As machine learning and automation gain more of a stronghold in digital marketing, we need a way to analyse all the data we obtain. Data science does this by turning theories into tangible evidence.
One data science term you might come across is big data. It refers to large and complex data sets used to identify trends and patterns that can help businesses make better, more personalised decisions for their customers.
In PPC, that kind of power can work wonders for optimising budget spend, improving CTR, and overall performance.
In this article, I will look at the concept of data science, the tools and knowledge you need to make it work, some common PPC issues, and how data science can help fix them.
What is data science?
Data science is the study of data through various scientific processes and algorithms with the goal of deducing insights. As mentioned above, big data plays a major role in showing the effectiveness of data science and techniques like machine learning and automation are just as important.
Why is it so important?
The short answer is: data science solves problems. Before technology was as sophisticated as it is now, certain decisions were made by best guesses and past trends. That didn’t always work and lost people and businesses a lot of money. Now, data science can help find anomalies and trends to save people millions and give businesses the chance to improve customer relationships and services.
Some examples that show how important data science can be include:
- Airbnb, who used data science to grow its business by 43,000% in just 5 years
- Netflix uses data insights to produce original series as well as all the personalised recommendations you see. This was done by using big data to understand its users and what they liked and disliked
- Spotify has a team of data scientists that use its API to categorise music by type (eg. energy, loudness, danceability)
Tools, terminology, and techniques
Data science isn’t reserved for use with large, complex data sets and you don’t need a degree to understand the basics.
- Python is a programming language synonymous with data science due to its clear syntax and ability to scale for projects of all sizes. It also has a great number of libraries made specifically for data science including pandas, numpy, and scipy.
- R is another programming language used extensively in data science, particularly with data mining.
- There are frameworks used for creating machine learning models – including TensorFlow (Google) and Pytorch (Facebook) – and data processing such as Apache Hadoop (Apache).
- BigML uses a cloud-based GUI environment to process machine learning algorithms.
- Data set – a collection of structured data.
- Data mining – a process of identifying models and insights in data sets.
- Deep learning – a subset of machine learning that tries to copy the thought processes of human beings. AI uses deep learning to improve things like speech recognition and translation.
- Clustering is a technique where data is grouped together.
- Machine learning performs tasks based on patterns and trends it gets from datasets. The larger and more accurate the dataset, the better the results.
- Classification helps to predict the categories that data points should go in.
How can data science help PPC?
Whether you use Google Ads or other PPC alternatives, automation is at the forefront. But you need to know what to automate and why, and that’s where data science comes in handy. Other benefits of data science in PPC include:
- Identifying outliers in impressions and clicks over time to confirm whether significant changes are due to seasonality or something else.
- Tailoring PPC campaigns aligned to particular audiences and when they’re online.
- Understanding buyer’s preferences.
- Deeper analysis of ad copy.
- Creating and optimising remarketing lists.
- Identifying unusual traffic.
- Creating better split tests.
Practical applications of data science in PPC
1. Find data sources and cluster them
Chris Pitt, Head of Marketing at Vertical Digital did a talk at brightonSEO about the benefits of data science in PPC campaign management and one of his suggestions was to “collect and combine everything”, regarding data sources.
In his example, he combined the OpenWeatherMap API or Google Distance Matrix API to ask deeper questions about the overall performance of PPC campaigns when it rained and the behaviour of users who were within a 20-mile radius of business locations. These insights might sound superfluous but acting on these details could save a lot of money in the long run.
The quality of your data sources can affect your insights so use the best tools you can. Besides the tools mentioned above, software like SEMrush, Google Data Studio, and Tableau can help collate and analyse data quicker and easier.
2. PPC needs to work together with all marketing channels
PPC professionals don’t operate in vacuums. Their strength lies in combining their efforts with everyone within a marketing team. That means collaborating with sales and using CRM data, developers who help create landing pages and websites, SEO, and customer support.
Data insights can provide goals and best practices for everyone to follow to ensure a smooth customer journey from impression to goal conversion. Those insights can also identify key areas for improvement between two teams and they can work together in fixing them. Is CTR high but conversions low? Maybe there’s a page speed issue that PPC, devs, and SEO could work on together. That’s the power of data science.
3. Visualize PPC performance
Data is awesome but on its own, it’s columns and rows of numbers and letters. How do you turn all that into insightful information you can act on? With data visualisation. Performance reports are paramount to showing where PPC is succeeding or needs improvement and the ability to visualise that data is important too.
Data visualisation can:
- Find trends
- Compare data over time
- Normalise larger datasets
- Help further testing
Sometimes, all it takes is a simple bar chart or line graph to show you where things are going right or wrong. The advantage of data science with regard to data visualisation is it can be done very quickly and you can generate reports in minutes if not seconds.
4. Test, test, test
After Google made their infamous changes to the SERPs on desktop, I decided to test whether the Google SERP changes had an impact on ad CTR. I had a hypothesis, I used a large dataset to test it and the results were different to what I had predicted. There was still scope for further testing and for better classification but the power of testing can help you confirm or refute any preconceptions you might have had.
Data in isolation has no meaning and being able to test it in controlled conditions can lead to important insights. That’s the beauty of data science – it provides knowledge you can use to make better judgements. In PPC, those judgements can impact metrics like ROAS (return on ad spend), Quality Score, impression share, and cost per conversion.
Using data science in PPC can solve ongoing problems, dependent on your campaigns. There’s no need for guesswork when you have the data in front of you. All you need is the means to interpret what you see, find certain trends, and improve performance and that’s where data science excels.