#1Meta Earnings Presentation Q4 2023 investor.fb.com Meta#2Advertising Revenue by User Geography In Millions $38,706 $4,447 $33,643 $32,639 $31,254 $31,498 $4,137 $3,220 $7,316 $28,152 $3,377 $28,101 $3,664 $26,998 $27,237 $6,183 $3,169 $3,229 $6,829 $2,949 $3,047 $5,968 $6,435 $5,835 $5,893 $9,159 $5,661 $5,717 $8,174 $6,904 $7,721 $7,268 $6,360 $6,364 $5,707 $6,269 ∞ Meta Rest of World $17,784 Asia-Pacific $15,062 $15,005 $12,024 $12,788 $12,766 $12,710 $14,131 $14,956 Europe US & Canada Q4'21 Q1'22 Q2'22 Q3'22 Q4'22 Q1'23 Q2'23 Q3'23 Q4'23 Our 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 consolidated financial statements where revenue is geographically apportioned based on the addresses of our customers. 2#3Revenue by User Geography In Millions $40,111 $4,573 $34,146 $33,671 $32,165 $31,999 $3,244 $4,251 $7,512 $28,822 $3,429 $28,645 $3,739 $27,908 $27,714 $6,244 $3,213 $3,292 $2,992 $3,100 $6,928 $6,050 $6,515 $9,441 $5,759 $5,908 $5,960 $5,782 $8,357 $7,050 $7,777 $7,323 $6,486 $6,452 $5,797 $6,345 Meta Rest of World $18,585 $15,826 $15,636 Asia-Pacific $12,671 $13,249 $14,422 $15,190 $13,035 $13,048 Europe US & Canada Q4'21 Q1'22 Q2'22 Q3'22 Q4'22 Q1'23 Q2'23 Q3'23 Q4'23 Our 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 consolidated financial statements where revenue is geographically apportioned based on the addresses of our customers. 3#4Segment Results In Millions Meta Q4'21 Q1'22 Q2'22 Q3'22 Q4'22 Q1'23 Q2'23 Q3'23 Q4'23 Advertising $ 32,639 $ 26,998 $ 28,152 $ 27,237 $ 31,254 $ 28,101 $ 31,498 $ 33,643 $ 38,706 Other 155 215 218 192 184 205 225 293 334 Family of Apps 32,794 27,213 28,370 Revenue 27,429 31,438 28,306 31,723 33,936 39,040 Reality Labs Revenue 877 695 452 285 727 339 276 210 1,071 Total Revenue $ 33,671 $ 27,908 $ 28,822 $ 27,714 $ 32,165 $ 28,645 $ 31,999 $ 34,146 $ 40,111 Family of Apps Operating Income $ 15,889 $ 11,484 $ 11,164 $ 9,336 $ 10,678 $ 11,219 $ 13,131 $ 17,490 $ 21,030 Reality Labs Operating (Loss) (3,304) (2,960) (2,806) (3,672) (4,279) (3,992) (3,739) (3,742) (4,646) Total Income from $ Operations 12,585 $ 8,524 $ 8,358 $ 5,664 $ 6,399 $ 7,227 $ 9,392 $ 13,748 $ 16,384 Operating Margin 37% 31% 29% 20% 20% 25% 29% 40% 41% We report our financial results based on two reportable segments: Family of Apps (FOA) and Reality Labs (RL). FoA includes Facebook, Instagram, Messenger, WhatsApp, and other services. RL includes augmented, mixed, and virtual reality related consumer hardware, software, and content. 4#5Expenses as a Percentage of Revenue 12% 10% 10% 8% 10% 13% 14% 14% 11% 10% 12% 6% 6% 12% 10% 8% 8% 13% 30% 33% 33% 28% 30% 29% 27% 26% 21% 26% 22% 21% 21% 19% 18% 19% 18% 19% Q4'21 Q1'22 Q2'22 Q3'22 Q4'22 Q1'23 Q2'23 Q3'23 Q4'23 ∞ Meta General & Administrative Marketing & Sales Research & Development Cost of Revenue 5#6Effective Tax Rate In Millions, Except for Percentages Meta Q4'21 Q1'22 Q2'22 Q3'22 Q4'22 Q1'23 Q2'23 Q3'23 Q4'23 Income before provision $ for income taxes 12,702 $ 8,908 $ 8,186 $ 5,576 $ 6,149 $ 7,307 $ 9,293 $ 14,020 $ 16,808 Provision for income taxes 2,417 1,443 1,499 1,181 1,497 1,598 1,505 2,437 2,791 Effective Tax Rate 19% 16% 18% 21% 24% 22% 16% 17% 17% 6#7Net Income In Millions $10,285 $7,465 $6,687 $5,709 $4,652 $4,395 $7,788 $11,583 $14,017 Q4'21 Q1'22 Q2'22 Q3'22 Q4'22 Q1'23 Q2'23 Q3'23 Q4'23 00 Meta 7#8Diluted Earnings Per Share $5.33 Meta $3.67 $2.72 $2.46 $2.20 $2.98 $4.39 Im...ill Q4'21 $1.76 $1.64 Q1'22 Q2'22 Q3'22 Q4'22 Q1'23 Q2'23 Q3'23 Q4'23 80#9Capital Expenditures In Millions $9,223 $7,899 Q4'22 Q4'23 Quarterly $32,036 $28,103 2022 2023 Annual Capital expenditures for periods presented were related to purchases of property and equipment, net and principal payments on finance leases. Meta 9#10Family Daily Active People (DAP) In Billions 3.02 3.07 3.14 3.19 2.93 2.96 2.82 2.87 2.88 IT Q4'21 Q1'22 Q2'22 Q3'22 Q4'22 Q1'23 Q2'23 Q3'23 Q4'23 DAP/MAP Q4'21 Q1'22 Q2'22 Q3'22 Q4'22 Q1'23 Q2'23 Q3'23 Q4'23 79% 79% 79% 79% 79% 79% 79% 79% 80% Meta We 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 through 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 link 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 number 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 significant 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. We view DAP, and DAP as a percentage of MAP, as measures of engagement across our products. For additional information, see "Limitations of Key Metrics and Other Data" located in the Appendix of this 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 30 million DAP to our reported worldwide DAP in September 2022. Beginning in the fourth quarter of 2023, our Family metrics no longer include Messenger Kids users. 