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Sample Report_Global Buy Now Pay Later Industry and Trends 2021_by yStats.comyStats.com
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-What are the Buy Now Pay Later Trends and Benefits for Merchants in 2021?
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Sample Report_Global Blockchain and Cryptocurrency Market 2021_by yStats.comyStats.com
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-How are the Global Developments of the Europe Cross-Border B2C E-Commerce Market 2021?
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Sample Report_Global Subscriptions and Recurring Payments Market 2021_by ySta...yStats.com
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Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
4. 4
GENERAL METHODOLOGY OF MARKET REPORTS
Secondary Market Research
The reports are compiled based on secondary market research. Secondary research is information gathered from previously published sources. Our reports
are based on information and data acquired from national and international statistical offices, industry and trade associations, business reports, business and
company databases, journals, company registries, news portals and many other reliable sources. By using various sources we ensure maximum objectivity
for reported data. As a result, companies gain a precise and unbiased impression of the market situation.
Cross referencing of data is conducted in order to ensure validity and reliability.
The source of information and its release date are provided on every chart. It is possible that the information contained in one chart is derived from several
sources. If this is the case, all sources are mentioned on the chart.
The reports include mainly data from the last 12 months prior to the date of report publication. Exact publication dates are mentioned in every chart.
Definitions
The reports take into account a broad definition of B2C E-Commerce, which might include mobile commerce. As definitions may vary among sources, exact
definition used by the source (if available) is included at the bottom of the chart.
Chart Types
Our reports include text charts, pie charts, bar charts, rankings, line graphs and tables. Every chart contains an Action Title, which summarizes the main
idea/finding of the chart and a Subtitle, which provides necessary information about the country, the topic, units or measures of currency, and the applicable
time period(s) to which the data refers. With respect to rankings, it is possible that the summation of all categories amounts to more than 100%. In this case,
multiple answers were possible, which is noted at the bottom of the chart.
Report Structure
Reports are comprised of the following elements, in the following order: Cover page, preface, legal notice, methodology, definitions, table of contents,
management summary (summarizing main information contained in each section of report) and report content (divided into sections and chapters). When
available, we also include forecasts in our report content. These forecasts are not our own; they are published by reliable sources. Within Global and
Regional reports, we include all major developed and emerging markets, ranked in order of importance by using evaluative criteria such as sales figures.
Notes and Currency Values
If available, additional information about the data collection, for example the time of survey and number of people surveyed, is provided in the form of a note.
In some cases, the note (also) contains additional information needed to fully understand the contents of the respective data.
When providing information about amounts of money, local currencies are most often used. When referencing currency values in the Action Title, the EUR
values are also provided in brackets. The conversions are made using the average currency exchange rate for the respective time period. Should the
currency figure be in the future, the average exchange rate of the past 12 months is used.
5. 5
METHODOLOGY OF THE CURRENT REPORT
Report Coverage
This report covers European buy now, pay later market. The report covers activities of various governments, businesses, banks, and market trends, as well as
consumers preferences and demand drivers.
The following countries were mainly considered in this market report: Austria, France, Germany, Poland, Russia, Switzerland, and the UK. Moreover, the report
provides an overview of two major BNPL service providers active in Europe: Klarna and Afterpay. The choice of the companies was based on the number of
customers their serve, as well as on the fact that all three have different countries of origin.
The report contains relevant information about regulations, international comparisons, market trends, BNPL adoption rates and usage, consumer preferences,
and players. Not all types of information were included for each country, due to varying data availability.
Report Structure
The report starts with an overview of the global overview of the regulations applied to the BNPL service providers, BNPL trends and benefits for merchants.
Regional chapter follow the global overview and include information on countries’ activities, consumers’ attitude towards BNPL services, and other data points.
The market report concludes with the profiles of two major BNPL service providers and their activities during the COVID-19 health crisis.
6. 6
DEFINITIONS
B2C E-COMMERCE
the sale of products (and services) through electronic transactions via the Internet from businesses to
consumers (B2C). A broader definition of retail E-Commerce applied by some sources also includes consumer-
to-consumer (C2C) sales.
