Analytix

analytix

The AI Agent Blind Spot: A New Frontier for Security Analytics

In modern analytics teams, Cohort Analysis and Customer Lifetime Value (CLTV) modeling are two foundational tools used to drive insights from customer behavior. While both tap into user-level transactional and event data, they are fundamntally different in scope, objective, and output. Cohort Analysis Customer Lifetime Value (CLTV) scope, objective, and output In this guide, I’ll dive deep into how each technique works, where they intersect, and how they fuel strategic decisions—especially when powered by predictive models like Gamma-Gamma + BG/NBD. Gamma-Gamma + BG/NBD 📊 Cohort Analysis: Behavior Over Time Cohort analysis groups users by shared characteristics—most commonly by acquisition date or first activity—and tracks behavior over time. acquisition date or first activity 🔧 What It Answers: How does user retention vary by acquisition channel?
Which product changes improved Week-1 activation or Week-4 retention?
What’s the impact of onboarding redesign for users acquired after a certain date? How does user retention vary by acquisition channel? Which product changes improved Week-1 activation or Week-4 retention? What’s the impact of onboarding redesign for users acquired after a certain date? 🧠 Technical Setup: Group by signup_date → Create cohorts
Track downstream events: purchases, logins, upgrades
Normalize time as "cohrt age" (e.g. Week 0, Week 1...) Group by signup_date → Create cohorts signup_date Track downstream events: purchases, logins, upgrades Normalize time as "cohrt age" (e.g. Week 0, Week 1...) You typically visualize this with retention heatmaps or rolling conversion charts: retention heatmaps rolling conversion charts Cohort

Week 0

Week 1

Week 2

Week 3



Jan 01

100%

42%

28%

21%



Jan 08

100%

48%

33%

24% Cohort

Week 0

Week 1

Week 2

Week 3



Jan 01

100%

42%

28%

21%



Jan 08

100%

48%

33%

24% Cohort

Week 0

Week 1

Week 2

Week 3 Cohort Cohort Week 0 Week 0 Week 1 Week 1 Week 2 Week 2 Week 3 Week 3 Jan 01

100%

42%

28%

21% Jan 01 Jan 01 100% 100% 42% 42% 28% 28% 21% 21% Jan 08

100%

48%

33%

24% Jan 08 Jan 08 100% 100% 48% 48% 33% 33% 24% 24% 📌 Key Insight:
“Users who signed up after the new onboarding flow retained 15% better at Week 4, suggesting the new tutorial flow increased product stickiness.” 📌 Key Insight: 📌 Key Insight: “Users who signed up after the new onboarding flow retained 15% better at Week 4, suggesting the new tutorial flow increased product stickiness.” 💸 CLTV Modeling: Forecasting Future Revenue CLTV estimates the total expected revenue from a customer throughout their relationship with your business. Unlike cohort analysis, it’s forward-looking and requires probabilistic modeling. total expected revenue forward-looking probabilistic modeling 🧮 Why Not Just Use AOV x Repeat Rate? Because customer behavior is not deterministic. Many users will churn early, some will stay for years, and spending patterns vary dramatically. This is why data scientists turn to probabilistic models. probabilistic models 🔍 The Gamma-Gamma Model (with BG/NBD) The Beta Geometric/NBD model is used to estimate: Beta Geometric/NBD model Purchase frequency (how often a customer transacts)
Churn probability (how likely they are to return) Purchase frequency (how often a customer transacts) Purchase frequency Churn probability (how likely they are to return) Churn probability The Gamma-Gamma model, used in tandem, estimates: Gamma-Gamma model Monetary value per transaction Monetary value per transaction Monetary value per transaction Together, they allow us to predict total future value per customer. predict total future value 📐 Model Assumptions: Model

Assumptions



BG/NBD

Purchase frequency follows a Poisson process. Churn is unobserved but estimable.



Gamma-Gamma

Monetary value is independent of frequency and follows a gamma distribution. Model

Assumptions



BG/NBD

Purchase frequency follows a Poisson process. Churn is unobserved but estimable.



Gamma-Gamma

Monetary value is independent of frequency and follows a gamma distribution. Model

Assumptions Model Model Assumptions Assumptions BG/NBD

Purchase frequency follows a Poisson process. Churn is unobserved but estimable. BG/NBD BG/NBD Purchase frequency follows a Poisson process. Churn is unobserved but estimable. Purchase frequency follows a Poisson process. Churn is unobserved but estimable. Gamma-Gamma

Monetary value is independent of frequency and follows a gamma distribution. Gamma-Gamma Gamma-Gamma Monetary value is independent of frequency and follows a gamma distribution. Monetary value is independent of frequency and follows a gamma distribution. from lifetimes import BetaGeoFitter, GammaGammaFitter

# Fit frequency and recency
bgf = BetaGeoFitter().fit(frequency, recency, T)

# Fit monetary value
ggf = GammaGammaFitter().fit(frequency, monetary_value)

# Predict 6-month CLTV
cltv = ggf.customer_lifetime_value(
    bgf, frequency, recency, T, monetary_value,
    time=6, discount_rate=0.01
) from lifetimes import BetaGeoFitter, GammaGammaFitter

# Fit frequency and recency
bgf = BetaGeoFitter().fit(frequency, recency, T)

# Fit monetary value
ggf = GammaGammaFitter().fit(frequency, monetary_value)

# Predict 6-month CLTV
cltv = ggf.customer_lifetime_value(
    bgf, frequency, recency, T, monetary_value,
    time=6, discount_rate=0.01
) 🧪 Business Insight Examples Let’s look at two hypothetical outputs and their potential business impact: two hypothetical outputs 📈 1. CLTV by Acquisition Channel Channel

Avg. CLTV (6 Mo)

CAC

ROI



Instagram Ads

$48.10

$15

3.2x



Google Search

$31.75

$12

2.6x



Organic

$53.60

$2

26.8x Channel

Avg. CLTV (6 Mo)

CAC

ROI



Instagram Ads

$48.10

$15

3.2x



Google Search

$31.75

$12

2.6x



Organic

$53.60

$2

26.8x Channel

Avg. CLTV (6 Mo)

CAC

ROI Channel Channel Avg. CLTV (6 Mo) Avg. CLTV (6 Mo) CAC CAC ROI ROI Instagram Ads

$48.10

$15

3.2x Instagram Ads Instagram Ads $48.10 $48.10 $15 $15 3.2x 3.2x Google Search

$31.75

$12

2.6x Google Search Google Search $31.75 $31.75 $12 $12 2.6x 2.6x Organic

$53.60

$2

26.8x Organic Organic $53.60 $53.60 $2 $2 26.8x 26.8x 📌 Key Insight:
Even though Instagram has higher CAC, it still delivers the strongest ROI. Organic, unsurprisingly, dominates in efficiency—this suggests that SEO and referral loops may deserve more investment. 📌 Key Insight: 📌 Key Insight: Even though Instagram has higher CAC, it still delivers the strongest ROI. Organic, unsurprisingly, dominates in efficiency—this suggests that SEO and referral loops may deserve more investment. 💡 2. CLTV Segmentation by Cohort Signup Month

