《Meta Platforms,Inc.（META）2024年第二季度业绩报告「NASDAQ」（英文版）（18页）.pdf》由会员分享，可在线阅读，更多相关《Meta Platforms,Inc.（META）2024年第二季度业绩报告「NASDAQ」（英文版）（18页）.pdf（18页珍藏版）》请在三个皮匠报告上搜索。

1、Meta Earnings Presentation Q2 $12,788$12,766$15,005$12,710$14,131$14,956$17,784$15,451$16,593$6,360$5,707$6,904$6,269$7,268$7,721$9,159$8,327$9,135$5,835$5,717$5,968$5,893$6,435$6,829$7,316$7,338$7,721$3,169$3,047$3,377$3,229$3,664$4,137$4,447$4,519$4,880Q222Q322Q422Q123Q223Q323Q423Q124Q224Advertisi

2、ng Revenue by User GeographyIn MillionsOur revenue by user geography is geographically apportioned based on our estimation of the geographic location of our users when they perform a revenue-generating activity.This allocation differs from our revenue disaggregated by geography disclosure in our con

3、densed consolidated financial statements where revenue is geographically apportioned based on the addressesof our customers.2Rest of WorldAsia-PacificEuropeUS&Canada$28,152$27,237$38,329$31,254$28,101$31,498$33,643$38,706$35,635$13,249$13,035$15,636$13,048$14,422$15,190$18,585$15,824$16,847$6,452$5,

4、797$7,050$6,345$7,323$7,777$9,441$8,483$9,300$5,908$5,782$6,050$5,960$6,515$6,928$7,512$7,481$7,888$3,213$3,100$3,429$3,292$3,739$4,251$4,573$4,667$5,036Q222Q322Q422Q123Q223Q323Q423Q124Q224Revenue by User GeographyIn MillionsOur revenue by user geography is geographically apportioned based on our es

5、timation of the geographic location of our users when they perform a revenue-generating activity.This allocation differs from our revenue disaggregated by geography disclosure in our condensed consolidated financial statements where revenue is geographically apportioned based on the addressesof our

6、customers.3Rest of WorldAsia-PacificEuropeUS&Canada$28,645$28,822$32,165$27,714$31,999$39,071$34,146$40,111$36,455Segment ResultsIn MillionsWe report our financial results based on two reportable segments:Family of Apps(FoA)and Reality Labs(RL).FoA includes Facebook,Instagram,Messenger,WhatsApp,and

7、other services.RL includes our virtual,augmented,and mixed reality related consumer hardware,software,and content.Q222Q322Q422Q123Q223Q323Q423Q124Q224Advertising$28,152$27,237$31,254$28,101$31,498$33,643$38,706$35,635$38,329 Other 218 192 184 205 225 293 334 380 389 Family of Apps Revenue 28,370 27,

8、429 31,438 28,306 31,723 33,936 39,040 36,015 38,718 Reality Labs Revenue 452 285 727 339 276 210 1,071 440 353 Total Revenue$28,822$27,714$32,165$28,645$31,999$34,146$40,111$36,455$39,071 Family of Apps Operating Income$11,164$9,336$10,678$11,219$13,131$17,490$21,030$17,664$19,335 Reality Labs Oper

9、ating(Loss)(2,806)(3,672)(4,279)(3,992)(3,739)(3,742)(4,646)(3,846)(4,488)Total Income from Operations$8,358$5,664$6,399$7,227$9,392$13,748$16,384$13,818$14,847 Operating Margin 29%20%20%25%29%40%41%38%38%418%21%26%21%19%18%19%18%19%30%33%30%33%29%27%26%27%27%12%14%14%11%10%8%8%7%7%10%12%10%10%13%6%

