WiserBrand's Comprehensive Customer Call Dataset: A Decade of Insights

WiserBrand offers an unparalleled dataset comprising over 16 million customer call records, meticulously gathered over the past 10 years and updated daily. This extensive dataset includes:

• User ID and Firm Name: Identify and categorize calls by unique user IDs and company names.
• Call Duration: Analyze engagement levels through call lengths.
• Geographical Information: Detailed data on city, state, and country for regional analysis.
• Call Timing: Track peak interaction times with precise timestamps.
• Call Reason and Group: Categorized reasons for calls, helping to identify common customer issues.
• Device and OS Types: Information on the devices and operating systems used for technical support analysis.

We can build a dataset based on your request, by category, industry, company, date, etc.

Our dataset is designed for businesses aiming to enhance customer service strategies, develop targeted marketing campaigns, and improve product support systems. Gain actionable insights into customer needs and behavior patterns with this comprehensive collection, particularly useful for Consumer Data and Consumer Behavior applications.

The more you purchase, the lower the price will be.

This dataset provides a comprehensive collection of consumer behavior data that can be used for various market research and statistical analyses. It includes information on purchasing patterns, demographics, product preferences, customer satisfaction, and more, making it ideal for market segmentation, predictive modeling, and understanding customer decision-making processes.

The dataset is designed to help researchers, data scientists, and marketers gain insights into consumer purchasing behavior across a wide range of categories. By analyzing this dataset, users can identify key trends, segment customers, and make data-driven decisions to improve product offerings, marketing strategies, and customer engagement.

Key Features: Customer Demographics: Understand age, income, gender, and education level for better segmentation and targeted marketing. Purchase Behavior: Includes purchase amount, frequency, category, and channel preferences to assess spending patterns. Customer Loyalty: Features like brand loyalty, engagement with ads, and loyalty program membership provide insights into long-term customer retention. Product Feedback: Customer ratings and satisfaction levels allow for analysis of product quality and customer sentiment. Decision-Making: Time spent on product research, time to decision, and purchase intent reflect how customers make purchasing decisions. Influences on Purchase: Factors such as social media influence, discount sensitivity, and return rates are included to analyze how external factors affect purchasing behavior.

Columns Overview: Customer_ID: Unique identifier for each customer. Age: Customer's age (integer). Gender: Customer's gender (categorical: Male, Female, Non-binary, Other). Income_Level: Customer's income level (categorical: Low, Middle, High). Marital_Status: Customer's marital status (categorical: Single, Married, Divorced, Widowed). Education_Level: Highest level of education completed (categorical: High School, Bachelor's, Master's, Doctorate). Occupation: Customer's occupation (categorical: Various job titles). Location: Customer's location (city, region, or country). Purchase_Category: Category of purchased products (e.g., Electronics, Clothing, Groceries). Purchase_Amount: Amount spent during the purchase (decimal). Frequency_of_Purchase: Number of purchases made per month (integer). Purchase_Channel: The purchase method (categorical: Online, In-Store, Mixed). Brand_Loyalty: Loyalty to brands (1-5 scale). Product_Rating: Rating given by the customer to a purchased product (1-5 scale). Time_Spent_on_Product_Research: Time spent researching a product (integer, hours or minutes). Social_Media_Influence: Influence of social media on purchasing decision (categorical: High, Medium, Low, None). Discount_Sensitivity: Sensitivity to discounts (categorical: Very Sensitive, Somewhat Sensitive, Not Sensitive). Return_Rate: Percentage of products returned (decimal). Customer_Satisfaction: Overall satisfaction with the purchase (1-10 scale). Engagement_with_Ads: Engagement level with advertisements (categorical: High, Medium, Low, None). Device_Used_for_Shopping: Device used for shopping (categorical: Smartphone, Desktop, Tablet). Payment_Method: Method of payment used for the purchase (categorical: Credit Card, Debit Card, PayPal, Cash, Other). Time_of_Purchase: Timestamp of when the purchase was made (date/time). Discount_Used: Whether the customer used a discount (Boolean: True/False). Customer_Loyalty_Program_Member: Whether the customer is part of a loyalty program (Boolean: True/False). Purchase_Intent: The intent behind the purchase (categorical: Impulsive, Planned, Need-based, Wants-based). Shipping_Preference: Shipping preference (categorical: Standard, Express, No Preference). Payment_Frequency: Frequency of payment (categorical: One-time, Subscription, Installments). Time_to_Decision: Time taken from consideration to actual purchase (in days).