6-Mo CLTV

Repeat Purchase Rate

Avg. Order Value



Jan 2024

$37.40

22%

$19.40



Feb 2024

$42.15

28%

$18.20



Mar 2024

$49.80

35%

$20.10 Signup Month

6-Mo CLTV

Repeat Purchase Rate

Avg. Order Value



Jan 2024

$37.40

22%

$19.40



Feb 2024

$42.15

28%

$18.20



Mar 2024

$49.80

35%

$20.10 Signup Month

6-Mo CLTV

Repeat Purchase Rate

Avg. Order Value Signup Month Signup Month 6-Mo CLTV 6-Mo CLTV Repeat Purchase Rate Repeat Purchase Rate Avg. Order Value Avg. Order Value Jan 2024

$37.40

22%

$19.40 Jan 2024 Jan 2024 $37.40 $37.40 22% 22% $19.40 $19.40 Feb 2024

$42.15

28%

$18.20 Feb 2024 Feb 2024 $42.15 $42.15 28% 28% $18.20 $18.20 Mar 2024

$49.80

35%

$20.10 Mar 2024 Mar 2024 $49.80 $49.80 35% 35% $20.10 $20.10 📌 Key Insight:
CLTV is rising for newer cohorts, suggesting product-market fit is improving or LTV uplift initiatives (e.g., loyalty emails or better upsells) are effective. 📌 Key Insight: 📌 Key Insight: CLTV is rising for newer cohorts, suggesting product-market fit is improving or LTV uplift initiatives (e.g., loyalty emails or better upsells) are effective. 🔁 Cohort + CLTV: Why Use Both? A cohort analysis tells you what’s happening over time, while CLTV modeling estimates what will happen. When combined, they empower your team to: what’s happening what will happen Validate growth experiments (e.g., "Did Week 1 retention improve for A/B Test Group B?")
Forecast revenue per segment or channel
Prioritize CRM workflows (e.g., higher-CLTV cohorts get more re-engagement) Validate growth experiments (e.g., "Did Week 1 retention improve for A/B Test Group B?") Forecast revenue per segment or channel Prioritize CRM workflows (e.g., higher-CLTV cohorts get more re-engagement) As a Conclusion: Why Top Companies Invest in Both Cohort analysis and CLTV modeling are not just academic exercises—they are critical tools used by the world’s most successful data-driven companies to drive growth, retention, and profitability. growth, retention, and profitability 🚀 Real-World Benchmarks: Amazon uses cohort-based retention tracking to continuously optimize Prime onboarding and predict subscription renewal behaviors.
Netflix combines behavioral cohorts with predictive churn and CLTV models to tailor recommendations and prioritize personalized engagement campaigns.
Shopify leverages CLTV modeling across merchants to forecast revenue and optimize the partner ecosystem—knowing early which stores are worth high-touch support. Amazon uses cohort-based retention tracking to continuously optimize Prime onboarding and predict subscription renewal behaviors. Amazon Netflix combines behavioral cohorts with predictive churn and CLTV models to tailor recommendations and prioritize personalized engagement campaigns. Netflix Shopify leverages CLTV modeling across merchants to forecast revenue and optimize the partner ecosystem—knowing early which stores are worth high-touch support. Shopify These companies understand that descriptive analytics (cohort) helps explain what’s happening now, while predictive analytics (CLTV) guides what to do next. Without the combination, you’re flying blind in either direction. descriptive analytics (cohort) what’s happening now predictive analytics (CLTV) what to do next 📌 Key Takeaway: Cohort analysis helps you understand the path your users took. CLTV tells you whether that path is worth the investment. \  The smartest organizations use both to align product, marketing, and finance toward long-term impact. Cohort analysis helps you understand the path your users took. CLTV tells you whether that path is worth the investment. \  The smartest organizations use both to align product, marketing, and finance toward long-term impact. path If your team isn't yet leveraging both, you're likely leaving revenue, insight, and efficiency on the table. Thank you for your time; sharing is caring! 🌍 Thank you for your time; sharing is caring! 🌍

Unleash the Power of Cohort Analysis & CLTV Modeling in Analytics

Rohit Sindhu, a Senior Principal Engineer with over 22 years of experience in computer science, embedded systems, and high-performance interconnects, has made a significant mark in the storage industry with his recently granted US patent, US11237760B2: “Measuring performance metrics for data storage devices.” Sindhu’s career is defined by technical depth, innovation, and a passion for advancing the state of the art in PCIe Express, CXL, MCTP, and NVMe storage technologies. In addition to this milestone patent, Sindhu has multiple pending patents in the PCIe, CXL, and MCTP domains, further underscoring his ongoing contributions to next-generation interconnect and storage solutions. Notably, he has also contributed to the development of CXL and JEDEC specifications related to CXL and PCIe device management, reflecting his influence on industry standards and best practices. A Career Dedicated to Embedded, Memory and Storage Innovation Rohit Sindhu’s professional journey spans more than two decades, during which he has become widely recognized for his expertise in both hardware and software domains. He holds a Master’s degree in Computer Science from the University of Texas at Dallas with a Bachelor’s Degree in Computer Engineering from NIT Surat, Gujarat, India and has contributed to the development of embedded systems for mission-critical applications, architected solutions across the PCIe, MCTP and CXL protocol stacks, and driven innovation in Memory & storage performance, availability, manageability and reliability. Sindhu’s technical leadership and mentorship have impacted teams and products across the industry. He is known for bridging the gap between complex engineering challenges and practical, scalable solutions. Over the years, he has worked on a diverse range of projects, from firmware development and system-on-chip design to advanced debug systems and high-speed interconnect protocols. His work has spanned sectors including enterprise storage, data centers, defense, and emerging edge computing applications. As a Senior Principal Engineer in his current role, Sindhu continues to focus on the intersection of high-speed interconnects and data storage, developing solutions that address real-world bottlenecks and enable next-generation computing platforms. His broad technical background allows him to approach problems holistically, considering both hardware and software aspects to deliver robust, high-performance solutions. Sindhu is also an active mentor and thought leader in the technology community. He regularly shares insights on storage trends, PCIe and CXL advancements, and best practices for embedded systems design. His professional profile and further details about his career can be found on his LinkedIn page. US11237760B2: Raising the Bar for NVMe Latency Measurement Sindhu’s patent, “Measuring performance metrics for data storage devices,” addresses a long-standing challenge in the storage industry: accurately measuring the latency of NVMe commands in high-speed PCIe environments without introducing the overhead or inaccuracies common to traditional software-based tools. The Problem: Latency Measurement in Modern Storage In today’s data-driven world, the performance of NVMe-based storage systems is critical for applications ranging from cloud computing and artificial intelligence to real-time analytics and edge computing. Latency, the time it takes for a storage command to be processed and completed directly impacts application responsiveness, quality of service (QoS), and overall infrastructure efficiency. Traditional latency measurement methods, often software-based, can introduce significant observational overhead, skewing results and failing to capture the true performance characteristics of storage devices. Hardware probes, while useful, sometimes lack the granularity needed to isolate individual command latencies, especially in environments with high command throughput and parallelism. Sindhu’s Solution: An FPGA-Accelerated Approach Sindhu’s patented method leverages FPGA-based hardware logic to passively monitor PCIe transactions between the host and storage device, precisely timing the lifecycle of individual NVMe commands. The process unfolds in a series of orchestrated steps designed to ensure accuracy, scalability, and zero impact on normal system operation. Step-by-Step Breakdown: Queue Pair Creation: The host system initiates the process by creating a dedicated Submission Queue (SQ) and Completion Queue (CQ) pair for latency measurement. This isolation prevents interference from other I/O operations. The start addresses and lengths of these queues are configured in the FPGA’s test logic.



