Customer Purchase Analytics Dashboard

@Giovanni704

Hi there! As I have read, I see you need help building a SQL/Python to Power BI workflow for purchase data analysis and dashboard reporting, and I could help you! I'm a professional Database Administrator and Analytics Engineer with more than 10 years of experience and more than 350 projects done here on this platform. I offer unlimited revisions and I don't require any payment in front. I always meet delivery deadlines working until I get the desired results. Send me a message so I can show you my previous work and we can discuss more. Best Regards, Giovanni.

@skalogir

Hello, I understand you need a reproducible ecommerce reporting workflow that cleans purchase CSV data with SQL/Python and presents order frequency and average basket size clearly in Power BI. With 9+ years of data engineering and dashboard experience, I’ve built retail analytics pipelines using Python, pandas, SQL, Power BI, DAX measures, slicers, and executive-ready visual reporting. I will create the data-prep script, structured Power BI model, key DAX metrics, desktop/mobile dashboard views, and README so you can refresh future CSV exports easily. Best regards.

@koolvishruth

Hello, I have 8 years of experience in Python , Data analysis, Software Engineering.I can work on it

@devendrathakur12

i’ve done very similar recently building SQL + Python pipelines feeding Power BI with clean DAX for ecommerce metrics. Is each row line-item level (sku per row) or already aggregated per order? Do you want customer-level frequency over time or cohort-based analysis as well? I suggest creating a star schema (fact_orders, dim_customer, dim_date) because it keeps DAX simple and fast. I also suggest precomputing basket totals in SQL/Python to reduce heavy DAX and improve report performance. I’ll inspect the CSV, build a reproducible Python/SQL pipeline to clean and model data, then load into Power BI with proper relationships. Next I’ll write DAX for order frequency and basket size, design a clear dashboard with slicers, and document refresh steps. Best, Dev S.

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@alizain721

Putting order frequency and average basket size front-and-center is the right call — those metrics need to be accurate and obvious on first glance. Common snag is hidden noise: inconsistent timestamps, duplicate lines, and unflagged returns that silently skew frequency and basket numbers. I’ll load your CSV into pandas, normalize timestamps, dedupe by order_id+sku, compute line_total = quantity * price, then roll up to order-level to produce order_count, items_per_order and basket_value. I’ll output a clean model (parquet/CSV), then build a .pbix with clear table names, DAX measures for orders/customer and Avg Basket Value, time-range slicers and a desktop + mobile dashboard. README and inline comments included. Quick question: does the CSV include returns/cancellations or a refund flag?

@mayankinnovative

Hi, As per my understanding: You need an end-to-end pipeline that transforms raw ecommerce CSV data into a clean, structured model and delivers a polished Power BI dashboard highlighting key metrics like order frequency and average basket size. Implementation approach: I will first build a reproducible data-prep script (Python/SQL) to ingest the CSV, clean inconsistencies, handle missing values, and structure tables (orders, customers, products). I’ll ensure the model supports efficient analysis and future scalability. In Power BI, I’ll create a well-organized data model with optimized relationships and DAX measures for order frequency and average basket size. The dashboard will include intuitive slicers (date, customer segments), and executive-friendly visuals for quick insights. Both desktop and mobile layouts will be designed for clarity. You’ll receive a .pbix file, clean scripts, and a README for easy refresh and reuse. A few quick questions: 1. Do you want customer-level segmentation (new vs returning)? 2. Any preferred time granularity (daily, weekly, monthly)? 3. Should we include additional KPIs (revenue, AOV trends)? 4. Will this connect to live data later or remain CSV-based?

@Jairo51019

As a seasoned web developer with extensive experience in creating tailored and scalable web solutions, I am confident that I can excel on this project. My strong command of diverse programming languages, including Python, combined with a solid understanding of application architecture will ensure an efficient and effective data-prep script to convert your raw purchase data into a model ready for Power BI. Not only do I offer expertise in SQL and Python to clean and structure your data, but I also possess a deep understanding of Power BI. In fact, I have previously created retail analytics using Power BI, aligning perfectly with your project requirements. My familiarity with DAX measures and other key functions in Power BI will aid me in providing you with intuitive slicers, logically organized tables, and impactful visuals that align with your strategic reporting needs. In addition to the technical competence, my commitment towards presenting high-quality work is unwavering. Hence, you can expect meticulous documentation or inline comments in the data-prep script for easy future use. Looking forward to collaborating with you and bringing this project to life!

@saba106

I am an expert statistician, Research Writer, and data analyst with more than eight years of experience. I have full command of Excel analysis, SPSS, STATA, R LANGUAGE, AND PYTHON. I am an expert in creating time series prediction models, working with survey data, conducting marketing analysis, building estimators, and medical analysis. I am a perfect match for your project share other details of the work so I can start working on your project. Will complete task on time.

