BUSINESS ANALYTICS 

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A process of collating, sorting, processing, and studying useful business data, and using statistical models and iterative methodologies to transform data into real business insights. It is about solving problems solve problems and enhance or increase increase efficiency, productivity, and revenue.

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APPROACH

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Business analytics is more prescriptive- more about data analysis, recognizable data patterns and data models for predictions over future events and occurrences in order to generate positive outcomes.

 

At Drapsa, we use sophisticated data, quantitative analysis, and mathematical models to provide solutions for data-driven issues. We extend our data knowledge to address complex data sets, and  go further to cover areas of artificial intelligence, deep learning, and neural networks to micro-segment in our deep quest to understand data patterns.

 

COMPONENTS

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Drapsa's business dashboards include:

 

Data Aggregation

 

Prior to analysis, we collect, centralize, and clean data  to avoid duplication, and remove data inconsistencies , if any.

Data aggregation is handled either from: 

• Transactional records: Records that are part of a large dataset shared by an organization or by an authorized third party (banking records, sales records, and shipping records).

• Volunteered data: Data supplied via a paper or digital form that is shared by the consumer directly or by an authorized third party (usually personal information).

 

Data Mining

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Data models are created by mining through vast amounts of data. Data mining is handled by employing several statistical techniques and not limiting to:

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• Classification: Used when variables such as demographics are known and can be used to sort and group data

• Regression: A function used to predict continuous numeric values, based on extrapolating historical patterns

• Clustering: Used when factors used to classify data are unavailable, meaning patterns must be identified to determine what variables exist

 

Association and Sequence Identification

 

In many cases, consumers perform similar actions at the same time or perform predictable actions sequentially. In this case, data can reveal patterns such as:

• Association: For example, two different items frequently being purchased in the same transaction, such as a lipstick series or multiple books in a series or a toothbrush and toothpaste.

• Sequencing: For example, a consumer requesting a credit report followed by asking for a loan or booking an airline ticket, followed by booking a hotel room or reserving a car.

 

Text Mining

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Companies can also collect textual information from social media sites, blog comments, and call center scripts to extract meaningful relationship indicators. This data can be used to:

• Develop in-demand new products

• Improve customer service and experience

• Review competitor performance

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Forecasting

 

A forecast of future events or behaviors based on historical data can be created by analyzing processes that occur during a specific period or season. For example:

• Energy demands for a city with a static population in any given month or quarter

• Retail sales for holiday merchandise, including biggest sales days for both physical and digital stores

• Spikes in internet searches related to a specific recurring event, such as the Super Bowl or the Olympics

 

Predictive Analytics

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Companies can create, deploy, and manage predictive scoring models, proactively addressing events such as:

• Customer churn with specificity narrowed down to customer age bracket, income level, lifetime of existing account, and availability of promotions

• Equipment failure, especially in anticipated times of heavy use or if subject to extraordinary temperature/humidity-related stressors

• Market trends including those taking place entirely online, as well as patterns which may be seasonal or event-related

 

Optimization

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Companies can identify best-case scenarios and next best actions by developing and engaging simulation techniques, including:

• Peak sales pricing and using demand spikes to scale production and maintain a steady revenue flow

• Inventory stocking and shipping options that optimize delivery schedules and customer satisfaction without sacrificing warehouse space

• Prime opportunity windows for sales, promotions, new products, and spin-offs to maximize profits and pave the way for future opportunities

 

Data Visualization

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Information and insights drawn from data can be presented with highly interactive graphics to show:

• Exploratory data analysis

• Modeling output

• Statistical predictions

 

These data visualization components allow organizations to leverage their data to inform and drive new goals for the business, increase revenues, and improve consumer relations.

 

OUR OFFERINGS  

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Descriptive Analytics

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Descriptive analytics describes or summarizes a business’s existing data to get a picture of what has happened in the past or is happening currently. It is the simplest form of analytics and employs data aggregation and mining techniques. This type of business analytics applies descriptive statistics to existing data to make it more accessible to members of an organization, from investors and shareholders to marketing executives and sales managers.

Descriptive analytics can help identify strengths and weaknesses and provide insight into customer behavior. Strategies can then be developed and deployed in the areas of targeted marketing and service improvement, albeit at a more basic level than if more complex diagnostic procedures were used. The most common physical product of descriptive analysis is a report heavy with visual statistical aids.

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Diagnostic Analytics

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Diagnostic analytics shifts from the “what” of past and current events to “how” and “why,” focusing on past performance to determine which factors influence trends. This type of business analytics employs techniques such as drill-down, data discovery, data mining, and correlations to uncover the root causes of events.

Diagnostic analytics uses probabilities, likelihoods, and the distribution of outcomes to understand why events may occur and employs techniques including attribute importance, sensitivity analysis, and training algorithms for classification and regression. However, diagnostic analysis has limited ability to provide actionable insights, delivering correlation results as opposed to confirmed causation. The most common physical product of diagnostic analysis is a business dashboard.

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Predictive Analytics

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Predictive analytics forecasts the possibility of future events using statistical models and machine learning techniques. This type of business analytics builds on descriptive analytics results to devise models that can extrapolate the likelihood of select outcomes. Machine learning experts and trained data scientists are typically employed to run predictive analysis using learning algorithms and statistical models, enabling a higher level of predictive accuracy than is achievable by business intelligence alone.

A common application of predictive analytics is sentiment analysis. Existing text data can be collected from social media to provide a comprehensive picture of opinions held by a user. This data can be analyzed to predict their sentiment towards a new subject (positive, negative, neutral). The most common physical product of predictive analysis is a detailed report used to support complex forecasts in sales and marketing.

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Prescriptive Analytics

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Prescriptive analytics goes a step beyond predictive analytics, providing recommendations for next best actions and allowing potential manipulation of events to drive better outcomes. This type of business analytics is capable of not only suggesting all favorable outcomes according to a specified course of action, but recommending specific actions to deliver the most desired result. Prescriptive analytics relies on a strong feedback system and constant iterative analysis and testing to continually learn more about the relationships between different actions and outcomes.

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One of the most common uses of prescriptive analytics is the creation of recommendation engines, which strive to match options to a consumer’s real-time needs. The key to effective prescriptive analysis is the emergence of deep learning and complex neural networks, which can micro-segment data across multiple parameters and timelines simultaneously. The most common physical product of prescriptive analysis is a focused recommendation for next best actions, which can be applied to clearly identified business goals.

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These four different types of analytics may be implemented sequentially, but there is no mandate. In many scenarios, organizations may jump directly from descriptive to prescriptive analytics thanks to artificial intelligence, which streamlines the process.

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