Command Injection: The host queues an NVMe command and rings the doorbell, prompting the device to fetch the command from the SQ.



PCIe Transaction Snooping: The device issues a PCIe Transaction Layer Packet (TLP) to fetch the command. The FPGA’s snooping logic monitors for read requests within the SQ address range, retrieves the transaction Tag (a unique identifier for the transaction), and records it internally.



Tag Matching and Timer Start: As the FPGA continues monitoring, it matches the Tag in subsequent response packets, extracts the NVMe command ID from the TLP payload, and starts a high-resolution latency timer—typically driven by FPGA clock cycles in the nanosecond range.



Completion Monitoring and Timer Stop: The FPGA then watches for command completion responses in the CQ address range. When the NVMe command ID in the completion response matches the saved command ID, the timer stops, and the measured latency is reported back to the host. Queue Pair Creation: The host system initiates the process by creating a dedicated Submission Queue (SQ) and Completion Queue (CQ) pair for latency measurement. This isolation prevents interference from other I/O operations. The start addresses and lengths of these queues are configured in the FPGA’s test logic. Queue Pair Creation: The host system initiates the process by creating a dedicated Submission Queue (SQ) and Completion Queue (CQ) pair for latency measurement. This isolation prevents interference from other I/O operations. The start addresses and lengths of these queues are configured in the FPGA’s test logic. Queue Pair Creation Command Injection: The host queues an NVMe command and rings the doorbell, prompting the device to fetch the command from the SQ. Command Injection: The host queues an NVMe command and rings the doorbell, prompting the device to fetch the command from the SQ. Command Injection PCIe Transaction Snooping: The device issues a PCIe Transaction Layer Packet (TLP) to fetch the command. The FPGA’s snooping logic monitors for read requests within the SQ address range, retrieves the transaction Tag (a unique identifier for the transaction), and records it internally. PCIe Transaction Snooping: The device issues a PCIe Transaction Layer Packet (TLP) to fetch the command. The FPGA’s snooping logic monitors for read requests within the SQ address range, retrieves the transaction Tag (a unique identifier for the transaction), and records it internally. PCIe Transaction Snooping Tag Matching and Timer Start: As the FPGA continues monitoring, it matches the Tag in subsequent response packets, extracts the NVMe command ID from the TLP payload, and starts a high-resolution latency timer—typically driven by FPGA clock cycles in the nanosecond range. Tag Matching and Timer Start: As the FPGA continues monitoring, it matches the Tag in subsequent response packets, extracts the NVMe command ID from the TLP payload, and starts a high-resolution latency timer—typically driven by FPGA clock cycles in the nanosecond range. Tag Matching and Timer Start Completion Monitoring and Timer Stop: The FPGA then watches for command completion responses in the CQ address range. When the NVMe command ID in the completion response matches the saved command ID, the timer stops, and the measured latency is reported back to the host. Completion Monitoring and Timer Stop: The FPGA then watches for command completion responses in the CQ address range. When the NVMe command ID in the completion response matches the saved command ID, the timer stops, and the measured latency is reported back to the host. Completion Monitoring and Timer Stop This approach ensures that performance metrics are captured with nanosecond-level accuracy and zero impact on the normal operation of the host or device, a significant advancement over existing methods. Technical Advantages and Industry Impact Sindhu’s invention offers several key advantages over traditional software and hardware-based measurement tools: Zero Observational Overhead: The FPGA operates passively on the PCIe bus, leaving host and device workloads unaffected. There is no need for intrusive software agents or kernel modifications. Zero Observational Overhead: The FPGA operates passively on the PCIe bus, leaving host and device workloads unaffected. There is no need for intrusive software agents or kernel modifications. Zero Observational Overhead: Protocol-Agnostic and Scalable: While optimized for NVMe, the method can be adapted to any PCIe-based protocol, including emerging standards like CXL.io, by reconfiguring address ranges and command parsing logic. Protocol-Agnostic and Scalable: While optimized for NVMe, the method can be adapted to any PCIe-based protocol, including emerging standards like CXL.io, by reconfiguring address ranges and command parsing logic. Protocol-Agnostic and Scalable Multi-Command Parallelism: The FPGA’s ability to track multiple Tags simultaneously enables concurrent latency measurement across many NVMe commands—critical for assessing real-world, high-throughput workloads. Multi-Command Parallelism: The FPGA’s ability to track multiple Tags simultaneously enables concurrent latency measurement across many NVMe commands—critical for assessing real-world, high-throughput workloads. Multi-Command Parallelism Integration Flexibility: The solution can be implemented as standalone hardware or embedded within Smart NICs, computational storage devices, or CXL-attached memory controllers. Integration Flexibility: The solution can be implemented as standalone hardware or embedded within Smart NICs, computational storage devices, or CXL-attached memory controllers. Integration Flexibility: The solution can be implemented as standalone hardware or embedded within Smart NICs, computational storage devices, or CXL-attached memory controllers. Integration Flexibility Applications Across the Storage Ecosystem Data Center Optimization: Cloud providers and enterprise IT teams can use this technology for real-time latency analytics across large fleets of NVMe devices, enabling dynamic QoS management and proactive troubleshooting.



Storage OEM Validation: Manufacturers can integrate this IP into test platforms to validate SSD latency under extreme workloads, replacing expensive and less flexible protocol analyzers.



