What are the best ways to develop data cleaning skills for marketing campaigns?

In my experience with being a data scientist specifically for performance marketing data, below are my major takeaways: - Standardise Campaign Names: Ensure consistency in campaign names for accurate aggregation. - Handle UTM Parameters: If using UTM parameters then group, clean, and standardize UTM_source, UTM_medium, and UTM_campaign fields. - Attribution Modeling: Choose or refine an attribution model to properly credit conversions to marketing touchpoints.This was a major feature that I handled for Google analytics tracking data. - Remove Test Campaigns Data: Exclude test or internal data from your analysis to prevent skewing results. Lastly I would always cross verify data with other platforms like google analytics, CRM

Data cleaning is one of the crucial steps in the data analysis process because it deals with maintaining data quality. To undergo data cleaning for a marketing campaign, you need to; - Understand the goal and objectives - identify the unnecessary variables - Handling missing values to maintain inconsistencies - identify the outlier - Remove the duplicates in the data The goal is to maintain the data qualities while cleaning the data which brings us back to the understanding of the basic goals of the data.

There's a saying in AI "Garbage in Garbage out". Your AI model will learn what you give it as input/training data. Cleaning the data is one of the most IMP step of the AI lifecycle. ?? Key point: You need to know where your data is coming from, because a lot of other steps will be decided according to your data source. For example if your data is coming from survey forms??, there might be some missing values or duplicates in it, if its coming from customer's review??, it'll need lots of processing to understand and make the reviews usable for any AI models, like removing or modifying #tags, decoding?? abbreviations etc. ?? One should also look at the bigger picture after cleaning the data and make sure that there is no bias in the data.

Data sources are never in good shape we always need to investigate and scrutinize them to ensure: 1- We know are dataset 2- We know its weaknesses 3- We know whether it fits our problem or not Now that we made ourselves acquainted with the dataset we can address its shortcomings and biases.

Start from basics: 1. Ensure date formats are uniform to avoid analytical errors, especially with varying day and month formats for every feature. 2. Confirm numerical data types, using floats for countable numbers and integers otherwise. 3. Standardize variables to maintain a consistent format throughout. Such foundational steps are crucial for accurate and reliable data analysis.

Before you start cleaning your data, you need to define what you mean by "clean data." This will help you to identify the most important problems to fix and to measure your progress. Some common data quality criteria include: Accuracy: The data is correct and consistent with the real world. Completeness: All of the required data is present. Consistency: The data is consistent within itself and across different sources. Timeliness: The data is up-to-date. Uniqueness: Each record is unique and does not represent multiple entities.

Defining data quality criteria is crucial for effective data cleaning in marketing campaigns. Start by identifying key attributes that impact your analysis. These could include accuracy, completeness, consistency, timeliness, and relevance. Set clear standards for each attribute. For example, define what constitutes a complete record, acceptable error rates, or outdated information. Remember, quality criteria may vary based on the specific needs of your campaign. Always align your criteria with your marketing objectives.

??More data isn't always the answer to making the model better ??Only good quality data may give you good results but it'll never help ur model generalize You have a classification model, you have 1000 imgs, 500 imgs for each cat & dog class. While looking into the data one might decide to remove all blurry & small imgs and only keep HD imgs for quality data, but let's be honest, we won't get all HD imgs in the real world. That's where the model will fail. On the other hand one decide to keep all the imgs, even the ones with watermark at the bottom of the img. In this case model might start learning the imgs mainly using the watermark, becase 60% of dog images had watermark. It's a disaster. Find a sweet spot b/w data quality & quality.

La calidad del dato es importante ya sea para marketing o para cualquier otro ámbito o sector. Cualquier error o base de datos sin un buen tratamiento puede generar problemas y por consiguiente no obtener los resultados objetivo.

Defining what is clean data is an important part of a working relationship with your client or business stakeholders - Set up time and attention up front in working with your users to understand what they expect as their final 'clean' data outcome and the importance behind various domains, attributes, and calculations. 'Clean' is subjective but with the right buy in and proper conversations with your stakeholders, you can create trust and buy in on data cleansing and therefore data accuracy.

To develop data cleaning skills for marketing campaigns, it’s essential to use the right tools and techniques. Familiarize yourself with data cleaning tools like Excel, SQL, Python libraries (Pandas), and specialized software like Trifacta or OpenRefine. Learn techniques such as filtering, sorting, deduplication, normalization, and validation. Practice using these tools and techniques on real datasets. Remember, the tool you choose should align with your data’s complexity and the scale of your marketing campaign.

