Customer Order Frequency

This scenario involves understanding customer order frequency, specifically determining the count of customers who made varying numbers of orders. While calculating the number of orders per customer is straightforward, discerning how many customers placed one, two, or multiple orders requires breaking down the count of customers based on order frequency. Utilizing LOD (Level of Detail) Expressions becomes essential in transforming the count of orders into a dimension that segregates customers by their order count. This process aids in unraveling insights about customer behavior in relation to their order frequency within a sales database where multiple items are present per order.

Cohort Analysis

In the pursuit of understanding the impact of customer tenure on sales contributions, cohort analysis is employed to assess whether longer-tenured customers hold more significant sales influence. The presented view categorizes customers based on the year of their initial purchase, facilitating an annual comparison of sales contributions among these cohorts. To determine the first purchase date for each customer, the minimum order date per customer is crucial. However, as the displayed data isn’t structured by customer, employing an LOD (Level of Detail) Expression becomes necessary to establish and retain the minimum order date per individual customer for accurate cohort analysis.

Daily profit KPI

In evaluating daily profit as a key performance indicator (KPI), the focus shifts from observing profit trends over time to quantifying success based on total profit per business day. Understanding the count of profitable days per month or year becomes essential, particularly in investigating potential seasonal impacts. Utilizing LOD (Level of Detail) Expressions, this view demonstrates the seamless creation of bins for aggregated data, like profit per day, despite the underlying data being recorded at a transactional level. This approach allows for efficient analysis and visualization of daily profitability trends within the context of the broader business calendar.

Percent of Total

Determining each country's revenue contribution to global sales is crucial for assessing market performance. When visualized by coloring contributions as percentages, it's apparent that the US holds the highest share of global sales revenue. However, focusing on markets like the EU, which might have a relatively smaller absolute contribution, becomes challenging without LOD Expressions. Without this capability, filtering by market could lead to recalculating the percent of total, displaying each country's contribution relative to its market. Using a straightforward LOD Expression enables filtering by market while preserving the measurement of each country's global contribution, facilitating a more nuanced analysis of market performance within the broader global context.

New customer acquisition

Analyzing the daily trend of total customer acquisition across different markets serves as a crucial metric in assessing the effectiveness of regional marketing and sales efforts in generating new business. By tracking this trend, we gain insights into the performance of these organizations. A steeper line signifies a stronger acquisition trend, while a flattening line suggests a need for increased lead flow.

To accurately measure this trend, it's imperative to ensure that repeat customers aren't erroneously counted as new customers. This necessitates using an LOD (Level of Detail) Expression, allowing data evaluation at the customer level despite its visual representation being segmented by market and day. This meticulous approach ensures a precise assessment of new customer acquisition, enabling strategic actions to be taken based on the observed trends.

Comparative Sales Analysis

When aiming to determine the difference from a selected category rather than the average, the process becomes more intricate. Initially, isolating the sales figures of the chosen category is necessary. Subsequently, employing an EXCLUDE Expression becomes crucial to reiterate that value across all other categories. This technique enables a straightforward calculation of the difference between each category's sales and the rest, allowing for a comparative sales analysis that emphasizes the disparity between the selected category and others.

Average of top deals by sales rep

Determining the largest deal closed by each sales representative and subsequently computing the average of these top deals by country is a multi-layered analysis. LOD (Level of Detail) Expressions play a pivotal role in dissecting data down to the sales rep level, even when the visualization displays information at the country level.
The presented view showcases the average top deal size by sales rep, offering insights where countries colored blue exhibit higher average top deal sizes, while those colored orange indicate comparatively lower averages. This information serves as a guide for further drill-down analysis from the country level to the sales rep level, facilitating a deeper understanding of performance variations across both geographical and individual sales rep perspectives.

Actual vs. Target

Within this visualization, we present the variance between actual and target profits per state for a chain of coffee houses. The top view distinctly showcases states surpassing or falling short of set targets. Yet, this aggregated view might overlook subtleties: some states exceed targets due to every product sold meeting or exceeding goals, while others rely on a single product surpassing its target to compensate for others missing theirs. Employing an LOD Expression enables the identification of the percentage of products sold within a state that surpass their set targets, offering a more nuanced assessment.

Value on the Last Day of a Period

Data reflecting specific day statuses—like inventory, employee headcounts, or daily stock values—require distinct handling compared to aggregatable metrics like sales or profit. Displaying the value on the last calendar day of a month holds significance in such cases. Moreover, transitioning from a monthly to a weekly view should dynamically update to showcase the last day of the week. For instance, in the stock data example below, assessing multiple ticker values at a daily level compares the average daily close value against the close value on the final day of the period. Employing a straightforward LOD Expression enables diving into daily granularity even within a visual display at a higher level of aggregation.