Autonomous and Edge Systems: In latency-sensitive environments like autonomous vehicles or industrial edge computing, Sindhu’s method provides the granularity needed to certify storage subsystems for real-time operation.



CXL and Next-Gen Storage: As CXL adoption grows for memory pooling and computational storage, the patent’s PCIe snooping framework lays the groundwork for similar measurement techniques across new protocols. Data Center Optimization: Cloud providers and enterprise IT teams can use this technology for real-time latency analytics across large fleets of NVMe devices, enabling dynamic QoS management and proactive troubleshooting. Data Center Optimization: Cloud providers and enterprise IT teams can use this technology for real-time latency analytics across large fleets of NVMe devices, enabling dynamic QoS management and proactive troubleshooting. Data Center Optimization Storage OEM Validation: Manufacturers can integrate this IP into test platforms to validate SSD latency under extreme workloads, replacing expensive and less flexible protocol analyzers. Storage OEM Validation: Manufacturers can integrate this IP into test platforms to validate SSD latency under extreme workloads, replacing expensive and less flexible protocol analyzers. Storage OEM Validation Autonomous and Edge Systems: In latency-sensitive environments like autonomous vehicles or industrial edge computing, Sindhu’s method provides the granularity needed to certify storage subsystems for real-time operation. Autonomous and Edge Systems: In latency-sensitive environments like autonomous vehicles or industrial edge computing, Sindhu’s method provides the granularity needed to certify storage subsystems for real-time operation. Autonomous and Edge Systems CXL and Next-Gen Storage: As CXL adoption grows for memory pooling and computational storage, the patent’s PCIe snooping framework lays the groundwork for similar measurement techniques across new protocols. CXL and Next-Gen Storage: As CXL adoption grows for memory pooling and computational storage, the patent’s PCIe snooping framework lays the groundwork for similar measurement techniques across new protocols. CXL and Next-Gen Storage A Visionary Leader and Mentor Beyond his technical achievements, Rohit Sindhu is recognized as a mentor and advocate for innovation in the embedded, memory, and storage communities. He has guided teams through complex engineering challenges, shared his expertise through technical talks and publications, and actively supports the next generation of engineers. Sindhu’s approach is characterized by a relentless pursuit of precision, efficiency, and scalability. His work on US11237760B2 exemplifies his commitment to solving real-world problems with elegant, practical solutions that have a lasting impact on the industry. Looking Forward As storage technologies continue to evolve to meet the demands of AI, big data, and cloud-scale infrastructure, the need for accurate and efficient performance measurement will only grow. Rohit Sindhu’s patented approach provides a robust foundation for the next wave of innovation in storage analytics and validation. With multiple pending patents in PCIe, CXL, and MCTP domains, and with his direct contributions to CXL and JEDEC specifications related to CXL and PCIe device management, Sindhu’s influence on the next generation of high-speed interconnects and storage technologies is set to expand even further. To learn more about Rohit Sindhu’s professional journey, connect with him on LinkedIn. LinkedIn LinkedIn US11237760B2 is more than a patent—it is a testament to Rohit Sindhu’s enduring impact on the technology industry, and a blueprint for the future of precise, hardware-accelerated storage performance measurement. US11237760B2 is more than a patent—it is a testament to Rohit Sindhu’s enduring impact on the technology industry, and a blueprint for the future of precise, hardware-accelerated storage performance measurement. This story was distributed as a release by Echospire Media under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Echospire Media under HackerNoon’s Business Blogging Program. Learn more about the program here. here here

A Breakthrough in Storage Analytics: How Rohit Sindhu's Patent Enables Nanosecond-Level NVMe Latency

Un avance en el análisis de almacenamiento: cómo la patente de Rohit Sindhu permite la latencia NVMe a nivel de nanosegundo

Un avance na análise de almacenamento: como a patente de Rohit Sindhu permite a latencia NVMe a nivel de nanosegundos

פריצת דרך בניתוח אחסון: כיצד פטנט רוהיט סינדו מאפשר ננו-שנייה רמות NVMe איחור

Yon Breakthrough nan Analytics depo: Ki jan Rohit Sindhu a patant pèmèt nan nan-sekond-nivo NVMe latency

A Breakthrough in Storage Analytics: How Rohit Sindhu's Patent Enables Nanosecond-Level NVMe Latency (ストレージアナリティクスにおける突破:ロヒット・シンドゥーの特許がどのようにNVMeレベルの遅延を可能にするか)

스토리지 분석의 획기 : 로히트 시드루 (Rohit Sindhu)의 특허가 어떻게 나노초 수준의 NVMe 지연을 가능하게 하는가

Przełom w analizie przechowywania: Jak patent Rohita Sindhu umożliwia opóźnienie NVMe na poziomie nanosekundowym

Prelom v analýze ukladacieho priestoru: Ako patent Rohit Sindhu umožňuje nanosekundovú latenciu NVMe

Shuruudaha ah ee Analysis Storage: Sida Patent Rohit Sindhu waxay la isticmaali karaa NVMe nanosecond-level latency

Ufanisi katika Uchambuzi wa Uhifadhi: Jinsi Patent ya Rohit Sindhu Inawezesha Latency ya NVMe ya Nano-Second

Depolama Analizinde Çarpışma: Rohit Sindhu'nun Patentinin Nanosekundelik NVMe Latensiyeni Nasıl Etkilediği

I-Breakthrough ku-Storage Analytics: Indlela I-Patent ye-Rohit Sindhu Inikeza I-Nanosecond-Level NVMe Latency

