Anupam Rajey’s Post

Project Title: E-Commerce Customer Lifetime Value Prediction The objective of this project is to predicting customer lifetime value to enhance marketing strategies and improve business decisions. Methods Used: RFM analysis, BG/NBD, Gamma-Gamma model, statistical methods. Key Findings: The analysis reveals that customer frequency and recency are the strong predictors of CLV, where higher frequency is associated with higher expected spending. The projected CLV over six-month and twenty-four-month periods allow insight into customer behaviors to be used in targeted marketing strategies and resource allocation. Conclusion: This study employed a rich dataset of customer transactions to analyze CLV in e-commerce. With the Gamma-Gamma and BG/NBD models, we undertook successful customer spend prediction and future value estimation. Key Highlights: There is huge variability in CLV, with a small share of high-value customers being responsible for an enormous part of the revenue. Key predictors for modelling customer behavior are recency, frequency, and monetary value. Indeed, high-frequency customers show higher CLV, indicating targeted retention efforts. The Gamma-Gamma model had robust estimates of expected spend that would help in forecasting and supporting marketing activities. Insights: Personalized marketing strategy for high-value customers will help in retaining customers and will drive sales. This analysis focuses on data-driven decision-making to show advanced modeling and better knowledge about customer behavior. Limitations The analysis was limited by the scope of the dataset itself, which may not take into consideration all of the interactions of the customers and other outside factors that may influence behavior. Recommendations: It is recommended that the business carry out targeted campaigns focused on high-value customers while developing custom plans to prevent at-risk customers. Any further research should extend this dataset with more attributes and alternative ways of modeling in order to make better predictions of CLV. The project underlines that it is great to apply CLV analysis because, finally, this allows a company to reach specific strategic business decisions, accelerating the growth in e-commerce through better positions in marketing efforts and improving customer retention and increasing profitability. Impact: These insights, once identified, go a long way in impacting the business strategy in terms of strategic marketing campaigns that improve customer acquisition costs, retention strategies, and result in higher profitability continuous growth in the competitive e-commerce space. #CustomerLifetimeValue #CLV #Ecommerce #DataAnalysis #MarketingStrategy #GammaGamma #BGNBD #CustomerInsights #PredictiveModeling #BusinessIntelligence #MarketingAnalytics #CustomerInsights #DataScience #Python #Analytics #DataVisualization #MachineLearning #FutreSales #customerBehaviour #AI #ArtificialIntelligence #BsinessGrowth

Gain leverage in retail marketing with advanced performance analytics! 🛒 Dive into our ultimate guide to uncover actionable insights and best practices for optimizing your marketing strategies. Discover how data-driven decisions can drive growth and enhance customer engagement. Read the full guide here: https://lnkd.in/gMxMN9AU #RetailAnalytics #PerformanceAnalytics #MarketingStrategy #DataDrivenDecisions #CustomerEngagement

Over the years, I've had countless discussions about dynamic pricing and its complexities. It's an essential topic that requires a thoughtful and structured approach. MarTech and Alicia Arnold, in their article, provide valuable insights into implementing dynamic pricing effectively without alienating your customers. 🔑 Transparency, ethical practices, and the right pricing models are key. I encourage anyone interested in dynamic pricing to dive into this discussion— it's an important part of finding the balance between profitability and customer loyalty. #DynamicPricing #CustomerLoyalty #Transparency #EthicalBusiness #PricingStrategy #Profitability #MarketingInsights #BusinessGrowth #CustomerSatisfaction #MarketingStrategy

Using Analytics to Drive Sales: The Magic of Tracking Tools If you've ever wondered how the big brands keep their sales numbers growing, the answer might be simpler than you think: analytics. At Purple Cow Services, we’re excited to share how using the right tools to track eCommerce performance metrics can turn your online store into a sales powerhouse. Picture this: Every click, every purchase, and every customer interaction is a piece of data. By using analytics tools, you can see not just numbers, but stories. What pages do visitors linger on? Which products are they zooming in on? Understanding these behaviors can help tailor your offers and boost your sales sky-high! But wait, there’s more! Predictive analytics can help you anticipate market trends and customer needs, making sure you're always a step ahead. Imagine being able to stock up on what will sell before the demand even peaks. With the right tools, that’s your new reality. Don’t just chase your tail with random strategies. Make informed decisions with analytics and watch your sales grow. Want to learn more about the tools that can help you achieve this? Contact Purple Cow Services today. We have the expertise and tools to transform your eCommerce site into a customer magnet. 📧 [email protected] 📞 +1 (914) 977 5459 🌐 www.purplecowservices.com #SalesAnalytics #ECCommerceMetrics #DataDriven #PerformanceTracking #CustomerBehavior #MarketTrends #SalesStrategies #AnalyticsTools #ECommerceTips #OnlineMarketing #DigitalSales #PurpleCow #PCIS #MUB #PurpleCowServices #SalesGrowth #OnlineStore #ECommerceStrategy #B2B #RetailAnalytics #PredictiveAnalytics #TrackPerform

