Beinex Bags the Title: Best in Data & Analytics at the 12th Edition of Digital Transformation Summit, UAE

By proactively utilising retail analytics, companies can rapidly act upon essential insights drawn from their data, resulting in notable improvements in their business outcomes. The most successful retailers also leverage external datasets to support their business strategy.

The initial and critical step towards achieving this is establishing a data foundation that combines internal and external data sources, providing a complete view of the customer experience.

To deliver the experience customers want and to remain competitive, companies must harness the power of retail analytics. In this blog, we will cover the top trends in retail analytics retailers use to get ahead.

The Primary Developments in Retail

There are six main trends in retail analytics that companies are using to gain an advantage. These include:

1. Develop Individualized Experiences for a Single Customer
2. Utilise Predictive Analysis to Anticipate
3. Develop Dynamic Automated Pricing Models
4. AR and VR in Retail
5. Subscription-based Business Models
6. Integrate AI & ML

These capabilities can only be achieved when your data is combined into a single, unified source of truth. To stay ahead, many retailers seek the assistance of data analytics consultants (such as those provided by Beinex) to create and implement the necessary modern data infrastructure.

1. Develop Individualized Experiences for a Single Customer

A survey was conducted on 1,000 individuals aged 18-64 to assist brands in improving relationships and building customer loyalty. The research found that 80% of respondents are likelier to engage with a company that provides personalised experiences. This demand for personalisation has grown even further since then. In a 2020 article on creating differentiation in retail, McKinsey suggests that the most effective retail experiences "involve the customer in the conversation and use data to establish tailored personalisation."

Of course, accomplishing this requires a Customer 360 perspective, which entails a comprehensive understanding of customer data across all brand interactions. These interactions may include transactional data, customer feedback, shopping preferences, website and mobile app activity, and more.

2. Utilise Predictive Analysis to Anticipate

Retailers are utilising sophisticated analytics that employs machine learning algorithms to make predictions based on patterns identified in customer data. These advanced algorithmic models allow retailers to determine how much of a particular product or service customers will likely buy during a specific timeframe.

Business executives use demand forecasting to encourage their most profitable customers to return to the store by providing timely notifications and valuable offers on relevant items. As a result, retailers can ensure that they are timing their shipments to ensure that the products their customers desire are available on store shelves while enhancing their supply chain.

3. Develop Dynamic Automated Pricing Models

Retailers frequently need to maintain a portion of their prices at a superficial level to remain competitive. These low-priced items, also known as doorbusters and key-value items (KVIs), are often the top sellers and traffic generators that shape a retailer's pricing reputation. KVIs can account for up to 80% of revenue but only half of a retail company's profit. To compensate for the low margin on KVIs, retailers tend to increase the prices of their higher-margin items and position them strategically alongside doorbusters and KVIs in creative ways to encourage shoppers to add higher-margin products to their shopping carts.

Retailers use dynamic pricing algorithms to adjust their product prices according to market demand and inventory levels, optimising their profit margins. These algorithms provide automated recommendations for pricing that enable retailers to make timely, informed decisions to improve their financial performance. To achieve maximum effectiveness, it is recommended that retailers work with a data analytics consulting firm to develop a tailored solution that aligns with their specific business objectives, operational processes, and customer needs.

4. AR and VR in Retail

The use of augmented reality (AR) and virtual reality (VR) in the retail industry is becoming more common, offering new and creative ways for customers to shop and experience products. By allowing customers to virtually try on products and creating immersive shopping experiences, AR and VR are changing how retailers interact with their customers. As per current retail technology trends, more and more retailers are integrating AR and VR into their strategies to improve customer experiences and boost sales.

5. Subscription-based Business Models

Subscription-based business models are gaining popularity as consumers seek convenient and personalised experiences, especially in industries like beauty and fashion. These models offer a unique opportunity for retailers to build a loyal customer base by regularly providing curated and customised products. Moreover, subscription models ensure a predictable revenue stream for businesses and reduce customer churn. As the subscription economy evolves, retailers must develop innovative ways to differentiate themselves and provide added value to their customers.

6. Integrate AI & ML

Artificial Intelligence (AI) and Machine Learning (ML) in retail is becoming more common. Retailers can use AI and ML for chatbots, personalisation, and predictive analytics to enhance the shopping experience for customers. By analysing large amounts of data, retailers can predict consumer behaviour and offer customised recommendations and promotions, resulting in better customer engagement and loyalty. As AI and ML technologies continue to advance, their potential impact on the retail industry is anticipated to be significant.