Use Cases: Market Segmentation: Segment customers based on demographics, preferences, and behavior. Predictive Analytics: Use data to predict customer spending habits, loyalty, and product preferences. Customer Profiling: Build detailed profiles of different consumer segments based on purchase behavior, social media influence, and decision-making patterns. Retail and E-commerce Insights: Analyze purchase channels, payment methods, and shipping preferences to optimize marketing and sales strategies.

Target Audience: Data scientists and analysts looking for consumer behavior data. Marketers interested in improving customer segmentation and targeting. Researchers are exploring factors influencing consumer decisions and preferences. Companies aiming to improve customer experience and increase sales through data-driven decisions.

This dataset is available in CSV format for easy integration into data analysis tools and platforms such as Python, R, and Excel.

Success.ai’s Consumer Behavior Data for Consumer Goods & Electronics Industry Leaders in Asia, the US, and Europe offers a robust dataset designed to empower businesses with actionable insights into global consumer trends and professional profiles. Covering executives, product managers, marketers, and other professionals in the consumer goods and electronics sectors, this dataset includes verified contact information, professional histories, and geographic business data.

With access to over 700 million verified global profiles and firmographic data from leading companies, Success.ai ensures your outreach, market analysis, and strategic planning efforts are powered by accurate, continuously updated, and GDPR-compliant data. Backed by our Best Price Guarantee, this solution is ideal for businesses aiming to navigate and lead in these fast-paced industries.

Why Choose Success.ai’s Consumer Behavior Data?

1. Verified Contact Data for Precision Engagement

   • Access verified email addresses, phone numbers, and LinkedIn profiles of professionals in the consumer goods and electronics industries.
   • AI-driven validation ensures 99% accuracy, optimizing communication efficiency and minimizing data gaps.

2. Comprehensive Global Coverage

   • Includes profiles from key markets in Asia, the US, and Europe, covering regions such as China, India, Germany, and the United States.
   • Gain insights into region-specific consumer trends, product preferences, and purchasing behaviors.

3. Continuously Updated Datasets

   • Real-time updates capture career progressions, company expansions, market shifts, and consumer trend data.
   • Stay aligned with evolving market dynamics and seize emerging opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible use and legal compliance for all data-driven campaigns.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with industry leaders, marketers, and decision-makers in consumer goods and electronics industries worldwide.
• Consumer Trend Insights: Gain detailed insights into product preferences, purchasing patterns, and demographic influences.
• Business Locations: Access geographic data to identify regional markets, operational hubs, and emerging consumer bases.
• Professional Histories: Understand career trajectories, skills, and expertise of professionals driving innovation and strategy.

Key Features of the Dataset:

1. Decision-Maker Profiles in Consumer Goods and Electronics

   • Identify and engage with professionals responsible for product development, marketing strategy, and supply chain optimization.
   • Target individuals making decisions on consumer engagement, distribution, and market entry strategies.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (consumer electronics, FMCG, luxury goods), geographic location, or job function.
   • Tailor campaigns to align with specific industry trends, market demands, and regional preferences.

3. Consumer Trend Data and Insights

   • Access data on regional product preferences, spending behaviors, and purchasing influences across key global markets.
   • Leverage these insights to shape product development, marketing campaigns, and customer engagement strategies.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing and Demand Generation

   • Design campaigns tailored to consumer preferences, regional trends, and target demographics in the consumer goods and electronics industries.
   • Leverage verified contact data for multi-channel outreach, including email, social media, and direct marketing.

2. Market Research and Competitive Analysis

   • Analyze global consumer trends, spending patterns, and product preferences to refine your product portfolio and market positioning.
   • Benchmark against competitors to identify gaps, emerging needs, and growth opportunities in target regions.

3. Sales and Partnership Development

   • Build relationships with key decision-makers at companies specializing in consumer goods or electronics manufacturing and distribution.
   • Present innovative solutions, supply chain partnerships, or co-marketing opportunities to grow your market share.

