This is a case study for the company to improve sales

Business Goal
Date: 2023/09/15
Dataset: Sales quantity of a certain brand from January to December 2022 and from January to September 2023.

Please describe what you observe (no specific presentation format required). Among your observations, identify at least three valuable insights and explain why you consider them valuable.
If more resources were available to you (including time, information, etc.), what would you need, and what more could you achieve?

Metadata of the file Data Period: January 2022 - September 2023 Data Fields: - item - store_id - sales of each month

Metadata of the file Data Period: January 2022 - September 2023 Data Fields: - item - store_id - sales of each month

Sample question & answer 1. Product insights: identify the product sales analysis, such as BCG matrix 2. Store insights: identify the sales performance of the sales 3. Supply chain insights: identify the demand 4. Time series forecasting: identify tread, seasonality

Feel free to leave comments on the discussion. I'd appreciate your upvote if you find my dataset useful! 😀

In 2019, retail e-commerce sales in Brazil amounted to approximately 25.3 billion U.S. dollars, and are projected to increase to over 31 billion dollars by 2023. According to estimates, e-commerce retail sales in Argentina will grow to reach more than five billion U.S. dollars by 2023.

The chart provides an insightful analysis of the estimated sales amounts for Consumer Electronics stores across various platforms. Custom Cart stands out, generating a significant portion of sales with an estimated amount of $1.23T, which is 94.93% of the total sales in this category. Following closely, Shopify accounts for $28.86B in sales, making up 2.23% of the total. Salesforce Commerce Cloud also shows notable performance, contributing $14.42B to the total sales, representing 1.11%. This data highlights the sales dynamics and the varying impact of each platform on the Consumer Electronics market.

Retail Sales in Finland increased 0.90 percent in April of 2025 over the previous month. This dataset provides - Finland Retail Sales MoM - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States - Total Business Sales was 1733841.00000 Mil. of $ in February of 2025, according to the United States Federal Reserve. Historically, United States - Total Business Sales reached a record high of 1974148.00000 in December of 2024 and a record low of 478951.00000 in January of 1992. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Total Business Sales - last updated from the United States Federal Reserve on May of 2025.

Online Retail Transaction Data

UK Online Retail Sales and Customer Transaction Data

By UCI [source]

About this dataset

Comprehensive Dataset on Online Retail Sales and Customer Data

Welcome to this comprehensive dataset offering a wide array of information related to online retail sales. This data set provides an in-depth look at transactions, product details, and customer information documented by an online retail company based in the UK. The scope of the data spans vastly, from granular details about each product sold to extensive customer data sets from different countries.

This transnational data set is a treasure trove of vital business insights as it meticulously catalogues all the transactions that happened during its span. It houses rich transactional records curated by a renowned non-store online retail company based in the UK known for selling unique all-occasion gifts. A considerable portion of its clientele includes wholesalers; ergo, this dataset can prove instrumental for companies looking for patterns or studying purchasing trends among such businesses.

The available attributes within this dataset offer valuable pieces of information:

• InvoiceNo: This attribute refers to invoice numbers that are six-digit integral numbers uniquely assigned to every transaction logged in this system. Transactions marked with 'c' at the beginning signify cancellations - adding yet another dimension for purchase pattern analysis.

• StockCode: Stock Code corresponds with specific items as they're represented within the inventory system via 5-digit integral numbers; these allow easy identification and distinction between products.

• Description: This refers to product names, giving users qualitative knowledge about what kind of items are being bought and sold frequently.

• Quantity: These figures ascertain the volume of each product per transaction – important figures that can help understand buying trends better.

• InvoiceDate: Invoice Dates detail when each transaction was generated down to precise timestamps – invaluable when conducting time-based trend analysis or segmentation studies.

• UnitPrice: Unit prices represent how much each unit retails at — crucial for revenue calculations or cost-related analyses.

Finally,

• Country: This locational attribute shows where each customer hails from, adding geographical segmentation to your data investigation toolkit.

This dataset was originally collated by Dr Daqing Chen, Director of the Public Analytics group based at the School of Engineering, London South Bank University. His research studies and business cases with this dataset have been published in various papers contributing to establishing a solid theoretical basis for direct, data and digital marketing strategies.

Access to such records can ensure enriching explorations or formulating insightful hypotheses about consumer behavior patterns among wholesalers. Whether it's managing inventory or studying transactional trends over time or spotting cancellation patterns - this dataset is apt for multiple forms of retail analysis

How to use the dataset

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance. You can use the Quantity and UnitPrice fields to calculate metrics like revenue, and further combine it with InvoiceNo information to understand sales over individual transactions.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description). You could analyse which products are most popular based on Quantity sold or look at popularity per transaction by considering both Quantity and InvoiceNo.

