The Segment Tool provides information on the causes of death and age groups that are driving inequalities in life expectancy at local area level. Targeting the causes of death and age groups which contribute most to the life expectancy gap should have the biggest impact on reducing inequalities.

The tool provides data tables and charts showing the breakdown of the life expectancy gap in 2020 to 2021 for 2 comparisons:

• England: the gap between each local area or region as a whole and England as a whole
• within area: the gap between the most deprived quintile of each area and the least deprived quintile of the area

The tool contains data for England, English regions and upper tier local authorities.

Segmentation of the adult England population with interactive tool and raw data to help understand where different types of people are located and how to reach them. Postcode level data with segment counts available to download. Youth segmentation is being developed and will be added to this tool in autumn 2013

Segmentation study final dataset (anonymised). The variables included attitudes to climate change and transport, information on transport behaviour and classification information e.g. age; gender; ethnicity; income group. Data collection ceased.

Overview This product, with over 100 actual and modelled variables, is designed to help you gain better insight into your customers and prospects. The Enhance dataset provides users with a set of predictive and descriptive attributes which support more informed, targeted and relevant marketing to consumers.

What is it? Enhance Core is an individual level data set, containing self-declared, freely given socio-demographic data on over 90m individuals. The data is obtained from a range of sources, including; Satisfaction & Lifestyle surveys, Website Registrations, Newsletter & Service subscriptions, Offers & Competition websites and public Social Media feeds.

Use cases -Using key information, appended from Enhance, to create personalised messaging for direct mail & digital marketing campaigns - Using Profiling & Predictive messaging to identify important cohorts within the customer base, and those that can be “Forgotten” - Seeing how the current customer base compares to the UK base, so you can identify which potential audiences you are missing and also those that your business excels in. - Segment your customers into distinct groups so that you can offer them the right products through the most appropriate channels

Additional Insights Enhance Core, Property & Geo (Individual, Property & Postcode level data) can all be used modularly, allowing you to understand the full picture of your customer base, considering not only their individual variance but also where they live & those around them.

This profile is designed to accompany the Joint Strategic Needs Assessment (JSNA) chapter on Demographics, which looks at segmenting the borough’s population by their most significant health and social care need. This supplement looks at adults (aged 18 and over) instead of the overall population, because the health and social care need segments covered in this section are more common in adults.

The Segment Tool provides information on the causes of death and age groups that are driving inequalities in life expectancy at local area level. Targeting the causes of death and age groups which contribute most to the life expectancy gap should have the biggest impact on reducing inequalities.

The tool provides data tables and charts showing the breakdown of the life expectancy gap in 2015 to 2017 for 2 comparisons:

• England: the gap between each local authority or region as a whole and England as a whole
• within area: the gap between the most deprived quintile of each area and the least deprived quintile of the area

This is the spatial framework around which the Living England Phase II habitat classification is based. The segmentation was created in the Trimble eCognition software using Sentinel-2 Analysis Ready Data (ARD) image mosaics for winter (February 2019) and summer (June 2019).

This is a transnational data set which contains all the transactions that occurred between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique and all-occasion gifts.

Overview The better you understand your customers, the better you can communicate with them. Better communication means improved response rates and customer retention, whilst also creating a better perception of your company. In heavily saturated markets, opinion is one of the few drivers of choice - help customers choose you and save money while you’re at it. More targeted campaigns reduce wasted spend across all marketing endeavours.

What is it? We receive data from multiple contributors and sources to create the Enhance dataset, which contains over 90m records with over 350 potential variables across three matching levels: Individual (Enhance Core), Household (Enhance Property), and Postcode (Enhance Geo). These variables can be used to profile, model, and “Enhance” your current customer base.

