Our product deciphers hidden insights between data and revenue
FUSE LOCATIONS WITH ARTIFICIAL INTELLIGENCE
FUSE LOCATIONS WITH ARTIFICIAL INTELLIGENCE
Manage probabilities and adapt new certanties with confidence, reduce customer churn, also increase revenue by assessing potential new customers.
Identify your risks very early, maximize the value of your investments (ROI) with power of perspective into usage, spend, vulnerabilities.
Unleash the wisdom of data science by modeling, predicting, aligning your business with evolving needs and prescribing your organization.
Clearly visualize, manage and understand data on a highly customizable map to see associations and casualities, both on-prem or in cloud servers.
Help your organization to raise the bar to the most pragmatic height for sales, marketing, stock optimization, replenishment planning by forecasting the demand.
Analyze location based circumstances about your competitors, identify existing or possible revenue cannibalism to make a leap in the right direction.
Get location and area reports, either to open new branches or to do right advertisement for your target audience and their profiles.
Use predictive maintenance to detect possible anomalies in your business domain, identify outliers, a broken equipment or a fraud attempt.
Broad street analytics can be implemented in servers on premise or as a SaaS solution which means data doesn't have to leave your company servers.
You will get access to our statistics database which contains ~350k locations in Sweden and demographics data in postalcode level from the get-go.
Name of "Broad Street Analytics" was inspired from Dr. John Snow and his study during 1850's about cholera outbreak in London. One of the most powerful examples of astute data visualization eventually leading to the establishment of causality dates back more than 150 years. It was not yet known that germs cause disease and cholera was among the most feared. The disease arrived suddenly and was almost immediately deadly: people died within a day or two of contracting it, hundreds could die in a week, and the total death toll in a single wave could reach tens of thousands. The leading theory was that "miasmas" were the main culprit. Miasmas manifested themselves as bad smells, and were thought to be invisible poisonous particles arising out of decaying matter.
Dr. Snow had noticed that the onset of the disease almost always involved vomiting and diarrhea. He therefore believed that the infection was carried by something people ate or drank, not by the air that they breathed.
At the end of August 1854, cholera struck in the overcrowded Soho district of London. As the deaths mounted, Snow recorded them diligently, using a method that went on to become standard in the study of how diseases spread: he drew a map. On a street map of the district, he recorded the location of each death.
You can see Snow's original map above and click to expand. Each black bar represents one death. When there are multiple deaths at the same address, the bars corresponding to those deaths are stacked on top of each other. The black discs mark the locations of water pumps. The map displays a striking revelation—the deaths are roughly clustered around the Broad Street and its water pump. Although association and causality are two different things in science, noticing this association was the first hint. He later discovered sewage water had been contaminating some parts of River Thames, hence, drinking water which had been supplied by Broad Street pump.
Reference and more details: Computational and Inferential Thinking
30-page-report that compares 4 different impact areas of a location
Area analysis on top of a heatmap which represents male population between age 30 and 35
Classification refers to supervised predictive modeling where a class label is predicted for a given example of input data.
Clustering is an unsupervised machine learning task. Unlike supervised learning methods, clustering involves discovering natural grouping in data without class labels.
Deep learning is a type of artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Can be supervised, semi-supervised, unsupervised.
A type of supervised machine learning where the data is continuously split according to certain parameters. The leaves represent the possibility of the decisions or the final outcomes.
Very briefly, the goal of regression model is to build a mathematical equation that defines the output as a function of the input variables.
Imputation means replacing missing data with substituted values. Missing data can introduce substantial amount of bias, challenges in model interpretation and reduced accuracy.
Outlier Analysis is a process that involves identifying the anomalies in the dataset. Action to take about outliers depends on the challenge.
Refers to the techniques of preparing (cleaning and organizing) the raw data to make it suitable for building and training machine learning models.
These are the techniques to reduce number of input variables in a dataset increasing interpretability but at the same time minimizing information loss.
Broad Street Analytics is here to help!
You don't have to be a data scientist to use this product and to benefit from data science. If you are a potential client or looking for partnership or just curious, let's talk!