Understanding OCR

OCR is a technology used to convert scanned documents or images containing text into computer-readable text, allowing automated data extraction and analysis.

Real-life Applications of OCR in Automation Testing

Extracting Data: Extract crucial information like invoice numbers from invoices, receipts, or forms. By using this, we can perform validations, ensuring that software correctly processes and stores such information.

Test Data Generation: Reads test data from legacy systems or documents and creates test scenarios and test cases, reducing manual effort in data preparation.

Example 1: Extract product details, prices, and customer information from invoices and purchase orders. This is used to perform end-to-end testing, ensuring accurate order processing and improving customer experience.

Example 2: Digitize prescriptions and medical reports which are used in automated testing of EHR systems, guaranteeing the correct storage and recovery of patient information, medications, and treatment histories.

Introduction to NLP

NLP is a branch of artificial intelligence that helps computers understand, interpret, and generate human language. Its role is to bridge the gap between human communication and machine understanding, allowing software to process, analyze, and respond to text and speech data in a way that resembles human language comprehension.

Real-Time Examples of NLP in Automation Testing

Log Analysis: Identifies patterns and errors in log data, automates the detection of exceptions, and reduces the need for physical log inspection.

Test Case Generation: Converts natural language requirements into executable test cases. By translating textual descriptions of desired functionalities, NLP streamlines test case creation, ensuring that test cases accurately reflect intended behavior and reducing the time required for test design and scripting.

Chatbot Testing: By simulating user conversations with natural language, NLP ensures the chatbot’s understanding and ability to provide appropriate responses, improving overall functionality and user experience.

Accessibility Testing: Assesses the clarity and correctness of textual content for screen readers and visually impaired users.

Localization Testing: Automatically compares source and target language content to ensure that localized versions of software or websites accurately reflect the original text and cultural requirements for various global audiences.

Integration of OCR and NLP

Combining OCR and NLP in automation testing allows for advanced capabilities, such as extracting and comprehending text from images or documents, enabling sophisticated data validation and test case generation.

Extracting Text from Images: OCR can extract text from images, making content machine-readable. NLP can then analyze the extracted text, allowing automation scripts to validate the information in image-based UI testing.

Sentiment Analysis on User Reviews: NLP can perform sentiment analysis on user reviews, categorizing opinions as positive, negative, or neutral. Combined with OCR, you can extract textual reviews from images or unstructured data sources, enabling automation to assess user sentiment without manual data entry.

Benefits of Using OCR and NLP in Automation Testing

The integration of OCR and NLP minimizes manual effort in data entry and test case generation, allowing testing teams to focus on higher-level tasks. Additionally, these technologies excel at handling complex scenarios, such as analyzing vast amounts of textual and visual data, enhancing test coverage, and overall testing effectiveness.

Conclusion

In conclusion, the synergy of OCR and NLP in automation testing promises a transformative leap in efficiency, accuracy, and coverage, ushering in a new era of software quality assurance where intricate testing challenges can be met with ease, precision, and speed.

The post The Role of OCR and NLP in Automation Testing appeared first on Indium.

If you’ve been dealing with banks and financial organizations, chances are you’ve chatted with these super-smart virtual assistants and chatbots. The use of Natural Language Processing (NLP) in the financial sector has been on the rise worldwide. More and more financial institutions are embracing advanced tech innovations, taking NLP beyond banking, insurance, and hedge funds (especially for sentiment analysis).

Artificial Intelligence and Machine Learning, alongside NLP, are making their mark in various areas of the financial sector like, operations, risk assessment, sales, research and development, customer support, and many other fields. This expansion boosts efficiency, productivity, cost-effectiveness, and time and resource management.

Take, for instance, the convenience it brings: Instead of the hassle of logging into individual accounts to check your balance, users can now effortlessly access their account information through chatbots and voice assistants. These digital companions are everywhere, from chatbots to voice assistants like Amazon Alexa, Google Assistant, and Siri.

Sentiment Analysis, often hailed as the next game-changer in the finance sector, plays a central role in chatbots, voice assistants, text analysis, and NLP technology. It’s a key component of natural language processing used to decipher the sentiments behind data. Companies frequently employ sentiment analysis on various text sources such as customer reviews, social media conversations, support tickets, and more to uncover genuine customer sentiments and evaluate brand perception.