10#11Family Monthly Active People (MAP) In Billions 3.88 3.81 3.71 3.74 3.59 3.64 3.65 Q4'21 Q1'22 Q2'22 Q3'22 Q4'22 I Q1'23 Q2'23 3.96 Q3'23 3.98 Q4'23 Meta We define a monthly active person (MAP) as a registered and logged-in user of one or more Family products who visited at least one of these Family products through a mobile device application or using a web or mobile browser in the last 30 days as of the date of measurement. The numbers for MAP do not include users on our other products unless they would otherwise qualify as MAP based on their other activities on our Family products. We do not require people to use a common identifier or link 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 MAP rely upon complex techniques, algorithms, and machine learning models that seek to estimate the underlying number 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 significant 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. We view MAP as a measure of the size of our global active community of people using our products. For additional information, see "Limitations of Key Metrics and Other Data" located in the Appendix of this 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 40 million MAP to our reported worldwide MAP in September 2022. Beginning in the fourth quarter of 2023, our Family metrics no longer include Messenger Kids users. 11 11#12Family Average Revenue per Person (ARPP) $9.39 $8.63 $8.71 $8.32 $7.91 $7.72 $7.53 $7.59 $10.10 Q4'21 Q1'22 Q2'22 Q3'22 Q4'22 Q1'23 Q2'23 Q3'23 Q4'23 Meta We define average revenue per person (ARPP) as our total revenue during a given quarter, divided by the average of the number of MAP at the beginning and end of the quarter. While ARPP includes all sources of revenue, the number of MAP used in this calculation only includes users of our Family products as described in the definition of MAP in the previous slide. 12#13Facebook Daily Active Users (DAUS) In Millions 1,929 1,960 1,968 1,984 2,000 2,037 2,064 2,085 2,110 619 629 631 638 643 657 665 676 683 Meta 806 827 836 845 854 873 891 899 914 Rest of World Asia-Pacific 309 307 303 303 304 307 307 307 308 Europe 195 196 197 197 199 200 202 203 205 US & Canada Q4'21 Q1'22 Q2'22 Q3'22 Q4'22 Q1'23 Q2'23 Q3'23 Q4'23 DAUS/MAUS Q4'21 Q1'22 66% 67% Q2'22 67% Q3'22 Q4'22 67% 67% Q1'23 68% Q2'23 Q3'23 Q4'23 68% 68% 69% Please see Meta's most recent quarterly or annual report filed with the SEC for definitions of user activity used to determine the number of our Facebook DAUs and MAUS. The numbers for DAUS and MAUS do not include users on Instagram, WhatsApp, or our other products unless they would otherwise qualify as DAUS or MAUS, respectively, based on their other activities on Facebook. 13#14Facebook Monthly Active Users (MAUS) In Millions 2,912 2,936 2,934 2,958 2,963 2,989 3,030 3,049 3,065 945 957 959 971 979 986 1,002 1,013 1,018 1,278 1,297 1,305 1,312 1,312 1,324 1,349 1,357 1,367 Meta Rest of World Asia-Pacific 427 418 407 408 407 411 409 408 408 Europe 262 Q4'21 263 264 266 266 269 270 271 272 US & Canada Q1'22 Q2'22 Q3'22 Q4'22 Q1'23 Q2'23 Q3'23 Q4'23 Please see Meta's most recent quarterly or annual report filed with the SEC for definitions of user activity used to determine the number of our Facebook DAUs and MAUS. The numbers for DAUS and MAUS do not include users on Instagram, WhatsApp, or our other products unless they would otherwise qualify as DAUS or MAUS, respectively, based on their other activities on Facebook. 