With the breakneck pace of progress in today’s world, any self-sustaining business needs to ensure that they maximize their profits while also cutting down on losses, essentially described as optimization. For the same optimization in advertisements and marketing, various factors, vendors, and methods must be considered before making any significant business decisions. This paper primarily reviews Google AdWords and Meta Ads, two advertising services. Comprehensive reviews of all factors, including interest targeting an estimated reach of the advertisement to the target audience, are conducted while comparing the advantages and disadvantages of both platforms.
A marketing strategy known as advertising involves purchasing real estate to advertise a good, bad, or intangible idea. Ads, as they are commonly known, are the real advertising messaging. Advertising aims to reach the demographics most likely to be interested in purchasing a company's goods or services. Delivering the right message to current and potential customers starts with advertising. Advertising serves a variety of purposes, including informing consumers about a product, persuading them that a company's goods or services are the best, enhancing the company's reputation, identifying and generating demand for goods or services, showcasing new applications for existing goods, announcing new goods and services, supporting salespeople's messages, luring customers to a business, and keeping hold of current clients. To reach consumers in the way they desire to be engaged, targeted advertisements rely on data gathered about consumers, employing demographics, interest trends, and behaviour patterns. Utilizing customer data minimizes spending on customers who are unlikely to convert and takes the guessing out of customer acquisition.
When customers search for relevant terms, google ads  allows you to advertise and promote your goods and services. It has the potential to accelerate leads and sales when done correctly. Let's examine google ads, their operation, and the steps you can take to set them up for your company immediately. Facebook advertisements are sponsored messages from businesses that help them target the customers who matter to them the most. They are written in their voice. Advertising objectives are the same goals that advertisers set for their campaigns and then produce commercials inside those campaigns to assist them in achieving those goals.
Feature Comparison of both the platforms
For businesses, items, or services like plumbers that your audience is actively looking for, Google Ads works best.
Facebook Ads, also known as Meta Ads or Meta for Business, work best for promoting products your target market is likely interested in but isn't necessarily actively searching for, like new headphones.
Google Ads work best for businesses offering goods or services consumers actively seek out and want or need. These advertisements primarily target consumers who are in the purchase stage. Thus the most effective ad language will be concise and straightforward, making it simple to contact or visit and emphasizing the apparent benefits of the brand's product or service.
The easiest way for companies to spread the word about their brand or the goods or services they sell, particularly to potential new customers, is through Facebook ads.
Facebook offers advertisers a wide range of audience targeting options, enabling them to expose their products to the intended target population through text, images, and engaging video ads.
III. MATHEMATICAL FACTORS FOR COMPARISON
A. Pay-Per-Click (PPC)
PPC  is an online form of paid advertising that is settled after a user clicks on the advertisement. Paid search is the term used for PPC in search engines and is one of many PPC models.
According to the most recent definition of PPC, it is a brief, typically text-based form of advertising that appears in search engines and is dependent on keywords.
Additionally, it is only paid for user clicks. AdWords, the PPC component of Google's search engine, is undoubtedly more popular in Europe than other recognized e-marketing channels.
Short-term activities and the requirement to optimize campaign parameters to maintain high efficiency are the main drawbacks of PPC.
B. Conversion Rate (CR)
The conversion rate (CR), which compares the number of people who click on an advertisement to the number of people who complete a specific task, like making a purchase, accurately describes the effectiveness of advertising activities in search engines.
C. Click-Through Rate (CTR)
The frequency with which the click measures Internet users who have viewed the advertising click on it through rate. The success of keywords and the overall campaign is assessed using CTR in PPC advertising. The CTR measures the proportion of ad clicks to views.
A high CTR shows that people believe the relevant advertising is relevant to their queries. In the AdWords advertising ecosystem, CTR is crucial. It simultaneously affects the quality of the results for the campaign as a whole and the keywords themselves.
D. Cost per Click (CPC)
You pay for each click on your adverts when you use cost-per-click (CPC) bidding. Suppose you need to utilize Enhanced CPC or make bid modifications. In that case, you set a maximum cost-per-click bid, also known as "max. CPC," which is the most you're prepared to spend for a click on your ad.
Both the ad platforms reviewed in this paper can be compared based on these factors:
Traffic Source: Meta ads are paid social traffic sources, which means the user that comes on social media platforms, i.e., Facebook and Instagram, are served ads in their feed. Whereas Google Ads is a paid search traffic source, the user that searches something on Google is served with an ad related to that search phrase.
Reach: Google handles more than 40,000 search queries per second and around 3.5 billion queries per day, so the traffic is incredibly high compared to social media platforms. Facebook has about 2.9 billion active users, and Instagram has about 1.4 billion active users.