3. Customer Segmentation:

If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better. Concatenating invoice numbers (which stand for separate transactions) per client will give insights about your clients as well.

4. Geographical Analysis:

The Country column enables analysts to study purchase patterns across different geographical locations.

Practical applications

Understand what products sell best where - It can help drive tailored marketing strategies. Anomalies detection – Identify unusual behaviors that might lead frau...

The FCA has published the latest edition of its Product Sales Data (PSD) statistics. The FCA publishes the aggregated PSD received from firms operating in the mortgages, retail investments 1 January 2018 to 31 December 2023.

The FCA uses this data to assist it in regulating firms and to spot trends in the products sold in the UK market. It publishes this data so that consumers and market participants can see what firms are selling and understand the trends.

The statistic presents the total specialty food sales in the United States from 2012 to 2020. U.S. specialty food sales amounted to about 170.4 billion U.S. dollars in 2020.

Data showing how many books were sold in 2024 revealed that the printed book market remains healthy: a total of 782.7 million units were sold that year among outlets which reported to the source. Whilst this marked a small jump from the previous year, the figure peaked in 2021 and has not surpassed 800 million since. Trade paperbacks remained the dominant format. Book sales statistics Looking at book sales by year, 2005 to 2010 were the most lucrative for the printed book market, with well over 700 million units sold annually during that five-year period. After dropping below 600 million in 2012, gradual and consistent increases can be seen each year, with the exception of between the years 2018 and 2019. For bookstores though, how many books are sold each year depends on the success of key months across a twelve-month period. Bookstore sales in the United States are at their highest in December, January, and August, but figures for December are consistently higher than other months. Books are popular holiday gifts, with around 30 to 40 percent of consumers responding to annual surveys in each year from 2012 to 2020 saying that they planned to purchase books as presents during the festive season.

Introduction

Social Media Marketing Statistics: Social media marketing is a key part of any digital marketing plan today. With over 50% of the world’s population using social media, brands need to be active on these platforms. But it’s not just about making profiles and posting content. Effective social media marketing involves keeping up with changing algorithms and trends and understanding the behaviors of your target audience. Social media’s interactive and engaging nature helps businesses connect with their audience in ways they couldn’t before.

This opens up new opportunities for engaging with people, building the brand, and doing direct marketing. We shall shed more light on Social Media Marketing Statistics through this article.

China Retail Sales of Consumer Goods: MoM: SA data was reported at 0.580 % in Mar 2025. This records a decrease from the previous number of 0.620 % for Feb 2025. China Retail Sales of Consumer Goods: MoM: SA data is updated monthly, averaging 0.810 % from Feb 2011 (Median) to Mar 2025, with 170 observations. The data reached an all-time high of 4.980 % in May 2020 and a record low of -10.770 % in Jan 2020. China Retail Sales of Consumer Goods: MoM: SA data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HA: Retail Sales of Consumer Goods: National Statistical Bureau.

This table contains 3 series, with data for years 2016 - 2017 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Sales (3 items: Retail trade; Electronic shopping and mail-order houses; Retail E-commerce sales).

Retail Sales - Table 620-67001 : Total Retail Sales

Retail Trade, sales by industries based on North American Industry Classification System (NAICS), monthly.

Key information about Lithuania Retail Sales Growth

• Lithuania Retail Sales grew 1.5 % YoY in Jan 2025, compared with a 7.8 % increase in the previous month.
• Lithuania Retail Sales Growth YoY data is updated monthly, available from Jan 1999 to Jan 2025, with an average growth rate of 5.4 %.
• The data reached an all-time high of 34.9 % in Apr 2021 and a record low of -26.2 % in Nov 2009.
• In the latest reports, Car Sales of Lithuania recorded 47,382.0 units in Dec 2019, representing a growth of 30.7 %.

