Google Ads Sales Dataset for Data Analytics Campaigns (Raw & Uncleaned) 📝 Dataset Overview This dataset contains raw, uncleaned advertising data from a simulated Google Ads campaign promoting data analytics courses and services. It closely mimics what real digital marketers and analysts would encounter when working with exported campaign data — including typos, formatting issues, missing values, and inconsistencies.

It is ideal for practicing:

Data cleaning

Exploratory Data Analysis (EDA)

Marketing analytics

Campaign performance insights

Dashboard creation using tools like Excel, Python, or Power BI

📁 Columns in the Dataset Column Name ----- -Description Ad_ID --------Unique ID of the ad campaign Campaign_Name ------Name of the campaign (with typos and variations) Clicks --Number of clicks received Impressions --Number of ad impressions Cost --Total cost of the ad (in ₹ or $ format with missing values) Leads ---Number of leads generated Conversions ----Number of actual conversions (signups, sales, etc.) Conversion Rate ---Calculated conversion rate (Conversions ÷ Clicks) Sale_Amount ---Revenue generated from the conversions Ad_Date------ Date of the ad activity (in inconsistent formats like YYYY/MM/DD, DD-MM-YY) Location ------------City where the ad was served (includes spelling/case variations) Device------------ Device type (Mobile, Desktop, Tablet with mixed casing) Keyword ----------Keyword that triggered the ad (with typos)

⚠️ Data Quality Issues (Intentional) This dataset was intentionally left raw and uncleaned to reflect real-world messiness, such as:

Inconsistent date formats

Spelling errors (e.g., "analitics", "anaytics")

Duplicate rows

Mixed units and symbols in cost/revenue columns

Missing values

Irregular casing in categorical fields (e.g., "mobile", "Mobile", "MOBILE")

🎯 Use Cases Data cleaning exercises in Python (Pandas), R, Excel

Data preprocessing for machine learning

Campaign performance analysis

Conversion optimization tracking

Building dashboards in Power BI, Tableau, or Looker

💡 Sample Analysis Ideas Track campaign cost vs. return (ROI)

Analyze click-through rates (CTR) by device or location

Clean and standardize campaign names and keywords

Investigate keyword performance vs. conversions

🔖 Tags Digital Marketing · Google Ads · Marketing Analytics · Data Cleaning · Pandas Practice · Business Analytics · CRM Data

TechCorner Mobile Purchase & Engagement Data (2024-2025)

Context

TechCorner Mobile Sales & Customer Insights is a real-world dataset capturing 10 months of mobile phone sales transactions from a retail shop in Bangladesh. This dataset was designed to analyze customer location, buying behavior, and the impact of Facebook marketing efforts.

The primary goal was to identify whether customers are from the local area (Rangamati Sadar, Inside Rangamati) or completely outside Rangamati. Since TechCorner operates a Facebook page, the dataset also includes insights into whether Facebook marketing is effectively reaching potential buyers.

Additionally, the dataset helps in determining: ✔ How many customers are new vs. returning buyers ✔ If customers are followers of the shop’s Facebook page ✔ Whether a customer was recommended by an existing buyer

This dataset is valuable for:

Retail sales analysis to understand product demand fluctuations.

Marketing impact measurement (Facebook engagement vs. actual purchase behavior).

Customer segmentation (local vs. non-local buyers, social media influence, word-of-mouth impact).

Sales trend analysis based on preferred phone models and price ranges.

With a realistic, non-uniform distribution of daily sales and some intentional missing values, this dataset reflects actual retail business conditions rather than artificially smooth AI-generated data.

Marketing & Customer Queries

Does he/she Come from Facebook Page? → Whether the customer came from a Facebook page (Yes/No). Used to analyze Facebook marketing reach.

Does he/she Followed Our Page? → Whether the customer is already a follower of the shop’s Facebook page (Yes/No). Helps measure brand loyalty and organic engagement.