14#15Facebook Average Revenue per User (ARPU) $68.44 $58.77 $53.53 $56.11 $13.12 $48.85 $10.86 $10.63 $11.23 $9.62 Meta $23.14 $17.29 $17.88 $19.04 $15.51 Q4'22 Q1'23 Q2'23 Q3'23 Q4'23 Q4'22 Q1'23 Q2'23 Q3'23 Q4'23 Q4'22 Worldwide US & Canada Q1'23 Q2'23 Q3'23 Q4'23 Europe $5.52 $4.88 $5.12 $4.50 $4.61 $4.52 $4.22 $3.76 $3.52 $3.35 Q4'22 Q1'23 Q2'23 Q3'23 Q4'23 Asia-Pacific Q4'22 Q1'23 Q2'23 Q3'23 Q4'23 Rest of World We define ARPU as our total revenue in a given geography during a given quarter, divided by the average of the number of MAUS in the geography at the beginning and end of the quarter. While ARPU includes all sources of revenue, the number of MAUS used in this calculation only includes users of Facebook and Messenger as described in the definition of MAU in the previous slide. Our 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 consolidated financial statements where revenue is geographically apportioned based on the addresses of our customers. 15#16Appendix ∞ Meta#17Free Cash Flow Reconciliation In Millions Q4'21 Q1'22 Q2'22 Q3'22 Q4'22 Q1'23 Q2'23 Q3'23 Q4'23 Net cash provided by $ operating activities 18,104 $ 14,076 $ 12,197 $ 9,691 $ 14,511 $ 13,998 $ 17,309 $ 20,402 $ 19,404 Less: Purchases of property and 5,370 5,315 7,528 9,355 8,988 6,823 6,134 6,496 7,592 equipment, net Less: Principal payments on finance leases 172 233 219 163 235 264 220 267 307 Free Cash Flow $ 12,562 $ 8,528 $ 4,450 $ 173 $ 5,288 $ 6,911 $ 10,955 $ 13,639 $ 11,505 Meta Free 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 activities. 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 their usefulness as comparative measures. 17#18Meta Limitations of Key Metrics and Other Data The 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), monthly active people (MAP), and average revenue 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. We have historically reported the numbers of our daily active users (DAUS), monthly active users (MAUS), and average revenue per user (ARPU) (collectively, our "Facebook metrics") based on user activity only on Facebook and Messenger and not on our other products. We believe our Family metrics better reflect the size of our community and the fact that many people are using more than one of our products. While these numbers are based on what we believe to be reasonable estimates 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 algorithm 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 inaccuracies 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 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 and Facebook metrics estimates will differ from estimates published by third parties due to differences in methodology or other factors such as data limitations or other challenges in measuring large online and mobile populations. For example, our Family metrics estimates in some instances exceed estimates of addressable online and mobile populations that are based on data published by third parties. Family Metrics 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 that 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 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 matching 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 below, and inherently are subject to statistical variances and uncertainties. We estimate the potential error in our Family metrics primarily based on user survey data, which itself is subject to error as well. While we expect the error margin for our Family metrics to vary from period to period, we estimate that such margin generally will be approximately 3% of our worldwide MAP. 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 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. 18#19Limitations of Key Metrics and Other Data Meta To calculate our estimates of Family DAP and MAP, 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 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 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 certain 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 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, whether 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 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 otherwise 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 MAP consisting solely of "violating" accounts. We define "violating" accounts as accounts which we believe are intended to be used for purposes that violate our terms of service, including bots and spam. In the fourth quarter of 2023, we estimated that approximately 3% of our worldwide MAP consisted solely of violating accounts. Such estimation is based on an internal review of a 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 activity 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 accounts, make product changes, or take other actions to reduce the number of violating accounts among our users, which may also reduce our DAP and MAP estimates in a particular period. Violating 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. The numbers of Family DAP and MAP discussed in this presentation, as well as ARPP, do not include users on our other products, unless they would otherwise qualify as DAP or MAP, respectively, based on their other activities on our Family products. 