Sentiment analysis aids in recognizing the polarity of information (positive or negative), emotional cues (like anger, happiness, or sadness), and intent (e.g., interest or disinterest). It is crucial in brand reputation management by providing insights into overall customer attitudes, challenges, and needs. This allows for data categorization by different sentiments, resulting in more accurate predictions and informed strategic decisions.

So, how can BFSI make the most of sentiment analysis? This emerging field has firmly rooted itself in the financial industry. Banks and financial institutions can employ AI-driven sentiment analysis systems to understand customer opinions regarding their financial products and the overall brand perception.

Of course, this approach may necessitate a certain level of data proficiency that financial companies must acquire before launching full-fledged sentiment analysis projects. Sentiment analysis stands as a highly promising domain within NLP and is undoubtedly poised to play a substantial role in the future of financial services.

Here, we’ll delve into the seven most prominent applications of sentiment analysis in financial services.

1. 1. Portfolio Management and Optimization: NLP can help financial professionals analyze vast amounts of textual data from financial news and market trends to assess the sentiment surrounding specific investments. This sentiment analysis can aid in making informed decisions about portfolio management, identifying potential risks, and optimizing investment strategies.
2. 2. Financial Data Analytics: Sentiment analysis enables financial firms to gauge the market’s sentiment toward specific assets or companies by analyzing news articles, social media, and reports. This information can be used to assess the volatility of investments and make data-driven decisions.
3. 3. Predictive Analysis: NLP can be used to analyze historical data and predict the future performance of investment funds. This involves assessing sentiment and other textual data to identify high-risk investments and optimize growth potential, even in uncertain market conditions.
4. 4. Customer Services and Analysis: Financial institutions employ NLP-driven chatbots and virtual assistants to enhance customer service. These AI-driven tools use NLP to process and understand customer queries, improving customer experience and satisfaction.
5. 5. Gathering Customer Insights: By applying sentiment analysis and intelligent document search, financial firms can gain insights into customer preferences, challenges, and overall sentiments. This information is valuable for personalizing offers, measuring customer response, and refining products and services.
6. 6. Researching Customer Emotional Responses: AI-powered tools process vast amounts of customer data, such as social media posts, chatbot interactions, reviews, and survey responses, to determine customer sentiments. This allows companies to better understand customer attitudes toward their products, services, and brands and analyze responses to competitors’ campaigns.
7. 7. Credit Market Monitoring: Sentiment analysis tracks credit sentiments in the media. Financial institutions can use NLP to process information from news articles and press releases to monitor the sentiment related to specific bonds or organizations. This data can reveal correlations between media updates and credit securities’ market performance, streamlining financial research efforts.

Future of NLP – Sentimental Analysis: Where does it stand today and tomorrow?

NLP has made significant strides in the banking and financial sector, supporting various services. It enables real-time insights from call transcripts, data analysis with grammatical parsing, and contextual analysis at the paragraph level. NLP solutions extract and interpret data to provide in-depth insights into profitability, trends, and future business performance in the market.

Soon, we can anticipate NLP, alongside NLU and NLG,  being extensively applied to sentiment analysis and coherence resolution, further enhancing its role in this domain.

Training computers to comprehend and process text and speech inputs is pivotal in elevating business intelligence. Driven by escalating demand, Natural Language Processing (NLP) has emerged as one of AI’s most rapidly advancing subsectors. Experts anticipate reaching a global market value of $239.9 billion by 2032, boasting a robust Compound Annual Growth Rate (CAGR) of 31.3%, per Allied Market Research.

NLP-based sentiment analysis is an innovative technique that enables financial companies to effectively process and structure extensive volumes of customer data, yielding maximum benefits for both banks and customers. This technology is positioned to empower traditional financial institutions and neo-banks alike, as it enhances current customer experiences, diminishes friction in financial services, and facilitates the creation of superior financial products.

In the finance and banking sectors, NLP is harnessed to streamline repetitive tasks, reduce errors, analyze sentiments, and forecast future performance by drawing insights from historical data. Such applications enable firms to realize time and cost savings, enhance productivity and efficiency, and uphold the delivery of quality services.

