior, an intricate aspect of market dynamics, forms the core subject of analysis within these models. Furthermore, econometric techniques provide the mathematical framework necessary for calibrating the market response model, enabling businesses to forecast demand with greater precision.

The Data-Driven Marketing Imperative: Navigating the Modern Marketing Landscape

In contemporary marketing, data-driven decision-making is no longer optional; it is an absolute imperative. The sheer volume of data available, coupled with increasingly sophisticated analytical tools, has fundamentally altered the dynamics of marketing strategy.

Marketers are now expected to demonstrate a clear return on investment (ROI) for every campaign, and this demands a rigorous, data-backed approach. Strategies built on intuition or outdated assumptions are increasingly vulnerable.

Quantifying Marketing Impact: An Analytical Overview

To effectively quantify marketing impact, a range of methodologies must be employed. These approaches enable marketers to measure the effectiveness of campaigns, identify areas for improvement, and optimize resource allocation.

These methodologies, from regression analysis to machine learning, provide the framework for transforming raw data into actionable insights. This transformation is critical for achieving marketing goals and driving business growth.

The Interconnected Ecosystem: Activities, Tools, and Data

The success of data-driven marketing hinges on the symbiotic relationship between marketing activities, analytical tools, and data sources. Each element is interdependent, and the overall effectiveness of the system is determined by the strength of these connections.

Marketing Activities: The Foundation

Marketing activities, such as advertising campaigns, content creation, and social media engagement, generate a wealth of data points. These activities are the starting point of the analytical process, providing the raw material for analysis.

Analytical Tools: The Engine

Analytical tools, including CRM systems, web analytics platforms, and statistical software, are the engines that power the data-driven marketing process. These tools enable marketers to collect, process, and analyze data, revealing patterns and trends.

Data Sources: The Fuel

Data sources, ranging from customer databases to market research reports, provide the fuel for analytical tools. The quality and comprehensiveness of these data sources are critical for generating accurate and reliable insights.

Data Quality: The Cornerstone of Reliable Insights

While advanced methodologies and sophisticated tools are essential, the importance of data quality cannot be overstated. Garbage in, garbage out: inaccurate or incomplete data will inevitably lead to flawed conclusions and misguided marketing decisions.

Ensuring data accuracy, consistency, and relevance is a prerequisite for generating reliable insights. Marketers must prioritize data governance and implement robust data management practices to maintain data quality.

In conclusion, data-driven marketing is not merely a trend but a fundamental shift in how marketing is conducted. By embracing data-driven decision-making, marketers can optimize their strategies, improve ROI, and drive sustainable business growth. This transformation requires a commitment to data quality, a deep understanding of analytical methodologies, and a recognition of the interconnected ecosystem of marketing activities, analytical tools, and data sources.

Core Methodologies: Quantifying Marketing Effectiveness

With the stage set regarding the importance of data-driven marketing, it’s time to delve into the core methodologies that underpin the measurement of marketing impact. These techniques provide the framework for understanding the effectiveness of various marketing activities and optimizing strategies for maximum ROI.

Marketing Mix Modeling (MMM)

Marketing Mix Modeling (MMM) serves as a cornerstone for quantifying the impact of diverse marketing activities. It is designed to disentangle the effects of different marketing levers, such as advertising, promotions, and pricing, on sales and revenue.

MMM employs statistical techniques like regression analysis and time series analysis. Regression analysis allows marketers to understand the relationship between independent variables (marketing inputs) and a dependent variable (sales output).

Time series analysis, on the other hand, is used to account for trends, seasonality, and other time-related factors that may influence sales.

A key application of MMM lies in optimizing advertising spend allocation. By understanding the contribution of each advertising channel to overall sales, marketers can allocate their budget more effectively, maximizing their return on investment.

Attribution Modeling

Attribution modeling addresses the challenge of assigning credit for sales to different marketing touchpoints. In today’s complex customer journeys, consumers interact with numerous marketing channels before making a purchase.

Attribution models aim to determine which touchpoints played the most significant role in driving the conversion.

Various attribution models exist, each with its own approach. Common models include first-touch (assigning all credit to the first interaction), last-touch (assigning all credit to the last interaction), linear (distributing credit evenly across all interactions), and time-decay (giving more credit to touchpoints closer to the conversion).