Summing Up

Retailers must prioritise adopting a customer-centric data and analytics approach to remain competitive against online and offline rivals. As technology advances and AI models become more sophisticated through advanced machine learning algorithms, retailers must utilise retail analytics to uncover valuable insights that can lead to novel methods of enhancing customer loyalty.

Advanced Analytics services from Beinex explain the why and how of change in your enterprise – the top line, bottom line behaviours and everything in between, from your organisational data. With minimal human intervention, it gives decision-makers the ability to have a firm grip on credible but previously hidden insights. As a decision-maker, you can employ data-driven insights and execute insights-led planning for your enterprise with telling and far-reaching effects.

Serverless Computing

Serverless computing, often referred to as "serverless," is a cloud computing model where developers can build and deploy applications without having to manage the underlying server infrastructure. In a traditional server-based architecture, developers need to provision, configure, and manage servers to run their applications, which can be complex and time-consuming.

In a serverless architecture, the cloud provider (such as Amazon Web Services with AWS Lambda, Microsoft Azure with Azure Functions, or Google Cloud with Google Cloud Functions) abstracts away the server management aspect. Developers can focus solely on writing code for the specific functions or tasks their application needs to perform without worrying about server provisioning, scaling, or maintenance.

Serverless services offer several significant benefits that can have a positive impact on application development, deployment, and management. Some of the key significances of serverless services include:

1. Simplified Infrastructure Management
2. Auto-Scaling
3. Rapid Development and Deployment
4. Event-Driven Architecture
5. Reduced Administrative Overhead
6. Global Scalability
7. Resource Optimization
8. Improved Fault Tolerance

Amazon Web Services (AWS) continuously introduces new capabilities and features to their serverless services for several reasons aimed at improving the developer experience, expanding use cases, and meeting evolving customer needs.

Latest buzz in AWS serverless services

1. General availability of AWS Database Migration Service Serverless

On June 2nd, 2023, AWS unveiled the widespread availability of AWS Database Migration Service (AWS DMS) Serverless. This release simplifies database migrations by automating the provisioning and scalability of migration resources. With AWS DMS Serverless, users gain the ability to seamlessly replicate data across a diverse range of widely used databases, analytics engines, and services—think PostgreSQL, MySQL, Oracle, Amazon Redshift, Amazon DynamoDB, Amazon Aurora, and more. By handling the often-cumbersome work of database migration, AWS DMS Serverless minimises the need for manual resource estimation, provisioning, monitoring, and scaling. This advancement translates to migration timeframes measured in hours, and cost savings realised through payment solely for consumed data migration resources.

2. Provisioned Concurrency for Amazon SageMaker Serverless Inference

As of May 10th, 2023, AWS has introduced the general availability of Provisioned Concurrency support for Amazon SageMaker Serverless Inference. This innovative feature ensures that models deployed on serverless endpoints offer consistent performance and impressive scalability. Through the integration of provisioned concurrency, users can infuse their serverless endpoints with a predetermined volume of concurrency, effectively maintaining the readiness and responsiveness of SageMaker endpoints. This offering particularly suits scenarios where traffic is predictable, yet throughput remains relatively low.

3. Amazon Aurora Serverless v2 is now available in 4 additional regions

The footprint of Amazon Aurora Serverless v2 has now expanded to include four additional regions. Aurora Serverless v2, an adaptive, automatic scaling configuration for Amazon Aurora, has the remarkable ability to instantaneously scale to accommodate even the most resource-intensive applications. By making precise capacity adjustments, Aurora Serverless v2 ensures that an application always receives the optimal database resources it demands. This dynamic resource allocation extends to encompass an impressive range of Amazon Aurora features, spanning read replicas, multi-AZ support, Performance Insights, and Global Database functionality. This powerful suite is ideally positioned to serve a diverse spectrum of applications. Enterprises dealing with an extensive array of applications or Software as a Service (SaaS) providers managing multi-tenant environments replete with numerous databases can harness the capabilities of Aurora Serverless v2 to deftly manage database capacity across their entire infrastructure.

