Data Mining in Media Buying

Data Mining is a complex process of analyzing raw data for informative insights to target your ads and campaigns better. Today, information streams are more abundant than ever before. While many marketers are acquainted with the detailed data obtained from platforms like Facebook and Google, you can harness information from your previous campaigns to form strategies based on your own experiences.

However, this tailored marketing, though efficient, walks a thin line. While some users might appreciate the personal touch, feeling that their choices are being anticipated and catered to, others might feel their privacy is invaded. The unease is evident when shoppers express concerns over how their data is harvested and analyzed.

The first step in the process is Data Harvesting, which focuses on extracting data from various sources to facilitate analysis. Then comes actual Data Mining, which analyzes extensive data sets to unearth trends. 

When paired effectively, these processes can be immensely beneficial. However, these practices can infringe upon users’ privacy rights without ethical guidelines.

How to Harvest Data for an Ad Campaign

Advertisers providing the offer usually have a good idea of their target audience and its interests. Partner networks can also provide insights into which products or services are popular with specific audience segments. However, often the information you can get from them is not enough. And to implement data-driven advertisement techniques to get that competitive edge, improve your strategy, and reduce costs and risks with predictive analysis, you require more data. But where does one get it?

The new data protection regulations shift the focus toward first-party data. You can harness it using website analytics tools like Google Analytics, which provides information on visitors, including demographics, behaviors, and interests, upon the user’s permission. Information such as geos and time of action, among others, can be harnessed through your tracker. And you could use heatmaps on your website to track the customer journey and see which elements of your website are more appealing to the visitors. If you use mobile apps – tools like Firebase can provide insights into user behavior. 

Even though third-party data has a bad reputation due to multiple privacy scandals, it’s still available and can be bought through data brokers like Experian or Acxiom. Some publishers provide granular audience data, including reader/viewer interests, typical reading/viewing times, and device types. This type of data is still a valuable source of information to augment your first-party insights. Keep in mind that soon, third-party data might become obsolete, with Google setting the trend by beginning a sunset policy on it in 2024.

If you’re working on social media platforms, you can get insight into your followers and how they interact with your content. Ads Manager software can grant access to data about potential reach and audience demographics. 

You can also conduct competitor analysis using platforms like SimilarWeb or SEMrush to gain insights into competitors’ web traffic and audience. The same tools, among many others, can be used for keyword analysis to understand which keywords are popular within your vertical or product type.

DSPs allow advertisers to utilize real-time bidding in programmatic media buying, offering real-time data on which ads perform well. DMPs allow for consolidating massive data sets from various sources, offering insights like audience overlap and segmentation possibilities.

The Data Mining Process

Data mining can often seem like a sophisticated task exclusive to large corporations. However, with a structured approach, anyone can harness its power. CRISP-DM (Cross Industry Standard Process for Data Mining) is a systematic method with six well-defined stages:

1. Understanding the Objective

At this initial stage, the primary goal is to clarify what you hope to achieve through data mining. Whether boosting sales, identifying potential clients, or refining marketing strategies, the objective should be specific and data-analyzable.

2. Getting Familiar with the Data

Now, you’ll pinpoint the datasets crucial to your goal. For example, if the aim is sales growth, relevant data might include the existing customer count, churn rates, and average transaction values.

Collate this high-quality data in an accessible format. Novices to data mining might find Google Sheets very straightforward. Intermediate users could benefit from tools like HubSpot’s data sync. And solutions like Tableau might be optimal for those well-versed in data mining.

3. Preparing the Data

This step involves refining your data by eliminating duplicates, correcting inaccuracies, and ensuring it mirrors your business landscape faithfully. Utilizing tools like Operations Hub can streamline this process. It’s recommended that his task is performed by a single individual.

4. Building the Model

Using designated software, the data undergoes transformations using algorithms, AI, and machine learning to create associations, classifications, predictions, and groupings. If you’re a beginner, you might use the pivot table, filtering, and data visualization tools in your spreadsheet software.

5. Assessing the Outcome

Upon modeling, it’s essential to scrutinize the results. Do the insights drawn align with and answer the question proposed in the first stage? If they fall short, revisiting the modeling phase is acceptable and often necessary.

6. Implementation Phase

In this concluding stage, consolidate the findings into a comprehensive report and derive actionable strategies based on the data insights. 

The seventh step for media buyers is to A/B test your findings before diving head-first into a campaign.

It seems like everyone is using data mining in their campaigns. Online retailers like Amazon provide personalized recommendations based on an individual’s browsing and purchasing history. Credit card companies use it to identify potential customers, tailor credit offers, or detect fraudulent activities. Airlines and hotels use data mining to offer personalized packages or promotional offers based on the user’s travel history.

Data mining allows media buyers to make more informed decisions, ensuring their campaigns resonate with the targeted audience and deliver a greater ROI. You can unlock meaningful insights from vast data sets through meticulous data harvesting and a structured approach like CRISP-DM. When employed strategically, these insights can vastly improve campaign performance and overall marketing outcomes. Stay tuned for more articles!

Keitaro Team

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