The following 6 examples illustrate how level of detail expressions can be applied to more advanced scenarios:

Evaluating the return purchases among customers holds significance, especially considering the costliness of acquiring new customers. Understanding the patterns of customers making repeat purchases within varying quarters—whether it's the first, second, third, or beyond—is essential. Additionally, assessing the count of customers who have never made a repeat purchase contributes valuable insights. This analysis, segmented by quarterly cohorts, sheds light on customer behavior over time.
Leveraging a FIXED Expression becomes instrumental in identifying each customer's first and second purchase dates, enabling the derivation of the time span in quarters for a repeat purchase. This nuanced approach offers a comprehensive understanding of customer return behavior within distinct quarterly cohorts.

Percent Difference from Average Across a Range

While Example 6 highlights comparing against a single selected item, what if the aim is to assess comparisons across a spectrum of values? Consider a scenario where one desires to evaluate the daily close value of a stock against the average daily close value before a significant industry-impacting event occurs.
In such instances, examining the percent difference becomes essential. By comparing the daily stock close values against the pre-event average, insights into the magnitude and impact of the event on stock performance can be gleaned. This analysis offers a broader perspective, aiding in understanding the deviation from the average within the context of industry-wide fluctuations.

Relative period filtering

When analyzing performance through year-to-date (YTD) and month-to-date (MTD) comparisons relative to the previous year, filtering relative to today is straightforward. However, when data undergoes weekly refreshes, discrepancies can arise. For instance, if the last refresh was on March 1 but the current day is March 7, a month-to-date comparison might inadvertently compare March 1 through March 7 of the previous year against March 1 of the current year, potentially causing unwarranted concern.
Employing a simple LOD (Level of Detail) Expression resolves this issue by identifying the maximum date within the dataset. This approach ensures accurate time-based comparisons, preventing misleading contrasts between different periods and providing a more precise evaluation of performance trends.

User login frequency

Understanding user login frequency is pivotal for assessing user engagement on websites or applications. This analysis aims to segment users based on their login frequency—whether it's monthly, bi-monthly, quarterly, and so on—and derive insights regarding the average login rate and its distribution around this average.
The dataset's granularity involves a log-in date per user ID, implying a row for each day a user accesses the platform. Slicing the number of customers by their login rate entails a more intricate analysis, necessitating the slicing of one measure by another measure. As showcased in Example 1, leveraging LOD (Level of Detail) Expressions streamlines this analysis, enabling an easy breakdown of user cohorts based on their login frequency and facilitating a comprehensive understanding of user behavior.

Proportional Brushing

In the realm of analysis, the pivotal question often revolves around comparison—specifically, "Compared to what?" Proportional brushing introduces a valuable technique for filtering where the aim is not merely to narrow down to the selection but to compare the selection against the total context.
This approach allows for a more comprehensive analysis by providing insights into how the selected subset relates to the entirety of the dataset. Proportional brushing aids in understanding the significance and impact of the chosen subset within the broader context, offering a richer perspective for informed decision-making.

Examining the correlation between customer tenure, measured by the year of acquisition, and loyalty, gauged through annual purchase frequency, provides valuable insights into customer behavior.
While Example 1 illustrates customers purchasing a specific number of times, marketers often seek insights beyond exact counts—particularly identifying customers who purchased at least a certain number of times. Moreover, understanding the loyalty trends within different acquisition cohorts is crucial. Simply assessing absolute customer numbers across cohorts might not reveal nuanced insights. Therefore, a more insightful approach involves evaluating the percentage of total customers within each cohort based on their purchase frequency thresholds.
In essence, this analysis combines variations of the number of orders LOD Expression, cohort Expression, and percent-of-total Expression to determine what percentage of customers within each cohort made at least one, two, three, or more purchases in a year. This approach offers a comprehensive understanding of loyalty trends across different customer acquisition periods.

In the last few years, the KSA market has witnessed a definitive shift to self-service consumption tools anchored in data democratization. The transition is of paramount importance for products that can scale at an enterprise level, AI-ML products, and those that can address data governance and quality management issues.

The shift is also significant for enterprise transformation catalysts that take an ecosystem approach with proven expertise in developing and executing comprehensive and unified data strategies, data engineering and data governance paradigms.