The post BFSI’s Tech Ride with NLP and Sentiment Analysis! Chatting with Erica, EVA, Amy, and Aida. appeared first on Indium.

The healthcare paradigm is shifting from a reactive approach to a proactive and holistic model. Preventive care is important for staying healthy and identifying problems early before they lead to other complications or become more difficult to treat. While early intervention has proven instrumental in advancing diagnostics and treatments, a critical element has been missing until now: the incorporation of social determinants of health (SDOH). Recognizing that health outcomes are intricately woven into the fabric of our lives, the integration of SDOH into preventive care emerges as a transformative solution.

Beyond genetics and clinical data, social determinants encompass factors like socioeconomic status, living conditions, education, and access to nutritious food. By embedding these key influencers into preventive care, healthcare providers gain an unprecedented understanding of their patients’ lives, empowering them to offer personalized and proactive interventions.

Social Determinants of Health: Impact on healthcare outcomes

The non-medical elements that affect health outcomes are referred to as social determinants of health (SDOH). Socioeconomic position, education, physical environment and neighborhood, job, and social support systems are a few of these variables. SDOH has a major effect on health and can impact healthcare outcomes in a number of ways.

For example, a patient with a lower socioeconomic status is more likely to have chronic diseases, such as diabetes and heart ailment. By understanding this patient’s social determinants, a healthcare provider can recommend preventive care measures that are tailored to their needs, such as financial assistance for medication or enrolling them in wellness programs.

Patient 360: A holistic view of patient data

Patient 360 is a comprehensive view of a patient’s health information, including their medical history, social determinants, and other relevant data. By integrating SDOH into patient 360, healthcare providers can gain a better understanding of the factors that are affecting their patients’ health and make more informed decisions about preventive care.

Here are some of the benefits of leveraging SDOH parameters in the patient 360 framework:

Better patient care: Integrating SDOH elements into the patient 360 approach helps improve treatment efficiency by empowering physicians to address the factors that influence healthcare outcomes. This can save time and resources, which can be used to provide better care for patients.

Enhanced patient engagement: Addressing SDOH factors helps enhance patient engagement by giving patients more awareness of their health data. This can lead to patients being more involved in their care management and being more likely to follow treatment plans.

Clinical notes to actionable insights: Physician notes record important patient medical histories, symptoms, demographics, and clinical data. These observations provide a holistic picture of the patient’s health. SDOH factors are important predictors of preventive care needs, which is why it is important to include them in patient records.

The integration of SDOH into patient 360 is a promising way to improve preventive care and achieve better health outcomes for all patients.

Manual SDOH data extraction: Typical challenges in the current system

Manually extracting social determinants of health (SDOH) elements, poses numerous challenges that can hinder the efficiency and accuracy of the process. SDOH data is often embedded in unstructured sources such as physician notes, medical records, or social service assessments, making it laborious and time-consuming for healthcare professionals to extract relevant information. Here are some of the difficulties associated with manual data extraction for SDOH:

Unstructured data: SDOH elements are often scattered throughout free-text narratives, that lack a standardized format.

Human error: Human analysts are susceptible to making errors during data extraction, leading to inaccuracies in the collected information.

Incomplete data capture: Due to the sheer volume of information, manually extracting SDOH elements from various sources may result in incomplete data capture.

Limited scalability: As healthcare organizations grow and data volumes increase, manual data extraction becomes less scalable and impractical.

Cracking the code of health: Indium’s SDOH machine learning model 

Indium’s expertise in developing the SDOH ML model is based on two pillars: NLP technology and a deep understanding of the healthcare landscape. With a team of experts in data science, engineering, and healthcare, Indium is at the forefront of using AI to transform preventive care.

Indium’s journey began with a recognition of the importance of social factors in determining health outcomes. The company’s ML model is designed to identify and address these factors, which can help improve the health of individuals and communities. Recognizing that manually extracting these factors from unstructured physician notes is labor-intensive and prone to errors, Indium sought to create an efficient and accurate solution. Leveraging Natural Language Processing (NLP) techniques, the team precisely crafted a robust ML model that swiftly identifies key social determinants hidden within vast amounts of textual data.