Multi-Touch Attribution (MTA) represents a more sophisticated approach. MTA considers all touchpoints in the customer journey, assigning fractional credit to each based on its contribution to the conversion. MTA requires advanced analytical capabilities and granular data.

Econometrics

Econometrics leverages statistical methods to analyze economic data and inform marketing decisions. It provides a rigorous framework for understanding the impact of factors like price and promotions on sales.

Econometric models can help marketers optimize pricing strategies by identifying the price elasticity of demand. They can also be used to evaluate the effectiveness of promotional campaigns, determining the incremental sales generated by a particular promotion.

Econometrics can be integrated with other methodologies, such as MMM. This integration allows for more accurate models that account for both marketing and economic factors influencing sales.

Causal Inference

Identifying true cause-and-effect relationships is paramount in marketing analytics. Causal inference helps marketers distinguish between correlation and causation, ensuring that decisions are based on genuine insights.

Spurious correlations can lead to ineffective marketing strategies and wasted resources. Methods for establishing causality include experimental designs, such as A/B testing, and quasi-experimental techniques.

Causal inference is crucial for evaluating the effectiveness of marketing interventions. By understanding the true impact of different actions, marketers can optimize resource allocation and maximize ROI.

Machine Learning (ML)

Machine Learning (ML) has emerged as a powerful tool in marketing analytics. ML algorithms can analyze large datasets to identify patterns, predict outcomes, and personalize customer experiences.

ML applications in marketing include predictive modeling, customer segmentation, and personalized marketing. Predictive models can forecast future sales, identify potential churn, and optimize pricing strategies.

Customer segmentation uses ML to group customers based on their characteristics and behaviors, enabling targeted marketing campaigns.

Personalized marketing leverages ML to deliver customized content and offers to individual customers, improving engagement and conversion rates.

ML can enhance the accuracy and efficiency of attribution modeling and MMM. By incorporating ML algorithms, these methodologies can better account for complex interactions and non-linear relationships in marketing data.

A/B Testing (Split Testing)

A/B testing, also known as split testing, is a fundamental method for comparing two versions of a marketing element. It allows marketers to determine which version performs better in achieving a specific goal, such as increasing conversion rates or click-through rates.

A/B testing is widely used to optimize website design, email marketing campaigns, and ad creatives. By testing different variations of these elements, marketers can identify the most effective approaches and improve their marketing performance.

Statistical significance tests are used to ensure the validity of A/B testing results. These tests determine whether the observed differences between the two versions are statistically significant or simply due to random chance.

Measuring Impact: Key Marketing Activities and Metrics

With the stage set regarding the importance of data-driven marketing, it’s time to delve into the core methodologies that underpin the measurement of marketing impact. These techniques provide the framework for understanding the effectiveness of various marketing activities and optimizing strategies.

Now, let’s shift our focus to the practical application of these methodologies. This section will dissect how to measure the effectiveness of specific marketing activities, providing a granular view of the metrics and analytical approaches applicable to each.

Analyzing Advertising Spend

Tracking and analyzing advertising spend across various channels is paramount for ROI optimization. It’s not merely about allocating budgets; it’s about understanding how each dollar contributes to tangible outcomes.

Marketing Mix Modeling (MMM) plays a crucial role here. By employing MMM, marketers can determine the return on investment (ROI) of different advertising channels.

This understanding enables informed decisions on resource allocation and budget optimization. Effective MMM models consider both online and offline channels, providing a comprehensive view of advertising performance.

Price Optimization and Impact Analysis

Price wields a significant influence on sales volume and revenue. A nuanced understanding of price elasticity is essential for maximizing profitability.

Econometric models provide a robust framework for optimizing pricing strategies. These models analyze historical sales data, competitor pricing, and other relevant factors to determine the optimal price point.

Sophisticated pricing models consider not only direct impact but also brand perception and long-term customer value. The goal is to strike a balance between short-term gains and sustained growth.

Evaluating Promotional Effectiveness

Promotions can effectively drive short-term sales, but their long-term impact must be carefully assessed. Simply boosting sales numbers isn’t enough; you need to understand the true cost and consequences.

Analyzing promotional effectiveness requires a blend of Regression Analysis and A/B testing. Regression analysis can identify correlations between promotional activities and sales uplift, while A/B testing helps compare different promotional offers to identify the most effective strategies.