4. AWS Lambda introduces response payload streaming

AWS Lambda, the bedrock of serverless computing, has ushered in an exceptional enhancement: response payload streaming. This capability enables AWS Lambda functions to gradually stream response payloads back to clients, even accommodating payloads that surpass the 6MB threshold. A monumental leap forward for web and mobile applications, this feature marks a departure from the conventional request-response model. Previously, applications built on Lambda necessitated the complete generation and buffering of responses before they could be sent to clients—an approach that often resulted in delayed first-byte transmission times. With response payload streaming, Lambda functions can transmit partial responses to clients as they are ready, substantially improving the all-important first-byte transmission time, a facet crucial for web and mobile applications alike. This innovation stands to elevate the performance of AWS Lambda-powered applications to new heights.

As AWS continues to introduce new capabilities, it's evident that the serverless paradigm is here to stay. This shift isn't just a technological trend; it's a fundamental change in how we architect, deploy, and scale applications. With each enhancement, AWS reinforces its commitment to providing customers with the tools they need to succeed. With AWS's ongoing dedication to pushing boundaries, the future of cloud computing holds the promise of even more remarkable advancements.

Beinex+ AWS Offerings

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Beinex is an AWS consulting partner, and we empower customers to host their BI solutions, provide security services and much more on the cloud. Our cloud migration experts bring in best-in-class stability and reliability by understanding your business strategy and working closely with you to deploy AWS infrastructure as a service.

As a frequent user of Tableau Desktop and Server, check out my top picks from the list. • Improved dynamic parameter • Multiple marks layer support for maps • Block comments in calculations • Web authoring enhancements • Offline map support for Tableau Server • Tableau Server Management (TSM) improvements • Multiple key activation on Tableau Server prior to TSM initialization • Analytics Extension for Tableau Online • Merge duplicate external assets • Tableau Prep Builder in the browser Tableau Desktop Improved Dynamic Parameter The dynamic parameter was one of the features which I used extensively starting from 2020.1. The feature allows us to populate parameter values based on a specified field whenever the workbook is opened. We will be able to select only the field with matching data types. The feature update announced in Tableau 2020.4 gives me more privilege to customize the Dynamic Parameter. I can now set a parameter’s range to a field’s min and max value upon opening the workbook, or when the field’s data source is refreshed. So, whenever I open the workbook or the extract is refreshed, Tableau displays the unique values within the range specified, giving more control over the parameter values displayed. Multiple Marks Layer Support for Maps This is an exceptional feature to bring multiple spatial layers and context together to better understand and analyze geospatial data and map views. I will be able to include multiple marks layers from a data source to map visualizations and enhance the geospatial analysis. I can present more context in a single map view and perform further analysis with this feature. Block Comments in Calculations Block comments is a simple yet one of the most useful features for me, which Tableau has announced in 2020.4. I often used to add comments in complex calculations for easy understanding in future references. Earlier, only single-line comments were possible, limiting the description I could add. But from now on, I can add comments of any length to calculation windows with block comments by simply starting the comment with /* and ending with */. What makes it unique is that the new multi-line block comment feature is consistent with other popular programming languages. This feature is an example of Tableau’s on-going effort to provide its customers with an intuitive user experience. Tableau Server Web Authoring Enhancements Starting from Tableau 2020.4, it is possible to author dashboards from the browser just like how we design it in Tableau Desktop. With this web authoring enhancements, I can include Highlight actions, Format Mark Labels, apply filters to worksheets, create fixed sets, and even create extract in my workbooks from the browser itself. I no longer have to make changes from Tableau Desktop and publish it to the server. I can directly do it from the browser itself. Offline Map Support for Tableau Server Rendering dashboards with maps is now faster compared to previous versions. Now, I can create maps using the offline map style in web authoring, ensuring the performance of map views in Tableau Server. Offline map support is a great deal for organizations with strict internet access restrictions, assuring that map view access to all its users. Tableau Server Management (TSM) Improvements Tableau Server Administration activities like installation, upgrade and backup are now easy like never before. I can retry installation or upgrade from the last checkpoint in case of an unexpected issue or error during installation or upgrade, saving my efforts to obliterate tableau server. Backups can be performed twice as fast as previous versions, and I can monitor the progress with the new progress bar giving visibility into what step the backup is on and how much time is remaining. Backups can also be scheduled using TSM command starting from 2020.4 and that is awesome. I no longer need to prepare batch script and depend on the windows task scheduler to schedule the backups on regular intervals; instead, I can schedule it with just a single command. Multiple Key Activation on Tableau Server Prior to TSM Initialization During Tableau Server installation, it is now possible to activate multiple license keys prior to TSM initialization. I will be able to save a lot of time by eliminating the need to restart after the installation is completed to activate multiple licenses and experience a smoother installation. Analytics Extension for Tableau Online The power of the Analytics extension is now unlocked in Tableau Online too. The feature was already available in Tableau Server, and it helped us dynamically perform advanced analysis with models and functions in R, Python and other platforms. Analytics extension in Tableau Online significantly enhances the scope of using Advanced Analytics by the common users. Merge Duplicate External Assets Earlier, the Database or Table with similar names used to appear as multiple assets within Tableau Catalog. But, the new feature helps me to merge those multiple assets into a single one. I can manage assets easily and keep an organized view of External assets by merging the common ones. Tableau Prep Tableau Prep Builder in the Browser Prepare the data for visualizations from anywhere using a browser! Starting from 2020.4, Tableau is bringing the data prep process into one integrated platform on the web. Now I can easily prepare and manage prep flows from anywhere using a browser. Conclusion Tableau is progressively evolving as a single platform for data preparations, visualization, and collaboration with every update and version release. Author : Firdous Maqbool Images Courtesy : Tableau

