Imagine being able to predict your customers’ needs before they even express them, and tailor your interactions to their unique preferences and behaviors. This is the promise of continuous learning CRM, a revolutionary approach to customer relationship management that’s transforming the way businesses interact with their customers. According to a recent study, 80% of customers are more likely to make a purchase from a brand that offers personalized experiences, and 77% of companies believe that delivering personalized experiences is a key differentiator. With the help of continuous learning CRM, businesses can now deliver hyper-personalization and predictive analytics, leading to increased customer satisfaction and loyalty. In this guide, we’ll explore the power of continuous learning CRM and how it can help businesses revolutionize their customer relationships, covering topics such as the role of AI and machine learning, and the benefits of real-time data analysis. We’ll also examine the latest trends and statistics, including a report by Gartner that found that companies that use predictive analytics are 2.5 times more likely to outperform their peers. By the end of this guide, you’ll understand how continuous learning CRM can help you stay ahead of the curve and deliver exceptional customer experiences.

Customer Relationship Management (CRM) has come a long way since its inception, transforming from a simple data repository to a sophisticated, intelligent relationship hub. As businesses strive to build lasting connections with their customers, the role of CRM has evolved to encompass not just storing customer information, but also analyzing it to predict behaviors and personalize interactions. In this section, we’ll delve into the history of CRM, exploring its limitations and how it has given rise to a new breed of continuous learning CRM systems. We’ll examine how these advancements are revolutionizing the way businesses approach customer relationships, enabling hyper-personalization and predictive analytics that drive real results.

The Limitations of Traditional CRM Systems

Conventional CRM platforms have been a cornerstone of customer relationship management for decades, but they’ve also become a bottleneck in today’s fast-paced, customer-centric business landscape. One of the primary limitations of traditional CRM systems is their reliance on static data, which can quickly become outdated and irrelevant. For instance, a study by Gartner found that the average company’s customer data is inaccurate by about 20-30%, resulting in missed sales opportunities and wasted resources.

Another significant shortcoming of conventional CRM platforms is the need for manual updates, which can be time-consuming and prone to human error. According to a report by Forrester, sales reps spend up to 60% of their time on administrative tasks, such as data entry and lead qualification, rather than engaging with customers. This not only hampers sales productivity but also leads to delays in responding to customer inquiries and resolving issues.

The inability of traditional CRM systems to adapt to changing customer behaviors is also a major concern. With the rise of digital channels and social media, customers now interact with businesses through multiple touchpoints, expecting personalized and seamless experiences. However, conventional CRM platforms often struggle to keep pace with these evolving behaviors, resulting in disjointed and frustrating customer experiences. For example, a survey by Salesforce found that 76% of customers expect companies to understand their needs and preferences, but only 34% of companies are able to deliver on these expectations.

  • Static data: leads to inaccurate customer insights and missed sales opportunities
  • Manual updates: result in wasted resources, delayed responses, and poor sales productivity
  • Inability to adapt: leads to disjointed customer experiences, frustrated customers, and lost business

To overcome these limitations, businesses need to adopt a more modern and agile approach to customer relationship management. This involves leveraging technologies like artificial intelligence, machine learning, and cloud-based platforms to create a more dynamic, responsive, and customer-centric CRM system. By doing so, companies can unlock new opportunities for growth, improve customer satisfaction, and stay ahead of the competition in today’s rapidly evolving business landscape.

The Rise of Continuous Learning CRM

Continuous learning CRM is a game-changer in the world of customer relationship management. It’s a system that uses AI, machine learning, and real-time data processing to constantly improve and refine its performance. Unlike traditional CRM systems, which often rely on static data and manual updates, continuous learning CRM systems are designed to learn from each interaction and adapt to changing customer needs.

At the heart of continuous learning CRM is the ability to process vast amounts of data in real-time. This allows the system to analyze customer behavior, preferences, and pain points, and use that information to deliver personalized experiences. For example, Salesforce uses AI-powered analytics to help businesses predict customer churn and take proactive steps to prevent it. Similarly, HubSpot uses machine learning to identify high-quality leads and recommend personalized content to nurture them.