Did he/she buy any mobile before? → Whether the customer is a repeat buyer (Yes/No). Determines the percentage of returning customers.

Did he/she hear of our shop before? → Whether the customer knew about the shop before purchasing (Yes/No). Identifies the impact of referrals or previous marketing efforts.

Was this customer recommended by an old customer? → Whether an existing customer referred them to the shop (Yes/No). Helps evaluate the effectiveness of word-of-mouth marketing.

Acknowledgements

This dataset is derived from real-world mobile sales transactions recorded at TechCorner, a retail shop in Bangladesh. It accurately reflects customer purchasing behavior, pricing trends, and the effectiveness of Facebook marketing in driving sales. Special appreciation to TechCorner for providing comprehensive insights into daily sales patterns, customer demographics, and market dynamics.

This dataset can be used for:

📊 Predictive modeling of sales trends based on customer demographics and marketing channels. 📈 Marketing effectiveness analysis (impact of Facebook promotions vs. organic sales). 🔍 Clustering customers based on purchasing habits (new vs. returning buyers, Facebook users vs. walk-ins). 📌 Understanding demand for different smartphone brands in a local retail market. 🚀 Analyzing how word-of-mouth recommendations influence new customer acquisition.

💡 Can you build a model to predict if a customer is likely to return? 💬 How effective is Facebook in driving actual sales compared to walk-ins? 🔍 Can we cluster customers based on behavior and brand preferences?

In 2024, Meta (formerly Facebook Inc) generated over 160 billion U.S. dollars in ad revenues. Advertising accounts for the vast majority of the social network's revenue. Facebook advertising revenue – additional information Facebook’s business model heavily relies on ads, as the majority of social network’s revenue comes from advertising. In 2020, about 97.9 percent of Facebook's global revenue was generated from advertising, whereas only around two percent was generated by payments and other fees revenue. Facebook ad revenue stood at close to 86 billion U.S. dollars in 2020, a new record for the company and a significant increase in comparison to the previous years. For instance, the social network generated almost seven billion U.S. dollars in ad revenue in 2013, about 10 billion less than the 2015 figure. Facebook's average revenue per user also significantly increased in the same time span, going from 6.81 U.S. dollars in 2013 to 32.03 U.S. dollars in 2020. The U.S. and Canada are important markets for Facebook, considering the average revenue per user (ARPU) in these two countries is far above the global average. Facebook’s ARPU in the U.S. and Canada was 41.41 U.S. dollars in the last quarter of 2019, while the global average was 8.52 U.S. dollars. In Europe, Facebook’s average revenue per user was 13.21 U.S. dollars during the same time period. In terms of segments, mobile is the most promising advertising form for the company. In 2018, Facebook’s mobile advertising revenue already accounted for 92 percent of the social network’s total advertising revenue. Facebook’s mobile advertising revenue grew from an estimate of 13 billion U.S. dollars in 2015 to 50.6 billion U.S. dollars in 2018.

Introduction Paid Search is one of the most efficient way to promote your products or services nowadays as we all know. Companies ''fight'' to get the best keywords to appear in the first google search page.

About the dataset The dataset is a real dataset contanining data of a 2021 5 months paid search campaign of a Us shopping mall (that for privacy reasons for this project the name has been anonymized) promoting through paid search coupons and promo codes. The dataset contains information about the ad group advertising, clicks, conversions, impressions, revenues and costs(see below the dataset dictionary).

Dataset Dictionary:

Ad Group: category of the advert (coupon/promo code, desktop ad/mobile ad etc...)

Month: month of the campaign. The campaign started in July 2021 and ended in November 2021.

Impressions: metric used in digital marketing to quantify the number of digital views or engagements of an advertisement. Impressions are also referred to as an "ad view.

Clicks: how many clicks the ad received

CTR: Click Through Rate, the number of clicks that your ad receives divided by the number of times your ad is shown: clicks ÷ impressions = CTR.

