What are some examples of successful Marketing Analytics projects to include in your portfolio?

One of my experiences in segmentation was in email marketing, I grouped customers using the RFM model and was able to target customers based on the things I was looking for. In this case my goal was to reduce the churn rate. So I targeted the customer group with high frequency, high monetary and low recency, meaning that these customers used to make purchases often and usually they spent a lot too but it's been while since their lat purchase. So using a series of promotional emails I was able to reduce customer churn rate by 15%.

Customer segmentation is like sorting your favorite snacks into different categories based on how much you love them. It helps businesses figure out who their best customers are, so they can make them super happy with special offers and stuff. They use fancy computer tricks to group customers together, kinda like creating profiles for different types of friends, and then they see which group of customers brings in the most money and sticks around the longest.

Art of Audience Segmentation 1. Canvas: Envision a varied crowd. Group by identity, not chance. 2. Purpose: One universal note or a unique melody for every heart? 3. Palette: - Demographics: Jane the teacher vs. Bob the retiree. - Geography: New York's beat vs. Montana's serenity. - Interests: Sarah's books vs. Michael's hikes. - Behavior: Emma's daily coffee vs. Liam's cafe hopping. 4. Craft: - Gather tales over data. - Find clarity in chaos. - Shape groups as dream portraits. - Weave tailored tales. 5. Wisdom: - Depth in three strokes. - Evolve with the story. - Use technology's sharp eye. Echo dreams through masterful segmentation.

I have experienced two types of segmentation:- Ad platform based and proprietary data based (CRM, CDP and other databases). In either cases what matters is the intent and interest of the user with the media that they are exposed which may trigger them to take further action. Hence segmentation becomes important to serve them personalized journeys and explore the message market fit because they might be a product fit but they might be using the product differently.

Let's see a few examples of successful marketing analytics projects: 1. Social Media Analytics: In this inter connected world a company can easily utilize social media analytics to monitor brand sentiment, track customer engagement, and identify emerging trends in their industry. This will allow them to make data-driven decisions, engage with their audience more effectively, and improve their overall social media marketing strategy. 2. Predictive Modeling: This is an advanced analytics techniques to build predictive models that forecasted customer behavior, such as purchasing probability or churn likelihood.

Marketing Mix Modeling has emerged as a prevalent approach for gaining a comprehensive understanding of how various marketing channels impact overall business results. Facebook has introduced its open-source Marketing Mix Model framework, known as Project Robyn, which harnesses the power of artificial intelligence and machine learning to enable brands to develop their own customized Marketing Mix Models.

MMMs are a great place to start analysis of marketing investments but a lousy place to finish. MMMs are regression analysis, which while easy to initiate and compute, get increasingly muddled as subtle market influences refuse to submit to regression analysis constraints. MMMs make many assumptions or sometimes just outright ignore market subtleties such as lag effects, non-linear decay of adstock, and mixed effects. Many analytics practitioners get quite clever in addressing these nuances with various transforms and interactive features but lose the interpretability that makes an MMM a great first pass at insight. Yes, run the MMMs, let them inspire you to ask questions and generate hypotheses, but don't rush to conclusions of causality.

Marketing mix modeling helps businesses assess the effectiveness of different marketing strategies, enabling them to allocate resources more efficiently and make data-driven decisions. It involves analyzing the influence of various marketing channels on key performance metrics like sales and profit, providing valuable insights for optimizing marketing campaigns. For instance, a company could use marketing mix modeling to determine the most cost-effective channels for reaching their target audience and enhancing overall profitability.

Marketing mix models include projects like lead scoring models, personalized journeys, time and budget optimizations. We developed a model which bridged marketing and sales to understand the requirement of marketing and sales for each user segment. Developing lead scoring models helps identify high potential leads both inbound and outbound efforts.

Marketing mix modeling in Marketing Analytics involves analyzing various marketing variables to understand their impact on sales and profitability. It's akin to solving a puzzle, decoding the relationships between marketing inputs and outputs. This approach empowers data-driven decision-making, optimizing budget allocation for effective channels. Statistical techniques like regression analysis provide valuable insights, enabling businesses to simulate scenarios and predict outcomes based on different strategies. This predictive ability enhances strategic planning, ensuring marketing efforts yield optimal results.

Sentiment analysis plays a pivotal role in understanding where a brand stands and how people perceive its products and services. By systematically evaluating customer opinions, comments, and feedback across various platforms, companies can gain valuable insights into their brand image and reputation. By leveraging sentiment analysis, companies can make data-driven decisions to enhance customer satisfaction, refine their marketing strategies, and ultimately strengthen their brand position in the market. Continuous monitoring of public sentiment enables brands to stay agile and responsive to changing consumer preferences and expectations.

Sentiment analysis, a valuable facet of Marketing Analytics, involves assessing customer or prospect emotions, opinions, or attitudes toward a brand, product, service, or topic. By employing natural language processing (NLP) techniques to extract sentiment from textual data and visualizing sentiment distributions and trends, you can gain insights into customer perceptions and enhance your offerings.

