Fundamentals

For Small to Medium-sized Businesses (SMBs), understanding and anticipating customer needs is not just a competitive advantage; it’s a cornerstone of survival and growth. In its simplest form, Predictive Customer Needs is about using available information to guess what your customers will want or need in the future. This isn’t about crystal balls or magic; it’s about smart business practices, observation, and a little bit of data analysis Meaning ● Data analysis, in the context of Small and Medium-sized Businesses (SMBs), represents a critical business process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting strategic decision-making. tailored to the resources and scale of an SMB.

Why Predictive Customer Needs Matter for SMBs

Imagine you run a local bakery. You notice that every Saturday morning, there’s a rush for croissants. That’s reactive customer understanding. Predictive customer needs take it a step further.

Perhaps you analyze sales data and see that croissant sales spike on Saturdays, but also on weekdays after local school events. Or maybe you observe that customers buying croissants often also purchase coffee. Predictive Customer Needs for your bakery might mean baking extra croissants on Fridays to prepare for the Saturday rush and school event days, and perhaps offering a coffee and croissant combo deal. This proactive approach, even in this simple example, can lead to:

• Increased Sales ● By having the right products or services available when customers want them, you reduce missed sales opportunities.
• Improved Customer Satisfaction ● Customers appreciate it when businesses seem to ‘know’ what they want, making their experience smoother and more enjoyable.
• Efficient Resource Allocation ● Knowing what to expect allows you to manage inventory, staffing, and marketing efforts more effectively, crucial for SMBs with limited resources.
• Competitive Edge ● In a crowded marketplace, anticipating needs can set you apart from competitors who are only reacting to current demand.

For an SMB, especially one just starting out, the idea of ‘predictive analytics’ might sound intimidating and expensive, conjuring images of complex software and data scientists. However, the fundamentals are accessible and can be implemented with tools and resources most SMBs already have or can readily adopt.

Basic Methods for SMBs to Start Predicting Customer Needs

You don’t need to be a tech giant to start predicting customer needs. Here are some foundational methods SMBs can easily implement:

1. Listening to Your Customers ● Direct Feedback

The most straightforward way to predict what customers want is to ask them directly. This can take many forms:

• Surveys and Feedback Forms ● Simple online surveys (using free tools like Google Forms or SurveyMonkey) or feedback forms at the point of sale can gather valuable insights. Ask questions about satisfaction, unmet needs, and future desires. For example, a local bookstore could ask, “What genres of books would you like to see more of in our store?”
• Direct Conversations ● Encourage your staff to have conversations with customers. Train them to listen actively and ask open-ended questions. A coffee shop barista could ask a regular customer, “What kind of new drinks are you interested in trying?”.
• Social Media Monitoring ● Pay attention to what customers are saying about your business and your industry on social media platforms. Tools like Mention or even basic social media platform search functions can help track mentions and sentiment. A clothing boutique might notice customers on Instagram asking for more sustainable fashion options.
• Review Analysis ● Online reviews (Google Reviews, Yelp, industry-specific review sites) are a goldmine of customer feedback. Analyze reviews to identify recurring themes ● what are customers praising? What are they complaining about? What are they wishing for? A restaurant might notice reviews consistently mentioning a desire for more vegetarian options.

These direct feedback methods are qualitative and provide rich, nuanced insights into customer thoughts and feelings. They are cost-effective and easily integrated into daily SMB operations.

2. Observing Customer Behavior ● Simple Data Analysis

Beyond what customers say, what they do is equally important. SMBs often have access to more data than they realize. Simple analysis of this data can reveal patterns and trends that predict future needs.

• Sales Data Analysis ● Even basic sales records can be incredibly informative. Track what products or services are selling well, when they are selling, and to whom. A hardware store might notice a spike in sales of gardening tools in the spring and snow shovels in the winter ● predictable seasonal needs.
• Website Analytics ● If you have a website, even a simple one, use free tools like Google Analytics to understand user behavior. What pages are they visiting? How long are they staying? What products are they viewing? An online craft store might see that customers are spending a lot of time on the ‘knitting supplies’ page, indicating a potential interest in expanding that product line.
• Customer Relationship Management (CRM) Basics ● Even a simple spreadsheet can act as a basic CRM. Track customer interactions, purchase history, and preferences. A small consulting firm could use a spreadsheet to note client industries and project types to anticipate future service needs based on industry trends.