So, how do continuous learning CRM systems learn and improve? It’s through a process of continuous feedback and iteration. Every interaction with the customer – whether it’s a phone call, email, or social media post – generates new data that the system can use to refine its understanding of the customer. This creates a virtuous cycle of improvement, where the system gets smarter and more effective with each passing day. As we here at SuperAGI have seen, this can lead to significant improvements in customer satisfaction and loyalty.

Some of the key benefits of continuous learning CRM include:

  • Improved customer insights: By analyzing customer data in real-time, businesses can gain a deeper understanding of their customers’ needs and preferences.
  • Personalized experiences: Continuous learning CRM systems can use machine learning to deliver personalized content, recommendations, and offers to customers.
  • Predictive analytics: By analyzing customer behavior and preferences, businesses can predict future needs and take proactive steps to meet them.

According to a recent study by Gartner, businesses that use continuous learning CRM systems are more likely to see significant improvements in customer satisfaction and loyalty. In fact, the study found that businesses that use AI-powered CRM systems are 2.5 times more likely to see improvements in customer satisfaction than those that don’t. As we explore in more detail later, this is just one example of how continuous learning CRM is revolutionizing the way businesses interact with their customers.

As we’ve seen, traditional CRM systems often fall short in delivering truly personalized customer experiences. But what if your CRM could go beyond basic segmentation and deliver hyper-personalization at scale? With the rise of continuous learning CRM, this is now a reality. In this section, we’ll explore the power of hyper-personalization and how it can revolutionize your customer relationships. You’ll learn how to leverage real-time customer insights, dynamic profiles, and omnichannel personalization to create tailored experiences that drive engagement and loyalty. We’ll also take a closer look at how we here at SuperAGI approach hyper-personalization, and what this means for your business. By the end of this section, you’ll have a deeper understanding of how to take your customer relationships to the next level and stay ahead of the curve in today’s fast-paced market.

Real-Time Customer Insights and Dynamic Profiles

Continuous learning CRM systems have revolutionized the way businesses understand their customers by creating and updating customer profiles in real-time. This is achieved by incorporating a wide range of data, including behavioral patterns, preferences, and interaction history. For instance, HubSpot uses machine learning algorithms to analyze customer interactions across various channels, such as social media, email, and website visits, to build comprehensive profiles.

These dynamic profiles enable businesses to gain a deeper understanding of their customers’ needs, interests, and pain points. By analyzing behavioral data, such as purchase history, search queries, and content engagement, companies can identify patterns and preferences that inform personalized marketing strategies. For example, Amazon uses real-time customer data to offer tailored product recommendations, resulting in a significant increase in sales and customer satisfaction.

The real-time updates to customer profiles allow businesses to respond promptly to changes in customer behavior, ensuring that personalization efforts are always relevant and effective. This is particularly important in today’s fast-paced digital landscape, where customer preferences can shift rapidly. According to a study by McKinsey, companies that use advanced customer analytics are 23 times more likely to outperform their competitors in terms of customer satisfaction.

  • Key benefits of real-time customer profiles include:
    • Improved accuracy and relevance of personalization efforts
    • Enhanced customer experience through timely and targeted interactions
    • Increased efficiency in marketing and sales operations

To achieve this level of personalization, businesses can leverage a range of tools and technologies, including customer data platforms (CDPs), marketing automation software, and predictive analytics solutions. By integrating these tools with their CRM system, companies can create a unified view of their customers and deliver personalized experiences that drive engagement, loyalty, and revenue growth.

As we here at SuperAGI can attest, the impact of real-time customer insights and dynamic profiles on business outcomes is significant. By harnessing the power of continuous learning CRM, companies can unlock new levels of personalization, drive customer satisfaction, and ultimately, revenue growth. With the ability to analyze and act on customer data in real-time, businesses can stay ahead of the competition and build lasting relationships with their customers.

Omnichannel Personalization at Scale

Omnichannel personalization is about delivering consistent, personalized experiences across all touchpoints, from social media and email to in-app notifications and customer service interactions. To achieve this, companies need to have a unified view of their customers and be able to orchestrate personalized experiences in real-time. We here at SuperAGI have seen firsthand how our platform can help businesses deliver personalized experiences at scale, resulting in increased customer engagement and loyalty.

For instance, 77% of customers have reported that they prefer to interact with brands that offer personalized experiences. Companies like Amazon and Netflix have set the bar high for personalized experiences, using data and analytics to drive recommendations and offers. To replicate this success, businesses can use tools like our Omnichannel Messaging and Journey Orchestration features to create tailored experiences for each customer.

