Are you using Python in your Marketing Team or are you planing to do so? Then this course is ideal for you. Proven with many marketing experts, we help your Team to improve the effectiveness of your campaigns and calculate your returns on investment get valuable insights from tons of data coming from your online advertising campaigns manage and coordinate omni-channel presence.
Day 1: Connect your data
On the first day, we will start bringing all your team to the same level. All participants by 10 AM have a running coding suite that caters all needs. No one is left behind.
We use the first day walking through data you will provide to the course. This way, you are highly familiar with the subject matter from the start – and develop own ideas what to look for in the data.
Over the course of the day, you will load data from your corporate sources, find flaws, clean it up, visualize it and draw first insights that you have not seen before.
Day 2: Build productive solutions
On the second day, you already know how to derive first insights from your data with Python. We will now focus on building productive solutions for your use case.
Do you want to analyze monthly changes in Net Promoter Score? Or evaluate the effectiveness of a series marketing campaign? You will get the required skills at hand to help yourself.
We will group data and analyze statistics of different customer groups. We will comprehend data as time series and monitor changes of relevant KPIs over time. And we will analyze returns from A/B testing and test for statistical significant shifts.
Experience the difference
This Python course is ready-built for your requirements. It will be based on data you provide, tasks you have at hand, and challenges you want to solve.
We are creating a uniquely targeted learning experience for you and enable you to build productive solutions with Python from Day 2.
You won’t learn a programming language. You will learn to think code – and open a new world for you, your job and your business.
Are you interested in a particular topic?
We design your Python course to your very needs. For beginner’s levels, we recommend covering:
- Reading data from Excel, SPSS, and databases
- Analyzing, cleaning and processing data using pandas, numpy and scipy
- Visualizing data using matplotlib and seaborn
Does your team already work with Python?
We recommend the following specialisations to advance in particular topics:
Use case time!
Now you are already on this page – thus, we suppose you are interested in using Python in your Marketing team. We would be curious to learn your very particular use case. To get inspired, have a read through the following generic ones for Python in Marketing:
Reporting
Do you get tons of data from multiple sources for which you need to produce periodic reports? Python is an efficient tool for data pre-processing, analytics, and data visualisation. You’ll need to write the code only once to create your first report. Afterwards, you can just run the code on a new dataset, and you’ll get the report within minutes.
Customer Segmentation
Delivering personalised experience to customers is a must for marketers these days. But before you can personalise your messaging and experience, you need to understand your customers’ behaviours, preferences, and habits. Proper customer segmentation is a key to understanding your customers and tailoring marketing campaigns accordingly. Python gives you access to the most sophisticated clustering techniques. A number of machine learning techniques that are easily implemented with Python will help you classify your customers by features that really matter, increasing your revenue and improving your customers’ experience.
Data Visualization
Marketers use visualisations of all types to support reporting and marketing analysis. Unfortunately, it usually takes quite a lot of time to create valuable and professional-looking plots. For your convenience, Python has a specialised library named seaborn that creates appealing, state-of-the-art plots with just one line of code. You’ll just need to pre-process your data first—but again, this process is straightforward with Python. For example, you can create Python heat maps for marketing campaigns in just a few lines of code.
Customer Feedback Analysis
Customers use multiple channels to leave feedback about the products they use. Large companies in particular struggle to manually analyse all the reviews left on different websites and social media platforms. This is a perfect opportunity for automation.
With natural language processing (NLP), you can automate the processing of customer feedback and get some valuable insights to answer questions such as the following, among many others:
- What are the things that customers like/dislike about our product?
- Do customers develop an emotional attachment to our product?
- How does the perception of our brand change with time?
Text processing and classification is not a trivial task for beginners. Fortunately, there are many open-source libraries and pre-trained models available online that will help you automate customer feedback analysis for your company.
Content Optimization
A/B testing is a popular marketing tool for comparing several versions of a website, app, or ad to determine which one performs best. For example, if you have two groups of customers with one being exposed to ad A and another group to ad B, you can compare these two ad groups’ conversion rates to find the winner. Of course, the difference should be statistically significant for you to conclude that one of the ads is indeed better. Python is a perfect tool for streamlining A/B testing and defining the statistical significance of the resulting difference.
With Python, you can go even further with content optimization. A/B testing is quite a good technique, but it inevitably includes a period of “regret,” when you’re not using the best option for part of your customers and thus lose some revenue. In contrast, multi-armed contextual bandits mitigate opportunity loss through dynamic optimization. With this technique, you don’t need to wait until the end of the test to define the best option since bandit tests explore and exploit different options simultaneously and gradually move to the better one. This advanced technique can be also implemented with Python. But of course, it will require a little bit more coding experience.
