<\/span><\/h3>\n\n\n\nFollow a brand on Instagram or Twitter and determine what they post that receives likes, comments and retweets. Does posting in the nighttime work better? Or does video take the place of photos? You’ll have straightforward responses that a social media manager can actually use.<\/p>\n\n\n\n
Dataset and Tools:<\/strong> Kaggle social media engagement datasets, or export data from your own page. Work in Excel, Google Sheets or Tableau Public.<\/p>\n\n\n\nSkills Learned:<\/strong><\/p>\n\n\n\n\n- Basic data cleaning<\/li>\n\n\n\n
- Spotting patterns and trends<\/li>\n\n\n\n
- Building simple charts<\/li>\n<\/ul>\n\n\n\n
<\/span>2. Email Campaign Performance Dashboard<\/strong><\/span><\/h3>\n\n\n\nGet some of the email data, such as open rates, click rates, and unsubscribes, and create a simple dashboard. You will now be able to see which Subject Lines were successful and which were ignored. A good first project as the numbers are simple to grasp and the story is apparent.<\/p>\n\n\n\n
Dataset and Tools:<\/strong> Data from the Kaggle or Mailchimp email marketing datasets. Please use Google Sheets or Power BI.<\/p>\n\n\n\nSkills Learned:<\/strong><\/p>\n\n\n\n\n- Dashboard building<\/li>\n\n\n\n
- Understanding marketing metrics like CTR<\/li>\n\n\n\n
- Comparing campaign results<\/li>\n<\/ul>\n\n\n\n
<\/span>3. Customer Segmentation Using RFM Analysis<\/strong><\/span><\/h3>\n\n\n\nThere are three factors of RFM: recency, frequency, monetary value. In essence, you segment customers according to their purchase frequency, amount and timing of purchase. Then you can see your best customers and those that are on the verge of losing you. A simple idea that companies use, and use all the time.<\/p>\n\n\n\n
Dataset and Tools:<\/strong> UCI Online Retail dataset. Use excel or python (pandas).<\/p>\n\n\n\nSkills Learned:<\/strong><\/p>\n\n\n\n\n- Grouping and segmenting customers<\/li>\n\n\n\n
- Creating pivot tables or using basic code.<\/li>\n\n\n\n
- Data to Marketing Actions<\/li>\n<\/ul>\n\n\n\n
<\/span>4. Website Traffic Analysis with Google Analytics<\/strong><\/span><\/h3>\n\n\n\nExplore Google’s free demo accounts to discover where traffic is coming from, what pages they are favouring and where they are going. Then write up what you\u2019d change on the site. It’s like real work because it is real data from a real online store.<\/p>\n\n\n\n
Dataset and Tools:<\/strong> Google Merchandise Store demo account. Use Google Analytics.<\/p>\n\n\n\nSkills Learned:<\/strong><\/p>\n\n\n\n\n- Reading traffic and bounce rate reports<\/li>\n\n\n\n
- Understanding where users come from<\/li>\n\n\n\n
- Writing practical recommendations<\/li>\n<\/ul>\n\n\n\n
<\/span>5. Social Media Sentiment Analysis<\/strong><\/span><\/h3>\n\n\n\nGather Tweets or comments on a brand or product and categorize them as positive, negative or neutral. Then observe the reactions over time. Are there reasons to believe that sentiment declined following a poor ad or product launch? A fun approach to some simple text analysis!<\/p>\n\n\n\n
Dataset and Tools:<\/strong> Sentiment140 or other Kaggle Twitter datasets. Use Python (TextBlob or VADER), or Excel for a smaller sample.<\/p>\n\n\n\nSkills Learned:<\/strong><\/p>\n\n\n\n\n- Basic text analysis<\/li>\n\n\n\n
- Working with messy, unstructured data<\/li>\n\n\n\n
- Tracking brand perception<\/li>\n<\/ul>\n\n\n\n
<\/span>6. Marketing Survey Data Analysis<\/strong><\/span><\/h3>\n\n\n\nTake one of the datasets of customer survey results and examine what they have to say about a product or service. Check out the satisfaction scores, age groups, and what customers are looking for more of. Follow this by presenting the answers as a short report with 3 or 4 key learning points.<\/p>\n\n\n\n
Dataset and Tools:<\/strong> Kaggle customer satisfaction survey datasets, or run your own with Google Forms. Use Excel or Tableau.<\/p>\n\n\n\nSkills Learned:<\/strong><\/p>\n\n\n\n\n- Summarizing survey results<\/li>\n\n\n\n
- Basic statistics like averages and percentages<\/li>\n\n\n\n
- Writing short, clear reports<\/li>\n<\/ul>\n\n\n\n