Thus the time is ripe for an innovation-led, experience-driven enterprise like Beinex to spearhead Digital and Analytics Transformations in KSA.

[sc name="quote" quote="“Beinex is pleased to formalize its presence in the KSA market by opening an Office in Riyadh. We, as an enterprise, are 100% aligned with Vision 2030 as put forth by the KSA and see tremendous value getting unlocked as the vision is realized. We look forward to expanding our footprint in the domains of Artificial Intelligence, Sustainability, Digital Transformation, Analytics and allied areas. The Kingdom envisions itself to be at the forefront of data and artificial intelligence-based economies, and Beinex is committed to playing its part in supporting and fulfilling this vision,”" author="Indumon Das, Founder and Managing Director of Beinex,
marking the occasion of the office’s opening, noted."][/sc]

Middle East Banking AI & Analytics Summit

Beinex is super excited to be a part of the 6th Middle East Banking AI & Analytics Summit on May 10, 2023. With the motto, "Accelerating Innovation in Banking with AI and Analytics Strategies", the summit aims to revolutionise the financial and banking space in KSA using AI. We are ready to witness and participate in panel discussions, fireside chats, keynote presentations, roundtable discussions, and conversational Q&A sessions with thought leaders on exploiting the Power of AI and Analytics for a futuristic banking ecosystem.

Middle East Enterprise AI & Analytics Summit

Also, we are enthusiastic to participate in the Middle East Enterprise Al and Analytics Summit on May 11, 2023. Its vision is to curate a world-class platform for tech leaders in the region to connect, communicate and collaborate under the theme "Accelerating Innovation in Enterprises with Applied Al and Analytics Strategies". Beinex is looking forward to connecting with thought leaders and high-level decision-makers in Al, and Data Analytics at #MEEAI 2023 to participate in discussions and to be a part of the transformation journey.

The Power of Beinex

Beinex drives a cohesive, unified digital ecosystem to help customers address their needs, assess products and operations, understand market requirements and evaluate overall business performance.

It is a multinational firm exploring the endless possibilities of data for Cloud, Analytics, Artificial Intelligence, Machine Learning, and Automation. In effect, Beinex architects, guides, leads, and implements solutions in Analytics, AI, and ML for the spheres of Digital Transformation, GRC, and Risk & Audit Transformation.

Partnerships make Beinex stronger. The company has solid partnerships with some of the leading technology firms, research labs, and universities around the globe. Businesses can leverage the power of the Beinex partner ecosystem to maximize the value of their end-to-end analytics journey.

Beinex Digital, a part of Beinex Holdings, is a digital transformation entity with a comprehensive suite of independent products focused on addressing specific business gaps, use cases, and needs. It incorporates a spectrum of solutions in the domains of Employee Health, Safety and Environment, Enterprise Product Management and Enterprise Performance Management.

Beinex is also the product champion for Aurex – Augmented Risk and Audit Analytics – a unique single-platform solution for Integrated Risk Management, Governance, Audit, Compliance, BCM, and Analytics functions. It is the first-of-its-kind product that streamlines risk and audit verticals for enterprises worldwide and is a Unified Digital Assurance Ecosystem.

Present in three continents, Beinex enables its clients to analyze data, mitigate risks, identify opportunities and automate processes.

Beinex Office Address (KSA):

Beinex Advanced Information Technology
3141, Anas Bin Malik,
8292 Al Malqa Dist
P. O. Box 13521,
Riyadh, Kingdom of Saudi Arabia
Email: Info@beinex.com