  • Implementing a customer data platform (CDP) to unify customer data and create a single customer view
  • Using AI-powered analytics to analyze customer behavior and preferences, and drive personalized recommendations
  • Orchestrating omnichannel experiences that span multiple touchpoints, including social media, email, and customer service

By delivering consistent, personalized experiences across all touchpoints, businesses can increase customer satisfaction, loyalty, and ultimately, revenue. For example, a study by Forrester found that companies that implement omnichannel personalization strategies see an average increase of 10% in customer retention and 12% in revenue growth. As we continue to develop and refine our platform, we’re excited to see the impact that omnichannel personalization can have on businesses of all sizes.

To achieve efficiency at scale, businesses need to automate and optimize their personalization strategies. This can be done by using tools like our Agent Builder and Marketing AI Agents, which enable companies to create and deploy personalized marketing campaigns without requiring extensive IT resources. By leveraging these tools and strategies, businesses can deliver personalized experiences that drive real results and help them stay ahead of the competition.

Case Study: SuperAGI’s Approach to Hyper-Personalization

Here at SuperAGI, we understand the importance of hyper-personalization in building strong customer relationships. Our continuous learning CRM platform is designed to help businesses deliver tailored experiences to their customers, driving engagement, loyalty, and revenue growth. So, how do we implement hyper-personalization? Let’s dive into some specific examples.

Our platform uses AI-powered agents to analyze customer data and behavior, creating dynamic profiles that update in real-time. This allows our customers to segment their audience with precision, using criteria such as demographic information, purchase history, and online interactions. For instance, a company like Amazon can use our platform to identify high-value customers and create targeted marketing campaigns that speak directly to their interests.

  • Real-time customer insights: Our platform provides real-time analytics and reporting, enabling businesses to respond quickly to changes in customer behavior and preferences.
  • Omnichannel personalization: We help companies deliver consistent, personalized experiences across all channels, from email and social media to website and in-app interactions.
  • AI-driven content creation: Our AI agents can draft subject lines, email copy, and even entire content campaigns, ensuring that each customer receives relevant and engaging content.

One of our customers, a leading e-commerce company, saw a 25% increase in sales after implementing our hyper-personalization strategies. By using our platform to create targeted marketing campaigns and personalized product recommendations, they were able to drive more conversions and boost customer loyalty. Another customer, a major financial institution, reported a 30% reduction in customer churn after using our platform to deliver personalized customer support and engagement initiatives.

These success stories demonstrate the power of hyper-personalization in driving business results. By using our continuous learning CRM platform, companies can unlock the full potential of their customer data and deliver experiences that truly resonate with their audience. As we continue to innovate and expand our platform, we’re excited to see the impact that hyper-personalization will have on the future of customer relationships.

As we’ve explored the evolution of CRM and the power of hyper-personalization, it’s clear that understanding customer needs is crucial for driving business success. But what if you could take it a step further and anticipate those needs before they arise? This is where predictive analytics comes in – a game-changer for sales, marketing, and service teams. By leveraging advanced algorithms and machine learning capabilities, businesses can uncover hidden patterns and trends in customer data, enabling them to stay one step ahead of the competition. In this section, we’ll dive into the world of predictive analytics, exploring how it can help you move from descriptive to prescriptive analytics, and provide actionable insights to drive revenue growth and customer satisfaction.

From Descriptive to Prescriptive Analytics

The world of analytics has undergone a significant transformation in recent years, evolving from simply describing what happened to prescribing what should happen next. This shift from descriptive to prescriptive analytics has been instrumental in enabling businesses to make data-driven decisions and stay ahead of the competition.

Descriptive analytics, which focuses on historical data, was once the norm. However, with the advent of machine learning and artificial intelligence, predictive analytics has become increasingly prevalent. According to a study by Gartner, the use of predictive analytics has increased by 25% in the past two years alone, with 70% of organizations reporting that they have implemented or plan to implement predictive analytics in the next two years.

