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About TengerData

There is a tremendous amount of raw data stored in corporate databases already and it is increasing daily. Yet, this raw data by itself does not provide any meaningful information. The Gartner survey shows that more than 50% of the businesses have no idea how to get any real value out of their data. Our mission is to turn raw data into a powerful marketing tool. Our single purpose is to create an environment in which organizations can grow exponentially. Guessing is risky business. We realize that understanding terabytes of unstructured data is impossible without the right tools. We will collect, process, analyze and visualize your raw data. We will turn your data into valuable information so you can make optimal business decisions. If you want to build wealth, making the right decisions is imperative to your success.

Industries Served

  • Internet Marketing/Social Media:
    Putting your products and services in front potential costumers. Growth Hacking Tracking user growth and trends. Managing affiliate programs.
  • Communications:
    Optimize customer relationship management (CRM) using real-time account history data. Increasing your costumer retention rate.
  • Financial Services:
    Target market analytics for financial products. Growth strategies and portfolio management. Optimized investment practices. Providing business intelligence to shareholders, delivering the information in any format, at any the time.
  • Media/Entertainment:
    Content development planning and targeted advertisement based on viewership measurement and/or user engagement data.
  • eCommerce:
    Machine Learning algorithms for sales recommendation engines. Customer retention strategies. Online Fraud identification.


Data Science

The art of information theory.

Is it art or science? Updated: 2017-8-20

The answer is both art and science. If you have a hard scientific question, and you can answer it with data, then it is a science. However, most small or medium size organizations do not need to answer complex scientific questions.

What is the difference between business analysts and data scientists? Data Science is an analytics program but data scientists are not always business analysts. Data scientists and engineers can customize software to automate analytics. Data scientists apply advanced algorithms to large data sets, aiming to discover hidden insights. In contrast with Data scientists, business analysts tend to have practical business solutions in mind, rather than complex algorithms.

Data Science Services:

  • Visualization: data visualization and communication.
  • Knowledge Discovery: advanced predictive analytics.
  • Machine Learning: pattern recognition - machine learning.
  • Coding: programming complex algorithms.
  • Big Data: high performance computing.
  • Analysis: setting up infrastructure and providing analysis.
  • Statistics: data preparation and statistics.
  • Models: building mathematical models.

Data Architecture

Data Architecture

Functionalities and data mechanisms

You envision it - we build it Updated: 2017-8-20

The goal is to discover the strategic insights in raw data. We collect the data, clean it, store it and process it. We can build and deploy storage systems based on each client’s specific business needs. Business managers don't have to understand technology. They just have to ask the right questions. It's our job to provide the technology solutions to business problems.

A coherent data architecture system can rapidly boost growth. In most cases, creating a data architecture is not so complicated and might not be expensive. In fact, for most small and medium size companies it's a simple straight-forward process. For these organizations, implementing an end-to-end database design strategy may not be necessary. Some clean-up, data migration, mapping and validation is usually more than enough. These techniques will give the business a competitive edge and will notably increase sales.

Data Architecture Services:

  • Database Design: strategies for data acquisitions and database implementation.
  • Relational Databases: designing and building relational databases from scratch.
  • Implementation: defining the target state, making developmental alignments then implementing the architecture.
  • Infrastructure: building the software infrastructure to handle big data
  • Large Data Sets: laying the groundwork to retrieve data for evaluations.
  • Smooth Data Flow: controlling data flow from source to destination.
  • Clean up Messy Data: robust pipelines to clean and aggregate raw data into databases.

CRM Solutions

Customer Relationship Management (CRM)

How to use Deep Learning to solve CRM problems. Updated: 2017-8-20

“65% of a company’s business comes from existing customers, and it costs five times as much to attract a new customer than to keep an existing one satisfied.” - source quoted as Gartner.

Accelerating Complexity

Content & product recommendation systems are important, but they don’t go deep enough when it comes to solving CRM problems. To minimize your customer churn rate, you have to dig much deeper into this problem. Recommending the right products to your customers is not nearly enough today to reduce churn rate.