Consider the cannibalization effect – how much of the promoted product’s sales come from other products in your range. A truly effective promotion increases overall sales, rather than simply shifting them from one area to another.

Distribution Channel Efficiency

Efficient distribution channels are vital for reaching target customers. Your distribution network needs to be seamless and effective for target audience reach.

The analysis of distribution effectiveness involves evaluating factors such as channel coverage, delivery speed, and customer satisfaction. Key metrics include market penetration, stockout rates, and distribution costs.

Strategies such as optimizing logistics, strengthening partnerships, and adopting innovative distribution models can significantly enhance channel performance.

Website Traffic Analysis and Optimization

Monitoring and analyzing website traffic using Web Analytics Platforms is a fundamental aspect of modern marketing. Your website is your digital storefront – how do people navigate it, and what actions do they take?

Key metrics to track include page views, bounce rate, conversion rate, and time on site. These metrics provide valuable insights into user behavior and website performance.

Effective website optimization involves improving site navigation, enhancing content relevance, and optimizing for search engines. The goal is to create a seamless and engaging user experience.

Social Media Engagement Strategies

Tracking Social Media Engagement metrics using Social Media Analytics Tools is crucial for understanding audience preferences and optimizing social media strategies. Engagement should be meaningful and aligned with your business objectives.

Metrics such as likes, shares, comments, and reach provide insights into content performance and audience engagement levels. Sentiment analysis can gauge the overall perception of your brand on social media.

Data-driven social media strategies involve tailoring content to resonate with your target audience, engaging in meaningful conversations, and leveraging social media for lead generation and customer acquisition.

Email Marketing Performance Measurement

Measuring the effectiveness of Email Marketing campaigns is essential for maximizing ROI and nurturing customer relationships. Email remains a powerful tool, but its effectiveness hinges on relevance and personalization.

Key metrics to track include open rates, click-through rates, conversion rates, and unsubscribe rates. A/B testing plays a crucial role in optimizing email content and delivery timing.

Experiment with subject lines, calls to action, and email design to identify the most effective strategies for driving engagement and conversions. Deliverability is key – ensure your emails reach the intended recipients.

Content Marketing Performance Metrics

Strategies for creating and distributing content to attract and engage target audiences are at the heart of effective content marketing. Content is king, but relevant and valuable content is emperor.

Measuring content performance involves tracking metrics such as website traffic, social media shares, lead generation, and sales conversions. Web analytics and social media metrics provide valuable insights into content effectiveness.

Tailoring content to specific audience segments, optimizing for search engines, and promoting content across multiple channels are essential for maximizing impact. Remember, content marketing is a long-term play.

Understanding Context: Critical Variables in Marketing Analysis

With a robust set of measurement methodologies in place, it’s crucial to acknowledge that marketing performance doesn’t exist in a vacuum. Various external and internal variables can significantly influence outcomes, and failing to account for these contextual factors can lead to flawed interpretations and misguided strategies. Let’s examine some critical variables that demand careful consideration in marketing analysis.

The Indelible Influence of Brand Awareness

Brand awareness, representing the degree to which consumers recognize and remember a brand, is a foundational element in the marketing landscape. It serves as a critical precursor to customer acquisition, brand loyalty, and overall market success. A strong brand presence can significantly reduce the friction in the customer journey, increasing the likelihood of purchase and fostering long-term relationships.

Measuring the Intangible: Quantifying Brand Awareness

Quantifying brand awareness requires a multi-faceted approach, combining both qualitative and quantitative methods. Surveys, a mainstay of market research, provide direct insights into consumer recall and recognition.

Social media monitoring offers a real-time gauge of brand mentions, sentiment, and engagement.

Search engine data can also reveal brand awareness levels, with increasing search volumes for branded keywords suggesting greater consumer recognition.

Brand Awareness and Marketing Effectiveness

The relationship between brand awareness and marketing effectiveness is cyclical. Successful marketing campaigns contribute to increased brand awareness, which, in turn, amplifies the impact of future marketing efforts.

Understanding the baseline brand awareness before launching a campaign allows for a more accurate assessment of its incremental impact.

