July 3, 2024

Harnessing generative AI for tailored marketing: Personalized content clusters for every account


Understanding generative AI and its applications in marketing 

Generative AI (Gen AI) is a transformative technology that uses machine learning models to create content autonomously. It can generate text, images, and even music that mimics human creativity. This capability is revolutionizing various industries by improving productivity and enabling the creation of highly personalized content. Marketers are benefiting from generative AI's ability to produce tailored content that resonates with specific target audiences.

Applications of generative AI in marketing 

      •   Personalization:
Generative AI can create highly customized content, ensuring that marketing materials are tailored to individual preferences and needs, which boosts engagement and conversion rates.
      •   Efficiency:
Gen AI significantly reduces the time and effort required to produce content, allowing marketers to focus on strategy and other high-value tasks.
      •   Scalability:
Generative AI enables the production of large volumes of content quickly, making it easier for companies to maintain a consistent and dynamic presence across multiple channels.

About our client 

Our client is a Design-and-Make platform provider that empowers people to design and create anything from buildings and cars to products and entertainment media.

Serving a wide range of industries, they faced challenges in creating targeted and personalized marketing content clusters for specific accounts. These challenges included time constraints, lack of resources, inconsistent brand voice, and difficulty in identifying relevant content. Therefore, they sought an AI-based solution to create personalized content clusters based on industry type and account.

Challenges being faced

      •   Personalized content challenges: 
Unable to deliver personalized content to individual customers or specific target segments because it was time-consuming and resource intensive.
      •   Engagement and conversion issues:
Failing to resonate with specific audience segments, leading to lower engagement and missed conversion opportunities due to using generic content.

Implementing Generative AI as the solution 

Phases of the approach
The implementation approach can be broken down into three phases (as summarized in the diagram below):

      •   Phase 1:
Input Content Classification, where data is categorized and tagged for relevance.
      •   Phase 2:
User Query Classification; the system interprets user inputs to understand specific content requests.
      •   Phase 3:
Content Retrieval and Generation involves generating tailored content based on the classified data and user queries.

Figure 1: Phased approach for the solution implementation

Figure 2: Key tasks under each step of the implementation

Considerations in this approach
      •   Choose the right LLM for classification accuracy.
      •   Consider pre-training on domain-specific data for improved performance.
      •   Incorporate techniques like NER for precise keyword extraction.
      •   Fine-tune the LLM with labeled data for improved performance.
      •   Implement a robust cross-questioning mechanism to capture essential information.
      •   Select an appropriate vectorization technique for accurate retrieval.
      •   Optimize the hybrid search strategy for retrieving relevant documents.
      •   Include re-ranking methods for more accurate retrieving of relevant documents.

Diving into the details
To implement the updated generative AI solution, we first onboarded various data types, including text, images, PDFs, webpages, emails, and more. These data types were mapped with relevant keywords and tags. Next, a chatbot was developed to handle user inputs and generate specific content for email campaigns and landing pages tailored to individual accounts.

With this AI-driven chatbot, marketers can quickly create personalized, account-specific content clusters. This improves the efficiency and effectiveness of their campaigns. This approach also ensures that the generated content aligns perfectly with the needs of targeted accounts, improving engagement and conversion rates.

Figure 3: Detailed solution flow diagram

About the solution architecture
The process begins with input documents being processed through text extraction and pre-processing steps, including text chunking, categorization, and vector embeddings. These processed document chunks are stored in a Weaviate Vector Database. User queries are then processed to extract entities & tasks and pre-processed to check for required entities. A retrieval engine fetches relevant documents using a hybrid ranking approach. The final processed input object is then used to generate a relevant email response using Azure OpenAI services.

Figure 4: Architecture diagram

Benefits of this approach
      •   Optimal chunking size with overlapping improves efficiency.
      •   The JSON output provides a structured format for subsequent information retrieval.
      •   The cross-questioning mechanism ensures the completeness of critical data for accurate content generation.
      •   The hybrid search and filtering approach ensures retrieved content is semantically relevant.
      •   This search and filtering method also makes sure the content retrieved matches the user's specific needs.

Benefits of the Gen AI solution

      •   Streamlined workflows:
Gen AI can seamlessly integrate into content creation processes, saving time and resources by automating routine tasks.
      •   Improved creativity:
The solution unlocks new avenues for generating personalized and engaging content. This offers fresh perspectives and ideas that might not be obvious to human creators.
      •   Effective marketing campaigns:
This efficiency leads to more effective and targeted marketing campaigns. Doing so ensures that the right content reaches the right audience at the right time.
      •   Efficiency and speed:
Organizations report a marked increase in the speed of content creation. Quantitative results show substantial improvements in production times.