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Data Analytics

Taking the guesswork out of business decisions

Do you ever have a hunch? Updated: 2017-8-20

Acting solely on a hunch is no longer necessary nor is it a good idea in today’s business environment. While good intuition is essential, it helps to know all the facts before making a decision. Most organizations compile huge volumes of raw data. This data is in a variety of different formats. Analyzing it may take millions or hundreds of millions of calculations and computations. Conventional business analytics solutions won’t be able to produce the desired results.

Data Analytics Services:

  • Statistical Methodologies On Demand: simple interactive environment for discovery and analysis of big data.
  • One size doesn't fit all: customized graphics for more accurate interpretation and visualization of the results.
  • Get reliable results: measuring and tracking performance, achieving better business results.
  • Multiple Facets: diverse set of skills and technologies to serve a variety of different enterprises and industries.
  • Predictive Analytics: using well-tested algorithms, so you can predict the future.

Sentiment Analysis

Sentiment Analysis

Also known as opinion mining. Updated: 2017-8-20

Sentiment Analysis is the process of establishing whether a text (i.e. comment, review, tweet etc.) is positive or negative. Our Machine Learning algorithms will help you to understand how people feel about a certain product or topic.

"What are the practical applications for sentiment analysis?"
The applications for Opinion Mining are endless. The possibilities are only bound by the limits of your imagination. There are many meaningful ways you can use sentiment analysis to add significant value to your business. One popular application is to track customer reviews, comments, survey responses and competitors. Sentiment analysis delivers amazing results in social media monitoring. You can apply it to any situations where you have to analyze text.

"So, what’s wrong with reading the comments or tweets?"
Well, there is nothing wrong with reading text if you have 10 - 20 or even 100. But, what if you have to read thousands, hundreds of thousands or millions of pieces of text? We can provide you the tools to analyze millions of comments, reviews or tweets. You will never have to read any of them. Our machine learning algorithms will read the text for you. Our tools will allow you to gain valuable insight into any topic.

Outdated market research methods like surveys, interviews or focus groups don't work anymore. They are time consuming, inaccurate and expensive. Our text mining and machine learning tools will achieve better results in a few seconds. And, the costs are lower than traditional methods.

Sentiment Analysis is more and more popular because it’s efficient. You can analyze topics, sentiment, named entities in millions of texts in only a few seconds. Recently, we analyzed a big data-set. It didn’t take us too long to do it. But, if you would want to perform this job manually, it will take you about 16 years.

“How can I afford this kind of technology?”
Our fees are lower than you think. We don’t believe in the one size fits all approach, we will tailor our solution to your specific needs and budget.

Sentiment Analysis - Machine Learning Services:

  • Social Media: classification of social media texts as positive or negative.
  • Online Product or Service reviews: analyzing the sentiment in online reviews
  • Opinion Mining: categorizing opinions expressed in any text .
  • Article Summarization: creating a summary of any text document .
  • Brand Sentiment: customer sentiment with regards to any brand online.
  • Go to Tools

    Tools

    We like to use Python and many of Python's libraries for Big Data & Machine Learning.

    • NumPy
    • Pandas
    • Scikit-Learn
    • Matplotlib
    • SciPy
    • Theano
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    New Products


    New Recommendation System
    Achieve higher conversion rates on your website, using our recommendation system. Our Deep Learning based Recommender will predict your user's responses to options.

    Read More


    New Ad Campaign Optimization
    Tracking Offline Advertisement Effectiveness
    We all know how simple it is to track the effectiveness of online advertisement. It is simple because we can count the clicks. Analyzing which online ad caused a conversion is a fairly straight-forward process.

    But, tracking offline advertisement campaigns is a major challenge. For example, analyzing the effectiveness of TV, Radio or Printed Media ad campaigns is a hard problem.

    Tenger Data Technologies built a highly interpretable Machine Learning model to solve this problem. This model allows us to input the TV, Radio and Newspaper ad spending in a given market and predict the increase in sales.

    Read More

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    • Artificial Neural Networks

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    • Integrated AI Systems

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