Achieve 100x Speedups in Graph Analytics Using Nx-cugraph

Meet Fabi.ai, Member of San Francisco/Analytics's Momentum 10 

Building a BNB Chain Analytics Dashboard with GetBlock

Building Privacy‑First Generative AI Chat Analytics Pipelines

In an era where retail success increasingly depends on data-driven decision making, the implementation of innovative promotional analytics at a major US department store stands as a compelling example of how advanced machine learning can drive substantial business value. Under the leadership of Shashank Shekhar Katyayan, a breakthrough project in promotional propensity modeling has set new benchmarks for personalization at scale, delivering remarkable financial results and establishing new standards for retail analytics implementation. The project, which centered on developing sophisticated predictive models using CatBoost technology, emerged as a significant innovation in retail personalization. Leading a focused team of five analytics professionals, Shashank Shekhar Katyayan took on the challenge of transforming vast amounts of customer data into actionable insights that would drive promotional effectiveness and margin optimization. The complexity of the task was magnified by the need to process and analyze massive datasets while ensuring real-time applicability in a dynamic retail environment. At the heart of this success story was a methodical approach to model design and implementation. Shashank Shekhar Katyayan's innovative concept leveraged the power of CatBoost's advanced algorithmic capabilities to create multiple predictive models that could accurately forecast promotional propensity across diverse customer segments. This sophisticated approach to personalization enabled the retailer to optimize promotional strategies at an unprecedented scale, taking into account numerous variables including customer behavior patterns, historical purchase data, and seasonal trends. The integration of multiple predictive models represented a significant advancement in retail analytics. Under Katyayan's guidance, the team developed a comprehensive framework that could not only predict customer responses to promotions but also optimize the timing and targeting of promotional activities. This nuanced approach to personalization went beyond traditional segmentation methods, enabling micro-targeting capabilities that significantly enhanced promotional effectiveness. The impact of this initiative extended far beyond technical achievement. Through rigorous A/B testing conducted over a three-month period, the project demonstrated remarkable results - achieving a two-percentage point increase in margins. This improvement translated to an annualized run rate of $100 million in additional value creation, a significant achievement in the highly competitive retail sector where margin improvements of this magnitude are exceptionally rare. The success of these tests validated not only the technical sophistication of the solution but also its practical applicability in driving business results. The implementation process itself showcased Shashank Shekhar Katyayan's ability to navigate complex organizational challenges while maintaining focus on deliverable outcomes. His leadership style emphasized both technical excellence and practical business application, ensuring that the sophisticated analytical models translated directly into actionable insights for the retail operation. This balanced approach was crucial in gaining stakeholder buy-in and ensuring successful adoption of the new systems. The success of this project highlighted Katyayan's expertise in leveraging advanced analytics for business value creation. His ability to bridge the gap between complex technical implementation and practical business outcomes has established him as a leading voice in retail personalization. The project's success has become a benchmark for future retail analytics initiatives, demonstrating how effective leadership in data science can deliver exceptional results across multiple performance indicators. For Shashank Shekhar Katyayan personally, the project represented a significant career milestone, showcasing his ability to conceive, design, and implement sophisticated analytical solutions while managing and mentoring high-performing teams. The success has already translated into tangible career advancement, with management recognizing his achievements through rapid promotion within the firm. This success story illustrates how strategic analytics leadership, when combined with innovative technical implementation, can transform retail operations. The promotional propensity modeling project not only contributed to immediate financial gains but also established new standards for personalization at scale in the retail sector. As the industry continues to evolve, this project serves as a compelling example of how focused leadership in advanced analytics can drive exceptional business results. Looking ahead, the implications of this project’s success extends beyond immediate achievements. It demonstrates how effective application of machine learning technologies can overcome complex retail challenges while delivering exceptional value to stakeholders. As the retail sector continues to embrace digital transformation, this project stands as a model for future analytics implementations, showcasing the powerful combination of technical innovation, strategic leadership, and business acumen in driving project success under Shashank Shekhar Katyayan's capable leadership. About Shashank Shekhar Katyayan A distinguished professional in advanced analytics and machine learning, Shashank Shekhar Katyayan has established himself as a leading expert in retail personalization and predictive modeling. His comprehensive experience spans the development and implementation of sophisticated analytical solutions for major retail organizations, with a particular focus on personalization at scale in B2C pricing. With deep expertise in machine learning technologies, particularly CatBoost and other gradient boosting frameworks, Shashank has demonstrated exceptional ability in translating complex analytical concepts into tangible business value. His proven track record in leading high-performance teams and delivering significant financial impacts has marked him as an innovative leader in the retail analytics space. Through his work, he has consistently shown an ability to bridge the gap between technical sophistication and practical business application, driving substantial improvements in promotional effectiveness and margin optimization. This story was distributed as a release by Echospire Media under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Echospire Media under HackerNoon’s Business Blogging Program. Learn more about the program here. here here

How Advanced Analytics Delivered $100M in Retail Value: A Personalization Breakthrough by S.Katyayan

የ Advanced Analytics እንዴት $ 100M በሽያጭ ዋጋ ይሰጣል: በ S.Katyayan የተመሠረተ የግል ልምድ

كيف قدمت تحليلات متقدمة 100 مليون دولار في قيمة التجزئة: تحول شخصي من S.Katyayan

Cómo Advanced Analytics entregó 100 millones de dólares en valor minorista: un avance en la personalización de S.Katyayan

Como Advanced Analytics entregou 100 millóns de dólares en valor de venda polo miúdo: un avance na personalización por S.Katyayan

Come Advanced Analytics ha consegnato 100 milioni di dollari di valore al dettaglio: un passo avanti nella personalizzazione di S.Katyayan

How Advanced Analytics Delivered $100M in Retail Value: A Personalization Breakthrough by S.Katyayan(アドバンテージ・アナリティクス)

How Advanced Analytics Delivered $100M in Retail Value: A Personalization Breakthrough by S.Katyayan에 대한 리뷰 보기

Kā Advanced Analytics piegādāja 100 miljonus ASV dolāru mazumtirdzniecības vērtībā: S.Katyayan personalizācijas sasniegums

Bagaimana Analisis Lanjutan Membekalkan $ 100M dalam Nilai Retail: Kejayaan Peribadi oleh S.Katyayan

Hoe Advanced Analytics $ 100 miljoen in Retail Value leverde: een personalisatie doorbraak door S.Katyayan

Как Advanced Analytics предоставила $100 млн в розничной стоимости: прорыв в персонализации S.Katyayan

วิธีการวิเคราะห์ขั้นสูงส่งมอบ 100 ล้านดอลลาร์ในมูลค่าขายปลีก: ประวัติความเป็นส่วนตัวโดย S.Katyayan