4. Product Development and Innovation

   • Utilize consumer trend insights to inform product design, pricing strategies, and feature prioritization.
   • Develop offerings that align with regional preferences and purchasing behaviors to maximize market impact.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality consumer behavior data at competitive prices, ensuring maximum ROI for your outreach, research, and ma...

This AI-Driven Consumer Behavior Dataset captures key aspects of online shopping behavior, including purchase decisions, browsing activity, customer reviews, and demographic details. The dataset is designed for research in consumer behavior analysis, AI-driven recommendation systems, and digital marketing optimization.

Key Features: ✔ Consumer Purchase Data – Tracks product purchases, prices, discounts, and payment methods. ✔ Clickstream Data – Includes browsing behavior, pages visited, session duration, and cart abandonment. ✔ Customer Reviews & Sentiments – Provides ratings, textual reviews, and sentiment analysis scores. ✔ Demographic Information – Includes age, gender, location, and income levels. ✔ Target Column (purchase_decision) – Indicates whether a customer completed a purchase (1) or not (0).

• Audience Data 1P Data Audience ResolveID™ Platform - Audience Identity Cookieless Technology • Audience Data Identity Global US Graph – 670m + Identity Records • Access to 14 Billion Identity Consumer Profile Data Identifiers • Over 500+ Consumer Attributes, Online & Offline Data Behavior & Signals • IAB™ Seller-Defined Cookieless-Contextual Category – Intent & Behavior Signal Audience Cohorts
• Access to Customer Data Enrichment & Customer Data Ingestion • First-Party Data Ingestion & Data Appending

Dataset Description: E-commerce Customer Behavior

Overview: This dataset provides a comprehensive view of customer behavior within an e-commerce platform. Each entry in the dataset corresponds to a unique customer, offering a detailed breakdown of their interactions and transactions. The information is crafted to facilitate a nuanced analysis of customer preferences, engagement patterns, and satisfaction levels, aiding businesses in making data-driven decisions to enhance the customer experience.

Columns:

1. Customer ID:

   • Type: Numeric
   • Description: A unique identifier assigned to each customer, ensuring distinction across the dataset.

2. Gender:

   • Type: Categorical (Male, Female)
   • Description: Specifies the gender of the customer, allowing for gender-based analytics.

3. Age:

   • Type: Numeric
   • Description: Represents the age of the customer, enabling age-group-specific insights.

4. City:

   • Type: Categorical (City names)
   • Description: Indicates the city of residence for each customer, providing geographic insights.

5. Membership Type:

   • Type: Categorical (Gold, Silver, Bronze)
   • Description: Identifies the type of membership held by the customer, influencing perks and benefits.

6. Total Spend:

   • Type: Numeric
   • Description: Records the total monetary expenditure by the customer on the e-commerce platform.

7. Items Purchased:

   • Type: Numeric
   • Description: Quantifies the total number of items purchased by the customer.

8. Average Rating:

   • Type: Numeric (0 to 5, with decimals)
   • Description: Represents the average rating given by the customer for purchased items, gauging satisfaction.

9. Discount Applied:

   • Type: Boolean (True, False)
   • Description: Indicates whether a discount was applied to the customer's purchase, influencing buying behavior.

10. Days Since Last Purchase:

    • Type: Numeric
    • Description: Reflects the number of days elapsed since the customer's most recent purchase, aiding in retention analysis.

11. Satisfaction Level:

    • Type: Categorical (Satisfied, Neutral, Unsatisfied)
    • Description: Captures the overall satisfaction level of the customer, providing a subjective measure of their experience.

Use Cases:

1. Customer Segmentation:

   • Analyze and categorize customers based on demographics, spending habits, and satisfaction levels.

2. Satisfaction Analysis:

   • Investigate factors influencing customer satisfaction and identify areas for improvement.

3. Promotion Strategy:

   • Assess the impact of discounts on customer spending and tailor promotional strategies accordingly.

4. Retention Strategies:

   • Develop targeted retention strategies by understanding the time gap since the last purchase.

5. City-based Insights:

   • Explore regional variations in customer behavior to optimize marketing efforts based on location-specific trends.