Interactiveness: Facebook and Instagram offer excellent user granularity, which helps publishers target users of a specific type based on their interests. Whereas Google Ads solely relies on keywords where the publisher must bid for the keyword, and ads are shown when that particular keyword is searched.
Available Ad Formats: Both have various formats like images/photos, videos, etc. One that stands out from Google is call-only ads (When people click on the ad, it triggers a phone call directly to your business, driving leads and business. Call ads appear in Google Search, but only on devices that can make phone calls.). In contrast, meta has a few which are pretty impressive, like Carousels (Carousel ads let you showcase up to ten images or videos in a single ad, each with its link. Highlight different products or tell a brand story that develops across each card) and playable (Playable ads offer people an interactive preview before they download an app. Find higher intent users for your app with this try-before-you-buy experience.). Thus, depending on the requirements, one can choose the format.
Return On Investment: Meta Ads offers an excellent return on investment due to its custom user targeting approach. Publishers benefit from testing specific products on a tiny group of people based on interest. Google Ads offers more reach with a high chance of conversion, but this depends on the keyword chosen and the competition.
After a comprehensive review of both platforms, it is found that both have different merits and demerits. Meta ads can serve photo and video ads on two social media platforms, i.e., Facebook and Instagram, in the form of feed posts, stories, and reels. On the other hand, Google AdWords serves three types of ads on its display network, i.e., Textual ads through Google Search, which promote websites for specific keywords to appear on top. These Visual ads display on websites and apps, which Google AdSense monetizes, and Video ads are shown on YouTube before every video. If the publisher wants to promote something based on interests, then Meta Ads are a better custom-target option. If the publisher wants to leverage the traffic from google search, then Google Ads is the better choice. Both platforms have bidding involved in their ranking process, which ranks ads based on the bid placed by the publisher. The higher bidder gets the top spot.
 Yoon, S., Koehler, J., & Ghobarah, A. (2010). Prediction of advertiser churn for google AdWords.
 McMahan, H. B., Holt, G., Sculley, D., Young, M., Ebner, D., Grady, J., ... & Kubica, J. (2013, August). Ad click prediction: a view from the trenches. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1222-1230).
 Szymanski, G., & Lininski, P. (2018, September). Model of the effectiveness of Google Adwords advertising activities. In 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT) (Vol. 2, pp. 98-101). IEEE.
 Shi, L., & Li, B. (2016). Using machine learning methodologies, predict the click-through rate and average cost per click for keywords. In Proceedings of the International Conference on Industrial Engineering and Operations Management; Detroit, Michigan, USA.
 Tahmaz, ?., Ünal?r, M. O., Giray, G., & Koçer, S. (2020, October). Improving Cost Estimation in Internet Advertising Using Machine Learning: Preliminary Results. In 2020 Turkish National Software Engineering Symposium (UYMS) (pp. 1-5). IEEE.
 Mittas, N., & Angelis, L. (2012). Ranking and clustering software cost estimation models through a multiple comparisons algorithm. IEEE Transactions on software engineering, 39(4), 537-551.
 D. Agarwal, B.-C. Chen, and P. Elango. Spatio-temporal models for estimating click-through rate. In Proceedings of the 18th international conference on World wide web, pages 21–30. ACM, 2009.
 R. Ananthanarayanan, V. Basker, S. Das, A. Gupta, H. Jiang, T. Qiu, A. Reznichenko, D. Ryabkov, M. Singh, and S. Venkataraman. Photon: Fault-tolerant and scalable joining of continuous data streams. In SIGMOD Conference, 2013. To appear.
 R. Bekkerman, M. Bilenko, and J. Langford. Scaling up machine learning: Parallel and distributed approaches. 2011.
 B. H. Bloom. Space/time trade-offs in hash coding with allowable errors. Commun. ACM, 13(7), July 1970.
 A. Blum, A. Kalai, and J. Langford. Beating the hold-out: Bounds for k-fold and progressive cross-validation. In COLT, 1999.
 O. Chapelle. Click modeling for display advertising. In AdML: 2012 ICML Workshop on Online Advertising, 2012.
 C. Cortes, M. Mohri, M. Riley, and A. Rostamizadeh. Sample selection bias correction theory. In ALT, 2008.
 J. Dean, G. S. Corrado, R. Monga, K. Chen, M. Devin, Q. V. Le, M. Z. Mao, M. Ranzato, A. Senior, P. Tucker, K. Yang, and A. Y. Ng. Large scale distributed deep networks. In NIPS, 2012.
 T. G. Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine learning, 40(2):139–157, 2000.
 J. Duchi, E. Hazan, and Y. Singer. Adaptive subgradient methods for online learning and stochastic optimization. In COLT, 2010.