What is Automated Analytics?

Think about your day's tedious tasks: organizing, cleaning, and formatting data. These repetitive processes are ripe for automation, and that's where automated analytics shines. Automated analytics combines the power of software and AI, including machine learning (ML) and generative AI, to streamline the analytics lifecycle. Diverse systems like data warehouses, analytics platforms, and reporting tools can be connected with it into an integrated, end-to-end workflow. It also saves time and improves productivity and accuracy, turning raw data into actionable insights more accessible than ever.

Key Forms of Automated Analytics:

1. Automated Machine Learning (AutoML):  With low-code or no-code platforms, model creation is simplified, enabling faster deployment of predictive models. From defining problems to fine-tuning models, AutoML handles every step with ease.
2. Generative AI: Adds a creative edge by automating documentation, generating summaries, and crafting stakeholder-ready presentations.
3. ETL Automation: Platforms like Alteryx automate data ingestion, transformation, and reporting, allowing you to build workflows once and let them run indefinitely.
4. Business Intelligence Automation: Enhances visualization and dashboarding by automatically surfacing insights and generating interactive reports.

Top Benefits of Automated Analytics

Analytics automation is indispensable, enabling organizations to tackle challenges at scale while maintaining agility and precision. The top benefits of analytics automation are listed below: Efficiency
Analytics automation significantly reduces the time required for data collection, preparation, and analysis. Automating daily tasks helps data professionals focus on deriving meaningful insights that drive business growth.

Improved Accuracy
Human error is a common pitfall in manual data handling, but automation ensures consistent logic and precision while safeguarding data quality and reliability.

Real-Time Insights
Gone are the waiting weeks for reports. Automated analytics deliver frequent updates, allowing businesses to respond swiftly to trends and opportunities.

Streamlined Scalability
Automation provides the flexibility to scale processes without additional resources as your data grows. Whether adding data sources or increasing analysis frequency, automation adapts seamlessly.

Empowered Decision-Making
Automated analytics empowers decision-makers with timely, accurate insights, ensuring they always have the information to steer their organizations forward.

Foster Collaboration
Cloud-based solutions promote teamwork by making workflows accessible and lowering barriers to advanced analytics like machine learning.

Getting Started with Analytics Automation

Your organization can get started with analytics automation with little hassles. Here's a roadmap to guide your journey:

1. Define Your Objectives: First, identify the challenges you aim to solve with automation, whether it's optimizing data prep, deploying machine learning models, or improving reporting efficiency.
2. Choose the Right Tools: Secondly, choose a solution that aligns with your goals and integrates with your existing infrastructure. Always go for enterprise-grade platforms that offer robust security and scalability.
3. Gather Relevant Data: Always ensure you have access to suitable datasets, e.g., from CRM systems, social media analytics, or financial platforms, and prepare them for automation.
4. Implement and Optimize: Finally, start with automating specific workflows, then expand to more complex processes. Continuous improvement will reveal even greater efficiencies over time.

Real-world applications of Analytics Automation

1. Demand Forecasting: Retailers leverage automation to predict future demand by connecting historical sales data, blending it for analysis, and creating predictive models and all without manual effort.
2. What-If Analysis in Financial Forecasting: Financial professionals can harness greater accuracy and flexibility with automated scenario modeling. It reduces errors and accelerates decision-making.
3. Month-End Close: By automating data preparation, validation, and reporting, you can easily simplify the reconciliation process, allowing accountants to focus on strategic financial analysis.