Note: This dataset is synthetically generated for illustrative purposes, and any resemblance to real individuals or scenarios is coincidental.

Access consumer behavior data for 700M+ consumer goods and electronics professionals globally with Success.ai. Includes detailed contact information, professional histories, and business locations. GDPR-compliant. Best price guaranteed.

Harness the power of Success.ai’s Consumer Behavior Data to unlock profound insights into B2B and Consumer actions and preferences across global markets. Our comprehensive data is tailored to enhance your marketing and sales effectiveness at the best price.

During a May 2022 survey, ** percent of responding customers stated that a positive customer service experience made them more likely to purchase again. Moreover, ** percent of customers would recommend a company based solely on excellent customer service.

The Survey of Consumer Attitudes and Behavior series (also known as the Surveys of Consumers) was undertaken to measure changes in consumer attitudes and expectations, to understand why such changes occur, and to evaluate how they relate to consumer decisions to save, borrow, or make discretionary purchases. The data regularly include the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations. Since the 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter. The surveys conducted in 2016 focused on topics such as evaluations and expectations about personal finances, employment, price changes, and the national business situation. Opinions were collected regarding respondents' appraisals of present market conditions for purchasing houses, automobiles, computers, and other durables. Also explored in this survey, were respondents' types of savings and financial investments, loan use, family income and retirement planning. Other topics in this series typically include ownership, lease, and use of automobiles, respondents' use of personal computers at home and in the office, and respondents' familiarity with and use of the Internet. Demographic information includes ethnic origin, sex, age, marital status, and education.

Description: This dataset includes detailed demographic and behavioral information about restaurant consumers. It is designed to provide insights into consumer profiles, preferences, and habits, which can be useful for improving customer experience and developing targeted marketing strategies.

Features:

Consumer_ID: A unique identifier assigned to each consumer in the dataset. City: The city where the consumer resides. State: The state or province where the consumer is located. Country: The country where the consumer lives. Latitude: The geographical latitude of the consumer’s location. Longitude: The geographical longitude of the consumer’s location. Smoker: Indicates whether the consumer is a smoker (e.g., Yes/No). Drink_Level: The consumer’s level of alcohol consumption (e.g., None, Light, Moderate, Heavy). Transportation_Method: The mode of transportation the consumer uses to travel to the restaurant (e.g., Car, Public Transit, Walking). Marital_Status: The consumer’s marital status (e.g., Single, Married, Divorced, Widowed). Usage:

Consumer Profiling: Understand the demographics and habits of different consumer segments to tailor marketing strategies and restaurant offerings. Location Analysis: Analyze consumer location data to identify key markets and optimize restaurant placement or delivery areas. Behavioral Insights: Study smoking and drinking habits to adjust menu options and enhance customer experience. Transportation Trends: Assess how consumers travel to the restaurant to improve accessibility and convenience. Source: The data is collected from restaurant surveys, customer profiles, and demographic studies.

Notes:

Ensure that personal data is handled securely and in compliance with privacy regulations. Regular updates may be necessary to reflect changes in consumer behavior and demographics.

1) Data Introduction • The Consumer Behavior and Shopping Habits Dataset is a tabular collection of customer demographics, purchase history, product preferences, shopping frequency, and online and offline purchasing behavior.

2) Data Utilization (1) Consumer Behavior and Shopping Habits Dataset has characteristics that: • Each row contains detailed consumer and transaction information such as customer ID, age, gender, purchased goods and categories, purchase amount, region, product attributes (size, color, season), review rating, subscription status, delivery method, discount/promotion usage, payment method, purchase frequency, etc. • Data is organized to cover a variety of variables and purchasing patterns to help segment customers, establish marketing strategies, analyze product preferences, and more. (2) Consumer Behavior and Shopping Habits Dataset can be used to: • Customer Segmentation and Target Marketing: You can analyze demographics and purchasing patterns to define different customer groups and use them to develop customized marketing strategies. • Product and service improvement: Based on purchase history, review ratings, discount/promotional responses, etc., it can be applied to product and service improvements such as identifying popular products, managing inventory, and analyzing promotion effects.