E-COMMERCE SALES
the total sales generated either on the E-Commerce market in a certain country or region, or the total sales
generated through E-Commerce by a player on the market.
CROSS-BORDER B2C E-COMMERCE
the sale of goods (and services) directly to consumers in other countries, with orders delivered from the country
where the foreign shop is based or from a central logistics facility, i.e. across the country’s borders. Both B2C
and C2C transactions might be included in the definition of retail cross-border E-Commerce.
FIAT MONEY inconvertible paper money made legal tender by a government decree
INTERNET USERS the total number of inhabitants in a certain country that regularly accesses the Internet.
ONLINE SHOPPERS the total number of inhabitants in a certain country or region that participates in B2C E-Commerce.
E-COMMERCE MARKETPLACE
platforms where third-party merchants (B2C) or individuals (C2C) can sell their digital or physical products
online whereas the marketplace operator acts as an intermediary.
BUY NOW, PAY LATER (BNPL)
a payment platform or service that allows shoppers to receive goods/services prior to making a payment by
splitting the payment across a set time period or pay back the full amount later
The following expressions and definitions are used in this market report*:
Note: *the definitions used by the original sources might differ from the ones stated on this chart; the exact definition used by each source, where available,
is included on the respective charts
7. 7
TABLE OF CONTENTS (1 OF 3)
1. Management Summary
2. Global Overview
• BNPL Regulations Overview, March 2021
• BNPL Trends and Benefits for Merchants, March 2021
• Breakdown of Payment Methods When Booking a Service Related to Travel, incl. BNPL, in % of Adult Travelers, 2020
3. Europe
• Share of Cross-Border BNPL, in % of Total Cross-Border BNPL Transaction Value, 2025f
• Overview of BNPL Startups, incl. Tymit, Montonio, Revo, Zilch, and Alma, March 2021
• Breakdown of Select Payment Methods Offered by Merchants, by “Growth” and “Non-Growth” Mindsets of Merchants, incl. BNPL,
in %, September 2020
• Breakdown of Credit and BNPL Providers Used, in % of Respondents, October 2020
• Share of Consumers Who Would Like BNPL Added to the Credit Card or Current Account, in % of BNPL Users, October 2020
• Share of BNPL Users who Prefer to Pay via Installments, and in One Later Lump-Sum Payment, in %, October 2020
• Average Customer Debt, in GBP, by BNPL Service Providers, and by Age Groups, August 2020
• Average BNPL Time to Pay Back, in months, by BNPL Service Providers, and by Age Groups, August
• Share of Respondents Who Spent More due to BNPL Option Available, and Share of Respondents Who Missed a BNPL
Payment, in %, October 2020
• Breakdown of BNPL Usage Cases, in % of BNPL Users, August 2020
• Breakdown of Payment Methods in Online Shopping, in % of the Total E-Commerce Transaction Value, July 2020
• Breakdown of Payment Methods Used for Online Purchases, in % of Respondents, December 2020
• Breakdown of Reasons to Choose an Online Payment Methods, in % of Respondents, December 2020
• Overview of Oney Bank’ BNPL Activity, March 2021
• Breakdown of Top Requirements to a BNPL provider, in % of Respondents, 2020
• Breakdown of the Channels of the Last BNPL Payment, and the Potential BNPL Channel, in % of Respondents, 2020
• BNPL Adoption Rate, in % of Respondents Aware of BNPL, and Breakdown of Respondents Attitude Towards BNPL Usage in the
Future, by Consumer Type, in % of BNPL Users, 2020
• Breakdown of Payment Methods Used for Online Purchases, in % of Respondents, December 2020
8. 8
TABLE OF CONTENTS (2 OF 3)