Seasonality: Riding the Waves of Consumer Demand

Seasonality, the predictable fluctuation in demand tied to specific times of the year, is a pervasive force in many industries. From the surge in retail sales during the holiday season to the increased demand for swimwear in the summer months, understanding seasonal patterns is vital for effective marketing planning.

Failing to account for seasonality can lead to misinterpretations of sales data and ineffective resource allocation.

Time Series Analysis: Decoding Seasonal Patterns

Time series analysis provides a powerful toolkit for modeling and forecasting seasonal variations. By analyzing historical sales data, marketers can identify recurring patterns and develop strategies to capitalize on peak seasons and mitigate the impact of slower periods.

Techniques such as decomposition and seasonal ARIMA models can help isolate the seasonal component of sales data, allowing for more accurate predictions and informed decision-making.

Strategic Implications of Seasonal Understanding

Understanding seasonal demand allows for optimized marketing campaigns, inventory management, and staffing decisions. Marketing budgets can be strategically allocated to align with peak seasons, maximizing return on investment.

Inventory levels can be adjusted to meet anticipated demand, preventing stockouts and lost sales.

Staffing levels can be optimized to ensure adequate customer service during busy periods.

The Analytics Toolkit: Essential Data Sources and Tools

Effectively quantifying marketing impact necessitates a robust toolkit comprising various data sources and analytical instruments. These tools, when strategically integrated, provide a comprehensive view of marketing performance, enabling informed decision-making and optimized strategies. Let’s delve into the critical components of this toolkit and their respective functionalities.

Customer Relationship Management (CRM) Systems

CRMs are at the heart of customer-centric marketing. These systems meticulously track every interaction a customer has with the brand, from initial contact to post-purchase support.

This data is invaluable for:

• Personalizing marketing messages
• Improving customer retention
• Identifying high-value customers

By centralizing customer data, CRMs facilitate a deeper understanding of customer behavior and preferences. This understanding allows marketers to tailor their approaches for maximum impact.

Web Analytics Platforms

Web analytics platforms, such as Google Analytics and Adobe Analytics, provide deep insights into website traffic and user behavior. These tools are essential for understanding how users interact with a brand’s online presence.

Key metrics tracked include:

• Page views
• Bounce rate
• Conversion rate
• Time spent on site

These metrics reveal which content resonates with users, identify areas for website improvement, and measure the effectiveness of online marketing campaigns. Web analytics empowers marketers to optimize the user experience and drive conversions.

Advertising Platforms

Advertising platforms, such as Google Ads and Facebook Ads Manager, offer a wealth of data on ad campaign performance. This data is crucial for optimizing ad spend and maximizing ROI.

Key metrics to monitor include:

• Impressions
• Clicks
• Conversions
• Cost per acquisition (CPA)

By analyzing these metrics, marketers can identify underperforming ads. They can refine targeting parameters, and optimize ad creative to improve campaign performance. Data-driven optimization ensures that advertising budgets are used efficiently.

Social Media Analytics Tools

Social media analytics tools provide valuable insights into audience engagement, reach, and sentiment on social media platforms. These tools are essential for understanding how a brand is perceived on social media.

They help marketers:

• Track brand mentions
• Identify trending topics
• Measure the impact of social media campaigns

By analyzing social media data, marketers can tailor their content strategies to resonate with their target audience. They can also proactively address negative sentiment and foster positive brand perceptions.

Market Research Data

Market research data provides a broader understanding of consumer behavior, market trends, and competitive landscapes. This data can be obtained through:

• Surveys
• Focus groups
• Industry reports

By analyzing market research data, marketers can identify unmet needs, anticipate future trends, and develop products and services that align with market demand. This proactive approach is vital for maintaining a competitive edge and driving sustainable growth.

The Role of Statistical Software

Statistical software forms the bedrock of advanced marketing analytics. Tools like R, Python (with libraries like Pandas and Scikit-learn), and specialized packages such as SAS and SPSS are critical.

They serve multiple vital functions:

• Rigorous Analysis: Statistical software empowers marketers to perform sophisticated analysis. This includes regression analysis, hypothesis testing, and cluster analysis.
• Model Validation: These tools provide the means to validate marketing models. Validation ensures that the models are accurate and reliable.
• Actionable Insights: They reveal previously hidden patterns and relationships within marketing data.
• Causal Inference: They uncover true causal impacts in campaigns.
• Data Cleaning and Manipulation: It enables analysts to prepare raw data for modeling.
• Advanced Modeling: It delivers the capacity for predictive analytics and more complex custom model building.