The consumer decision software market is experiencing robust growth, driven by the increasing need for businesses to leverage data-driven insights for informed decision-making. The market's expansion is fueled by several key factors, including the rising adoption of cloud-based solutions, the proliferation of big data, and the growing demand for advanced analytics capabilities. Companies across diverse sectors are recognizing the value of utilizing sophisticated software to analyze consumer behavior, preferences, and market trends, enabling them to optimize marketing campaigns, personalize customer experiences, and enhance overall operational efficiency. This market is witnessing a shift towards solutions that offer predictive modeling, AI-powered recommendations, and real-time data visualization, fostering more agile and responsive decision-making processes. The competitive landscape is characterized by a mix of established players and emerging technology providers, leading to innovation and price competition. Looking ahead, the market is poised for continued expansion, with a projected Compound Annual Growth Rate (CAGR) of approximately 15% from 2025 to 2033. This growth trajectory will be fueled by the ongoing adoption of advanced technologies such as machine learning and artificial intelligence within consumer decision software. Increased investment in research and development by key players, coupled with the growing awareness of the value proposition of data-driven decision-making, will further propel the market forward. However, factors such as data security concerns and the high cost of implementation could potentially pose challenges to market growth. Nevertheless, the overall outlook for the consumer decision software market remains positive, indicating significant opportunities for both established companies and new entrants.

Echo offers unmatched data curation with an in-depth picture of location activity over time. Explore cross-shopping patterns, consumer profiles and untapped sites. Our Market Analysis reveals your store's reach, optimizes site selection, and enhances market intelligence for growth opportunities.

VisitIQ™ Consumer Data is a robust B2C dataset that empowers businesses to identify and connect with their target audiences effectively. This data set offers an extensive and detailed identity graph, providing you with the tools needed to link, model, and train your AI to understand and reach the right prospect audience for your marketing and sales campaigns.

Key Features of VisitIQ™ Consumer Behavior Data:

Comprehensive Coverage: Includes a wide array of U.S. consumer behavior data, covering millions of contacts and households in the US. This expansive dataset ensures that you have access to the most up-to-date and reliable identity graph available for audience prospecting.

Rich Demographic Data: Understand and identify your prospect audience and customers on a deeper level with linking and modeling B2C data points such as age, gender, income level, education, marital status, occupation, and household size. This granular demographic information allows for more precise segmentation. linking, modeling, and AI training and targeting, helping you to tailor your campaigns to the specific characteristics of your desired audience.

In-Depth Psychographic Data: Go beyond basic demographics with psychographic data that captures consumer interests, lifestyle choices, purchasing behavior, and brand affinities. This information allows for creating highly personalized marketing strategies, tapping into the motivations, preferences, and values that drive consumer decisions.

Enhanced Data Accuracy: The identity graph audience is meticulously collected, verified, and regularly updated to ensure accuracy and relevance. This commitment to data integrity helps to minimize bounce rates, reduce wasted marketing spend, and improve overall campaign performance.

Diverse Use Cases: Whether you're looking to launch a new product, conduct targeted email marketing, run a direct mail campaign, or optimize digital advertising efforts, VisitIQ's™ Consumer Behavior Data can be used across multiple channels to drive more effective marketing and sales efforts.

Customizable Data Solutions: Tailor the dataset to suit your specific business needs. Whether you need highly targeted lists for niche markets or broader segments for mass marketing, the flexibility of VisitIQ's™ data ensures that you can access the most relevant information for your unique objectives.

Compliance and Privacy: VisitIQ™ is committed to maintaining the highest standards of data privacy and compliance. All consumer data is ethically sourced and complies with data protection regulations, giving you peace of mind when using the dataset for your marketing campaigns.

Access 561M+ behavioral and location signals from 8 key markets for audience modeling and predictive marketing. GeoLifestyle enables detailed audience segmentation, predictive analytics, and behavior-driven marketing, with full on-premise security and regulatory compliance.

Data for Consumer behavior on YouTube. Visit https://dataone.org/datasets/sha256%3A623901173126421ba0ddc0fec30e563d8e09ee90eb0c527de6c22dbb25fd8c95 for complete metadata about this dataset.