Build your portfolio and practice yourAnalytical and data cleaning skills with these free datasets on marketing, sales, and customer analysis 1. Mall Customers Dataset Description Information about customers visiting a shopping mall. [Kaggle](https://lnkd.in/dBJYEYWS) 2. Uk Online Retail Dataset This is a transnational data set that contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers. https://lnkd.in/dbWA4sfz 3. Sales Transactions Dataset Description: Sales transactions for different items. Consumer brands often offer discounts to attract new shoppers to buy their products. The most valuable customers are those who return after this initial incented purchase. With enough purchase history, it is possible to predict which shoppers, when presented with an offer, will buy a new item. However, identifying the shopper who will become a loyal buyer -- prior to the initial purchase -- is a more challenging task.The Acquire Valued Shoppers Challenge asks participants to predict which shoppers are most likely to repeat purchase. To aid with algorithmic development, we have provided a complete, basket-level, pre-offer shopping history for a large set of shoppers who were targeted for an acquisition campaign. The incentive offered to that shopper and their post-incentive behavior is also provided. Source: [Kaggle] (https://lnkd.in/d6FDyS48) 4. Customer Personality Analysis Description: Customer personality data for marketing strategies. Source: [Kaggle] (https://lnkd.in/dbcNpUuC) 5. E-commerce Data Description: Data of e-commerce orders, including customer data. Source: [Kaggle] (https://lnkd.in/dxa_3j4y) 6. Marketing-Customer-Value-Analysis Description: Customer data for value analysis. Source:[Kaggle] https://lnkd.in/df5wZ8nK 7. Bank Marketing Dataset Description: Direct marketing campaigns of a Portuguese banking institution. Source : [UCI Machine Learning Repository](https://lnkd.in/divFa9tc) 8. Consumer Complaint Dataset Description: Consumer complaints data for analysis. Source : Kaggle https://lnkd.in/dpaN2Fui #dataanalyst #datacleaning #dataanalysis #datascientist #data #datascience

In today’s data-driven world, understanding your customers and making informed decisions is essential for success. Explore how data analytics can enhance your e-commerce marketing strategies, improve customer engagement, and boost sales. Discover actionable insights in our latest blog! 💡 👉 Read the full article here: https://shorturl.at/i1P1D #EcommerceMarketing #DataAnalytics #MarketingStrategy #BusinessGrowth #DigitalTransformation

When we talk about growth in eCommerce, most people immediately think of flashy marketing strategies, huge sales events, or influencer endorsements. These are all great tools, but - Real, sustainable growth comes from something far less glamorous—data. I’ve learned over the years that every big decision has to be data-driven. It’s tempting to follow trends, but you need hard facts to back your choices. Here’s how I approach it: - Customer Insights: Data tells you who your customers are, what they need, and how they behave. From browsing patterns to purchase histories, the answers are always in the data. - Personalization: Your customer doesn’t want a generic experience. Use data to personalize every touchpoint—emails, product recommendations, even discounts. This goes beyond just knowing their name. Know what matters to them. - Inventory Management: Data helps you anticipate demand and adjust supply. Overstocking eats up your cash flow, and understocking means lost sales. The sweet spot is found in data patterns—seasonal trends, customer demand cycles, and predictive analytics. - Customer Retention: It’s not always about getting new customers. Keeping the ones you have is more valuable. Data gives you insights into customer satisfaction, and most importantly, churn rates. - Optimization at Every Step: From your product pages to checkout processes, small optimizations based on data can make a massive difference. Every click, every bounce, every conversion rate—track it, analyze it, and improve it. In the world of eCommerce, gut feelings won’t get you far. Data will. It’s the difference between wishful thinking and measurable success. Focus on the numbers, and the growth will follow. 📈 #zopoxo #datadrivendecisions #ecommercesuccess

🚀 Harnessing Data Analytics to Predict Customer Behavior and Boost Retention Rates in E-Commerce 📊 In the fast-paced world of e-commerce, retaining customers is just as crucial as attracting them. By leveraging data analytics, businesses can gain valuable insights into customer behavior, personalize the shopping experience, optimize marketing campaigns, implement effective loyalty programs, and enhance customer support. With predictive modeling and advanced segmentation, companies can forecast future actions and tailor strategies to keep customers engaged and loyal. Discover how data analytics can transform your e-commerce strategy and drive sustainable growth. 💡 #Ecommerce #DataAnalytics #CustomerRetention #PredictiveAnalytics #CustomerExperience #Personalization #MarketingStrategy #LoyaltyPrograms #CustomerSupport #BusinessGrowth https://lnkd.in/ddRPa4uC

👨🔧 Customer Purchasing Habits 💰 performed via RFM Analysis !! RFM analysis: Recency - When did they last order ? Frequency - How often they make purchases ? Monetary value - How much money they spent ? It is a marketing analysis tool used to identify a firm's best clients based on the nature of their spending habits. It also assists in focusing on high value customers / convert occasional buyer into habitual ones by marketing strategies. Checkout Tableau dashboard: https://lnkd.in/dYYPSEBG Checkout my Portfolio website: https://lnkd.in/dygHrGYS Inspired by: Andy Kriebel #tableaudashboard #marketinganalytics #dataanalysis

Results More data sources, variables and values through Customer Journey Analytics Data-driven optimisations ensure sales increases Useful product information has proven to be the key to a good customer journey