Starting with basic data analysis doesn’t require expensive software or specialized skills. Spreadsheet programs like Microsoft Excel or Google Sheets are powerful enough for many SMB needs. The key is to start collecting data consistently and looking for patterns.

3. Staying Informed About Industry Trends

Customer needs are not static; they evolve with broader industry trends, technological advancements, and societal changes. SMBs need to stay informed about what’s happening in their industry and the wider world to anticipate how customer needs might shift.

• Industry Publications and Blogs ● Subscribe to industry-relevant newsletters, magazines, and blogs. These often highlight emerging trends and predict future customer demands. A fitness studio owner should follow fitness industry publications to understand new workout trends and dietary preferences.
• Competitor Analysis ● Pay attention to what your competitors are doing. What new products or services are they offering? How are they marketing themselves? This can provide clues about evolving customer expectations. A local cafe might observe a competitor adding a plant-based milk option, signaling a growing customer demand for such alternatives.
• Attend Industry Events ● Trade shows, conferences, and webinars are excellent opportunities to learn about the latest innovations and hear from industry experts about future trends. A small software company should attend industry conferences to understand emerging technology needs of their target market.

Staying informed is an ongoing process. Dedicate time regularly to research and learning to keep your SMB ahead of the curve.

Predictive Customer Needs, at its core, is about proactive business thinking for SMBs, using available resources to anticipate what customers will require, thereby enhancing customer satisfaction and business efficiency.

For SMBs just beginning to think about Predictive Customer Needs, the most important thing is to start. Begin with simple methods like direct customer feedback Meaning ● Customer Feedback, within the landscape of SMBs, represents the vital information conduit channeling insights, opinions, and reactions from customers pertaining to products, services, or the overall brand experience; it is strategically used to inform and refine business decisions related to growth, automation initiatives, and operational implementations. and basic data observation. As you become more comfortable and see the benefits, you can gradually incorporate more sophisticated techniques. The goal is to move from simply reacting to customer demands to proactively shaping your business to meet their future needs.

Intermediate

Building upon the foundational understanding of Predictive Customer Needs, the intermediate stage for SMBs involves leveraging more structured approaches and readily available technologies to enhance prediction accuracy and operational efficiency. At this level, SMBs move beyond basic observation and direct feedback to implement systematic processes and utilize digital tools for deeper customer insight and automated prediction.

Refining Data Collection and Analysis for Predictive Insights

While basic methods provide a starting point, intermediate strategies require a more refined approach to data. This means not just collecting data, but collecting the right data, organizing it effectively, and using more robust analytical techniques.

1. Enhanced Customer Relationship Management (CRM)

Moving beyond spreadsheets, implementing a dedicated CRM system is a crucial step. Modern CRMs designed for SMBs are often cloud-based, affordable, and user-friendly. They offer features that significantly enhance predictive capabilities:

• Centralized Customer Data ● CRMs consolidate customer interactions from various touchpoints (website, email, phone, social media) into a single, unified view. This provides a holistic picture of each customer, making it easier to identify patterns and preferences.
• Sales and Marketing Automation ● CRMs automate tasks like email marketing, lead nurturing, and sales follow-ups. This automation is not just about efficiency; it generates valuable data on customer engagement with marketing campaigns and sales processes, revealing what works and what doesn’t.
• Customer Segmentation ● CRMs allow for advanced customer segmentation Meaning ● Customer segmentation for SMBs is strategically dividing customers into groups to personalize experiences, optimize resources, and drive sustainable growth. based on demographics, purchase history, behavior, and engagement levels. This segmentation is fundamental for predictive analysis, as it allows you to tailor predictions and strategies to specific customer groups. For example, a CRM might segment customers into ‘high-value,’ ‘medium-value,’ and ‘low-value’ based on purchase frequency and amount, enabling targeted predictive marketing efforts for each segment.
• Reporting and Analytics Dashboards ● CRMs often come with built-in reporting and analytics dashboards that visualize key customer metrics and trends. These dashboards provide readily accessible insights into customer behavior Meaning ● Customer Behavior, within the sphere of Small and Medium-sized Businesses (SMBs), refers to the study and analysis of how customers decide to buy, use, and dispose of goods, services, ideas, or experiences, particularly as it relates to SMB growth strategies. and preferences, making it easier to spot emerging needs and predict future trends.

