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Leveraging the Power of AI for Digital Asset Management

Leveraging the Power of AI for Digital Asset Management. Digital asset management is a process to store, organize, retrieve, and share digital assets.

Published

4 years ago

March 11, 2020

Gilad David Maayan

Leveraging the Power of AI for Digital Asset Management

Artificial Intelligence (AI) technologies are in the process of integrating into every field of work, including marketing workflows. AI, however, is not a replacement for a human workforce.

Instead, AI capabilities support your work by automating repetitive tasks, thus enabling marketers and developers to do more in less time. In this article, you’ll learn how AI improves digital asset management (DAM) tasks, and how to implement AI in DAM ecosystems properly.

Table of Contents

1. What Is Digital Asset Management?

What Is Digital Asset Management

Digital asset management (DAM) is a process you can use to store, organize, retrieve, and share digital assets. DAM uses a centralized content library that is accessible by employees, contractors, clients, and stakeholders. Assets stored in a DAM include images, audio, video, documents, and original files.

2. AI and Digital Asset Management

Artificial intelligence (AI) is the use of machine learning to perform tasks that were previously only capable of manual human effort. You can use AI technologies to automate and facilitate a variety of functions within DAM systems. AI can help organizations and teams retain control over significant amounts of data with less effort.

3. AI Technologies That Power Dynamic DAM Systems

AI Technologies That Power Dynamic DAM Systems

Several AI technologies can be incorporated into DAM systems. The most commonly integrated technologies are covered below.

a) Smart Tagging

A tag is a piece of metadata that you use to classify and identify assets. Cards are used for searching, tracking, and sorting assets within your DAM. Smart tagging is the use of AI to apply these tags. It is based on API services offered by organizations such as Microsoft, Google, and Amazon.

These services use image recognition to determine the content of media and suggest appropriate tags. Image recognition technologies scan images to identify specific characteristics associated with a category or classification. AI then weighs these characteristics to determine the most likely content of the picture.

Some DAM systems enable you to incorporate business intelligence and marketing data to categorize assets with tags better. This can allow you to distribute content across multiple media channels automatically reliably.

b) OCR

Optical Character Recognition (OCR) is an AI technology you can use to identify text inside images. You can use it to extract text from images and convert that text to an editable document. Extracting text from images is not a new capability, but previously it could only be performed on images with defined structure or layout. OCR uses the same image recognition strategies that are used for smart tagging.

Modern AI, however, enables you to extract text more dynamically. This is possible because AI algorithms can learn from feedback supplied from previous text extractions. This feedback is applied to future removals, refining the sensitivity and accuracy of the OCR algorithm.

c) Natural Linguistics Processing (NLP)

NLP is a field of AI that deals with how computers process and analyze linguistic information. It is commonly used in speech to text conversion and chatbots. In a DAM system, you can use NLP to extract speech content from video or audio files. This content can then be used to create transcripts, subtitles, and captions. It can also be used as a guide for speech removal from files.

NLP capabilities incorporated in DAM systems can also enable you to serve content according to requests in a variety of languages. This can be accomplished through translations of search requests.

4. Best Practices for Implementing AI in a DAM

When incorporating AI into your DAM systems, there are several best practices you should follow. Below are the essential methods to start with.

a) Keep AI-Generated Metadata Separate

You should find a way to identify AI metadata separately from user-generated data. This enables you to verify the accuracy of your AI more efficiently and to ensure that tags are appropriate. By keeping metadata separate, you can ensure that your tagging is as reliable as possible and that user-generated tags are not automatically overwritten.

b) Track AI Like Your Users

You shouldn’t provide users unfettered access to your DAM; this is as true for AI. You need to be able to track the actions of your AI services and vendors to ensure the integrity of your data. Tracking can also help you identify when AI is not working correctly or if it is causing conflicts with other integrations or users.

c) Ensure That You Can Filter AI-Generated Metadata

You should be able to filter whether or not AI-generated metadata is used for searches, content serving, or analytics. Some users may not trust AI-generated data or may prefer to use only specific AI sources. Likewise, users may wish to use only AI-generated metadata created after an algorithm has been refined.

d) AI-Generated Metadata Should be Convertible

Many DAMs enable you to convert embedded metadata into a regular asset attribute, such as a keyword. This ability should extend to AI-generated metadata. Convertibility can help you ensure that AI-generated information remains available even if AI services are removed or changed. You may also wish to set protocols that automatically convert metadata once enough users have approved it.

e) Use Feedback to Improve Your AI Accuracy

Any AI services you use should improve over time. This could mean that algorithms are refined with each analysis or suggestion or that algorithms are periodically retrained. Sources of feedback can include manual audits, accumulated user verification of accuracy, or asset use statistics.

Check if you have specific tags that are consistently rejected, or assets that you know are relevant that aren’t’ being returned. Both of these situations can indicate a fault in your AI and can help you target areas for improvement.

5. Conclusion

AI enhances DAM systems with capabilities that save time and enable personalization. By delegating repetitive tasks to the machine, you can free up your time and focus on sophisticated and creative jobs. In many cases, once you set up an organization system, the AI mechanism will take care of the grunt work of classifying the assets. You can then set up a dynamic process that delivers personalized, and sometimes hyper-personalized, content to users.

AI Tools

Unlocking Effective Customer Journey Management: Leveraging AI in Microsoft Dynamics CRM Services

Unlocking Effective Customer Journey Management: Leveraging AI in Microsoft Dynamics CRM Services. AI-driven CRM solutions have become a potent tool.

