Essay Example on Maximizing Sales with Facebook Ads: A Comprehensive Guide

Introduction

In any business, it is quite essential to be able to reach the target audience to maximize sales and profits. With more than a billion monthly users, Facebook gives a unique platform for advertisers to gather potential clients’ data, analyze the target by their demographics, and advertise to them directly. Compared to content marketing and Search Engine Optimization (SEO), Facebook ads provide substantial and rewarding results within a short time (Trattner & Kappe, 2013). The right ad needs to be placed to the right audience, which will undoubtedly spike the sales at low acquisition costs. Leveraging Facebook ads data has several purposes. First, a business needs to automate the manual process of duplicating successful campaigns. This is the scenario where one can make an exact copy of a Facebook ad that is active, archived, or paused. More details will be discussed in the main body of the paper. Secondly, leveraging allows a business to pause a failed campaign. Pausing is done when an ad is not performing as predicted. Thirdly, leveraging can be used to increase budgets for scaling (Carter, Levy, & Levy, 2012).

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Using Python API on Facebook gives marketers an essential tool to perform the above purposes. Through Python, marketers can be able to perform predictive analysis for the target market. Predictive analysis helps vendors enrich their customers' profiles, measure the performance of the available ads, and provide analytics insight. Predictive analytics through Python makes marketing campaign to focus more on the customers’ behaviors. The objective is to customize the Facebook ads to target the best segment of the customers’ base. If this is done effectively, there is a high probability that the segment will perform the action that the marketer is highly hoping for-buying. Determining the segment that will respond best to the ad will save the business acquisition costs and improve efficiency. Facebook offers a Python API extension (Facebook Business Python Software Development Kit) to interact with the data in it and filter the required information (Freelon, 2018). This paper will describe how Python is used to achieve automation in Facebook ads and its advantages over other data modeling tools. We shall go into detail to explain how Python collects data, manipulates it, determine and evaluate the trends, and measure performance.

Building a Predictive Model

The first step towards data exploration in Python involves performing three tasks:

• Identifying ID, Target features, and Input.
• Classifying numerical and categorical features.
• Connecting columns with missing values.

A marketer can create a Python code to extract data from Facebook by registering as a developer. Through this, the marketer will be given an access token. Various steps should be followed to access the token. First, the marketer will have to create an account on the developers’ extension on Facebook (Russell, 2013). On creating the account, the marketer will need to click the tools bar and go to "explorer." On this page, the marketers will be able to create "App ID," where they will choose a display name and the category. At this point, the app will be accessed from the explorers' page, where the marketer can access the "User Token." Next, the marketer needs to download urllib3, Facebook, and request libraries and define a variable token. The next step is to set the value of this token to the one set in the previous step – “User Access Token.” From this, Python will be able to access all the public data required from Facebook – events, pages, groups, etc.

To find the potential clients' information, the marketer can be able to search; for example, if the marketer deals with furniture, he can search for the term “furniture.” The python code will filter all the data related to this search item (Russell, 2013). Some of the variables that can be set include the number of clicks on the ad, timestamp, daily internet usage, age, gender, daily time spent on site, region/country, Ad Topic Line, and area income. For instance, let’s assume that most of the people who clicked the ad and spent the most time on the site are aged between 30-40 years. If the smallest area income is $10,000 and the highest income is $120,000, then the marketer will use the first two variables to select the target population.

Predicting the Success of a Marketing Campaign

Advertisers can create a Python algorithm to predict the success of a campaign. The decision to duplicate or pause an ad will depend on the variables set on the algorithm (Freelon, 2018). The main variable that a marketer should rely on is the first one listed: number of clicks. There are two possible outcomes in this variable: 1 or 0, where 1 refers to the case where a potential client clicked the ad, while 0 means that the client did not click the ad. The other variables are used to predict the main variable through which the marketer can get a clear picture of the target population.

The decision to pause an ad or duplicate it will depend on the number of clicks on the ad. Python can be used to analyze further the data, e.g., by plotting a histogram with Kernel density estimation for any of the variables (Russell, 2013). Let’s take, for instance, the "income." If the histogram displays that other people outside the 30-40 years range clicked the ad, but in the same salary range, the ad can be duplicated to this demographic. The marketer decides to pause an ad when fewer people than expected clicks on the ad in a given timeframe.

Increasing Budget for Scaling

A marketer can decide to increase or decrease the budget for a particular campaign depending on the predictors set (Chatterjee & Krystyanczuk, 2017). This is done by using the Python algorithm to analyze the variable that has either the highest number of clicks or the lowest. For instance, the budget can be increased for the “age” variable if this is where the highest number of clicks occurred, and decrease the budget for the “gender” if this is where the lowest number of clicks occurred.

Conclusion

Python can enable marketers to do quite a lot with the data provided on Facebook’s Python API. Python generates a standard performance report that allows the marketer to run attribution models for the targeted market. However, Python needs to update the version to work on the keyword and ad-group level. Presently, Python algorithms only calculate modifiers on the campaign level and then assigns them to every keyword in this campaign. Also, a future version should enable the script to factor on the day of the week. Although the conversion rate from search engine marketing does not change much daily, this data would be beneficial to some businesses.

References

Carter, B., Levy, J., & Levy, J. R. (2012). Facebook Marketing: Leveraging Facebook's features for your marketing campaigns. Que Publishing.

Chatterjee, S., & Krystyanczuk, M. (2017). Python Social Media Analytics. Packt Publishing Ltd.

Freelon, D. (2018). Computational research in the post-API age. Political Communication, 665-668.

Russell, M. A. (2013). Mining the Social Web: Data Mining, Facebook, Twitter, LinkedIn, Google+, GitHub, and More. O'Reilly Media, Inc.

Trattner, C., & Kappe, F. (2013). Social stream marketing on Facebook: a case study. International Journal of Social and Humanistic Computing, 86-103.