10、6%9%9%Q222Q322Q422Q123Q223Q323Q423Q124Q224Expenses as a Percentage of Revenue5General&AdministrativeMarketing&SalesResearch&DevelopmentCost ofRevenueEffective Tax RateIn Millions,Except for PercentagesQ222Q322Q422Q123Q223Q323Q423Q124Q224Income before provision for income taxes$8,186$5,576$6,149$7,30

11、7$9,293$14,020$16,808$14,183$15,106 Provision for income taxes$1,499$1,181$1,497$1,598$1,505$2,437$2,791$1,814$1,641 Effective Tax Rate 18%21%24%22%16%17%17%13%11%6Q222Q322Q422Q123Q223Q323Q423Q124Q224Net IncomeIn Millions7$12,369$11,583$7,788$5,709$4,652$4,395$6,687$14,017$13,465Q222Q322Q422Q123Q223

12、Q323Q423Q124Q224Diluted Earnings Per Share$2.46$5.16$2.20$1.76$1.64$4.71$5.33$4.39$2.988Q223Q22420232024Capital ExpendituresIn Millions9QuarterlyYTDCapital expenditures for periods presented were related to purchases of property and equipment,net and principal payments on finance leases.$15,187$13,4

13、41$8,472$6,354Q222Q322Q422Q123Q223Q323Q423Q124Q224Family Daily Active People(DAP)In Billions10We define a daily active person(DAP)as a registered and logged-in user of Facebook,Instagram,Messenger,and/or WhatsApp(collectively,our Family of products)who visited at least one of these Family products t

14、hrough a mobile device application or using a web or mobile browser on a given day.The numbers for DAP do not include users on our other products unless they would otherwise qualify as DAP based on their other activities on our Family products.We do not require people to use a common identifier or l

15、ink their accounts to use multiple products in our Family,and therefore must seek to attribute multiple user accounts within and across products to individual people.Our calculations of DAP rely upon complex techniques,algorithms,and machine learning models that seek to estimate the underlying numbe

16、r of unique people using one or more of these products,including by matching user accounts within an individual product and across multiple products when we believe they are attributable to a single person,and counting such group of accounts as one person.As these techniques and models require signi

17、ficant judgment,are developed based on internal reviews of limited samples of user accounts,and are calibrated against user survey data,there is necessarily some margin of error in our estimates.For additional information,see Limitations of Key Metrics and Other Data located in the Appendix of this

18、presentation.In the third quarter of 2022,we updated our Family metrics calculations to maintain calibration of our models against recent user survey data,and we estimate such update contributed an aggregate of approximately 30million DAP to our reported worldwide DAP in September 2022.Beginning in

19、the fourth quarter of 2023,our Family metrics no longer include Messenger Kids users.2.883.273.243.142.963.073.022.933.19Q222Q322Q422Q123Q223Q323Q423Q124Q224Family Average Revenue per Person(ARPP)11Beginning in the first quarter of 2024,we define average revenue per person(ARPP)as our FoA revenue du

20、ring a given quarter,divided by the average of the number of DAP at the beginning and end of the quarter.We have recast ARPP in prior periods for comparative purposes.$11.89$12.33$11.20$10.93$10.42$9.47$10.68$9.44$9.87Q223Q323Q423Q124Q224Ad Impressions Delivered YoY Percentage Change12Worldwide Our

21、ad impressions growth by user geography is geographically apportioned based on our estimation of the geographic location of our users when an ad impression is delivered.Q223Q323Q423Q124Q224Q223Q323Q423Q124Q224US&CanadaEuropeQ223Q323Q423Q124Q224Q223Q323Q423Q124Q224Rest of WorldAsia-Pacific8%34%8%16%1

22、0%20%21%31%26%16%28%6%12%17%25%26%27%31%28%30%39%42%15%17%17%Q223Q323Q423Q124Q224Average Price Per Ad YoY Percentage Change13Worldwide Our average price per ad growth by user geography is geographically apportioned based on our estimation of the geographic location of our users when an ad impression