The success of Indium’s SDOH ML model lies in its ability to provide healthcare providers and payers with invaluable insights. By seamlessly integrating social determinants into preventive care, the model empowers stakeholders to offer personalized preventive interventions, optimize patient care, and drive cost savings within the healthcare ecosystem.

SDOH ML model

ML techniques can be used to identify and extract SDOH from physician notes. These techniques can identify patterns in text, such as the presence of certain words or phrases that are associated with SDOH. For example, the phrase “food insecurity” might be associated with the SDOH of food insecurity. By using the SDOH ML model, healthcare providers can make right interventions to help improve healthcare outcomes and reduce costs.

Once SDOH have been identified and extracted from physician notes, they can be integrated into preventive care management. This information can be used to provide a more comprehensive understanding of the patient’s overall well-being and to develop a more personalized treatment plan.

The power of precision: Partner with Indium

As a leading healthcare service provider and a leader in the digital engineering space, Indium has developed the SDOH machine learning model. Understanding the profound influence that social factors have on health outcomes, and recognizing the value of this information is crucial to bring transformative advancements in patient care, the SDOH model is trained to accurately extract social factors from patient records. Beyond improving patient care, the integration of social determinants also serves as a strategic tool in reducing healthcare costs by proactively addressing health issues. Unlike the traditional method, our model is 90% accurate and can identify SDOH attributes from thousands of patient records in a matter of seconds.

The post How the SDOH machine learning model improves patients’ health and your bottom line appeared first on Indium.

The integration of AI and NLP in data warehousing has opened new horizons for organizations, enabling them to unlock the hidden patterns, trends, and correlations within their data that were previously inaccessible. AI, with its cognitive computing capabilities, empowers data warehousing systems to learn from vast datasets, recognize complex patterns, and make predictions and recommendations with unprecedented accuracy. NLP, on the other hand, enables data warehousing systems to understand, analyze, and respond to human language, making it possible to derive insights from non-formatted data sources such as social media posts, customer reviews, and textual data.

The importance of AI and NLP in data warehousing cannot be overstated. These technologies are transforming the landscape of data warehousing in profound ways, offering organizations unparalleled opportunities to drive innovation, optimize operations, and gain a competitive edge in today’s data-driven business landscape.

Challenges Faced by C-Level Executives

Despite the immense potential of AI and NLP in data warehousing, C-level executives face unique challenges when it comes to implementing and leveraging these technologies. Some of the key challenges include:

• Data Complexity: The sheer volume, variety, and velocity of data generated by organizations pose a significant challenge in terms of data complexity. AI and NLP technologies need to be able to handle diverse data types, formats, and sources, and transform them into actionable insights.

• Data Quality and Accuracy: The accuracy and quality of data are critical to the success of AI and NLP in data warehousing. Ensuring data accuracy, consistency, and integrity across different data sources can be a daunting task, requiring robust data governance practices.

• Talent and Skills Gap: Organizations face a shortage of skilled professionals who possess the expertise in AI and NLP, making it challenging to implement and manage these technologies effectively. C-level executives need to invest in building a skilled workforce to leverage the full potential of AI and NLP in data warehousing.

• Ethical and Legal Considerations: The ethical and legal implications of using AI and NLP in data warehousing cannot be ignored. Organizations need to adhere to data privacy regulations, ensure transparency, and establish ethical guidelines for the use of AI and NLP to avoid potential risks and liabilities.

Also check out our Success Story on Product Categorization Using Machine Learning To Boost Conversion Rates.

The Current State of Data Warehousing

• Increasing Data Complexity: In today’s data-driven world, organizations are grappling with vast amounts of data coming from various sources such as social media, IoT devices, and customer interactions. This has led to data warehousing becoming more complex and challenging to manage.

• Manual Data Processing: Traditional data warehousing involves manual data processing, which is labor-intensive and time-consuming. Data analysts spend hours sifting through data, which can result in delays and increased chances of human error.

• Limited Insights: Conventional data warehousing provides limited insights, as it relies on predefined queries and reports, making it difficult to discover hidden patterns and insights buried in the data.

• Language Barriers: Data warehousing often faces language barriers, as data is generated in various languages, making it challenging to process and analyze non-English data.