Published

1 week ago

September 25, 2023

TwinzTech

Unlocking Effective Customer Journey Management Leveraging AI in Microsoft Dynamics CRM Services

In today’s fast-moving corporate environment, (CRM) customer relationship management is more than a simple tool because of strategic necessity. For businesses that are trying to remain competitive and satisfy customer’s expectations, AI-driven CRM solutions have become a potent tool.

Table of Contents

1. How important is journey of a customer-

It is important to understand the connection of managing client journeys successfully before diving into the function of AI-driven CRM.

The path from initial awareness to final purchase is complicated for consumers in the digital era since they have so many options at their disposal.

It includes different help desks such as social media websites emails and more. Businesses obtain a competitive advantage by embracing and amplifying these experiences.

They are able to provide individual and experience forecast client demands and create experiencing connections. However, manually coordinating these journeys across several channels can be exhausting. Here comes the role of AI-driven CRM, such as Microsoft Dynamics CRM services.

2. Using AI to Gain Customer Insights

Deep customer understanding is one of the main advantages of AI-driven CRM systems.

To analyse voluminous client data, Microsoft Dynamics CRM services use advanced analytics and machine learning.

Previous buying habits, online activity and even social media sentiment research are included in this information.

By analysing this data the business can obtain a thorough snatch of the preferences problems and behavtof their customers.

Companies can adjust their marketing and sales tactics as a result because AI systems can spot underlying patterns that humans would overlook. Making personalised client experiences requires a high level of customer information.

3. Individual customer engagement

Effective management of the client journey depends on personalization. CRM platforms powered by AI, such as Microsoft Dynamics CRM services, let companies design highly customised interactions. They can use the appropriate channel to convey the appropriate message at the appropriate moment.

For instance, the CRM might automatically suggest related products or provide personalised promos if a consumer has expressed interest in a certain product category. According to historical behaviour, AI systems can even forecast when a customer is most likely to make a purchase, enabling firms to strategically time their marketing initiatives.

Microsoft Dynamics CRM Services

4. Improvement of Customer Support

Exceptional customer assistance is a key component of managing customer journeys. CRM solutions powered by AI have the potential to greatly improve customer service operations. AI-powered chatbots may respond quickly and accurately to common client enquiries around the clock.

Additionally, AI-driven CRM can help human customer support representatives by offering on-the-spot information during encounters.

For instance, if a customer calls with a problem, the CRM can show the agent’s screen pertinent data about the customer’s purchasing history and preferences. This enables the agent to deliver service that is more effective and individualised.

5. Automating Routine Work

A crucial component of AI-driven CRM solutions is automation. They can automate time-consuming, repetitive operations, freeing up personnel to concentrate on more strategic responsibilities.

For example, AI can automatically lead nurturing, email marketing campaigns and data entry.

Businesses may guarantee that clients receive notifications throughout their journey by automating these activities on time.

This improves consumer satisfaction and lowers the possibility of human error.

6. Sales Predictive Analytics:

AI-driven CRM has major advantages for sales teams as well, particularly in the field of predictive analytics. These systems can examine historical sales data and find trends that result in profitable transactions. They can forecast which leads are most likely to become clients, which helps sales teams focus their efforts.

Additionally, sales representatives can receive real-time recommendations and insights from AI-driven CRM during customer interactions. Based on the profile and behaviour of the consumer, it might, for instance, recommend upsell or cross-sell opportunities. This degree of assistance can greatly improve sales results.

7. Improvement of Marketing Campaigns

CRM powered by AI really excels in the marketing sector. By dividing up the client base into narrowly focused groups, these systems can optimise marketing strategies. In addition to suggesting the ideal times to send marketing messages, they may pinpoint the optimal channels for interacting with each category.

Customer Journey Management Leveraging AI in Microsoft Dynamics CRM Services

Additionally, AI-driven CRM can perform A/B testing on marketing campaigns to find the words, visuals, or deals that each segment responds to the most favourably. With this iterative process, campaign success rates increase and are continuously improved.

8. Security of Data and Compliance:

In today’s driven environment assuring data security and compliance with laws like GDPR is crucial.

CRM systems powered by AI, such as Microsoft Dynamics CRM services, come with advanced security measures. They set access controls, encrypt critical client data, and give audit trails to monitor data usage.

AI can also help automate compliance-related duties. It can alert users to possible data privacy violations and make sure that client data is managed in line with applicable laws, for example.

9. Scalability and integration:

Services provided by Microsoft Dynamics CRM are renowned for their adaptability and scalability. They can easily connect with other Microsoft tools and other programs to provide a cohesive ecosystem. This guarantees smooth client data flow throughout the business, giving a comprehensive understanding of the customer journey.

Additionally, AI-driven CRM solutions can scale to accommodate growing data volumes and consumer interactions as firms expand. This scalability guarantees that the CRM will continue to be a valued asset as the company grows.

Conclusion:

In conclusion, Microsoft Dynamics CRM services with a focus on AI-driven CRM play a crucial role in enabling organisations to manage customer journeys successfully. Deep consumer insights are provided by these technologies, which also allow for personalised engagement, streamlined assistance, task automation, and improved sales and marketing activities. Along with providing integration and scalability, they also place a priority on data security and compliance.

Businesses that use AI-driven CRM to their advantage will be better able to satisfy changing customer demands and create long-lasting customer relationships. The AI-driven CRM is the compass that leads enterprises on this trip to success since, in the digital era, managing customers means managing their journeys as well.