Another key aspect for improving data quality is data de duplication and data standardisation. If the data is sourced from multiple sources, chances are high that not all the data will have the same format and scale or even the metric system used. Another aspect is the normalisation of values across different features as this is very crucial in getting the distance based machine learning algorithms (e.g. k-nearest neighbours) right.

Choosing the right tools and techniques is pivotal in streamlining the data cleaning process. In my experience, the selection depends on factors like dataset size and complexity. For smaller datasets, Excel's user-friendly functions prove handy, while Python, with libraries like pandas, is my go-to for larger, intricate datasets. R, with its statistical prowess, becomes invaluable for in-depth analysis, especially with specialized packages like dplyr and ggplot2. SQL comes into play when dealing with relational databases, offering a structured query language for efficient data manipulation. The key is aligning the tools with the specific demands of each cleaning task to ensure accuracy and efficiency.

1. Campaign specific data profiling : Focus on customer crucial features such as demographics, purchase history, and other engagement metrics 2. Use of Marketing analysis libraries in python : explore and use python packages that contribute for customer segmentation, attribution analysis, and predictive modelling. 3.Customer segmentation Practice : Group and sub group customers into buckets and bins based on customer behaviour. This helps to select a targeted audience and will help to send out personalized campaigns. 4.Marketting data campaign rules : Set rules to identify Tags and patterns across campaign and remove those from analysis. 5. Integrate A/B testing Practices into pipeline 6. Automate feature engg and campaign execution.

Utilize data cleaning tools like Python libraries (Pandas), Excel functions, and techniques such as outlier detection, imputation, and normalization.

Developing data cleaning skills for marketing campaigns also involves adhering to best practices. Always backup your original data before cleaning. Use visualizations to understand data patterns and outliers. Apply cleaning techniques iteratively, checking for errors at each stage. Document your process, including the tools used and changes made. This ensures reproducibility and facilitates collaboration. Remember, data cleaning is an ongoing process that improves with experience and feedback.

Follow industry standards, leverage automation where possible, create data backup, and maintain proper documentation throughout the cleaning process.

Honing Data Cleaning Skills for Marketing Campaigns To effectively develop data cleaning skills, consider these practical approaches: Hands-on Experience: Practice cleaning datasets using data cleaning tools. Real-world Projects: Participate in real-world data cleaning projects. Open-Source Contributions: Contribute to open-source data cleaning projects. Workshops and Conferences: Attend data cleaning workshops and conferences. Mentorship: Seek mentorship from data cleaning experts. Personal Projects: Apply data cleaning skills to personal projects. Industry Trends: Stay updated with industry trends in data cleaning. Continuous Improvement: Seek continuous feedback and improvement.

Beyond the foundational tasks like handling duplicates and outliers, marketers need to actively address missing data to ensure their analyses are based on complete and reliable information. Inconsistencies in formats or units can be particularly problematic in performance marketing, where precise metrics matter. Moreover, dealing with noisy data becomes crucial to accurately assess the impact of marketing efforts. Practical application involves not only refining these skills iteratively but also integrating them seamlessly with analytics tools. Marketers must also develop the ability to handle diverse data sources and formats.

Practice on actual marketing datasets, encountering diverse issues. Understand nuances in different data sources and how cleaning impacts analysis.

Learning by doing is the most effective way to sharpen your skills. Every aspiring data scientist thinks that data science is about creating complex machine learning algorithms. The ugly reality is that we spend more than 70-80% of our time cleaning data. Master that and you'll thrive.

Regularly engage in data cleaning exercises, participate in challenges, and seek feedback to refine your skills continually. Stay updated with evolving tools and methods.

Additionally, effective data analysis and use in marketing initiatives depend on maintaining a uniform and standardised data nomenclature. Naming standards that are consistent help facilitate data interchange across teams. It also improves data comprehension and ensures data integrity.

Understand the corporate collective kpis and objective key results; the ones if designed properly anyone can rally around. Map out the pathways across silos of the dataflows insofar as value is added in the process contributing to net value. The data governance then is simply the framework needed to be in place to enable connecting the dots and expanding the okrs ultimately. All the value chains to okrs where clear value can be mapped - must be governed or liberated to contribute full value. Thats the best governance, the only governance needed.

Consider data governance, collaborate with domain experts, and invest time in learning domain-specific knowledge for effective cleaning in marketing contexts.