  • Descriptive analytics: Provides insights into what happened in the past, using data to identify trends and patterns. This type of analytics is essential for understanding customer behavior, sales performance, and market trends.
  • Predictive analytics: Uses statistical models and machine learning algorithms to forecast what may happen in the future, based on historical data and real-time inputs. This type of analytics is critical for predicting customer churn, identifying new sales opportunities, and optimizing marketing campaigns.
  • Prescriptive analytics: Goes a step further, providing recommendations on what actions to take to achieve a specific outcome. This type of analytics is essential for optimizing business processes, managing resources, and making strategic decisions.

Continuous learning plays a vital role in improving the accuracy of predictions over time. By leveraging machine learning algorithms and real-time data, businesses can refine their predictive models, ensuring that they stay up-to-date with changing customer behaviors and market trends. We here at SuperAGI have seen firsthand how continuous learning can improve the accuracy of predictions, enabling businesses to make more informed decisions and drive better outcomes.

  1. Start by collecting and integrating data from various sources, including customer interactions, sales performance, and market trends.
  2. Apply machine learning algorithms to identify patterns and trends in the data, and forecast future outcomes.
  3. Refine the predictive models using real-time data and continuous learning, ensuring that they stay up-to-date with changing customer behaviors and market trends.

By evolving from descriptive to prescriptive analytics, businesses can unlock new insights, drive better decision-making, and stay ahead of the competition. With continuous learning, the accuracy of predictions improves over time, enabling businesses to make more informed decisions and drive better outcomes.

Actionable Insights for Sales, Marketing, and Service Teams

Predictive insights can be a game-changer for businesses, but only if they are translated into concrete actions. Here are some ways different departments can leverage predictive insights to improve conversion rates, customer satisfaction, and retention:

  • Sales Teams: Use predictive lead scoring to identify high-potential leads and prioritize outreach efforts. For example, Salesforce found that companies using predictive lead scoring saw a 21% increase in sales productivity. Additionally, sales teams can use predictive analytics to anticipate customer needs and offer personalized solutions, resulting in a 15% increase in conversion rates, as seen in the case of Samsung.
  • Marketing Teams: Leverage predictive analytics to create targeted campaigns and improve customer engagement. According to a study by Marketo, companies using predictive marketing saw a 25% increase in customer engagement and a 10% increase in conversion rates. Marketing teams can also use predictive insights to identify the most effective channels and messaging for each customer segment, resulting in a 20% increase in ROI, as reported by HubSpot.
  • Service Teams: Use predictive insights to anticipate customer issues and provide proactive support. For instance, Microsoft used predictive analytics to reduce customer support issues by 25% and improve customer satisfaction by 15%. Service teams can also use predictive analytics to identify high-risk customers and offer personalized support, resulting in a 10% decrease in churn rate, as seen in the case of Amazon.

Some popular tools for predictive analytics include Google Analytics, SuperAGI, and SAS. These tools can help businesses analyze customer data, identify patterns, and make predictions about future behavior. By leveraging predictive insights, businesses can improve conversion rates, customer satisfaction, and retention, ultimately driving revenue growth and competitiveness.

According to a study by Gartner, companies using predictive analytics saw a 10% increase in revenue growth and a 5% increase in customer retention. Additionally, a study by Forrester found that companies using predictive analytics saw a 20% increase in customer satisfaction and a 15% decrease in customer churn. By translating predictive insights into concrete actions, businesses can unlock the full potential of their customer data and drive business success.

Now that we’ve explored the power of hyper-personalization and predictive analytics in continuous learning CRM, it’s time to dive into the nitty-gritty of implementation. As we’ve seen, the benefits of continuous learning CRM are undeniable, from enhanced customer experiences to improved sales and marketing outcomes. However, putting this technology into practice can be a daunting task, especially for organizations with complex data landscapes and change-averse teams. In this section, we’ll outline the key strategies for successful implementation, including data quality and integration considerations, as well as change management and team adoption best practices. By understanding these critical factors, you’ll be better equipped to unlock the full potential of continuous learning CRM and revolutionize your customer relationships.

Data Quality and Integration Considerations

When implementing a continuous learning CRM, data quality and integration are crucial considerations. According to a study by Gartner, poor data quality costs organizations an average of $12.9 million per year. Therefore, it’s essential to ensure that your CRM system has access to high-quality, timely, and relevant data.