This dataset contains 12730 rows of consumer behavior data for an e-commerce platform. The data provides insights into consumer demographics, purchasing habits, product preferences, and return behaviors. This dataset can be used to analyze patterns in customer purchasing decisions, predict product returns, and help optimize inventory for businesses aiming for sustainable e-commerce practices. By understanding purchasing trends, return rates, and customer behavior, businesses can enhance customer satisfaction, reduce waste, and optimize product offerings.

Customer_ID: A unique identifier for each customer (e.g., "CUST-1", "CUST-2", etc.).

Age: Customer's age (integer).

Gender: Gender of the customer (values: "Male", "Female").

Income_Level: Customer's income level (values: "Low", "Middle", "High").

Marital_Status: Customer's marital status (values: "Single", "Married", "Divorced", "Widowed").

Education_Level: Customer's highest completed education (values: "High School", "Bachelor's", "Master's", "Doctorate").

Occupation: Occupation of the customer (values: "Student", "Engineer", "Doctor", "Teacher", "Artist").

Location: Customer's location (city or region; values: "New York", "Los Angeles", "Chicago", "Houston", "Phoenix").

Purchase_Category: Product category purchased by the customer (values: "Electronics", "Clothing", "Groceries", "Furniture", "Books").

Purchase_Amount: The amount spent by the customer during the purchase (decimal values).

Frequency_of_Purchase: Number of purchases made per month (integer).

Purchase_Channel: Method of purchase (values: "Online", "In-Store", "Mixed").

Brand_Loyalty: Customer's loyalty to the brand (1-5 scale).

Product_Rating: Rating given by the customer to a purchased product (1-5 scale).

Time_Spent_on_Product_Research: Time (in hours or minutes) spent researching a product before purchase (integer).

Social_Media_Influence: Influence of social media on the customer's purchasing decision (values: "High", "Medium", "Low", "None").

Discount_Sensitivity: Sensitivity to discounts (values: "Very Sensitive", "Somewhat Sensitive", "Not Sensitive").

Return_Rate: Percentage of products returned by the customer (decimal).

Customer_Satisfaction: Overall satisfaction with the product (1-10 scale).

Engagement_with_Ads: Customer's engagement level with advertisements (values: "High", "Medium", "Low", "None").

Device_Used_for_Shopping: Device used for shopping (values: "Smartphone", "Desktop", "Tablet").

Payment_Method: Payment method used for the purchase (values: "Credit Card", "Debit Card", "PayPal", "Cash", "Other").

Time_of_Purchase: Timestamp of when the purchase was made (date/time).

Discount_Used: Whether the customer used a discount during the purchase (values: "True", "False").

Customer_Loyalty_Program_Member: Whether the customer is part of a loyalty program (values: "True", "False").

Purchase_Intent: The intent behind the purchase (values: "Impulsive", "Planned", "Need-based", "Wants-based").

Shipping_Preference: Customer's shipping preference (values: "Standard", "Express", "No Preference").

Payment_Frequency: Frequency of payment (values: "One-time", "Subscription", "Installments").

Time_to_Decision: Time (in days) taken from consideration to actual purchase.

Delivery_Status: Whether the product was delivered or returned (values: "1 for Delivered", "0 for Returned").

Kaufman Global, a leading provider of market research and customer insights, offers a rich trove of data on consumer behavior, market trends, and industry analysis. The company's extensive research reports and datasets provide valuable insights into the dynamic global marketplace, illuminating shifts in consumer preferences and purchasing habits.

Through Kaufman Global's vast repository of market intelligence, businesses can gain a deeper understanding of their target audiences, identify emerging trends, and inform data-driven decision-making. With a focus on delivering actionable insights, Kaufman Global's data offers unparalleled opportunities for market analysis and strategic planning, empowering organizations to stay ahead of the curve in today's fast-paced marketplace. By leveraging Kaufman Global's wealth of knowledge, businesses can optimize their marketing strategies, improve customer engagement, and drive growth in a rapidly changing world.

Discover the booming market for Consumption Decision-Making Customized Services! This in-depth analysis reveals a $15 billion market in 2025, projected to reach $50 billion by 2033, driven by AI, big data, and consumer demand. Explore key trends, regional breakdowns, and leading companies shaping this dynamic landscape.