The effective use of statistical software elevates marketing analytics from descriptive reporting to predictive and prescriptive insights, offering a competitive advantage in today’s data-driven landscape.

The Analytics Team: Roles and Responsibilities

Effectively quantifying marketing impact necessitates a robust toolkit comprising various data sources and analytical instruments. These tools, when strategically integrated, provide a comprehensive view of marketing performance, enabling informed decision-making and optimized strategies. Let’s delve into the essential roles within an analytics team, each contributing uniquely to extracting actionable insights from this data.

Core Roles in Marketing Analytics

The success of data-driven marketing hinges not only on technology and methodologies but also on the expertise of the individuals who interpret and apply the insights. A well-structured analytics team comprises diverse roles, each with distinct responsibilities and skill sets, working in synergy to unlock the full potential of marketing data.

The Marketing Analyst: Unveiling Insights from Data

Marketing analysts are the cornerstone of any data-informed marketing organization. They are responsible for sifting through vast datasets, identifying trends, and translating raw data into actionable recommendations.

Their primary function is to analyze marketing data to extract meaningful insights that inform strategic decisions. This involves a deep understanding of statistical techniques, data visualization tools, and marketing principles.

Specifically, marketing analysts employ statistical methods to evaluate campaign performance, segment customer bases, and forecast future trends. They utilize data visualization tools to create compelling reports and dashboards that communicate complex findings to stakeholders across the organization.

Ultimately, the marketing analyst serves as a vital bridge between data and action, empowering marketing managers to make informed decisions that optimize campaign performance and drive business growth.

The Data Scientist: Predictive Modeling and Advanced Analytics

Taking a more technical and forward-looking approach, data scientists delve deeper into the data, employing sophisticated statistical and machine learning techniques to address complex marketing challenges.

Their key role involves developing predictive models and algorithms for various applications, including customer segmentation, churn prediction, and personalized marketing. Data scientists leverage programming languages such as Python and R, along with specialized machine learning libraries, to build these models.

For instance, data scientists might create a model to identify customers at high risk of churn, enabling proactive intervention strategies to retain valuable clients. Or, they might develop a personalized recommendation engine that delivers tailored marketing messages to individual customers, increasing engagement and conversion rates.

By harnessing the power of advanced analytics, data scientists empower marketing organizations to anticipate future trends, personalize customer experiences, and achieve a competitive edge.

The Econometrician: The Impact of Price and Promotion

Econometricians bring a specialized skillset to the table, focusing on the statistical analysis of economic data related to marketing variables. Their expertise is particularly valuable in understanding the impact of pricing strategies, promotional campaigns, and other economic factors on sales and revenue.

Econometricians employ advanced statistical techniques, such as regression analysis and time series analysis, to quantify the relationship between marketing activities and economic outcomes. They can model the demand curve for a product, estimate the price elasticity of demand, and determine the optimal pricing strategy to maximize profitability.

Furthermore, econometricians play a critical role in evaluating the effectiveness of promotional campaigns, measuring the incremental sales lift generated by discounts, coupons, and other promotional tactics. Their insights enable marketing managers to optimize promotional budgets, select the most effective promotional tactics, and drive revenue growth.

The Marketing Manager: Strategic Leadership and Data Integration

Marketing managers are responsible for the overall strategic direction and execution of marketing campaigns. In a data-driven organization, marketing managers rely heavily on the insights provided by analysts, data scientists, and econometricians to inform their decisions.

Their role involves collaborating closely with these experts to understand key trends, evaluate campaign performance, and identify opportunities for improvement. They leverage data to develop and execute marketing strategies that are aligned with business objectives.

Moreover, marketing managers are responsible for allocating marketing budgets effectively, ensuring that resources are directed towards the most promising channels and tactics. They use data to track ROI, optimize marketing spend, and maximize the impact of their campaigns.

Essentially, the modern marketing manager must be a data-savvy leader, capable of translating complex analytical insights into actionable strategies that drive business growth.