Choosing the right CRM for your SMB is important. Consider factors like ease of use, integration with existing tools, scalability, and industry-specific features. Popular SMB CRM options include HubSpot CRM, Zoho CRM, and Salesforce Essentials.

2. Leveraging Marketing Automation for Predictive Engagement

Marketing automation platforms, often integrated within CRMs or available as standalone tools, play a vital role in predicting customer needs and proactively addressing them. They enable SMBs to:

• Behavioral Tracking and Analysis ● Marketing automation Meaning ● Marketing Automation for SMBs: Strategically automating marketing tasks to enhance efficiency, personalize customer experiences, and drive sustainable business growth. tracks customer behavior across digital channels ● website visits, email opens and clicks, social media interactions, content downloads. This behavioral data is crucial for understanding customer interests and predicting their next steps in the customer journey. For example, if a customer repeatedly visits pages about ‘project management software’ on your website and downloads related ebooks, it’s a strong indicator of their need for such software.
• Personalized Customer Journeys ● Based on behavioral data and segmentation, marketing automation allows for the creation of personalized customer journeys. This means delivering the right content, offers, and messages to the right customers at the right time, anticipating their needs at each stage of their interaction with your business. For instance, a customer who has shown interest in project management software could be automatically enrolled in a personalized email sequence showcasing the benefits of your software and offering a free trial.
• Predictive Lead Scoring ● Marketing automation platforms Meaning ● MAPs empower SMBs to automate marketing, personalize customer journeys, and drive growth through data-driven strategies. often include lead scoring features that predict the likelihood of a lead converting into a customer. Lead scoring algorithms analyze various data points (demographics, behavior, engagement) to assign scores to leads. This helps sales teams prioritize the most promising leads and tailor their approach based on predicted conversion potential.
• A/B Testing and Optimization ● Marketing automation facilitates A/B testing of different marketing messages, offers, and channels. Analyzing the results of these tests provides valuable data on what resonates best with different customer segments, enabling continuous optimization of marketing efforts and improved prediction of customer response.

Examples of SMB-friendly marketing automation platforms include Mailchimp, ActiveCampaign, and GetResponse. These tools empower SMBs to move from reactive marketing to proactive, predictive engagement.

3. Intermediate Data Analysis Techniques for SMBs

While advanced statistical modeling might be beyond the scope of many SMBs, intermediate data analysis techniques are accessible and powerful for predictive purposes:

• Trend Analysis and Forecasting ● Using historical sales data, website traffic, or customer engagement metrics, SMBs can identify trends and forecast future patterns. Spreadsheet software or basic data visualization tools can be used to plot data over time and identify seasonal trends, growth patterns, or cyclical fluctuations. For example, a retail store can analyze past years’ holiday sales data to forecast inventory needs for the upcoming holiday season.
• Cohort Analysis ● Cohort analysis involves grouping customers based on shared characteristics or experiences (e.g., customers who signed up in the same month, customers who made their first purchase during a specific campaign). Analyzing the behavior of these cohorts over time can reveal valuable insights into customer retention, lifetime value, and product adoption patterns. A subscription box service could use cohort analysis to understand how customer retention rates vary across different signup months and identify factors influencing long-term engagement.
• Basic Regression Analysis ● Regression analysis Meaning ● Regression Analysis, a statistical methodology vital for SMBs, facilitates the understanding of relationships between variables to predict outcomes. explores the relationship between different variables. For SMBs, this could involve analyzing how marketing spend impacts sales, how website features affect conversion rates, or how customer demographics correlate with product preferences. Spreadsheet software or online statistical tools can perform basic regression analysis to identify statistically significant relationships and build simple predictive models. For example, a restaurant could use regression analysis to determine the impact of online advertising spend on website reservations.
• Customer Segmentation Analysis (Beyond CRM) ● While CRMs provide basic segmentation, more advanced techniques like RFM (Recency, Frequency, Monetary value) analysis or simple clustering algorithms can provide deeper customer segmentation. RFM analysis segments customers based on how recently they made a purchase, how frequently they purchase, and how much they spend. Clustering algorithms can group customers based on multiple variables to identify more nuanced customer segments. These advanced segmentations allow for more targeted and effective predictive strategies.