Use cases

• Enriching current customer datasets (Fill in the gaps in your CRM)
• Obtaining customer insights (Find out what makes your customers special, and how you can best communicate with them)
• Profiling & Predictive modelling (Work out which customers are most likely to buy another product/churn/upsell, allowing you to plan your efforts accordingly)
• Identify which households within your customer base are the best kind of customers, using that to identify new, promising prospects
• Trigger communications based on home moves

Additional Insights Enhance can be utilised alongside our Suppression services to ensure that your data is not only capable of providing the highest level of insights, but it is also as clean & accurate as possible. Business decisions made from poor data end up being poor decisions.

To ensure that we can provide as much information as possible, Enhance can be combined with SmartLink to identify every permutation of an individual, reducing the gaps in your data and providing a holistic picture.

Doorda's UK Health Data provides a comprehensive database covering 1.8M postcodes sourced from 20 data sources, offering unparalleled insights for local area health insights and analytics purposes.

Volume and stats: - 1.8M Postcodes - UK Coverage - Age and Gender bands

Our Health Data offers a multitude of use cases: - Market Analysis - Geodemographic Insights - Risk Management - Location Planning

The key benefits of leveraging our Health Data include: - Data Accuracy - Informed Decision-Making - Competitive Advantage - Efficiency - Single Source

Covering a wide range of industries and sectors, our data empowers organisations to make informed decisions, uncover market trends, and gain a competitive edge in the UK market.

This factsheet breaks down Camden’s population by looking at health conditions, and then by their age, sex, ethnicity, and deprivation. Understanding the size and characteristics of each segment helps us plan healthcare resources and service delivery effectively for each group, as well as the population in general.

Society is becoming increasingly connected. Technology has allowed us to be online anytime and anywhere, through portable devices such as smart phones and tablets. This means that consumers have instant access to goods, products, and services. Connection is also extending through the Internet of Things, where big data is collected through wearable tech, connected cars, and smart homes. The result is that consumers expect and look for ever-responsive, convenient, tailored services via a channel they can access from wherever they choose. Catering for digitally active and tech-savvy individuals should be important for insurers, considering that 42% of consumers identified with the Digital Lifestyles trait according to Verdict Financials 2015 UK General Insurance Consumer Survey. Read More

The Segment Tool has been updated by Public Health England (PHE).

The tool provides information on life expectancy and the causes of death that are driving inequalities in life expectancy at local area level. Targeting the causes of death which contribute most to the life expectancy gap should have the biggest impact on reducing inequalities.

This update of the tool provides data for a more recent time period and incorporates some small changes to the data sources used. The changes are:

• update of data to 2012 to 2014
• update to use the new Index of Multiple Deprivation 2015 to define the most and least deprived areas
• adjustment of counts of deaths to take account of changes in ICD-10 coding made in 2014

This presentation of the tool is the same as the previous version but, because of these changes, results are not directly comparable.

As well as the tool, a summary report is available for each local authority which contains the charts and tables. Summary reports for the English regions and for England as a whole are also available.

http://fingertips.phe.org.uk/profile/segment">View the Segment tool

Explore Doorda's UK Health Data, offering insights into 1.8M postcodes sourced from 20 data sources. These cover Obesity, Smoking, and Life expectancy to name a few. Unlock local health insights and analytics capabilities.

Goals :

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description).

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.

4. Geographical Analysis:

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

5. Sales Performance Dashboard:

To track the sales performance of the online retail company, a sales performance dashboard can be created. This dashboard can include key metrics such as total sales, sales by product category, sales by customer segment, and sales by geographical location. By visualizing the sales data in an interactive dashboard, it becomes easier to identify trends, patterns, and areas for improvement.