In an era where healthcare organizations grapple with the dual challenges of data management and operational efficiency, the remarkable transformation at Federally Qualified Health Center (FQHC), stands as a testament to innovative data engineering and visionary leadership. Under the guidance of Bhumika Shah, a pioneering data engineer and AI professional, this ambitious data infrastructure project has set new benchmarks for healthcare analytics and operational excellence, demonstrating the transformative power of strategic data management in modern healthcare delivery. The comprehensive database system implementation, serving a diverse workforce of 500 healthcare professionals, emerged as a significant challenge in healthcare data management. Healthcare organizations traditionally struggle with fragmented data systems, manual reporting processes, and the critical need for real-time access to patient care metrics. Bhumika faced the complex task of integrating multiple data streams while maintaining data integrity, ensuring HIPAA compliance, and providing seamless access to critical healthcare metrics – a challenge that demanded both technical expertise and deep understanding of healthcare operations. At the heart of this success story was a methodical approach to data architecture and system design that went far beyond simple automation. Bhumika implemented a sophisticated solution leveraging SQL Server Management Studio (SSMS), SQL Server Reporting Services (SSRS), and Power BI, creating a robust and scalable infrastructure that could adapt to evolving healthcare needs. Through strategic integration with the Electronic Medical Record (EMR) system's API, she established an automated data pipeline that transformed time-consuming manual processes into efficient, real-time reporting systems. The technical implementation demonstrated exceptional attention to detail and forward-thinking design principles. Bhumika's solution incorporated advanced data modeling techniques, optimized query performance, and implemented sophisticated data validation protocols to ensure accuracy and reliability. The system's architecture was carefully planned to handle the complex hierarchical relationships common in healthcare data while maintaining optimal performance under heavy user loads. The project's success in data preparation and staging showcased Bhumika's expertise in handling healthcare-specific data challenges. She implemented specialized ETL processes that could handle diverse data types, from structured patient demographics to complex clinical metrics. The solution included robust error handling mechanisms and data quality checks, ensuring that the automated systems maintained the highest standards of data integrity – a critical requirement in healthcare settings. The impact of this implementation extended far beyond technical metrics, touching every aspect of the organization's operations. Through innovative automation and streamlined workflows, the project achieved a remarkable 60% reduction in report generation time, transforming what was once a bottleneck into a seamless process. Perhaps most notably, this transformation saved approximately 30 hours of manual reporting effort monthly while maintaining impeccable data accuracy – a significant accomplishment that allowed healthcare professionals to focus more time on patient care rather than administrative tasks. The visualization component of the project demonstrated particular innovation in healthcare analytics. Bhumika's implementation of Power BI dashboards provided intuitive access to complex healthcare metrics, enabling real-time monitoring of key performance indicators (KPIs) and facilitating data-driven decision-making across all organizational levels. The dashboards were carefully designed to balance comprehensive data presentation with user-friendly interfaces, ensuring that healthcare professionals could quickly access and interpret critical information without extensive training. Stakeholder engagement played a crucial role in the project's success, with Bhumika demonstrating exceptional ability in understanding and addressing diverse user needs. The implementation process included careful consideration of various departmental requirements, from clinical staff needing quick access to patient metrics to administrative teams requiring detailed operational reports. This comprehensive approach resulted in a 20% increase in operational transparency, enabling better resource allocation and improved patient care outcomes. The achievement has demonstrated the transformative potential of data engineering in healthcare settings, establishing new standards for what's possible in healthcare analytics. Bhumika's implementation has become a benchmark for healthcare analytics solutions, showcasing how effective technical leadership and strategic system design can deliver exceptional results across multiple performance indicators. The project's success has drawn attention from other healthcare organizations looking to achieve similar transformations in their data management practices. The automation architecture implemented by Bhumika showed particular innovation in its use of SQL Server Agent's job scheduler. The system was designed to handle complex scheduling requirements while maintaining system performance and data accuracy. This included implementing sophisticated error handling and notification systems, ensuring that any potential issues could be quickly identified and addressed before impacting healthcare operations. For Bhumika Shah personally, the project represented a significant career milestone, showcasing her ability to bridge complex technical challenges while delivering substantial operational improvements. As a certified Scrum Master and Six Sigma Green Belt holder pursuing her Ph.D. in Information Technology, Bhumika brings a unique combination of technical expertise and healthcare domain knowledge to her work. Her approach to this project demonstrated how academic research could be effectively applied to solve real-world healthcare challenges. The project's success has implications beyond technical achievement, highlighting the importance of understanding healthcare-specific challenges in data management. Bhumika's implementation considered various healthcare-specific requirements, from ensuring HIPAA compliance to handling complex clinical workflows. The solution's architecture incorporated healthcare industry best practices while introducing innovative approaches to common challenges in healthcare data management. Looking ahead, the implications of this project success extend beyond immediate achievements. It demonstrates how effective data engineering can overcome complex healthcare operational challenges while delivering exceptional value to stakeholders. The solution's scalable architecture ensures that it can adapt to future healthcare needs, incorporating new data sources and analytics capabilities as they emerge. This forward-thinking approach positions Premier Health Center for continued success in an increasingly data-driven healthcare environment. As the healthcare sector continues to digitize, the Premier Health Center project stands as a model for future healthcare analytics implementations, showcasing the powerful combination of technical innovation, operational excellence, and healthcare domain expertise under Bhumika Shah's capable leadership. The project's success demonstrates how strategic technical leadership, combined with deep healthcare operational understanding, can transform healthcare analytics and improve patient care delivery. About Bhumika Shah A distinguished professional in healthcare data engineering, Bhumika Shah has established herself as a leading expert in analytics and system implementation. Her comprehensive experience spans across major organizations where she has consistently delivered innovative solutions to complex data challenges. With advanced certifications including Six Sigma Green Belt and Scrum Master, combined with ongoing Ph.D. research in Information Technology, Bhumika has demonstrated exceptional ability in optimizing healthcare operations through innovative data solutions. Bhumika Shah's academic contributions extend beyond practical implementations. Her published work on "Effective Leadership in Organization" in EJBMR has contributed valuable insights to the field of organizational leadership, while her ongoing research continues to advance the understanding of healthcare analytics. Her work consistently demonstrates the powerful intersection of academic research and practical application in healthcare settings. As a thought leader in healthcare data engineering, Bhumika continues to inspire the next generation of data professionals through her innovative approaches to healthcare analytics. Her success at Premier Health Center serves as a compelling example of how technical expertise, combined with healthcare domain knowledge, can drive meaningful improvements in healthcare delivery and patient outcomes. Her ongoing work promises to further advance the field of healthcare analytics, setting new standards for excellence in healthcare data management and analysis. This story was distributed as a release by Echospire Media under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Echospire Media under HackerNoon’s Business Blogging Program. Learn more about the program here. here here

Transforming Healthcare Analytics: Bhumika Shah's Impact at Federally Qualified Health Center

Transforming Healthcare Analytics: Bhumika Shah se Impact by Federale Gekwalifiseerde Gesondheidsentrum

"Transforming Healthcare Analytics: Bhumika Shah'ın Federal Qualified Health Center-dəki nəticəsi"

রূপান্তরিত স্বাস্থ্যসেবা বিশ্লেষণ: ফেডারেলভাবে যোগ্য স্বাস্থ্য কেন্দ্রে Bhumika Shah এর প্রভাব

Μετασχηματισμός της ανάλυσης της υγειονομικής περίθαλψης: Ο αντίκτυπος του Bhumika Shah στο Ομοσπονδιακό Πιστοποιημένο Κέντρο Υγείας

Transformando el análisis de la atención médica: el impacto de Bhumika Shah en el centro de salud federalmente calificado

Transformation de l'analyse des soins de santé: l'impact de Bhumika Shah sur le centre de santé qualifié fédéral

「Transforming Healthcare Analytics: Bhumika Shah's Impact at Federally Qualified Health Center」