These intermediate techniques, while requiring some analytical aptitude, are within reach for SMBs willing to invest in training or hire personnel with basic data analysis skills. They provide a significant step up in predictive capability compared to purely observational methods.

Moving to an intermediate level of Predictive Customer Needs for SMBs means systematically collecting, organizing, and analyzing customer data using CRM, marketing automation, and refined analytical techniques to anticipate future customer demands more accurately and proactively.

At the intermediate stage, the focus shifts to building systems and processes that enable ongoing predictive analysis and action. This involves investing in appropriate technology, developing data analysis skills within the team, and integrating predictive insights Meaning ● Predictive Insights within the SMB realm represent the actionable intelligence derived from data analysis to forecast future business outcomes. into marketing, sales, and customer service Meaning ● Customer service, within the context of SMB growth, involves providing assistance and support to customers before, during, and after a purchase, a vital function for business survival. strategies. The payoff is a more proactive, data-driven SMB that can anticipate customer needs and stay ahead of the competition.

Advanced

At the advanced level, Predictive Customer Needs transcends reactive analysis and simple forecasting, evolving into a strategic, deeply integrated organizational capability. For sophisticated SMBs, or those aspiring to become industry leaders, advanced predictive customer needs involves employing cutting-edge technologies, sophisticated analytical frameworks, and a profound understanding of the complex interplay between customer behavior, market dynamics, and emerging trends. This advanced perspective redefines Predictive Customer Needs as not merely anticipating immediate desires, but as Strategic Foresight into Evolving Customer Values, Latent Needs, and Future Market Landscapes, Enabling Proactive Innovation and Sustainable Competitive Advantage Meaning ● SMB Competitive Advantage: Ecosystem-embedded, hyper-personalized value, sustained by strategic automation, ensuring resilience & impact. for SMBs.

Redefining Predictive Customer Needs ● A Strategic Foresight Perspective

Traditional definitions often frame Predictive Customer Needs as simply forecasting demand for existing products or services. However, an advanced understanding recognizes that true predictive capability lies in anticipating needs that customers themselves may not yet articulate, or even be aware of. This requires a shift from reactive data analysis to proactive strategic foresight, encompassing:

• Anticipating Latent Needs ● Moving beyond expressed desires to identify underlying, unarticulated needs. This involves understanding customer motivations, pain points, and aspirations at a deeper psychological and sociological level. For instance, customers may express a need for ‘faster delivery,’ but the latent need might be ‘convenience’ and ‘time-saving,’ which could be addressed by various solutions beyond just delivery speed, such as subscription services or automated ordering.
• Forecasting Evolving Values ● Customer values are not static; they shift with societal trends, technological advancements, and cultural changes. Advanced Predictive Customer Needs involves anticipating these shifts and understanding how they will reshape customer preferences and priorities. For example, the growing emphasis on sustainability is not just a trend, but a value shift. SMBs need to predict how this value shift will impact product preferences, service expectations, and brand loyalty in the long term.
• Scenario Planning and Future Market Modeling ● Advanced prediction moves beyond point forecasts to scenario planning, exploring multiple plausible future scenarios and their implications for customer needs. This involves building dynamic models that incorporate various factors ● technological disruptions, economic shifts, regulatory changes, competitor actions ● to simulate different market futures and understand how customer needs might evolve under each scenario. For example, an SMB in the education sector might develop scenarios based on the future of remote learning, the integration of AI in education, and changing workforce skill demands to predict future learning needs.
• Proactive Innovation and Needs Shaping ● The ultimate goal of advanced Predictive Customer Needs is not just to react to predicted demands, but to proactively shape customer needs and create new markets. This involves leveraging predictive insights to drive innovation, develop products and services that anticipate future needs, and even educate customers about needs they were not previously aware of. For example, a tech SMB might predict the growing need for cybersecurity solutions in the SMB market and proactively develop user-friendly, affordable cybersecurity products, educating SMBs about the increasing cyber threats they face.

This strategic foresight Meaning ● Strategic Foresight: Proactive future planning for SMB growth and resilience in a dynamic business world. perspective requires a blend of advanced analytical techniques, deep domain expertise, and a culture of continuous learning and adaptation within the SMB.