23、 is delivered.Q223Q323Q423Q124Q224Q223Q323Q423Q124Q224US&CanadaEuropeQ223Q323Q423Q124Q224Q223Q323Q423Q124Q224Rest of WorldAsia-Pacific24%(16)%9%5%10%6%2%(6)%(7)%2%(14)%18%18%14%8%(9)%7%(12)%(3)%(6)%(14)%(22)%4%20%13%AppendixFree Cash Flow ReconciliationIn MillionsQ222Q322Q422Q123Q223Q323Q423Q124Q224

24、Net cash provided by operating activities$12,197$9,691$14,511$13,998$17,309$20,402$19,404$19,246$19,370 Less:Purchases of property and equipment,net 7,528 9,355 8,988 6,823 6,134 6,496 7,592 6,400 8,173 Less:Principal payments on finance leases 219 163 235 264 220 267 307 315 299 Free Cash Flow$4,45

25、0$173$5,288$6,911$10,955$13,639$11,505$12,531$10,898 15Free cash flow(FCF)is a non-GAAP financial measure that has limitations as an analytical tool,and you should not consider it in isolation or as a substitute for analysis of other GAAP financial measures,such as net cash provided by operating act

26、ivities.FCF is not intended to represent our residual cash flow available for discretionary expenses.Some of the limitations of FCF are:(i)FCF does not reflect our future contractual commitments,and(ii)other companies in our industry present similarly titled measures differently than we do,limiting

27、their usefulness as comparative measures.Limitations of Key Metrics and Other Data 16Family MetricsThe numbers for our key metrics are calculated using internal company data based on the activity of user accounts.We report our estimates of the numbers of our daily active people(DAP)and average reven

28、ue per person(ARPP)(collectively,our Family metrics)based on the activity of users who visited at least one of Facebook,Instagram,Messenger,and WhatsApp(collectively,our Family of products)during the applicable period of measurement.While these numbers are based on what we believe to be reasonable e

29、stimates of our user base for the applicable period of measurement,there are inherent challenges in measuring usage of our products across large online and mobile populations around the world.The methodologies used to measure these metrics require significant judgment and are also susceptible to alg

30、orithm or other technical errors.In addition,we are continually seeking to improve our estimates of our user base,and such estimates may change due to improvements or changes in our methodology.We regularly review our processes for calculating these metrics,and from time to time we discover inaccura

31、cies in our metrics or make adjustments to improve their accuracy,which can result in adjustments to our historical metrics.Our ability to recalculate our historical metrics may be impacted by data limitations or other factors that require us to apply different methodologies for such adjustments.We

32、generally do not intend to update previously disclosed Family metrics for any such inaccuracies or adjustments that are within the error margins disclosed below.In addition,our Family metrics estimates will differ from estimates published by third parties due to differences in methodology or other f

33、actors such as data limitations or other challenges in measuring large online and mobile populations.For example,our methodologies include measurements of our user base that have in some instances exceeded estimates of addressable online and mobile populations that are based on data published by thi

34、rd parties.Many people in our community have user accounts on more than one of our products,and some people have multiple user accounts within an individual product.Accordingly,for our Family metrics,we do not seek to count the total number of user accounts across our products because we believe tha

35、t would not reflect the actual size of our community.Rather,our Family metrics represent our estimates of the number of unique people using at least one of Facebook,Instagram,Messenger,and WhatsApp.We do not require people to use a common identifier or link their accounts to use multiple products in

36、 our Family,and therefore must seek to attribute multiple user accounts within and across products to individual people.To calculate these metrics,we rely upon complex techniques,algorithms and machine learning models that seek to count the individual people behind user accounts,including by matchin

37、g multiple user accounts within an individual product and across multiple products when we believe they are attributable to a single person,and counting such group of accounts as one person.These techniques and models require significant judgment,are subject to data and other limitations discussed b

38、elow,and inherently are subject to statistical variances and uncertainties.We estimate the potential error in our Family metrics primarily based on user survey data as described further below,which itself is subject to error as well.While we expect the error margin for our Family metrics to vary fro