3. Europe (cont.)
• Breakdown of Reasons to Choose an Online Payment Method, in % of Respondents, December 2020
• Breakdown of Payment Methods Used for Online Purchases, in % of Respondents, December 2020
• Breakdown of Reasons to Choose an Online Payment Methods, in % of Respondents, December 2020
• Overview of BNPL Service Providers, incl. Alma and PayPal, April 2021
• Breakdown of Payment Methods Offered, incl. Deferred and Installment Payments, in % of Online Retailers, October 2020
• Top 5 Banks Offering BNPL Financing, by Value, in RUB billion, by Market Share, in % of the Total BNPL Financing Value, and
Year-on-Year Change, in %, October 2020
4. Major BNPL Service Provider: Klarna
• Klarna’s Activity Overview, March 2021
• Top 10 CNBC Disruptor Companies, incl. Klarna, June 2020
• Klarna’s Total Net Operating Income, and Net Profit, in SEK thousands, 2016 – 2020
• Overall Number of Klarna’s Users, and Klarna’s Monthly Active App Users, in millions, 2019 & 2020
• Terms of Use of Klarna’s Services, incl. Pay in 4, Pay in 30, Pay Now, and Financing, March 2021
• Share of Klarna’s User Who Ever Missed a BNPL Payment, in %, October 2020
5. Major BNPL Service Provider: Afterpay
• Afterpay’s Activity Overview, March 2021
• Overview of Afterpay’s, PayPal’s and CommBank’s BNPL Service, incl. credit check, individual purchase price, max credit limit,
Use Cases, Payment Plan, and Late Fees, March 2021
• Afterpay’s Underlying Sales, Net Transaction Margin, Gross Losses, Total Income, Active Customers, Merchants, FY 2016 – FY
2020, and Year-on-Year Change, in %, FY 2020
• Share of Transactions via Afterpay that are Paid Back on Time, in %, February 2021
• Afterpay’s Share of Late Fees, in % of Afterpay’s Total Income, February 2021
9. 9
TABLE OF CONTENTS (3 OF 3)
6. Major BNPL Service Provider: Affirm
• Affirm’s Activity Overview, March 2021
• Overview of Affirms’s Key Financial Metrics, incl. GMV, Total Revenue, Transaction Costs, and Adjusted Operating Loss, Q4
2019, Q4 2020, H2 2019, H2 2020
• Overview of Affirms’s Key Financial Metrics, incl. GMV, Total Revenue, Transaction Costs, Adjusted Operating Loss, and
Weighted Average Shares Outstanding, Fiscal Q3 2021e, and Fiscal 2021f
• Affirms’s Active Users, in millions, December 2019, June 2020, and December 2020
7. Payment Facilitators Offering BNPL Services
• Various Facilitators’ BNPL Activity Overview, incl. Stripe, Mastercard, and Shopify, March 2021
• Share of PayPal Credit’s User Who Ever Missed a BNPL Payment, in %, October 2020
10. 10
1.
2.
3.
4.
5.
Management Summary
Global Overview
Europe
Major BNPL Service Provider: Klarna
Major BNPL Service Provider: Afterpay
10 - 13
14 - 18
19 - 44
45 - 52
53 - 58
6.
7.
Major BNPL Service Provider: Affirm
Payment Facilitators Offering BNPL Services
59 - 63
64 - 66
11. 11
Online
59%
In-store
26%
Both online and in-
store
15%
Channel of the potential BNPL
Channel of the last BNPL
Online
56%
In-store
44%
The majority (59%) of respondents in Germany in 2020 chose to use
BNPL online, and 56% would prefer to do so in the future.
Germany: Breakdown of the Channels of the Last BNPL Payment, and the Potential BNPL Channel, in %
of Respondents, 2020
Survey: based on a survey of 495 respondents in Germany for the question of the last BNPL, and 61 respondents for the question of the potential BNPL, conducted in 2020;
question asked: “Which channel did you use to complete the BNPL payment? Which channel would you use to complete a BNPL payment?“
Source: IGH Köln, 2020
12. 12
Ever missed a payment
29%
Other
71%
In the UK, 29% of PayPal Credit’s users admitted in an October 2020
survey that they had at least one BNPL missed payment.
UK: Share of PayPal Credit’s User Who Ever Missed a BNPL Payment, in %, October 2020
Survey: based on a survey of 2,016 UK respondents conducted in October 2020; question asked: “Would you like BNPL on your credit card or in your current account?”
Source: Capco, November 2020