Conversions: Conversions are those valuable actions that users take on your site like buying something or filling in a form. The success can be measured in the number of conversions generated at a particular cost.

Conv Rate: Conversion Rate. It is the percentage of people who convert after clicking on your ads. Depending on your goals, a conversion may mean they make a purchase, complete a contact form, request a free trial, or take another desired action.

Cost: Cost is the actual money spent by the advertiser (the "shop") for the related ad group.

CPC: Cost Per Click, it is the cost of the specific ads divided by the click. It is one of the metrics used to evaluate the effectiveness of the campaign in terms of ROI (Return on Investment), therefore a low or decreasing CPC is better than a high or increasing CPC.

Revenue: Revenue is the total amount of income generated by advertisment.

Sale Amount: Sale Amount for this dataset means the quantity of sales derived by the single ad group.

P&L: Profit and Loss, based on the formula Revenue - Cost. For this dataset mesaures the profit of the specific Ad Group.

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Data can be accessed in two flexible ways:

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2. Via API — automate contact discovery, data enrichment, or validation at scale

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Bytemine’s phone number dataset is trusted by sales teams, marketing agencies, growth hackers, product teams, and data-driven platforms that rely on accurate contact information to engage the right audience.

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Context

Digital advertising is promotional material delivered to a target audience through digital platforms, including social media, email, search engines, mobile apps, affiliate programs, and websites. One of the main benefits of digital advertising is an advertiser can track in real-time the success of the campaign. The goal of digital advertising is to inorganically advertise where consumers are and to customize ads to the target audience's preferences.

Content

Technology in the digital advertising space is fast-paced and evolving at lightning speed. Competing effectively requires every company to constantly transform itself with the latest industry trends and standards. Whether you want to integrate new technology solutions into your existing advertising cloud or want to build a proprietary solution from scratch. Through this Dataset, you will be able to explore how different attributes of Digital Advertisement are impacting the revenue of a product company. There are many factors of Digital advertisement which impact business campaign such as WebSites, Geographical conditions, Type of devices, Monetization channels, total impressions, etc. In this Ad-Tech Dataset, we have 17 columns which will help you to explore the world of Digital Advertisement - date

site_id

ad_type_id

geo_id

device_category_id

advertiser_id

order_id

line_item_type_id

os_id

integration_type_id

monetization_channel_id

ad_unit_id

total_impressions

total_revenue

viewable_impressions

measurable_impressions

revenue_share_percent

Use your Analytical and ML skills to explore data, find corelation between fields, and predict total revenue in order to know the Impact of Digital Advertisement for any Business.

Inspiration

By exploring this dataset you will able to understand how much digital advertisement can impact your business in today's world.

Discover the ultimate resource for your B2B needs with our meticulously curated dataset, featuring 148MM+ highly relevant US B2B Contact Data records and associated company information.

Very high fill rates for Phone Number, including for Mobile Phone!

This encompasses a diverse range of fields, including Contact Name (First & Last), Work Address, Work Email, Personal Email, Mobile Phone, Direct-Dial Work Phone, Job Title, Job Function, Job Level, LinkedIn URL, Company Name, Domain, Email Domain, HQ Address, Employee Size, Revenue Size, Industry, NAICS and SIC Codes + Descriptions, ensuring you have the most detailed insights for your business endeavors.

Key Features:

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Phone Number Data Inclusion: Benefit from our comprehensive Phone Number Data, ensuring you have direct and effective communication channels. Explore our Phone Number Datasets and Phone Number Databases for an even more enriched experience.

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Reach out to us today to explore flexible pricing options and discover how Salutary Data Company Data, B2B Contact Data, B2B Marketing Data, B2B Email Data, Phone Number Data, Phone Number Datasets, and Phone Number Databases can transform your business strategies. Elevate your decision-making with top-notch B2B intelligence.

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