39、m period to period,we estimate that such margin generally will be approximately 3%of our worldwide DAP.At our scale,it is very difficult to attribute multiple user accounts within and across products to individual people,and it is possible that the actual numbers of unique people using our products

40、may vary significantly from our estimates,potentially beyond our estimated error margins.As a result,it is also possible that our Family metrics may indicate changes or trends in user numbers that do not match actual changes or trends.Limitations of Key Metrics and Other Data 17To calculate our esti

41、mates of DAP,we currently use a series of machine learning models that are developed based on internal reviews of limited samples of user accounts and calibrated against user survey data.We apply significant judgment in designing these models and calculating these estimates.For example,to match user

42、 accounts within individual products and across multiple products,we use data signals such as similar device information,IP addresses,and user names.We also calibrate our models against data from periodic user surveys of varying sizes and frequency across our products,which survey questions are base

43、d on monthly usage,and which are inherently subject to error.The timing and results of such user surveys have in the past contributed,and may in the future contribute,to changes in our reported Family metrics from period to period.In addition,our data limitations may affect our understanding of cert

44、ain details of our business and increase the risk of error for our Family metrics estimates.Our techniques and models rely on a variety of data signals from different products,and we rely on more limited data signals for some products compared to others.For example,as a result of limited visibility

45、into encrypted products,we have fewer data signals from WhatsApp user accounts and primarily rely on phone numbers and device information to match WhatsApp user accounts with accounts on our other products.Any loss of access to data signals we use in our process for calculating Family metrics,whethe

46、r as a result of our own product decisions,actions by third-party browser or mobile platforms,regulatory or legislative requirements,or other factors,also may impact the stability or accuracy of our reported Family metrics,as well as our ability to report these metrics at all.Our estimates of Family

47、 metrics also may change as our methodologies evolve,including through the application of new data signals or technologies,product changes,or other improvements in our user surveys,algorithms,or machine learning that may improve our ability to match accounts within and across our products or otherwi

48、se evaluate the broad population of our users.In addition,such evolution may allow us to identify previously undetected violating accounts(as defined below).We regularly evaluate our Family metrics to estimate the percentage of our DAP consisting solely of violating accounts.We define violating acco

49、unts as accounts which we believe are intended to be used for purposes that violate our terms of service,including bots and spam.In the first quarter of 2024,we estimated that less than 3%of our worldwide DAP consisted solely of violating accounts.Such estimation is based on an internal review of a

50、limited sample of accounts,and we apply significant judgment in making this determination.For example,we look for account information and behaviors associated with Facebook and Instagram accounts that appear to be inauthentic to the reviewers,but we have limited visibility into WhatsApp user activit

51、y due to encryption.In addition,if we believe an individual person has one or more violating accounts,we do not include such person in our violating accounts estimation as long as we believe they have one account that does not constitute a violating account.From time to time,we disable certain user

52、accounts,make product changes,or take other actions to reduce the number of violating accounts among our users,which may also reduce our DAP estimates in a particular period.We intend to disclose our estimates of the percentage of our DAP consisting solely of violating accounts on an annual basis.Vi

53、olating accounts are very difficult to measure at our scale,and it is possible that the actual number of violating accounts may vary significantly from our estimates.User GeographyOur estimates for revenue by user location,as well as year-over-year percentage changes in ad impressions delivered and

54、the average price per ad by user location,are also affected by data limitations and other challenges in measuring user geography.Our data regarding the geographic location of our users is estimated based on a number of factors,such as the users IP address and self-disclosed location.These factors ma

55、y not always accurately reflect the users actual location.For example,a user may appear to be accessing our products from the location of the proxy server that the user connects to rather than from the users actual location.The methodologies used to measure our metrics are also susceptible to algorithm or other technical errors.Meta Earnings Presentation Q2