Advanced Analytical Techniques for SMB Predictive Customer Needs

To achieve this level of strategic foresight, SMBs need to employ more sophisticated analytical tools and methodologies. While resource constraints are a reality, the increasing accessibility of cloud-based AI and machine learning Meaning ● Machine Learning (ML), in the context of Small and Medium-sized Businesses (SMBs), represents a suite of algorithms that enable computer systems to learn from data without explicit programming, driving automation and enhancing decision-making. platforms, coupled with the growing availability of open-source tools and skilled freelance talent, makes advanced analytics increasingly viable for SMBs.

1. Machine Learning and Artificial Intelligence (AI)

Machine learning (ML) and AI are no longer the exclusive domain of large corporations. SMBs can leverage these technologies for advanced predictive capabilities:

• Predictive Modeling with ML Algorithms ● ML algorithms can analyze vast datasets to identify complex patterns and build highly accurate predictive models. For customer needs prediction, this could involve using algorithms like ●
  • Regression Algorithms (e.g., Linear Regression, Support Vector Regression) ● For predicting continuous variables like future purchase value, customer lifetime value, or demand volume.
  • Classification Algorithms (e.g., Logistic Regression, Random Forests, Neural Networks) ● For predicting categorical variables like customer churn risk, product preference categories, or likelihood of responding to a specific marketing campaign.
  • Clustering Algorithms (e.g., K-Means, DBSCAN) ● For advanced customer segmentation Meaning ● Advanced Customer Segmentation refines the standard practice, employing sophisticated data analytics and technology to divide an SMB's customer base into more granular and behavior-based groups. based on complex behavioral patterns and identifying niche customer groups with specific unmet needs.
  • Time Series Forecasting Algorithms (e.g., ARIMA, Prophet, LSTM Recurrent Neural Networks) ● For highly accurate forecasting of demand, sales, and other time-dependent customer behavior metrics, especially useful for businesses with seasonal or cyclical demand patterns.

  SMBs can utilize cloud-based ML platforms like Google AI Platform, Amazon SageMaker, or Microsoft Azure Machine Learning, which offer user-friendly interfaces and pre-built algorithms, reducing the need for deep coding expertise. They can also leverage open-source ML libraries like scikit-learn, TensorFlow, and PyTorch, often with the support of freelance data scientists or specialized consulting services.

• Natural Language Processing (NLP) for Sentiment and Needs Analysis ● NLP techniques enable SMBs to analyze unstructured text data ● customer reviews, social media posts, survey responses, customer service interactions ● to extract sentiment, identify recurring themes, and uncover hidden customer needs and pain points. Sentiment analysis can gauge customer emotions and attitudes towards products, services, and brands. Topic modeling can automatically identify key topics and themes discussed by customers, revealing emerging needs and areas for improvement.

  SMBs can use cloud-based NLP APIs like Google Cloud Natural Language API, Amazon Comprehend, or Azure Text Analytics, which provide pre-trained models for sentiment analysis, topic extraction, and entity recognition, making NLP accessible even without in-house NLP expertise.

• Recommendation Engines and Personalized Experiences ● AI-powered recommendation engines Meaning ● Recommendation Engines, in the sphere of SMB growth, represent a strategic automation tool leveraging data analysis to predict customer preferences and guide purchasing decisions. go beyond simple product recommendations based on past purchases. They analyze a wide range of data ● browsing history, purchase behavior, demographics, preferences, contextual information ● to predict individual customer needs and deliver highly personalized product recommendations, content suggestions, and service offerings. This enhances customer experience, increases engagement, and drives sales. SMBs can implement recommendation engines using cloud-based recommendation APIs or by integrating open-source recommendation libraries into their e-commerce platforms or customer portals.

While implementing advanced AI/ML requires investment and expertise, the potential return in terms of predictive accuracy, customer personalization, and strategic foresight is significant, particularly for SMBs aiming for rapid growth and market leadership.