Ensuring Reliability: Critical Considerations for Accurate and Ethical Measurement

The effectiveness of any marketing analytics initiative hinges not only on the sophistication of the methodologies employed but also, and perhaps more fundamentally, on the reliability of the insights derived. This reliability is contingent upon several crucial factors, including data quality, rigorous model validation, and interpretability. Neglecting these considerations can lead to flawed conclusions, misinformed decisions, and, ultimately, wasted resources. Furthermore, ethical considerations must be at the forefront of all marketing measurement activities to maintain consumer trust and adhere to legal standards.

The Primacy of Data Quality

Data quality is the bedrock of any sound analytical endeavor. Garbage in, garbage out – this maxim holds particularly true in marketing analytics. If the data used to train models or inform decisions is inaccurate, incomplete, or inconsistent, the resulting insights will inevitably be unreliable.

Sources of Data Quality Issues

Several factors can compromise data quality. These include:

• Data entry errors: Human error during data collection or input can introduce inaccuracies.

• Inconsistent data formats: Data from different sources may use different formats or units of measurement, leading to inconsistencies.

• Missing data: Gaps in the data can bias the results and limit the scope of analysis.

• Data integration challenges: Combining data from disparate systems can be complex, leading to errors and inconsistencies.

Strategies for Enhancing Data Quality

Improving data quality requires a multifaceted approach. Strategies include:

• Data Cleaning: Identifying and correcting errors, inconsistencies, and duplicates in the data.

• Data Validation: Implementing rules and checks to ensure that data conforms to predefined standards and constraints.

• Data Standardization: Transforming data into a consistent format and structure.

• Data Governance: Establishing policies and procedures for managing data quality throughout the data lifecycle.

By prioritizing data quality, organizations can lay a solid foundation for reliable marketing analytics.

The Imperative of Model Validation

Even with high-quality data, it is crucial to validate the models used for analysis. Model validation is the process of assessing the accuracy and reliability of a model by testing it on data that was not used to train it.

Techniques for Model Validation

Several techniques can be used for model validation, including:

• Holdout Samples: Dividing the data into two sets: a training set used to build the model and a holdout set used to test its performance.

• Cross-Validation: Dividing the data into multiple subsets and iteratively training the model on some subsets and testing it on the remaining subsets.

• Backtesting: Testing the model on historical data to assess its ability to predict past outcomes.

By rigorously validating models, organizations can gain confidence in their accuracy and reliability.

The Necessity of Interpretability

The ultimate goal of marketing analytics is not simply to generate predictions, but to provide actionable insights. This requires that the models used are interpretable, meaning that their predictions can be understood and explained.

Approaches to Enhancing Interpretability

Several techniques can be used to make models more interpretable, including:

• Feature Importance Analysis: Identifying the features that have the greatest impact on the model’s predictions.

• Model Visualization: Using graphs and charts to visualize the model’s behavior and predictions.

• Explainable AI (XAI) Techniques: Employing advanced techniques to understand and explain the inner workings of complex models.

By prioritizing interpretability, organizations can ensure that their marketing analytics efforts lead to meaningful and actionable insights. Transparency is paramount. Stakeholders should understand how conclusions are derived.

The Ethical Dimension of Marketing Analytics

Beyond accuracy and reliability, ethical considerations are paramount in marketing analytics. With increasing data collection and sophisticated analytical techniques, the potential for misuse and ethical violations is ever-present. Organizations must prioritize ethical data handling and usage.

Key Ethical Considerations

• Data Privacy: Protecting the privacy of individuals and complying with data privacy regulations, such as GDPR and CCPA. Obtain consent where necessary and be transparent about data use.

• Data Security: Implementing measures to protect data from unauthorized access and breaches. Employ robust security protocols.

• Transparency: Being transparent about how data is collected, used, and shared.

• Bias Mitigation: Identifying and mitigating biases in data and models to ensure fairness and equity.

• Responsible AI: Developing and deploying AI-powered marketing solutions in a responsible and ethical manner.

By adhering to ethical principles, organizations can build trust with consumers and maintain a positive brand reputation. Failing to do so can result in reputational damage, legal penalties, and loss of customer loyalty.

So, the next time you’re wondering why sales are up or down, remember the power of the market response model. It’s not a crystal ball, but it’s a pretty darn good way to understand and, more importantly, predict how consumers will react to your marketing efforts. Give it a try – you might just be surprised at what you discover!