<\/span>7. Sales Trend Analysis by Product and Region<\/strong><\/span><\/h3>\n\n\n\nConsider a retail sales data set and determine which merchandise is most popular, in what areas and in what months. Seasonal trends will likely be visible, such as during holidays. After that, recommend an area in which a business should allocate more of its marketing budget. This last part is what makes it feel like a real marketing project.<\/p>\n\n\n\n
Dataset and Tools:<\/strong> Kaggle Superstore Sales dataset. Use Excel or Power BI.<\/p>\n\n\n\nSkills Learned:<\/strong><\/p>\n\n\n\n\n- Time-based trend analysis<\/li>\n\n\n\n
- Comparing categories and regions<\/li>\n\n\n\n
- Linking data to budget decisions<\/li>\n<\/ul>\n\n\n\n
<\/span>Marketing Analytics Project Ideas for Students (Intermediate)<\/strong><\/span><\/h2>\n\n\n\nYou’ve tried a few of the easy ones and you’re looking for something more challenging. The following marketing analytics project ideas require a bit more thought and some coding or modeling, but nothing too crazy. Spend time with them.<\/p>\n\n\n\n
<\/span>8. Customer Churn Prediction<\/strong><\/span><\/h3>\n\n\n\nFigure out which customers are likely to stop using a service. You consider the duration they’re customers, their bills, and how frequently they ask technical support. Next, create a basic model to warn the dangerous ones before leaving.<\/p>\n\n\n\n
Dataset and Tools:<\/strong> Telco Customer Churn dataset on Kaggle. Use Python (scikit-learn) or R.<\/p>\n\n\n\nSkills Learned:<\/strong><\/p>\n\n\n\n\n- Logistic regression or decision trees<\/li>\n\n\n\n
- Feature selection<\/li>\n\n\n\n
- Understanding why customers leave<\/li>\n<\/ul>\n\n\n\n
<\/span>9. Ad Campaign ROI and ROAS Analysis<\/strong><\/span><\/h3>\n\n\n\nSample some ad data from a few different channels, such as Google, Facebook and email, and see which one, if any, were profitable. Don’t believe that if you spend a lot you will make a lot of money. There are some channels that are over budget, and that is a positive thing to report.<\/p>\n\n\n\n
Dataset and Tools:<\/strong> Kaggle digital marketing campaign datasets. Use Excel, Power BI, or Python.<\/p>\n\n\n\nSkills Learned:<\/strong><\/p>\n\n\n\n\n- Calculating ROI and ROAS<\/li>\n\n\n\n
- Comparing channel performance<\/li>\n\n\n\n
- Making budget recommendations<\/li>\n<\/ul>\n\n\n\n
<\/span>10. Product Review Sentiment Analysis<\/strong><\/span><\/h3>\n\n\n\nGather customer feedback for a product and determine the strengths and weaknesses of the feedback. Is it the price? Delivery? Quality? Then identify themes of the complaints. A company might use this to resolve genuine issues.<\/p>\n\n\n\n
Dataset and Tools:<\/strong> Amazon product review datasets on Kaggle. Use Python (NLTK or VADER) and Tableau.<\/p>\n\n\n\nSkills Learned:<\/strong><\/p>\n\n\n\n\n- Text cleaning and analysis<\/li>\n\n\n\n
- Finding common themes<\/li>\n\n\n\n
- Presenting customer feedback visually<\/li>\n<\/ul>\n\n\n\n
<\/span>11. Market Basket Analysis<\/strong><\/span><\/h3>\n\n\n\nHave you ever seen the \u201cpeople also bought\u201d section in stores? This is how this works. You see things that are frequently purchased together and propose product placement or products bundles, such as bread and butter.<\/p>\n\n\n\n
Dataset and Tools:<\/strong> Instacart or Online Retail dataset. Use Python (mlxtend, Apriori algorithm) or R.<\/p>\n\n\n\nSkills Learned:<\/strong><\/p>\n\n\n\n\n- Association rules (support, confidence, lift)<\/li>\n\n\n\n
- Working with transaction data<\/li>\n\n\n\n
- Suggesting cross-selling ideas<\/li>\n<\/ul>\n\n\n\n
<\/span>12. A\/B Testing of Landing Pages<\/strong><\/span><\/h3>\n\n\n\nCompare two versions of a page and see which version gets more sign-ups or purchases. The hard part is figuring out if it’s due to real change or just chance. Truthfully, this is a statistics class you won’t find in most classrooms.<\/p>\n\n\n\n
Dataset and Tools:<\/strong> A\/B testing dataset on Kaggle (Marketing A\/B Testing). Use Python or Excel for the significance test.<\/p>\n\n\n\nSkills Learned:<\/strong><\/p>\n\n\n\n\n- Hypothesis testing and p-values<\/li>\n\n\n\n
- Conversion rate analysis<\/li>\n\n\n\n
- Explaining test results simply<\/li>\n<\/ul>\n\n\n\n