Аюулгүй байдлын аналитикийг өөрчилж: Бхумика Шахад Федераль ажилтнуудын эрүүл мэндийн төвд нөлөө

Transforming Healthcare Analytics: Bhumika Shahs inverkan på federalt kvalificerade hälsocenter

Transforming Healthcare Analytics: Bhumika Shah's Impact at Federally Qualified Health Center (அரசியல் சிகிச்சை அறிகுறிகள் மாற்ற)

การเปลี่ยนแปลงการวิเคราะห์การดูแลสุขภาพ: ผลกระทบของ Bhumika Shah ในศูนย์สุขภาพที่มีคุณสมบัติทางพันธรัฐ

Sağlık Analizi Değiştirme: Bhumika Şah'ın Federal Nitelikli Sağlık Merkezi Etkisi

Welcome to another weekly round-up from the HackerNoon team celebrating promising startups around the globe. New to HackerNoon Startups of the week series? See our past features here. Each week, the HackerNoon team highlights three startups from our Startups of the Year database. These featured companies have earned nominations in their respective tech categories and/or regions, recognizing them as some of the most promising ventures in the industry. This week, we chose Neo Auth, Peris.ai, and Midas Analytics! Startups of The Year 2024 voting ended on March 31st, 2025. Winners are expected to be announced April 22nd, 2025. Subscribe now to be first notified! Meet Neo Auth As the name suggests, Neo Authis an identity and access management solution. Its core offering, the Neo SSO SDK, uses Attribute-Based Access Control (ABAC) for accurate and secure identity verification. Designed for simplicity, the SDK streamlines access to cloud applications without compromising on security. Based in Toronto Canada, Neo Auth is nominated as a top startup in the city, with additional nominations in the Data Privacy and Compliance and Cybersecurity Services and Products Industries. Meet Peris.ai Peris.ai is based in Singapore, providing experts and solutions in cybersecurity. From their Agentic-AI Hyperautomation Cybersecurity Platform to 1-on-1 human expert consultations, they have all the arrows in their quiver to help organizations defend against cyber threats. They are nominated as the Startup of The Year in the Singapore region, as well as in the Network Security, Cybersecurity Services and Products, and Data Privacy and Compliance industries. Meet Midas Analytics ​Midas Analytics is a Hong Kong-based market intelligence platform that leverages AI to deliver actionable insights. Founded in 2021, Midas combines structured and unstructured data, ranging from financial reports to real-time news, to help businesses make faster, smarter decisions. Midas Analytics is nominated as Startup of The Year in Hong Kong, China, as well as in the Programming and SaaS industries. Want to be featured on HackerNoon’s Startups of the Week? Share your startup's story with our Business Blogging Program! https://business.hackernoon.com/business-blogging?embedable=true What's Business Blogging? HackerNoon’s Business Blogging Program is one of the many ways we help brands grow their reach and connect with the right audience. This program lets businesses publish content directly on HackerNoon to boost brand awareness and build SEO authority by tapping into ours. Here's what you get: Backlinks to your website (yes, including CTAs)
A personalized Tech Company News Page featuring your logo, intro, call-to-action, and socials
Full editorial support to make your story shine
Multiple permanent placements on HackerNoon and social media promotions
Stories converted into audio format and distributed via audio RSS feeds
Automatic translation into 12 languages for global reach
Your brand also gains domain authority and SEO via canonical links and the story is distributed across 8 different relevant keyword/tagged pages for better organic discoverability. Share your startup's story today! That’s all, folks! Please note that the startup's special packages have now ended, and we will be announcing some exciting prizes for the finalists and winners soon! Cheers, The HackerNoon Team About HackerNoon’s Startups of The Year Startups of The Year 2024 is HackerNoon’s flagship community-driven event celebrating startups, technology, and the spirit of innovation. Currently in its third iteration, the prestigious Internet award recognizes and celebrates tech startups of all shapes and sizes. This year, over 150,000 entities across 4200+ cities, 6 continents, and 100+ industries will participate in a bid to be crowned the best startup of the year! Millions of votes have been cast over the past few years, and many stories have been written about these daring and rising startups. The winners will get a free interview on HackerNoon and an Evergreen Tech Company News page. Visit our FAQ page to learn more. Download our design assets here. Check out the Startups of the Year Merch Shop here. HackerNoon’s Startups of The Year is a branding opportunity unlike any other. Whether your goal is brand awareness or lead generation, HackerNoon has curated startup-friendly packages to solve your marketing challenges. Meet our sponsors: Wellfound: Join the #1 global, startup-focused community. At Wellfound, we're not just a job board—we're the place where top startup talent and the world's most exciting companies connect to build the future. Notion: Notion is trusted and loved by thousands of startups as their connected workspace—from building product roadmaps to tracking fundraising. Try Notion with unlimited AI, FREE for up to 6 months, to build and scale your company with one powerful tool. Get your offer now! Hubspot: If you’re looking for a smart CRM platform that meets the needs of small businesses, look no further than HubSpot. Seamlessly connect your data, teams, and customers in one easy-to-use scalable platform that grows with your business. Get started for free. Bright Data: Startups that leverage public web data can make faster, data-driven decisions, giving them a competitive edge. With Bright Data’s scalable web data collection, businesses can grow from a small operation to an enterprise by harnessing insights at every stage. Algolia: Algolia NeuralSearch is the world's only AI end-to-end Search and Discovery Platform combining powerful keyword and natural language processing in a single API. Welcome to another weekly round-up from the HackerNoon team celebrating promising startups around the globe. New to HackerNoon Startups of the week series? See our past features here . Each week, the HackerNoon team highlights three startups from our Startups of the Year database. These featured companies have earned nominations in their respective tech categories and/or regions, recognizing them as some of the most promising ventures in the industry. here Startups of the Year This week, we chose Neo Auth , Peris.ai , and Midas Analytics ! Neo Auth Peris.ai Midas Analytics Startups of The Year 2024 voting ended on March 31st, 2025. Winners are expected to be announced April 22nd, 2025. Subscribe now to be first notified! Startups of The Year 2024 voting ended on March 31st, 2025. Winners are expected to be announced April 22nd, 2025. Subscribe now to be first notified ! Subscribe now to be first notified Subscribe now to be first notified Meet Neo Auth Meet Neo Auth As the name suggests, Neo Auth is an identity and access management solution. Its core offering, the Neo SSO SDK, uses Attribute-Based Access Control (ABAC) for accurate and secure identity verification. Designed for simplicity, the SDK streamlines access to cloud applications without compromising on security. Neo Auth Based in Toronto Canada , Neo Auth is nominated as a top startup in the city, with additional nominations in the Data Privacy and Compliance and Cybersecurity Services and Products Industries. Toronto Canada Data Privacy and Compliance Meet Peris.ai Peris.ai Peris.ai is based in Singapore, providing experts and solutions in cybersecurity. From their Agentic-AI Hyperautomation Cybersecurity Platform to 1-on-1 human expert consultations, they have all the arrows in their quiver to help organizations defend against cyber threats. Peris.ai They are nominated as the Startup of The Year in the Singapore region, as well as in the Network Security , Cybersecurity Services and Products , and Data Privacy and Compliance industries. Singapore Network Security Cybersecurity Services and Products Data Privacy and Compliance Meet Midas Analytics Midas Analytics ​ Midas Analytics is a Hong Kong-based market intelligence platform that leverages AI to deliver actionable insights. Founded in 2021, Midas combines structured and unstructured data, ranging from financial reports to real-time news, to help businesses make faster, smarter decisions. Midas Analytics Midas Analytics is nominated as Startup of The Year in Hong Kong, China , as well as in the Programming and SaaS industries. Hong Kong, China Programming SaaS Want to be featured on HackerNoon’s Startups of the Week? Share your startup's story with our Business Blogging Program! Share your startup's story with our Business Blogging Program! Share your startup's story https://business.hackernoon.com/business-blogging?embedable=true https://business.hackernoon.com/business-blogging?embedable=true What's Business Blogging? What's Business Blogging ? What's Business Blogging HackerNoon’s Business Blogging Program is one of the many ways we help brands grow their reach and connect with the right audience. This program lets businesses publish content directly on HackerNoon to boost brand awareness and build SEO authority by tapping into ours. HackerNoon’s Business Blogging Program HackerNoon’s Business Blogging Program many ways many ways Here's what you get: Here's what you get: Backlinks to your website (yes, including CTAs) A personalized Tech Company News Page featuring your logo, intro, call-to-action, and socials Full editorial support to make your story shine Multiple permanent placements on HackerNoon and social media promotions Stories converted into audio format and distributed via audio RSS feeds Automatic translation into 12 languages for global reach Your brand also gains domain authority and SEO via canonical links and the story is distributed across 8 different relevant keyword/tagged pages for better organic discoverability. Backlinks to your website (yes, including CTAs) A personalized Tech Company News Page featuring your logo, intro, call-to-action, and socials Tech Company News Page Full editorial support to make your story shine Multiple permanent placements on HackerNoon and social media promotions Stories converted into audio format and distributed via audio RSS feeds audio format Automatic translation into 12 languages for global reach 12 languages Your brand also gains domain authority and SEO via canonical links and the story is distributed across 8 different relevant keyword/tagged pages for better organic discoverability. Share your startup's story today! Share your startup's story today! Share your startup's story today! Share your startup's story today! That’s all, folks! Please note that the startup's special packages have now ended, and we will be announcing some exciting prizes for the finalists and winners soon! Cheers, The HackerNoon Team About HackerNoon’s Startups of The Year Startups of The Year 2024 is HackerNoon’s flagship community-driven event celebrating startups, technology, and the spirit of innovation. Currently in its third iteration, the prestigious Internet award recognizes and celebrates tech startups of all shapes and sizes. This year, over 150,000 entities across 4200+ cities, 6 continents, and 100+ industries will participate in a bid to be crowned the best startup of the year! Millions of votes have been cast over the past few years, and many stories have been written about these daring and rising startups. Startups of The Year 2024 many stories The winners will get a free interview on HackerNoon and an Evergreen Tech Company News page. free interview Evergreen Tech Company News Visit our FAQ page to learn more. FAQ Download our design assets here . here Check out the Startups of the Year Merch Shop here . here HackerNoon’s Startups of The Year is a branding opportunity unlike any other. Whether your goal is brand awareness or lead generation, HackerNoon has curated startup-friendly packages to solve your marketing challenges. startup-friendly packages Meet our sponsors: Meet our sponsors: Wellfound: Join the #1 global, startup-focused community . At Wellfound, we're not just a job board—we're the place where top startup talent and the world's most exciting companies connect to build the future. Wellfound: Join the #1 global, startup-focused community Notion: Notion is trusted and loved by thousands of startups as their connected workspace—from building product roadmaps to tracking fundraising. Try Notion with unlimited AI, FREE for up to 6 months , to build and scale your company with one powerful tool. Get your offer now ! Notion: Try Notion with unlimited AI, FREE for up to 6 months Get your offer now ! Hubspot: If you’re looking for a smart CRM platform that meets the needs of small businesses, look no further than HubSpot. Seamlessly connect your data, teams, and customers in one easy-to-use scalable platform that grows with your business. Get started for free . Hubspot: Get started for free Bright Data: Startups that leverage public web data can make faster, data-driven decisions, giving them a competitive edge. With Bright Data’s scalable web data collection , businesses can grow from a small operation to an enterprise by harnessing insights at every stage. Bright Data: Bright Data’s scalable web data collection Algolia: Algolia NeuralSearch is the world's only AI end-to-end Search and Discovery Platform combining powerful keyword and natural language processing in a single API. Algolia: AI end-to-end Search and Discovery Platform