@ianb65

Hello, there! I can build this end-to-end ecommerce analytics workflow from CSV to cleaned data model to a polished Power BI dashboard. I have strong experience with Python, SQL, Power BI, DAX, and retail-style reporting focused on order behavior, customer activity, and revenue insights. I will prepare a reproducible data-cleaning script that loads the CSV, validates fields, handles missing values, calculates clean order-level metrics, and outputs Power BI-ready tables. In the .pbix report, I’ll make order frequency and average basket size the main KPIs, with supporting slicers for time range, customer segments, SKU/category, and revenue trends. I’ll also include clean DAX measures, desktop and mobile-friendly dashboard layouts, and a short README so you can refresh future CSV exports without extra help. Best regards, Ian Brown

@DevDataAnalyst

Hi there I’ve spent the last 10+ years helping businesses make sense of their data — turning numbers and reports into clear, visual stories that drive smarter decisions. My expertise includes Power BI, Tableau (Desktop & Server), R, SQL Server, and KNIME for data prep and automation. Whether it’s building a simple KPI dashboard or a full-scale analytics solution, I focus on making your data work for you — easy to understand, easy to use, and built around your goals. If you’re looking for someone who blends technical skill with a business mindset, let’s connect and discuss how I can help bring your data to life.

@devanshkhandekar

Hi,I'm a seasoned Applied Data Scientist(6+ yoe) & I've strong hands-on experience in retail/ecommerce analytics,Python/SQL data prep & Power BI dashboard development. This project is a good fit for the kind of reporting workflows I’ve built in practice. Relevant past work: -Retail analytics dashboards in Power BI: built sales/performance dashboards with KPIs like order trends,category contribution,customer repeat behavior,AOV/basket metrics & region/product drilldowns -Customer/churn analysis projects: prepared raw transaction data in Python/SQL,engineered behavioral features & visualized repeat purchase patterns,drop-offs & retention cohorts in BI dashboards -Consumer & market analysis: created dashboards combining transaction patterns,segmentation & summary metrics for business users -Refreshable reporting pipelines: delivered Python/SQL prep scripts + Power BI models so clients could refresh monthly/weekly data without manual cleanup -Executive-facing BI reports: focused on DAX measures,clean data models,consistent labels,filters & layouts that are easy to understand My approach would be straightforward: clean the CSV in Python/SQL,create a reporting-ready model,define robust measures for order frequency & average basket size & build a clean Power BI report with intuitive slicers,exec-friendly visuals & mobile-ready layout For your project,I’d make sure the PBIX is cleanly structured,measures are clearly named & the README is practical enough that you can refresh

@KarthikTechCon

Hi, I’m Karthik with 15+ years in **Data Engineering, SQL, Python and Power BI**, and I can build your **customer purchase analytics dashboard** end-to-end with strong business insight + executive-ready visuals. ✔ Data prep pipeline in Python/SQL for cleaning, modeling and transformation from CSV ✔ Power BI dashboard with focus on **Order Frequency** and **Average Basket Size** KPIs ✔ Well-structured data model, clean DAX measures, intuitive slicers and exec-friendly visuals ✔ Desktop + mobile optimized Power BI layout ✔ Documented .pbix + reusable refresh workflow/README included ✔ Can extend to cohort, RFM, repeat purchase and customer segmentation insights if desired Approach: • Inspect and model raw order data • Build transformation pipeline (Python/Pandas or SQL) • Create optimized Power BI model + DAX metrics • Design polished dashboard for fast decision-making • Deliver refresh-ready assets and documentation I’ve delivered **retail/eCommerce analytics, KPI dashboards, Power BI reporting and customer behavior models**, with strong focus on both calculation accuracy and presentation quality. Can start immediately, review your sample CSV, and turn around a clean actionable dashboard quickly. Happy to share relevant Power BI examples. — Karthik

@chiragardeshna

Hi there, Strong alignment with this project comes from experience delivering analytics dashboards where data accuracy, clarity, and business insight are essential. Clear understanding of the requirement to process raw CSV data using SQL/Python, build reliable transformations, and create a Power BI dashboard highlighting order frequency and average basket size. Hands-on expertise with data modeling, DAX measures, and Power BI ensures clean visuals, intuitive filters, and executive-ready insights. Risk is minimized through validated data pipelines, clear documentation, and reproducible workflows. Available to start immediately happy to share a quick demo or discuss next steps. Recent work: https://www.freelancer.com/u/chiragardeshna Regards Chirag