I’ve always been a huge Tableau fan as they never fail to impress me by bringing out new exciting features in every release. Tableau has yet again released a new version after a thorough research based on people’s experience, thoughts, and comments. As a result, Tableau 2020.3 has an exciting feature with respect to Predictive modelling functions, especially useful to advanced analytics geeks. There are so many other new features which everyone will appreciate – such as IN operator for calculations, Search improvements in data pane, Data quality warnings on Tableau Server and many more! Here are my top picks from the list. The IN Operator The much-awaited feature for the SQL lovers. The IN operator is a shorthand replacement for multiple OR functions. By using IN operator, we will be able to create concise calculation fields instead of never-ending lines of ‘OR’. Let’s look into a simple use case by assuming that I am interested to know the sales value of ‘Apple Products’ compared to all other products, then I can use IN operator to with IF statement to group the products to “Apple Product” and “Other Products” IF [Product Name] IN (‘Apple iPhone 5′,’Apple iPhone 5C’,’Apple iPhone 5S’) THEN “Apple Products” ELSE “Other Products” END Predictive Modelling Function Now the predictive genes would be happy to see more features on the prediction module of Tableau. With the added advantages of the functions MODEL_QUANTILE and MODEL_PERCENTILE, now the prediction function goes beyond the existing trendline analysis. Both the functions will help to build a model that understands how your data is distributed around a best-fit line. MODEL_QUANTILE is a table calculation that returns a target value at a specified percentile, based on other predictors MODEL_PERCENTILE is a table calculation that returns the probability of the expected value being less than or equal to the observed mark, based on other predictors Search improvements in Data pane While developing business specific dashboards, we need to create several calculations and its needs to be adjusted or new calculations needs to be created from existing calculations as per the business unit’s requirements. With the newly added Search improvement feature, our lives are made easy by searching or filtering specific filed based on field name, type, or comments. Search improvements in Data pane Write to external databases in Tableau Prep Write to external databases in Tableau Prep The wow moment for prep is here with the introduction of the ability to output to a database. In all previous versions of Prep, we have been able to write to an extract file or csv, and now from the latest 2020.3 introduces the ability to output to a database. Currently supported databases are SQL Server, MySQL, PostgreSQL, Amazon Redshift, Snowflake, Oracle, and Teradata. As shown in the below example, result from the flow can be saved to a data base table from the output step by choosing “Database table” option, specify your server connection (with login credentials) , choose the data base and then the table. And now, Write to database will power the analytics journey by helping to solve the issues related to, data sources, data security and data governance Open or Upload Workbooks On The Web within Tableau Server Sharing your work and exploring insights from others just got easier. You can now open a Tableau workbook or upload it straight to the web without having to use Tableau Desktop. Simply select the workbook (.twb or .twbx) you want to upload and publish directly to your site on Tableau Server or Online. Only users with the appropriate publishing permissions will have the ability to upload content. Open or Upload Workbooks On The Web within Tableau Server Three refresh options are available, they are Earlier prep was used mainly for Set Data Quality Warnings on Tableau Server Data quality warnings make it possible to let users know when a data asset is flagged, whether because the data is under maintenance, it’s stale, or some other reason. With high visibility data quality warnings, not only let users know about the issue but make sure they do not miss it. Are you interested in learning more about Tableau’s new features or the features? Are you a Tableau user and would like to become a Tableau Champion? If the answer to the above questions is a big “YES”, Please contact us at training@beinex.com/ info@beinex.com and we would be happy to schedule a Tableau demo or training for you and your company.

AWS + Tableau: Together, a Match Made in Data Heaven

By embracing the synergy between Tableau and AWS, you're not just investing in tools; you're investing in a future fueled by data-driven insights. This powerful combination paves the way for a more agile, data-centric organization ready to thrive in the ever-evolving digital landscape. The digital landscape is evolving rapidly, and businesses are increasingly turning to the cloud for their analytics needs. This shift is driven by the cloud's ability to:

1. Faster Time to Insights: The seamless integration between Tableau and AWS allows you to quickly get up and running with your analytics, enabling you to make data-driven decisions sooner.
2. Effortless Data Management: Leverage the power of AWS data warehousing and management services to ensure your data is clean, organized, and readily accessible for analysis in Tableau.
3. Advanced Analytics Capabilities: Tap into the power of AWS machine learning and artificial intelligence services to uncover hidden patterns and gain deeper insights from your data within the Tableau environment.
4. Handle complex data integration: Seamlessly connect and analyze data from various sources, regardless of size or location.
5. Empower self-service analytics: Enable users to explore and gain insights from data independently, fostering data-driven decision-making across the organization.
6. Support digital transformation: Meet the growing demands of digital transformation with scalability, flexibility, and cost-efficiency.

Image source: https://aws.amazon.com/solutions/partners/tableau-server/

Tableau: The Master of Data Visualization

Imagine transforming raw data into captivating, interactive visualizations that tell a clear story. Tableau is a game-changer in the world of data visualization. It empowers users of all technical backgrounds to:

1. Connect to Diverse Data Sources: Tableau seamlessly connects to a wide range of data sources, both on-premise and in the cloud. This includes databases, spreadsheets, cloud applications, and even big data platforms.
2. Effortlessly Drag-and-Drop Analysis: The user-friendly interface allows users to drag and drop data fields, explore trends, and create stunning visualizations without writing a single line of code.
3. Craft Interactive Dashboards & Reports: Go beyond static reports. Tableau empowers you to create dynamic dashboards that users can interact with, filter data, and gain deeper insights on the fly.
4. Foster Data-Driven Culture: Tableau democratizes data by making it accessible and understandable to everyone in the organization, fostering a data-driven culture where decisions are based on evidence, not intuition.