Meet Neo Auth, Peris.ai, and Midas Analytics: HackerNoon Startups of the Week 

የ Neo Auth, Peris.ai, እና Midas Analytics ጋር ያነጋግሩ: ሳምንት የ HackerNoon Startups

Neo Auth, Peris.ai এবং Midas Analytics: সপ্তাহের HackerNoon স্টার্টআপস

Conoce a Neo Auth, Peris.ai y Midas Analytics: HackerNoon Startups de la Semana

Neo Auth、Peris.ai、およびMidas Analytics: HackerNoon Startups of the Week

Neo Auth, Peris.ai და Midas Analytics: HackerNoon საწყობები კვირაში

Neo Auth, Peris.ai 및 Midas Analytics: HackerNoon Startups of the Week에 대한 리뷰 보기

Запознајте ги Neo Auth, Peris.ai и Midas Analytics: HackerNoon Startups на неделата

Neo Auth, Peris.ai, Midas Analytics: Хакерноун долоо хоногийн эх үүсвэрүүд

Faceți cunoștință cu Neo Auth, Peris.ai și Midas Analytics: HackerNoon Startups of the Week

Kumuha ng Neo Auth, Peris.ai, at Midas Analytics: HackerNoon Startups ng Semana

Neo Auth، Peris.ai، اور Midas Analytics سے ملاقات کریں: HackerNoon ہفتے کے آغاز

了解Neo Auth,Peris.ai和Midas Analytics:周的HackerNoon初创公司

Stories, News & Mentions about ANALYTIX

Categories

Trending Topics

Stories, News & Mentions about ANALYTIX

Analytix’s Stories on HackerNoon

Categories

Trending Topics