Predictive Analytics

newData is a data science consulting agency located in the Nashville Metropolitan Area. Our objective is to use machine learning and statistics to assist businesses achieve their goals. We offer predictive analytics marketing, operational and scientific AI solutions tailored to the specific needs of our clients, as well as a range of AI solutions in partnership with top universities and niche AI businesses.

How can AI help
your business?

AI Modeling
Use Case Discovery
Data Exploration
Model Development
Model Deployment
Businesses throw away opportunities because they don't know what to do wih the data.
  • Identifying potential use cases is a key first step in an analytics journey.
  • Assessing existing tools, personnel skills and resources available for gathering relevant data helps to get a clearer picture of what needs to be done.
  • This enables stakeholders to better understand the business problem and identify useful data sources that can contribute towards solving it.
Common AI Use Cases
Exploring data is like opening a box of treasures. Each treasure bringing a new business insight or opportunity.
  • Once use cases requirements have been established, data needs to be collected.
  • Data can be gathered through surveys, sales reports, customer feedback or other sources of information.
  • After the data is collected it must then be evaluated and analyzed in order to gain insight into how the business should move forward.
Modeling is both art and science. A well-built model combines imagination and logical, data-driven approaches.
  • Data scientists must first obtain the necessary data and prepare it for analysis, which includes cleaning and transforming the data.
  • Once a suitable model is trained and tested on the sample dataset, it can be fine-tuned to optimize its performance against the desired task.
  • The machine learning model development process involves multiple stages of preparation, testing, and refinement.
By automating decision making processes, companies can save valuable time and resources.
  • Machine learning models enable businesses to automate decision-making processes and gain insights from data sets.
  • Models can be trained to predict customer behavior or identify trends in market activity that are not immediately apparent.
  • Deploying machine learning models gives companies the ability to make informed decisions quickly and accurately, leading to greater efficiency and improved overall performance.
Advanced Analytics
Data Mining
Anomaly Detection
Recommender Systems
Segmentation should not be viewed as a standalone process, but rather as a part of a larger data-driven decision-making process.

Segmentation divides data into smaller, more homogeneous groups based on common characteristics and behaviors. Examples of

  • Demographic: Age, gender, income, education, family size, etc.
  • Geographic: Region, climate, population density, and urbanization.
  • Psychographic: Personality, values, interests, and lifestyles.
  • Behavioral: Customer behaviors, such as purchase history, brand loyalty, and usage patterns.
  • Attitudinal: Customer attitudes, such as their perception of a brand, product, or service.
  • Industry-specific: Tailored to specific industries, such as healthcare, finance, or technology, and considers the unique characteristics and needs of the target market within that industry.
Digital analytics can be used to automate business processes, such as personalizing the customer experience based on their behavior and preferences.

Digital analytics is a critical component of modern business that involves measuring and analyzing data from digital channels. The goal is to understand how people are interacting with digital assets, such as websites and mobile apps, and to use this information to improve digital experiences and drive business outcomes. Digital analytics tools can range from simple website analytics platforms to advanced tools that incorporate machine learning algorithms, and they can provide insights into a wide range of metrics, such as website traffic, user behavior, and conversion rates.

The insights generated from digital analytics can inform key business decisions, such as website design and content, user experience, digital marketing strategies, and product development. By using digital analytics, organizations can gain a deeper understanding of their customers and their digital interactions, which can help them make data-driven decisions to improve their overall digital performance and drive business success.

Data mining combines techniques from computer science, statistics, and domain-specific knowledge to extract insights from data.

Data mining is the process of discovering patterns, relationships, and insights in large datasets. It is used to extract meaningful information from data and to make informed decisions. The techniques used in data mining can vary depending on the type of data being analyzed, but common methods include association rule learning, clustering, and decision tree analysis. Data mining can be applied in a variety of industries, such as finance, healthcare, marketing, and retail, and it can help organizations make more informed decisions, such as improving business processes, predicting customer behavior, or detecting fraud.

Data mining requires large datasets, powerful computational resources, and expertise in statistical and machine learning methods. The results of data mining can provide valuable insights, but it is important to consider the ethical implications of data mining, such as privacy and data security. Overall, data mining is a powerful tool for organizations looking to make data-driven decisions and to gain a deeper understanding of their customers and their business operations.

Anomaly detection can be used in any scenario where it is important to identify deviations from normal behavior Recommender Systems

Anomaly detection is a process of identifying unusual patterns or behaviors in data that deviate from the normal or expected patterns. It helps organizations identify data points that are rare, unusual or difficult to explain, providing insights into potential problems and opportunities. Anomaly detection can be applied in various industries such as finance, healthcare, marketing, and retail, helping organizations detect fraud, identify problems in business processes, and improve customer experiences.

The techniques used in anomaly detection vary and may include statistical methods, machine learning algorithms, and data visualization. The choice of technique depends on the nature of the data and the goals of the analysis. It is crucial to validate the results using subject matter expertise and domain knowledge to ensure that the results are meaningful and actionable. Anomaly detection is a valuable tool for organizations to better understand their data and to identify potential issues and opportunities in their operations.

Recommender systems personalize the user experience by suggesting products, movies, or music that are likely to be of interest to the individual user.

A recommender system, also known as a recommendation system, is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item. Recommender systems are utilized in a variety of applications, including movies, music, news, books, research articles, search queries, social tags, and products in general. These systems can operate using a single input, like music, or multiple inputs within and across platforms like news, books and music.

Recommender systems use a variety of algorithms to provide personalized recommendations to users based on their past behavior, preferences, and interests.

Synthetic Data

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The Intersection of Business and Data Science

newData's predictive analytics framework provides a simple and effective way to align company goals with data science.



Business Goals

  • Grow revenue
  • Decrease costs
  • Mitigate risks
  • Maximize profits
  • Increase efficiency
  • Enhance brand credibility
  • Improve Health
Business Goals Data Science Objectives AI Framework
  • Thousands of algorithms, data prep and data science functions
  • Built in scenario-planning technology
  • Fast and easy model deployment with automation where needed

Data Science Objectives

  • Predict future outcomes with accuracy and precision
  • Prioritize strategies using scenario-based predictive modeling
  • Optimize drivers of business performance

Companies that utilize newData's predictive analytics framework have discovered a powerful way to easily and quickly integrate their goals with the use of data science methods. This not only saves time, but also helps ensure that outcomes are achieved according to predetermined criteria. The integration of data science techniques into the overall goal-driven ecosystem allows for more accurate decision-making and higher ROIs over the long term. Automation of processes such as customer segmentation, risk management and predictive maintenance further streamlines operations while boosting efficiency.

newData created synthetic data that Funnel Metrics used to mimic a sales organization of different sizes, structures and complexities. We used this data to power Funnelocity® our Salesforce performance management app. newData also developed a custom machine learning model for us that predicts which salespeople are most likely to reach their potential. By using newData’s modelling approach, Funnelocity® provides continuous insights to analyze and identify the metrics, KPI’s & skills that have the greatest impact on sales achievement. Thus, enabling sales managers to focus on improving their sales team's performance, instead of spending valuable time analyzing data.

Michael Mooradian, CEO

Funnel Metrics

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