Obtain Clients through E-Commerce Data Science

Within the e-commerce industry, there is a widespread belief that all it takes for an ad campaign to be successful is superior creatives. It implies that companies should concentrate on producing excellent creatives and leave the rest to the ad networks. However, in reality, e-commerce companies should not rely solely on ad networks for a number of reasons:

• For e-commerce SMBs, accumulating a substantial amount of historical ad performance data is costly.
• Brands that advertise to different market segments with different products fragment performance data, which can trick the algorithm.

Thankfully, data science can be utilized by marketers and e-commerce executives to surmount these obstacles. This post will describe the workings of ad platform algorithms and offer some doable strategies for enhancing customer acquisition.

How Advertising Platforms Operate

Real-time auctions are used by ad networks to decide which users see which ads. Let us take the example of Meta. The Total Value score is used in its ad auction to determine which ad wins:

Bid × Estimated Action Rate + Relevance and Quality Score = Total Value

• The price an advertiser is willing to pay to get a desired action is known as the bid.
• The estimated probability that a user will take the desired action after viewing the advertisement is known as the "Estimated Action Rate."
• Relevance and Quality Score: The advertisement's level of quality and relevance for the intended user. Brands can affect this score by targeting and producing high-quality ads.

An advertisement has a better chance of getting placed if its Total Value score is higher. A greater Relevance and Quality Score also reduces expenses and aids in securing prominent placements.

Platforms require money and time to learn.

Ad networks use sophisticated machine learning models to target the appropriate audience with their ads. Algorithms go through a learning phase when a new advertisement is released, during which they gather information on how users interact with it. Positive engagement is indicated by interactions like clicks and conversions, which raise the ad's relevance score and create a positive feedback loop. Algorithms keep learning from user interactions after the initial learning phase and dynamically modify ad placements based on the most recent data.

It is not simple to optimize ads.

Our analysis indicates that raising the Relevance and Quality Score and enhancing algorithmic learning are the keys to raising ad performance. But there are obstacles in the way of resolving this issue:

• Relevance of ads is influenced by their delivery and content. A well-made advertisement that is shown to the wrong people will make a campaign less successful.
• Every advertisement produces a unique set of performance data. To determine the appropriate audience for each advertisement, algorithms rely on vast amounts of reliable ad performance data.
• Using a variety of criteria, ad networks segment users to improve the relevance and engagement of their ads. This division, meanwhile, might not always correspond with the optimal consumer groups for an online retailer.

These elements have significant effects on advertising in e-commerce.

Ads that are shown to uninvited audiences cost money.

Think of a sports company that caters to players who are competitive as well as casual. The brand's advertisement for its professional gear will be displayed to a wide audience by the algorithms if it is run without any kind of targeting. Because ad networks do not precisely segment consumers the way brands do, the advertisement may wind up in front of casual users, who are less likely to interact with it. The algorithms receive negative signals from this lack of interaction, which lowers the ad's relevance score and makes it more difficult to get prime ad placements.

Ad updates on a regular basis fragment performance data.

Every month, a clothing company might launch new lines and run advertisements. The algorithms have a hard time gathering enough performance data because each advertisement has a finite budget and runs for a brief amount of time. This makes determining who each advertisement's ideal audience is difficult. As a result, the algorithms display these advertisements to a large number of inappropriate users, which lowers the relevance score of the ad and raises its cost.

Big promotions interfere with algorithmic learning

Brands' advertisements seem more appealing for a short while when they run extensive promotions for important occasions or new product launches. Offering steep discounts can draw in clients who might not have otherwise purchased their goods. The algorithms may become confused by these brief bursts in user interaction and misinterpret normal user behavior as a result. After these large promotions, the cost per acquisition frequently increases, which forces brands to spend a lot of money on advertising to make up for the disruption.

Use data science to address marketing challenges

Fortunately, even with sparse ad performance data, brands can still leverage consumer behavior analysis to steer ad platforms toward the best possible outcomes.

Determine which customer cohorts to target

The purpose of customer segmentation for a brand is to pinpoint specific consumer preferences for messaging, products, and promotions. With this information, the brand can use these insights to enhance its sales and marketing strategies.

Every brand will have different pertinent segmentation factors, so it is critical to take into account as many as you can. Customer segmentation in e-commerce relies heavily on factors like growth trends, purchase patterns, demographics, and geography.

Machine learning models can be used by brands to precisely identify customer cohorts. After this procedure is finished, marketers can use these consumer insights to configure ad targeting and launch successful campaigns.

I was able to pinpoint specific cohorts for a sports brand I worked with according to variables like age, income, and competitiveness. Adults in their middle years who preferred to buy the brand's professional equipment and resided in affluent suburban neighborhoods made up one cohort. Another was young urban professionals who played the sport occasionally and preferred entry-level gear. Thanks to these insights, the brand was able to use age and location data to precisely target different cohorts and feature different products and sports environments in their advertising campaigns.

Create powerful creatives.

To produce an effective advertisement for each customer cohort, a number of factors come together in addition to precise ad targeting:

• Display the appropriate merchandise

Using their sales and marketing data, brands can determine which products are best for each customer cohort. Then, they can feature different products in the ad creatives for each cohort. Younger consumers of a home décor brand might favor items with eye-catching colors at reasonable price points, whereas wealthy consumers might favor items with opulent designs.

• Attend to the pertinent requirements.

When it comes to coming up with messaging ideas based on social listening from reviews and social media posts, the newest AI tools can be quite useful. Young adults are more likely to buy products for energy and fitness from a wellness brand than older retirees who are looking to address health and aging concerns.

• Show off a livable lifestyle

Brands can use sales, demographic, and geographic data to determine the lifestyle of each cohort. Once more, you can create a lifestyle profile for each of your cohorts with the aid of the newest AI tools. When it comes to recreational products, images of active families using the products together may resonate well with parents of small children. Scenes of elderly people spending time with their grandchildren may have a greater emotional resonance with grandparents.

Ads that are most relatable to their target audience are more likely to stand out and be successful, especially in light of today's short attention spans.

Customize your advertising offers.

In today's economy, promotional discounts are becoming more and more common, but brands have many other options for structuring their product offerings. Numerous companies have informed us that while they are aware that certain consumers can buy products without a discount, they are unsure of how to offer various promotions to different market segments. The good news is that new opportunities arise from customer segmentation and targeted advertising.

Offering distinct promotions for every type of product is a practical tactic, as different customer cohorts have varying preferences for different products. For instance, a clothing company may market limited-edition accessories to its wealthy clientele. In the interim, they may give discounts on apparel that appeals more to young adults.

Acquiring customers successfully requires more than just inventiveness.

Ad campaigns with high performance levels are based on well-targeted ads, compelling messaging, and appealing product offerings. Successful customer acquisition cannot be ensured by exceptional creativity alone. Because there are so many variables in ad optimization, brands should always be testing and figuring out what works best for their particular business. Data science can provide e-commerce companies with smart sales and marketing strategies to help them along this path.