2. Advanced Data Integration and Management

Effective advanced predictive customer needs requires seamless integration of data from diverse sources and robust data management practices:

• Data Warehousing and Data Lakes ● SMBs need to move beyond siloed data sources and establish centralized data repositories. Data warehouses are structured repositories optimized for reporting and analysis, suitable for well-defined data types. Data lakes are more flexible repositories that can store both structured and unstructured data in its raw format, ideal for exploratory data analysis and ML applications. Cloud-based data warehousing solutions like Google BigQuery, Amazon Redshift, or Snowflake, and data lake solutions like Amazon S3 or Azure Data Lake Storage, offer scalable and cost-effective options for SMBs.
• Real-Time Data Streaming and Processing ● In today’s fast-paced digital environment, real-time data Meaning ● Instantaneous information enabling SMBs to make agile, data-driven decisions and gain a competitive edge. is crucial for timely prediction and response. Implementing real-time data streaming pipelines to capture and process customer interactions as they happen ● website clicks, app usage, social media activity, in-store transactions ● enables dynamic predictive models Meaning ● Predictive Models, in the context of SMB growth, refer to analytical tools that forecast future outcomes based on historical data, enabling informed decision-making. that adapt to changing customer behavior in real-time. Technologies like Apache Kafka, Apache Flink, or cloud-based streaming services can be used to build real-time data pipelines for SMBs.
• Data Governance and Privacy Compliance ● As SMBs handle increasingly large and complex datasets, robust data governance Meaning ● Data Governance for SMBs strategically manages data to achieve business goals, foster innovation, and gain a competitive edge. and privacy compliance become paramount. This includes establishing data quality standards, implementing data security measures, and adhering to data privacy regulations like GDPR or CCPA. Proper data governance not only ensures data accuracy and security but also builds customer trust and mitigates legal risks. SMBs should invest in data governance tools and expertise to establish and maintain effective data governance frameworks.

Advanced data integration and management are foundational for leveraging the full potential of advanced analytical techniques and achieving strategic predictive customer needs capabilities.

3. Cross-Functional Predictive Customer Needs Integration

For Predictive Customer Needs to truly drive strategic advantage, it must be integrated across all functional areas of the SMB, not just marketing or sales:

• Predictive Customer Service ● Anticipating customer service needs and proactively addressing them. This includes using predictive models to identify customers at risk of churn and proactively reaching out with personalized retention offers, predicting customer service inquiries and optimizing staffing levels, and using NLP to analyze customer service interactions and identify areas for service improvement.
• Predictive Product Development ● Leveraging predictive insights to guide product innovation and development. This involves using predictive models to identify unmet customer needs and emerging market opportunities, analyzing customer feedback and market trends to inform product design and feature prioritization, and using scenario planning Meaning ● Scenario Planning, for Small and Medium-sized Businesses (SMBs), involves formulating plausible alternative futures to inform strategic decision-making. to anticipate future product requirements in different market scenarios.
• Predictive Operations and Supply Chain ● Optimizing operations and supply chain based on predicted customer demand. This includes using demand forecasting models to optimize inventory levels and reduce stockouts or overstocking, predicting equipment maintenance needs to minimize downtime, and optimizing logistics and delivery routes based on predicted order patterns.
• Predictive Financial Planning ● Integrating predictive customer needs into financial forecasting and planning. This involves using sales forecasting models to project future revenue and cash flow, predicting customer acquisition costs and optimizing marketing budgets, and using customer lifetime value Meaning ● Customer Lifetime Value (CLTV) for SMBs is the projected net profit from a customer relationship, guiding strategic decisions for sustainable growth. predictions to inform investment decisions and long-term financial strategy.

Cross-functional integration requires a shift in organizational culture, fostering data-driven decision-making across all departments and establishing clear communication channels to share predictive insights and collaborate on proactive strategies. This holistic approach transforms Predictive Customer Needs from a tactical tool to a strategic organizational capability.

Advanced Predictive Customer Needs for SMBs transcends basic forecasting, evolving into strategic foresight by leveraging AI, sophisticated data management, and cross-functional integration to anticipate latent needs, shape future markets, and achieve sustainable competitive advantage.

For SMBs venturing into advanced Predictive Customer Needs, the journey requires a commitment to continuous learning, technological investment, and organizational transformation. It’s not merely about adopting advanced technologies, but about cultivating a data-driven culture, fostering analytical expertise, and embracing a strategic foresight mindset. The rewards, however, are substantial ● the ability to not just react to market changes, but to proactively shape them, build stronger customer relationships, and secure a leading position in the evolving business landscape. This advanced approach, while demanding, represents the future of SMB competitiveness and sustainable growth in an increasingly complex and dynamic world.