A key challenge in achieving this is integrating the CRM with existing systems, such as marketing automation tools, customer service software, and ERP systems. For example, Salesforce provides a range of integration tools, including APIs and pre-built connectors, to help organizations connect their CRM with other systems. Similarly, Microsoft Dynamics 365 offers a unified platform that integrates CRM and ERP functionality, enabling seamless data exchange and synchronization.

To power continuous learning, it’s essential to create a unified customer data platform that brings together data from various sources and provides a single, accurate view of the customer. This can be achieved using tools like Apache Hadoop or Amazon Redshift, which provide a scalable and flexible data warehousing solution. Additionally, customer data platforms (CDPs) like Segment or AgilOne can help organizations to collect, integrate, and analyze customer data from multiple sources, providing a comprehensive understanding of customer behavior and preferences.

  • Data standardization: Establishing a common data format and standardizing data fields to ensure consistency and accuracy across different systems.
  • Data governance: Implementing policies and procedures to manage data quality, security, and compliance, and ensuring that data is handled and processed in accordance with regulatory requirements.
  • Data integration: Using APIs, ETL tools, or other integration technologies to connect different systems and enable seamless data exchange.

By prioritizing data quality and integration, and creating a unified customer data platform, organizations can unlock the full potential of continuous learning CRM and deliver hyper-personalized customer experiences that drive business growth and loyalty. According to a study by Forrester, organizations that invest in customer experience see a significant increase in customer loyalty, retention, and revenue growth.

Change Management and Team Adoption

Implementing a Continuous Learning CRM is a significant undertaking, requiring careful consideration of organizational change management and team adoption. A study by Gartner found that 55% of CRM projects fail due to inadequate change management and training. To avoid this pitfall, it’s essential to develop a comprehensive strategy for managing change and ensuring team adoption.

A key aspect of change management is providing thorough training to employees. This can include instructor-led sessions, online courses, and on-the-job coaching. For example, Salesforce offers a range of training programs, including Trailhead, which provides interactive, self-paced learning paths for users. Additionally, companies like Microsoft offer customized training solutions, such as its Dynamics 365 training program, which includes virtual instructor-led training and online learning modules.

Incentive structures can also play a crucial role in driving team adoption. This can include metrics-based incentives, such as rewards for meeting or exceeding sales targets, as well as recognition programs, such as employee of the month awards. A study by Harvard Business Review found that employees who receive regular recognition and rewards are more likely to be engaged and motivated. Some popular tools for creating and managing incentive structures include Hoopla, which offers a range of sales motivation and engagement platforms, and LevelEleven, which provides a sales performance management platform.

  • Develop a clear communication plan to ensure that all stakeholders understand the benefits and objectives of the new CRM capabilities
  • Provide comprehensive training programs to equip employees with the skills and knowledge needed to effectively use the new system
  • Establish metrics-based incentives and recognition programs to drive team adoption and motivation
  • Continuously monitor and evaluate the effectiveness of the change management strategy, making adjustments as needed

By following these strategies and providing ongoing support and training, organizations can successfully manage change and ensure team adoption of their new CRM capabilities. As noted by Forrester, companies that prioritize change management and employee adoption are more likely to achieve significant returns on their CRM investments, with some organizations reporting returns of up to 245%.

As we’ve explored the vast potential of Continuous Learning CRM in revolutionizing customer relationships, it’s clear that the future of CRM is intertwined with the advancements in artificial intelligence. With the ability to hyper-personalize and predict customer needs, businesses are poised to take their relationships to the next level. But as we look ahead, it’s essential to consider the ethical implications and privacy compliance that come with AI-powered CRM. In this final section, we’ll delve into the future of customer relationships, discussing the importance of responsible AI adoption and measuring the ROI of Continuous Learning CRM. By examining the latest trends and research insights, we’ll uncover the key considerations for businesses looking to stay ahead of the curve and unlock the full potential of their customer relationships.

Ethical Considerations and Privacy Compliance

As organizations embark on implementing advanced CRM capabilities, they must navigate a complex landscape of ethical considerations around data usage, algorithmic bias, and privacy regulations. With the rise of AI-powered CRM, companies like Salesforce and Microsoft Dynamics 365 are grappling with the implications of collecting and analyzing vast amounts of customer data.

A key concern is algorithmic bias, which can result in discriminatory outcomes for certain customer groups. For instance, a ProPublica investigation found that a widely used risk assessment tool in the US criminal justice system was biased against African American defendants. To mitigate such risks, companies can implement bias detection and correction techniques, such as those offered by FICO and SAS.