      •   Competitive advantage:
By breaking free from traditional methods, Gen AI facilitates the exploration of innovative formats and approaches, providing an edge in the market.


Interested in improving your marketing operation with Gen AI? Contact Sales@MAQSoftware.com today to see how we can elevate your business and improve your customer relationships.

June 12, 2024

Optimizing task management with smart recommendations

Impacts of manual operations

Our client, a marketing-focused company, aimed to streamline their operations. The company faced several challenges, including manual overload on their employees, complex decision-making, and issues with maintaining consistency.


The challenges

      •   Manual overload:
The company's workforce was burdened with the tedious task of manually creating service tasks for each request. This process led to inefficiencies and frustration among the employees.
      •   Complex decision making:
With a multitude of parameters influencing task creation, employees struggled to make informed decisions quickly, resulting in delays and errors.
      •   Consistency concerns:
Maintaining consistency across task creation processes was a challenge, leading to variations in quality and adherence to company standards.


Transforming task management

To tackle these challenges, a cutting-edge smart recommendation system was introduced within our Model-Driven App. This innovative solution uses recommendation configurations to automatically generate and manage lease tasks, greatly reducing manual effort and boosting operational efficiency.


About the solution

      •   Data analysis:
Upon initiation of a new record, the system analyzes the pre-configured parameters.


      •   Recommendation engine:
As mentioned, the system generates personalized task recommendations based on pre-configured parameters. These recommendations are customized for each request by matching pre-configured parameters with predefined configurations.


      •   Automatic task creation:
Upon user approval, the system creates recommended tasks, populates necessary fields, and triggers predefined workflows. This process greatly reduces manual intervention, streamlining operations and ensuring consistency in task execution.


The impact

Implementing the solution brought measurable benefits to the client:

      •   Increased efficiency:
By automating task creation, users experienced a large reduction in time spent on administrative tasks, allowing them to focus on strategic initiatives and value-added activities. With manual task creation, a user takes approximately 20 minutes to create 8 tasks. However, with the help of recommendations, it takes only 30 to 120 seconds without any manual intervention—a decrease of up to 97%.
      •   Improved decision making:
The smart recommendation system empowered users with data-driven insights and suggestions, enabling them to make informed decisions quickly and confidently.
      •   Enforced consistency:
With standardized task creation processes enforced by the recommendation system, the client achieved greater consistency and compliance with internal policies and industry regulations.


Through the implementation of a smart recommendation system within the Model-Driven App, we have redefined task management, driving efficiency, accuracy, and employee satisfaction. By embracing technology and innovation, the client has positioned itself as a leader in streamlining business processes and delivering exceptional value to its customers. 

For more information on how MAQ Software can optimize your operations, contact Sales@MAQSoftware.com.

May 20, 2024

Microsoft Fabric Features for Real-Time Analytics


About real-time analytics (RTA)

Real-time analytics captures, processes, and analyzes data instantly. Think of it as having your finger on the pulse of your data, constantly monitoring, and reacting to changes in real time. Whether it's website traffic, social media engagement, or stock market fluctuations, real-time analytics provides immediate insights for informed decision-making. Advantages of RTA are detailed further below:

      •   Accelerate and elevate decision making:
Identify trends and anomalies as they happen and respond rapidly. This is crucial in today's fast-paced digital landscape, where every second counts.
      •   Increase agility and optimize operations:
Adapt and pivot in real-time based on the latest data. Adjust marketing strategies, optimize operations, or personalize user experiences with agile decision-making.
      •   Improve customer service:
Track customer interactions across multiple channels in real time—from social media to live chat and phone calls—and respond to them. Identify patterns in customer behavior to proactively address potential issues and deliver hyper-personalized recommendations and support.


Current implementation and its challenges

      •   Scalability and cost management:
As data volumes grow, so do demands on infrastructure and processing power. Building a scalable real-time analytics system requires careful planning and investment.
      •   Maintaining data accuracy:
Ensuring data accuracy and consistency is paramount. In the rush to analyze data in real time, data quality issues can skew results and lead to faulty insights.
      •   Alerts and actions:
Set up appropriate thresholds, detect complex events, automate response actions securely, and establish feedback loops for effective alerting and actions.


Real-time implementation in Fabric

In the ever-evolving landscape of enterprise data management, Microsoft Fabric emerges as a beacon of integration and efficiency. This comprehensive platform streamlines the complex processes of data analytics, offering a unified solution that caters to the diverse needs of modern businesses. From data ingestion to real-time analytics, Microsoft Fabric encapsulates a suite of services that drive insights and foster informed decision-making.