@murtaza1981

Hi, I’ll turn your CSV into a clean pipeline + executive-ready Power BI dashboard—with order frequency & basket size front and center. Rate: $30/hr | Capacity: 40 hrs/week ? Budget-First (Fast Insights) Timeline: ~3–4 days (24–32 hrs) Cost: ~$720–$960 Scope: Python/SQL ETL, clean model, DAX for order frequency & avg basket size, slicers, one polished dashboard, basic README Best for: Quick, accurate reporting ? Premium (Polished + Scalable) Timeline: ~5–6 days (40–56 hrs) Cost: ~$1,200–$1,680 Scope: Robust data model, optimized DAX, time intelligence, mobile layout, performance tuning, detailed docs, reusable pipeline Best for: Long-term, production-ready analytics ? Milestones Data Audit + ETL – 25% Modeling + DAX – 30% Dashboard + UX – 30% QA + Handover – 15% ✔ Clear KPIs (frequency, basket size) ✔ Clean, maintainable code ✔ Fast, intuitive visuals No fluff—data → insight → decisions, delivered on time. Thanks, Murtaza

@johibkhan

Hi, With over 15 years of experience as a software developer, my expertise lies in handling and transforming data - a critical skill for your project. Having worked with renowned companies like Avaya and CGI, I've demonstrated my proficiency in Python, SQL, and data processing - the exact set of skills needed to manage your CSV extract and transform it into a clean model for Power BI consumption. My extensive experience includes not only coding but also understanding complex business requirements and translating them into effective technical solutions. My specialty in building strategic reporting aligns well with your project's goal. You specifically mentioned the need for order frequency and average basket size as front-and-center metrics. Utilizing my analytical skills, I can develop intuitive visualization through Power BI that executives can easily comprehend. Moreover, my strenghts in algorithmic thinking will ensure that data transformation is both efficient and robust, adding value to the final dashboard.

@CauaAlves

Dear Client, I am ready to build your Customer Purchase Analytics Dashboard in Power BI exactly as requested. My approach: Load and clean the raw CSV using Python (pandas) – reproducible script included Create a clean, optimized data model in Power BI Build clear DAX measures for Order Frequency and Average Basket Size Design an executive-friendly dashboard with intuitive slicers (time range, customer segments, etc.), KPIs, and visuals that can be understood in seconds Deliverables: Clean, well-commented Python data-prep script Fully functional .pbix file (desktop + mobile optimized) All DAX measures clearly labeled and documented Short README with instructions on how to refresh the data The dashboard will highlight the key metrics you need and look professional and clean. I specialize in turning raw purchase data into actionable Power BI dashboards and can start immediately. Looking forward to delivering this for you.

@Hrolgar

Before pricing this tightly, one shape question: when you say raw purchase data, do you mean CSV or Excel exports landing in a folder, a Postgres/MySQL dump, or live read access to a transactional DB? That answer flips the architecture. Batch ETL (cron or dbt running nightly against staging) versus a read-replica view feeding the dashboard live. The cleaning logic is similar; the refresh and ops story is not. Either way I'd build a proper dimensional layer (customer, product, date dims plus a purchase fact) rather than charting straight off transactional rows. That's what makes cohort, repeat-purchase, and product-mix views actually fast and reusable. Stack-wise, Metabase or Superset gets you there in days if you want zero-maintenance; custom React plus Recharts is the call only if you need bespoke UX or to embed in an existing app. M1: data audit, schema design, cleaning script proof on a sample. $200, 3d. M2: ETL pipeline plus analytics tables in Postgres or SQLite. $200, 4d. M3: dashboard scaffolding with core views (revenue, cohort, top products). $160, 4d. M4: polish, scheduled refresh, handover doc. $80, 3d. Total $640 over 14 days. Quick check before I tighten this: what does the raw input actually look like today (file drop, DB dump, live access)?

@VianRey1

Digging through messy purchase data just to answer basic questions like order frequency and basket size can slow down decision making and eats up valuable time, especially when reporting needs to impress execs as much as it informs. It’s frustrating when the numbers you need are buried in a spreadsheet, and even more so when the final report feels clunky or unclear. I’ll deliver a Power BI dashboard where order frequency and basket size are always clear and up front, with clean visuals that make sense at a glance. You can expect a setup that lets you refresh your insights anytime, without hassle. First, I’ll review your CSV and build a data-prep script in SQL or Python to handle all the cleaning and shaping. Then, I’ll craft a Power BI file with intuitive visuals and labeled measures so your key metrics are always front and center. Finally, I’ll include clear comments or a quick README so updating the data is simple. Is there a particular time period or product category you want to prioritize for the initial dashboard view?