Another crucial aspect is privacy compliance, with regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) imposing strict requirements on data handling and protection. To ensure compliance, organizations can implement measures such as:

  • Conducting regular data audits to identify and address potential vulnerabilities
  • Implementing transparent data collection and usage practices, as seen in companies like Spotify and Dropbox
  • Providing customers with clear opt-out options for data collection and analysis, as mandated by regulations like the GDPR and CCPA

According to a Gartner report, 70% of organizations will implement AI and machine learning by 2022, highlighting the need for proactive attention to ethical considerations and privacy compliance. By prioritizing these concerns and implementing responsible AI practices, organizations can build trust with their customers and ensure the long-term success of their CRM initiatives.

Measuring ROI and Business Impact

To truly gauge the effectiveness of continuous learning CRM, it’s essential to establish a robust framework for measuring return on investment (ROI) and business impact. This involves tracking a combination of quantitative and qualitative metrics that reflect the nuances of customer relationships and business outcomes.

Quantitatively, metrics such as customer lifetime value (CLV), customer retention rates, and sales revenue growth can provide clear insights into the financial impact of CRM implementation. For example, a study by Salesforce found that companies using CRM systems saw an average increase of 29% in sales revenue. Similarly, HubSpot reports that businesses leveraging CRM experience a 25% higher customer retention rate compared to those without.

  • CLV Increase: Measure the change in customer lifetime value before and after CRM implementation to understand the long-term revenue impact.
  • Customer Satisfaction (CSAT) Scores: Track changes in CSAT to evaluate the qualitative impact of CRM on customer relationships.
  • Return on Investment (ROI) Analysis: Calculate the financial return on investment in CRM systems to ensure it aligns with business objectives.

Qualitatively, metrics such as customer satisfaction (CSAT) scores, net promoter scores (NPS), and employee engagement can offer a more nuanced view of CRM’s impact on customer relationships and internal processes. For instance, a report by ASUG highlighted that 70% of businesses believe that CRM has a significant impact on customer satisfaction, further emphasizing the importance of continuous learning in maintaining and improving these relationships.

  1. Conduct regular customer feedback surveys to gauge the perceived value of CRM-powered interactions and identify areas for improvement.
  2. Implement employee training programs to ensure that teams are adept at leveraging CRM capabilities, thereby enhancing the quality of customer interactions.
  3. Utilize data analytics tools like Tableau or Microsoft Power BI to visualize CRM data and facilitate data-driven decision-making.

By adopting a multi-faceted approach to measuring ROI and business impact, organizations can accurately assess the value of their continuous learning CRM investments and make informed decisions to further enhance customer relationships and drive business success.

In conclusion, revolutionizing customer relationships requires a fundamental shift in how we approach customer relationship management (CRM). As we’ve explored throughout this post, continuous learning CRM is the key to unlocking hyper-personalization and predictive analytics, enabling businesses to anticipate and meet customer needs like never before. The evolution of CRM from a basic data repository to an intelligent relationship hub has been significant, and it’s essential to stay ahead of the curve to remain competitive.

Key takeaways from this post include the importance of moving beyond basic customer segmentation, using predictive analytics to anticipate customer needs, and implementing continuous learning CRM strategies for success. By doing so, businesses can reap numerous benefits, including improved customer satisfaction, increased loyalty, and enhanced revenue growth. According to recent research data, companies that have adopted continuous learning CRM have seen a significant improvement in customer engagement and retention rates.

Future-Proofing Customer Relationships

To stay ahead of the curve, it’s crucial to consider the future of customer relationships, including the role of AI-powered CRM and beyond. As technology continues to advance, businesses must be prepared to adapt and innovate to meet the evolving needs of their customers. For more information on how to revolutionize your customer relationships, visit Superagi to learn more about the latest trends and insights in CRM and customer experience.

Take the first step towards revolutionizing your customer relationships today. By implementing continuous learning CRM and embracing hyper-personalization and predictive analytics, you’ll be well on your way to delivering exceptional customer experiences that drive loyalty, retention, and growth. Don’t miss out on the opportunity to stay ahead of the competition – start your journey towards continuous learning CRM and discover the transformative power of AI-powered customer relationships.