Snapshot img

Sales Intelligence Market Size 2025-2029

The sales intelligence market size is forecast to increase by USD 4.86 billion at a CAGR of 17.6% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing demand for custom-made solutions that cater to the unique needs of businesses. This trend is fueled by the rapid advancements in cloud technology, enabling real-time access to comprehensive and accurate sales data from anywhere. However, the high initial cost of implementing sales intelligence solutions can act as a barrier to entry for smaller organizations. Furthermore, regulatory hurdles impact adoption in certain industries, requiring strict compliance with data privacy regulations. With the advent of cloud computing and SaaS customer relationship management (CRM) systems, businesses are able to store and access customer information more efficiently. Moreover, the exponential growth of marketing intelligence, driven by big data and natural language processing (NLP) technologies, enables organizations to gain valuable insights from customer interactions.
Despite these challenges, the market's potential is vast, with opportunities for growth in sectors such as healthcare, finance, and retail. Companies seeking to capitalize on these opportunities must navigate these challenges effectively, investing in cost-effective solutions and ensuring regulatory compliance. By doing so, they can gain a competitive edge through improved lead generation, enhanced customer insights, and streamlined sales processes.

What will be the Size of the Sales Intelligence Market during the forecast period?

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In today's business landscape, sales intelligence has become a critical driver of revenue growth. The go-to-market strategy of companies relies heavily on predictive lead scoring and sales pipeline analysis to prioritize opportunities and optimize resource allocation. Sales operations teams leverage revenue intelligence to gain insights into sales performance and identify trends. Data quality is paramount in sales analytics dashboards, ensuring accurate sales negotiation and closing. Sales teams collaborate using sales enablement platforms, which integrate CRM systems and provide sales performance reporting. Sales process mapping and sales engagement tools enable effective communication and productivity. Conversational AI and sales automation software streamline sales outreach and prospecting efforts. Messaging and alerting features help sales teams engage with potential customers effectively, while chatbots facilitate efficient communication.
Sales forecasting models and intent data inform sales management decisions, while salesforce automation and data governance ensure data security and compliance. Sales effectiveness is enhanced through sales negotiation training and sales enablement training. The sales market is dynamic, with trends shifting towards advanced analytics and AI-driven solutions. Companies must adapt to stay competitive, focusing on data-driven strategies and continuous improvement.

How is this Sales Intelligence Industry segmented?

The sales intelligence industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  Cloud-based
  On-premises


Component

  Software
  Services


Application

  Data management
  Lead management


End-user

  IT and Telecom
  Healthcare and life sciences
  BFSI
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period. In today's business landscape, sales intelligence platforms have become indispensable tools for organizations seeking to optimize their sales processes and gain a competitive edge. These solutions offer various features, including deal tracking, win-loss analysis, data mining, sales efficiency, customer journey mapping, sales process optimization, pipeline management, sales cycle analysis, revenue optimization, market research, data integration, customer segmentation, sales engagement, sales coaching, sales playbook, sales process automation, business intelligence (BI), predictive analytics, target account identification, lead generation, account-based marketing (ABM), sales strategy, sales velocity, real-time data, artificial intelligence (AI), sales insights, sales enablement content, sales enablement, sales funnel optimization, sales performance metrics, competitive intelligence, sales methodology, customer churn, and machine learning (ML) for sales forecasting and buyer person

Envestnet®| Yodlee®'s Consumer Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

China Industrial Enterprise: Product Sales Ratio data was reported at 98.100 % in 2018. This stayed constant from the previous number of 98.100 % for 2017. China Industrial Enterprise: Product Sales Ratio data is updated yearly, averaging 98.010 % from Dec 1999 (Median) to 2018, with 20 observations. The data reached an all-time high of 98.180 % in 2006 and a record low of 97.150 % in 1999. China Industrial Enterprise: Product Sales Ratio data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BF: Industrial Financial Data.

Introduction

Smartphone Sales Statistics: It's hard to imagine life without smartphones. They have become an essential part of our daily lives. Smartphones are everywhere, with over 80% of people in the United States using them for browsing the web, conducting business, messaging, and communication.

Despite being a relatively recent invention, smartphones can do a lot, from recognizing faces and enabling video chats to take high-quality photos and effortless internet browsing. Let's take a closer look at smartphone sales statistics

Hong Kong Electricity Sales: Total data was reported at 34,505.000 kWh mn in 2017. This records an increase from the previous number of 34,442.000 kWh mn for 2016. Hong Kong Electricity Sales: Total data is updated yearly, averaging 31,043.000 kWh mn from Dec 1989 (Median) to 2017, with 29 observations. The data reached an all-time high of 34,505.000 kWh mn in 2017 and a record low of 17,858.000 kWh mn in 1989. Hong Kong Electricity Sales: Total data remains active status in CEIC and is reported by CLP Power Hong Kong Limited. The data is categorized under Global Database’s Hong Kong SAR – Table HK.RB002: Electricity Sales Statistics.