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Tenger Data Technologies Always on Target

We are providing affordable data products and services.

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: 2017-5-23

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: 2017-5-23

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: 2017-5-23

“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.

Data Science Cartoon

By Gregory Piatetsky, KDnuggets.

Big Data is getting bigger

We are living in a hyper-connected world. In the world of digital interactions; from phone calls and page views to purchase – are adding to the never-ending long list of data. What’s more, with the arrival of Internet of Things (IoT), everything including inanimate objects such as cars, clothing, and refrigerators are generating more data by themselves every second. But can this Big Data be useful in your businesses? The short answer is “Yes”.

But, there is a catch!

A tremendous amount of raw data is 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.

Don’t try this at home!

How can we get any valuable strategic insights from our raw data?
It’s not realistic to expect an organization to make sense out of terabytes unstructured data. First, you have to collect, process, analyze and visualize your raw data. Only then, will you be able to turn data into valuable information. That being said, the Data Cleansing and Data Mining process is usually not for the faint-hearted. But, it has to be done; otherwise you cannot apply any Machine Learning algorithms to your data.

Artificial Intelligence (AI)

When data is analyzed and understood, it can help you increase your sales, develop better marketing strategies, and offer personalized and immediate services that your customers want. With artificial intelligence, you can turn your pre-processed data into a steadier insight stream you require to fulfill your many expectations.

Machine Learning (ML)

Machine Learning evolved from the computational learning theory in Artificial Intelligence. ML algorithms use statistical methods to make predictions. In business use, this is often referred to as predictive analytics.

Data Science Cartoon

Job Interview
"With such extensive experience in predictive analysis, you should have known we wouldn't hire you"
Source KDnuggets.

Deep Learning (DL)

Deep Learning is a branch of Machine Learning. DL algorithms, Artificial Neural Networks (ANN), are inspired by neuroscience’s interpretation of the information processing patterns in our central nervous system.

Applications

There are many examples of (ML) and (DL) application. Deep Learning technologies are used in cancer diagnosis, streamlining product development; spam filtering, fraud detection, image recognition, natural language processing, improving cyber security, development of robots for manufacturing operations and self-driving cars.

The applications for Artificial Intelligence are endless. Our imagination sets the limit for what we can do. There are plenty of ways you can use Artificial Intelligence to add value to your enterprise.

CRM Problems

Tenger Data is developing and deploying highly effective Machine Learning and Deep Learning technologies to solve a variety of different CRM problems.

One of the major CRM problems is churn prediction. As mentioned earlier, sources say that it costs at least 5 times more to acquire a new customer than it does to keep an existing one.

We can integrate and use Deep Learning algorithms to predict which customers are most likely to churn. Our DL solutions will provide you the insights to make optimal decisions when interacting with your customers and initiate preemptive actions to minimize churn.

Another major challenge is to predict the life time value for a customer. This is a common problem in companies with high customer acquisition costs.

Artificial Intelligence System Integration

The good news is that almost all key decisions can be supported by an integrated AI system. But, what is artificial intelligence system integration and how can we use it to take the engagement of our customers to another level?

For better understanding, think of artificial intelligence system integration as a software application. Deploying the application is more cost effective, faster and more accurate, which is contrary to what you would expect when doing the tasks manually. The result is better business prospects and more customer satisfaction.

Preventing Churn

To achieve high-growth, most companies should care about upselling products and services to existing customers. As mentioned earlier in this article, according to Gartner, 65% of a company’s business comes from existing customers. Thus, preventing churn should be the number one priority for any CRM strategy.

CRM Use Cases:

  • Sentiment Analysis (Opinion Mining):
    Our DL models can predict customer sentiment and behavior by tracking:

    - Causes for high churn likelihood
    - Levels of customer satisfaction
    - Trending support topics
    - Survey responses
    - Product reviews
    - Social media
    - Competitors

    Learn more about our Sentiment Analysis services here.
  • Recommendation Systems:
    ML & DL based recommendation engines will produce a list of recommendations to customers:

    - best product or service recommendations
    - best content recommendations
    - best promotional recommendations

    Learn more about our Recommendation Systems here.
  • Natural Language Processing (NLP):
    NLP can translate spoken languages to text or any other form for use as input to other systems. We can also do the reverse – translating the output of other systems to a spoken voice. NLP will also translate from one language to the other, or simply detect the language.
  • Natural Language Generation (NLG):
    Image caption generators will construct natural language outputs (captions) from images. NLG will automatically describe the content of an image, mapping the meaning to English sentences.
  • Complex Event processing:
    The algorithms can detect particular patterns (such as opportunities or threats) and initiate processes or actions accordingly.
  • Video and Image Analytics:
    - Scan unstructured data and look for common entities.
    - Scaling image and video analytics, using rules-based decision engines.
  • Considering the many areas, you can now start seeing the potential benefits of deep learning technologies in your business. In addition to the above mentioned services and use cases, Deep Learning can also help you with:
    - executing marketing tactics
    - predicting current customer value
    - predict customer lifetime value
    - optimizing prices dynamically
    - automate sales
    - most effective sales activity
    - improve customer services
    - price optimization for ad buys
    - forecasting demand
    - market response modeling
    - predicting advertising success
    ...to mention only a few of our CRM services. Please contact us for more info.

    Go to Analytics

    Data Analytics

    Taking the guesswork out of business decisions

    Do you ever have a hunch? Updated: 2017-5-23

    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: 2017-5-23

    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 Predictive Analytics
    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 (TDT) built a highly interpretable Linear Regression - 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.

    Moreover, TDT wrapped this Machine Learning model in a self-contained software application. The software comes with “batteries included”, it will work on any PC. The app doesn't require any network connections or external dependencies. Simply loading the data in the app will produce the desired results.


    New Recommendation System
    Achieve higher conversion rates on your applications, using our recommendation system. Our new system, Conversion++ will predict your user's responses to options.

    We can solve any Machine Learning problem that comes our way. And, we offer Machine Learning solutions at affordable prices.

    The Algorithms we use:

    • Support Vector Machine Algorithm.
    • Linear Regression
    • K Means Clustering Algorithm.
    • Logistic Regression.
    • Artificial Neural Networks
    • Random Forests
    • Naïve Bayes Classifier Algorithm.

    • Data Science

      Tenger Data Services 2017
    • Data Mining

      Tenger Data Services 2017
    • Machine Learning

      Tenger Data Services 2017
    • Recommendation Systems

      Tenger Data Services 2017
    • Sentiment Analysis

      Tenger Data Services 2017
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