Take the Leap with Analytics Automation

Analytics automation isn't just a tool; it's a transformative approach to data-driven decision-making. Whether you're a data analyst, scientist, or business leader, automation opens new possibilities, allowing you to work smarter, not harder. With platforms like Alteryx, you can automate the entire analytics lifecycle, from data prep to visualization, and transform how your organization handles data. Start automating today and redefine what's possible for your business.

Alteryx+ Beinex Offerings

Our Premier partnership with Alteryx empowers business users to automate manual data cleansing and tasks in minutes through a simple visual workflow while incorporating the latest technological advancements. Connect with us for a free demo: https://beinex.com/alteryx-partner/

1. Generative AI Features in Existing Products

Alteryx is proactively identifying areas where GenAI can improve the productivity and efficiency of its customers. These innovations are integrated into existing tools, such as Alteryx Designer, to streamline processes, automate routine tasks, and enhance the overall analytics experience. For example: OpenAI Connector: Users can now integrate GenAI directly into Alteryx Designer workflows to streamline communication and share data more effectively. AI-Generated Workflow Summaries: These summaries automate documentation processes, helping users enhance governance and auditability.

2. Enterprise-Ready Generative AI Platform

Alteryx’s GenAI platform enables businesses to create, train, and deploy custom AI models that operate securely within their organizational firewall. This approach ensures that data privacy and security are maintained while offering organizations the flexibility to tailor AI models to specific business needs. Alteryx also provides an environment for creating proprietary models that are customized to fit each organization’s workflows, making it easier to integrate AI-driven analytics into everyday operations.

3. New GenAI Applications and Interfaces

Data analytics is a collaborative process that involves various stakeholders, including analysts, data scientists, engineers, and knowledge workers. With Alteryx, these roles can now collaborate in real time through multi-modal analytics powered by GenAI. The flexibility to use different analytical tools—like SQL, Python, notebooks, or Alteryx workflows—opens doors for more seamless collaboration across different teams. GenAI applications like Magic Documents allow Alteryx users to automatically generate insight-rich reports in just a few clicks, drastically reducing time-to-insights and increasing productivity across business functions.

Introducing Alteryx AiDIN

AiDIN is Alteryx's umbrella for all AI-related capabilities, combining existing AI features with cutting-edge GenAI innovations. Alteryx AiDIN enables users to leverage advanced AI models for analytics, whether it's extracting insights, automating tasks, or generating complex reports. Some of the key benefits include: • Improved Time-to-Value: AiDIN accelerates the time it takes to derive insights from data, enabling quick decision-making for critical business tasks. • Increased Operational Efficiency: By automating repetitive tasks, AiDIN frees up time for users to focus on higher-value activities. • Enhanced Governance: Alteryx AiDIN ensures that AI capabilities meet stringent enterprise-grade governance and security standards.
Source: https://www.alteryx.com/blog/alteryx-announces-generative-ai-capabilities

Data Security and Trust: The Alteryx AiDIN Commitment

A key concern in any AI-driven platform is data security and the integrity of AI-generated outputs. Alteryx AiDIN prioritizes these through: 1. Mitigating Hallucinations In generative AI, "hallucinations" refer to scenarios where AI models produce plausible but incorrect information. Alteryx has implemented stringent quality checks and continuous feedback mechanisms to minimize these errors. This ensures that businesses can rely on the outputs generated by AiDIN for decision-making. 2. Fact-Checking Mechanisms Alteryx AiDIN integrates fact-checking tools to verify AI-generated insights against actual source data. This added layer of validation helps organizations maintain the accuracy and reliability of their analyses. 3. Data Privacy and Security Alteryx ensures that data privacy is maintained at all stages of the AI process. AiDIN offers two key deployment options: Private Data Handling and SaaS. Both options provide robust encryption and ensure that sensitive data is securely managed within a customer’s ecosystem, giving businesses peace of mind as they adopt AI.

The Future of AI-Driven Analytics

The integration of GenAI into the Alteryx platform paves the way for smarter, more accessible analytics. With capabilities like OpenAI integration, Magic Documents, and enterprise-level model customization, Alteryx is enabling organizations to maximize the value of their data, improve efficiency, and foster a more collaborative analytics environment. By combining GenAI’s potential with trusted, secure analytics, Alteryx is redefining how enterprises interact with data—delivering faster insights and more impactful results across industries. Get in touch with us for a free demo: https://www.alteryx.com/designer-trial/free-30-days?