Research Ideas ****:

1. Inventory Management: By analyzing the quantity and frequency of product sales, retailers can effectively manage their stock and predict future demand. This would help ensure that popular items are always available while less popular items aren't overstocked.
2. Customer Segmentation: Data from different countries can be used to understand buying habits across different geographical locations. This will allow the retail company to tailor its marketing strategy for each specific region or country, leading to more effective advertising campaigns.
3. Sales Trend Analysis: With data spanning almost a year, temporal patterns in purchasing behavior can be identified, including seasonality and other trends (like an increase in sales during holidays). Techniques like time-series analysis could provide insights into peak shopping times or days of the week when sales are typically high.
4. Predictive Analysis for Cross-Selling & Upselling: Based on a customer's previous purchase history, predictive algorithms can be utilized to suggest related products that might interest the customer, enhancing upsell and cross-sell opportunities.
5. Detecting Fraud: Analysing sale returns (marked with 'c' in InvoiceNo) across customers or regions could help pinpoint fraudulent activities or operational issues leading to those returns
6. RFM Analysis: By using the RFM (Recency, Frequency, Monetary) segmentation technique, the online retail company can gain insights into customer behavior and tailor their marketing strategies accordingly.

**************Steps :**************

1. Data manipulation and cleaning from raw data using SQL language Google Big Query
2. Data filtering, grouping, and slicing
3. Data Visualization using Tableau
4. Data visualization analysis and result

Success demands customer-centric models – a shift towards this approach is essential in order to create products that consumers want to buy and brands that consumers want to associate themselves with. Attitudinal traits are rarely mutually exclusive – products or services that cater to a trend crossover offer consumers multiple benefits and will be attractive to a wider range of consumers. Trust is key. A sense of honesty from an insurance provider is not only the most widely sought factor among consumers – regardless of customer group – but also drives among the strongest sources of sentiment. Read More

Please note: This is a large data product with 2.7 million polygon features (1.2GB file in ESRI File Geodatabase format). It is not possible to download in Shapefile format. Please access the data using the APIs or select another download format.This is the spatial framework around which the Living England Phase II habitat classification is based. The segmentation was created in the Trimble eCognition software using Sentinel-2 Analysis Ready Data (ARD) image mosaics for winter (February 2019) and summer (June 2019).

Sentinel-2 Analysis Ready Data (ARD) produced by the Earth Observation Data Service (JNCC / DEFRA) were used as the input for the segmentation. The Sentinel-2 ARD is available under an Open Government License (OGL). It is not intended that the 2019 segmentation will be revised, however, as Living England progresses and up-to-date image mosaics are created new habitat segmentation datasets will be developed from the up-to-date imagery.Full metadata can be viewed on data.gov.uk.

The Household Gas Disaggregation dataset provides appliance-level insights into how households use gas across key categories such as heating, hot water, cooking, and other uses. Each record corresponds to a single household (user_id) and reports monthly gas consumption in kilowatt-hours (kWh), along with the percentage contribution of each category to total household gas usage.

Currently, the dataset covers 26,000+ households, with coverage expanding monthly as new data becomes available. This growing dataset enables comprehensive analysis of domestic gas consumption patterns and appliance-level energy use.

Key attributes include:

1. user_id: Unique anonymised household identifier (can be linked with other datasets).
2. created_at: Timestamp for record creation.
3. id: Unique record identifier.
4. period_type & period: Aggregation period (e.g., month) and corresponding date.
5. type: Fuel type (gas).
6. category: End-use category ( heating, hot water, cooking, other.)
7. energy (kWh): Absolute gas consumption for each category.
8. percentage (%): Proportion of total household gas usage represented by each category (sums to 100% per household).

Ideal for: - Energy analysis: Understand household gas consumption patterns at the category level. - Demand forecasting: Support predictive models for heating and hot water demand. - Energy efficiency & decarbonisation research: Identify opportunities for reducing gas consumption and improving building performance. - Behavioural insights: Explore how different households allocate gas use between heating, hot water, and cooking. - Segmentation & policy design: Profile households based on gas use intensity or category distribution. - All data is anonymised to protect household privacy while offering high-value analytical insights.

When linked with other datasets, such as Household Profiles, Property Characteristics, or Half-Hourly Gas Consumption, it supports a wide range of applications in energy analytics, policy design, and data-driven innovation.