Leveraging sentiment analysis in social media through NLP (Natural Language Processing) techniques with Python offers powerful statistical insights. Through sentiment analysis, we can quantify consumer sentiment by analyzing text data from social media platforms. Python libraries such as NLTK (Natural Language Toolkit) and SpaCy provide robust tools for text processing and sentiment analysis. By applying statistical methods like classification algorithms (e.g., Naive Bayes, Support Vector Machines) or deep learning models (e.g., LSTM, BERT), we can accurately categorize posts into sentiment categories. These models enable us to not only understand overall sentiment trends but also identify specific topics or keywords driving sentiment.

Sentiment analysis in Marketing Analytics is a vital tool, decoding customer emotions and opinions about a brand or product. It transforms unstructured textual data from sources like social media into actionable insights. Utilizing natural language processing, businesses can swiftly gauge customer satisfaction, identify areas for improvement, and stay responsive to evolving perceptions. This nuanced understanding guides informed decision-making, enabling customer-centric strategies and ensuring brands resonate with their audience effectively.

For a successful Sentiment Analysis project: 1. Collect text data from sources like social media and reviews. 2. Preprocess data, removing noise and special characters. 3. Use NLP for sentiment classification (positive, negative, neutral). 4. Employ text mining for topic extraction. 5. Visualize sentiment trends. 6. Provide actionable insights to boost brand reputation or identify growth opportunities. These steps enhance your Marketing Analytics portfolio.

Using A/B testing I was able to increase conversion rate by 10%. This is how... I tracked the numbers of clicks on all the CTAs in an e-commerce website leading the customer to the purchase process. Then I start to optimize each CTA by changing and A/B testing different colors and texts. After more than 20 changes I was able to increase the Overall CR.

A/B testing is vitally requisite to optimize your marketing analytics projects. Conduct them periodically across a variety of channels from webpage landing pages to emails to social media and display ad campaigns. By testing variation on campaigns for effectiveness, it gives you insights on what's working and how to improve your marketing mix and achieve conversions.

In my recent experience with AB Testing, our performance marketing campaigns witnessed an impressive 15% increase in conversions. We were able to achieve this due to the following: 1) Constant optimisation of Ad Copies & Creatives 2) Change the Landing pages with minor tweaks to copies, colours and form positions. 3) We kept refining the audience targeting while looking at the holistic conversion data on the CRM. 4) Always monitor the Clicks on various CTAs to see which CTA gets more clicks. We were lucky that we had the data to play around with and I believe A/B testing requires you to monitor everyday what is the pattern of user behaviour. This will not only boost your marketing campaigns but also improve the quality of your product.

In my experience all marketing analytics techniques have a great impact on our clients at Beyond North. I practice A/B testing if clients are willing to spend a bit more. Hospitality and fashion industries are my favourite to see Marketing Analytics in action.

Your A/B testing program should be conducted to test multiple hypothesis but not A vs B. A/B testing programs should be conducted to test user behavior and extract insights which could be leveraged in terms of pricing, user journeys, product and other business decision making.

This should be used to influence decision making around supply, product and pricing to increase sales and margin over offerings.

Market basket analysis in Marketing Analytics uncovers patterns in customer purchases, revealing product associations and preferences. It's akin to deciphering a hidden language within transaction data. This technique enhances cross-selling and upselling, guiding targeted promotions and bundles. I find it fascinating because it transforms raw data into actionable insights, enabling businesses to tailor offerings effectively. Understanding customer buying behaviors not only boosts sales but also fosters loyalty, showcasing the power of data-driven strategies in maximizing revenue and customer satisfaction.

For a successful Market Basket Analysis project: 1. Data Collection: Gather transaction data containing items purchased. 2. Data Preprocessing: Clean and format data, handling missing values and outliers. 3. Association Rules Mining: Use techniques like Apriori to find item associations, support, confidence, and lift. 4. Visualization: Present discovered rules and their metrics visually for easy understanding. 5. Recommendations: Generate product recommendations or bundling strategies based on analysis. 6. Implementation: Apply findings to boost sales, revenue, or customer loyalty. 7. Following these steps leads to an effective Market Basket Analysis project.

Aunque es cierto que las recomendaciones que hacen las empresas, y que no siempre son realizadas en base a estudios, análisis y/o IA (lo digo por experiencia), pueden tener un impacto en la compra, es importante, explorar a detalle los datos para entender las preferencias de los usuarios y sobre todo determinar la complementariedad de los productos a efectos de elevar el ticket medio, y hacer las recomendaciones en base a estos análisis, para así elevar su eficiencia.

Elevating the retention rate stands as a paramount objective for marketing teams in certain businesses, such as mobile games. Retention rate is intricately linked to Customer Lifetime Value (CLV) analysis. Enhancing CLV directly contributes to increased overall revenue. Marketing analytics projects incorporating CLV analysis can be exceptionally successful and serve as strong portfolio pieces, showcasing the tangible impact on the business's bottom line.

In your portfolio, emphasize your marketing analytics prowess through projects that have yielded tangible outcomes. This might encompass accomplishments such as augmenting website traffic, curbing customer acquisition expenses, boosting sales figures, refining customer satisfaction levels, or amplifying brand recognition. Remember, the selection of projects to feature in your portfolio should align with your unique experiences and competencies, showcasing your ability to employ data-driven solutions for genuine marketing challenges.

Choose the case studies relevant to your next job. Showcase not just the skills, but results of applying those skills. Start with results, how you arrive at them, end with results.