19#20Limitations of Key Metrics and Other Data Meta Facebook Metrics We regularly evaluate our Facebook metrics to estimate the number of "duplicate" and "false" accounts among our MAUs. A duplicate account is one that a user maintains in addition to his or her principal account. We divide "false" accounts into two categories: (1) user-misclassified accounts, where users have created personal profiles for a business, organization, or non- human entity such as a pet (such entities are permitted on Facebook using a Page rather than a personal profile under our terms of service); and (2) violating accounts, which represent user profiles that we believe are intended to be used for purposes that violate our terms of service, such as bots and spam. The estimates of duplicate and false accounts are based on an internal review of a limited sample of accounts, and we apply significant judgment in making this determination. For example, to identify duplicate accounts we use data signals such as identical IP addresses and similar user names, and to identify false accounts we look for names that appear to be fake or other behavior that appears inauthentic to the reviewers. Any loss of access to data signals we use in this process, whether 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 estimates of duplicate and false accounts. Our estimates also may change as our methodologies evolve, including through the application of new data signals or technologies or product changes that may allow us to identify previously undetected duplicate or false accounts and may improve our ability to evaluate a broader population of our users. Duplicate and false accounts are very difficult to measure at our scale, and it is possible that the actual number of duplicate and false accounts may vary significantly from our estimates. In the fourth quarter of 2023, we estimated that duplicate accounts may have represented approximately 10% of our worldwide MAUS. We believe the percentage of duplicate accounts is meaningfully higher in developing markets such as the Philippines and Vietnam, as compared to more developed markets. In the fourth quarter of 2023, we estimated that false accounts may have represented approximately 4% of our worldwide MAUS. Our estimation of false accounts can vary as a result of episodic spikes in the creation of such accounts, which we have seen originate more frequently in specific countries such as Indonesia, Vietnam, and Nigeria. From time to time, we disable certain user accounts, make product changes, or take other actions to reduce the number of duplicate or false accounts among our users, which may also reduce our DAU and MAU estimates in a particular period. The numbers of DAUS and MAUS discussed in this presentation, as well as ARPU, do not include users on Instagram, WhatsApp, or our other products, unless they would otherwise qualify as DAUS or MAUS, respectively, based on their other activities on Facebook. User Geography Our data regarding the geographic location of our users is estimated based on a number of factors, such as the user's IP address and self-disclosed location. These factors may not always accurately reflect the user's actual location. For example, a user may appear to be accessing Facebook from the location of the proxy server that the user connects to rather than from the user's actual location. The methodologies used to measure our metrics are also susceptible to algorithm or other technical errors, and our estimates for revenue by user location and revenue by user device are also affected by these factors. 20#21Meta Earnings Presentation Q4 2023 investor.fb.com ∞ Meta