Microsoft Fabric simplifies end-to-end real-time system setup with low-code/no-code components and AI Copilot integration, while Apache Kafka delivers real-time data, consolidating changes from source systems and making them available to subscribers.

We developed a real-time solution using Microsoft Fabric by ingesting data from Confluent Kafka as the streaming data source. This showcases how easily Fabric enables real-time processing and reporting systems.
Figure 1: Architecture diagram

Stream sources and destinations
The real-time hub serves as a central location to manage all real-time components in Fabric, including event streams and KQL databases. This hub is to your streaming sources what OneLake is to your data.

The 'Microsoft Sources' tab displays all configured Azure sources in Fabric, such as event hubs, Change Data Feeds (CDC), and IoT hubs. The 'Connect' option allows for the immediate setup of new ingestion from existing sources, like tables from SQL sources to an event stream.

The 'Get Events' capability connects to stream sources like Azure event hub, Confluent Cloud Kafka, and CDC using a low-code/no-code interface, straight out of the box. Streams can then be sent to destinations like Lakehouse and KQL database, or to Fabric’s Reflex for alerts and actions.

You can also preview the data within event streams using the Real-Time hub.

Fabric Workspace Item events
'Fabric Workspace Item events' has been enabled as a source, allowing specific actions when a Fabric item is created, updated, or deleted, and even on the success or failure of these events. You can also set up actions such as sending notifications via email or Teams or running a notebook in a Fabric workspace.

Event stream interface
Once connected to an event stream, the data can then be sent to a Lakehouse, KQL database, Reflex, or Custom   endpoints. The destination can be provisioned from the event stream interface itself with the ability to define the database, table name, and data type. KQL databases are preferred for real-time reporting due to their optimization for large datasets.

Event streams enable you to define standardized transformations applied to data before it is written to storage. You can manage event fields by adding or removing fields, updating field names, applying filters, and performing unions and group by operations. The transformed data can then be made available as a derived event stream for organizational consumption.

Event streams also support content-based routing, facilitating data segregation and management across multiple KQL databases.

Event houses for managing data stores
Event houses can store multiple KQL databases, sharing compute and cache resources. Data is indexed and partitioned during storage, allowing for high-speed analysis and granular reporting. Event houses also introduce the shortcut feature to KQL databases. With the integration of AI Copilot, you can transform data effortlessly, even without detailed knowledge of the KQL language.

Reporting on data
In addition to Direct Query reports built with Power BI on KQL databases, Microsoft Fabric now supports real-time dashboards on KQL databases. This allows reading data from KQL databases using queries or existing KQL query sets as data sources. You can enable auto-refresh for the dashboards and set minimum frequency and default refresh rates to ensure the latest data is always displayed. 

It also enables the ability to set alerts based on the data displayed in the tiles, using event groups to track metric variations.

Cost optimization
Event houses are optimized to reduce costs by sharing resources across KQL databases. You can define a minimum number of CUs for service guarantees, with additional CUs consumed based on usage.

Integrated monitoring
Monitor metrics for all KQL databases within the event house, including storage consumed, activity, errors, and specific user actions and commands from the last 7 days.

Real-time data availability for analytics
KQL data can be made available in a Lakehouse for real-time analytics. Data Wrangler then offers quick insights, cleaning, formatting, and normalization.

Copilot integration with notebooks further transforms and enriches data for analytical reporting. Direct Lake mode enables fast reporting on Lakehouse data, with a default Semantic model for immediate Power BI report creation using Copilot.

Advantages of a Fabric solution

      •   Ease of setup and management:
With its low-code SaaS approach, Microsoft Fabric simplifies the setup and management of real-time solution components. By integrating with Copilot to assist with coding, Fabric enables the creation of an end-to-end solution in just minutes. All resources within the pipeline share the same Fabric capacity, streamlining resource management across the entire framework.
      •   Maintaining data accuracy with integrated alerts and actions:
The integration of Reflex with event stream allows for real-time event tracking and the triggering of alerts and custom actions based on incoming data. Any bad data can be isolated within the event stream and redirected to a separate KQL database/table, ensuring that even real-time reporting is based on accurate data.
      •   Automated scaling with performance guarantees:     
Fabric SKUs can automatically scale based on CU consumption. Combined with the ability to define a minimum size for the event house KQL database, this ensures a guaranteed level of performance for real-time reporting while optimizing costs.


Contact Sales@MAQSoftware.com to learn more about how MAQ Software can help you achieve your business goals with Microsoft Fabric. Explore our Fabric services and Marketplace offerings today.