AWS: The Cloud Powerhouse for Scalability and Security

While Tableau excels at data visualization, the underlying infrastructure needs to be robust and scalable. This is where AWS, the world's leading cloud computing platform, comes into play. Here's how AWS empowers your Tableau deployment:

1. Unmatched Scalability: AWS offers virtually limitless scalability to accommodate your growing data volumes and user base. As your data needs evolve, your cloud infrastructure can easily scale up or down to meet those demands.
2. Enhanced Security: Security is paramount when dealing with sensitive data. AWS offers robust security features and compliance certifications, ensuring your data remains protected throughout its lifecycle.
3. Cost-Effectiveness: The pay-as-you-go model of AWS allows you to optimize your costs. You only pay for the resources you use, eliminating the need for upfront investments in expensive hardware infrastructure.
4. Wide Range of Services: AWS offers a comprehensive suite of services beyond just compute power. These services include data warehousing, machine learning, and data management tools, giving you a complete cloud ecosystem to manage your entire data pipeline.

Tableau and AWS work together to accelerate your digital journey with Modern Cloud Analytics (MCA). Let’s see how:

Modern Cloud Analytics: A Collaborative Powerhouse

Modern Cloud Analytics is a collaborative initiative leveraging the expertise and resources of Tableau, AWS, and their extensive partner networks. Its objective is to maximize the value you extract from your data and analytics investments throughout your entire digital transformation journey, encompassing:

1. Data Strategy and Migration: Develop a comprehensive plan to securely and efficiently migrate your data and analytics operations to the cloud.
2. Optimization: Fine-tune your cloud analytics environment for peak performance and cost-effectiveness.
3. Deployment and Scaling: Securely deploy and seamlessly scale your Tableau environment on AWS to adapt to your evolving needs.

Benefits of Modern Cloud Analytics:

1. Faster Time to Value: Get up and running with cloud analytics quickly, enabling data-driven decisions sooner.
2. Reduced Costs: Leverage the cloud's inherent cost-efficiency and scalability to optimize your analytics spending.
3. Minimized Risks: Mitigate potential risks associated with cloud adoption by utilizing validated migration processes and expert guidance.

Unified Integration for Unparalleled Insights

Tableau and AWS offer a comprehensive solution for cloud-powered organizations. Both Tableau Server and Tableau Cloud operate flawlessly on AWS infrastructure, providing you with:

1. Effortless Data Access: Streamlined workflows and effortless access to data stored within various AWS sources directly within the AWS ecosystem.
2. Market-Leading Connectivity: Tableau serves as the ideal platform for analyzing data residing in diverse AWS data sources like:
3. Amazon Redshift: A blazing-fast data warehouse designed to handle large datasets with efficiency.
4. Amazon RDS: A managed relational database service offering high availability and scalability.
5. Amazon EMR: A managed Hadoop framework for processing and analyzing massive datasets.

Enhanced Security and Broader Connectivity

Tableau's commitment to continuous improvement extends to its AWS integrations. Here's a glimpse into the exciting advancements:

1. Enhanced Security: The updated Amazon Athena connector now supports secure authentication using third-party identity providers like Azure AD and Okta, adding an extra layer of security with multi-factor authentication options.
2. Expanded Connectivity: The Tableau Exchange offers a plethora of new connectors, further extending your connection options within the AWS ecosystem.
3. Amazon OpenSearch Connector: Effortlessly visualize and analyze data residing in Amazon OpenSearch.
4. Amazon DocumentDB Connector: Gain valuable insights from your DocumentDB data through seamless interaction.
5. Amazon Neptune Connector: Explore connections within your data by directly connecting to your Neptune graph database.

By embracing the synergy between Tableau and AWS, you're not just investing in tools, you're investing in a future fueled by data-driven insights. This powerful combination paves the way for a more agile, data-centric organization ready to thrive in the ever-evolving digital landscape.

How Beinex Can Assist You

Beinex is a premier Tableau partner and AWS consulting partner providing sustainable analytics solutions to organizations. Our Tableau and AWS-certified consultants help organizations build superior data visual analytics capabilities through bespoke training programs. We empower customers to host their BI solutions on the cloud with AWS infrastructure as a service. Get in touch with us for free trials and experience our expertise in providing analytics and cloud solutions.