The Future of Data Warehousing

• Augmented Data Management: AI and NLP are transforming data warehousing with augmented data management capabilities, including automated data integration, data profiling, data quality assessment, and data governance.
• Automation with AI & NLP: The future of data warehousing lies in leveraging the power of AI and NLP to automate data processing tasks. AI-powered algorithms can analyze data at scale, identify patterns, and provide real-time insights, reducing manual efforts and improving efficiency.
• Enhanced Data Insights: With AI and NLP, organizations can gain deeper insights from their data. These technologies can analyze unstructured data, such as social media posts or customer reviews, to uncover valuable insights and hidden patterns that can inform decision-making.
• Advanced Language Processing: NLP can overcome language barriers in data warehousing. It can process and analyze data in multiple languages, allowing organizations to tap into global markets and gain insights from multilingual data.
• Predictive Analytics: AI and NLP can enable predictive analytics in data warehousing, helping organizations forecast future trends, identify potential risks, and make data-driven decisions proactively. Example: By using predictive analytics through AI and NLP, a retail organization can forecast the demand for a particular product during a particular time and adjust their inventory levels accordingly, reducing the risk of stock outs and improving customer satisfaction.

Conclusion

In conclusion, AI and NLP are reshaping the landscape of data warehousing, enabling automation, enhancing data insights, overcoming language barriers, and facilitating predictive analytics. Organizations that embrace these technologies will be better positioned to leverage their data for competitive advantage in the digital era. At Indium Software, we are committed to harnessing the power of AI and NLP to unlock new possibilities in data warehousing and help businesses thrive in the data-driven world.

The post Revolutionizing Data Warehousing: The Role of AI & NLP appeared first on Indium.

At the beginning, we had simple apps that did very little. However, everything has changed in the last decade as a result of the Smartphone revolution.

These mobile apps influence everything from your daily chores to your social interactions to your business strategy.

When we think of artificial intelligence (AI), the first names that pop up are probably Siri, Bixby, Cortana or Alexa.

According to the most recent McKinsey Global Institute reports, Google and Apple have invested billions of dollars in artificial intelligence. According to the report, AI advancements brought in $139 billion in investment in 2020, which was more than three times the money invested in AI three years prior.

The concept of a ‘smart assistant’ which can solve everyday tasks has captivated millions of users across all business sectors, not to mention education, healthcare, and finance. However, AI is not limited to smart assistance; it is progressing at a rapid pace. Many mobile apps are now utilising AI to improve user satisfaction.

AI is continuing to improve mobile apps by acting as a catalyst. It enables the evolution of mobile apps by transforming them into intelligent pieces of software capable of predicting user behaviour and making decisions. AI algorithms also enable mobile apps to learn from user-generated data.

It is important to note that AI in this context does not refer to pure self-aware intelligence machines. Rather, it is a catch-all term for a variety of applications used by website and mobile app developers.

Contributions Of AI to Mobile Application Development

Facial recognition: Because of the ‘easy to use’ nature and added layer of security, face recognition lock has become one of the most popular features on Android smartphones. These systems use AI and ML-based algorithms to recognise a person’s face to unlock the phone and the various apps that are installed on it.

Smartphone manufacturers are expected to implement even more advanced AI and ML in the coming years to identify a person as their facial features change, such as growing a beard or wearing glasses.

Search Engines on mobile phones: The use of voice search and voice commands is perhaps one of the most common and popular advancement in artificial intelligence and machine learning. Customers used to type their queries into search bars. It is now as easy as asking your virtual assistant to look for something for you.

Instead of signing into your computer or unlocking your phone, something as easy and simple as “hey google what’s the best restaurant near me?” provides users with the quick answer they seek while also directing them to your business. Voice command allows you to respond to text messages without having to type.

Smart Camera apps: The smartphone camera is one of the most important areas in which custom android app development personnel and android mobile manufacturers are making significant advances in AI and ML. These advanced cameras can detect the subject within the frame, such as faces, fireworks, or food, and adjust the settings to produce the best possible image.

Artificial intelligence and machine learning can now automatically identify and enhance facial features for outstanding portrait images. More advanced features can even count the calories you eat from a simple photo of your food or provide information to businesses about how and where their products are being used when photos are shared on social media platforms.

Emotion recognition: Emotion recognition is a rising star in AI development. We have now advanced to the point where we can incorporate ML & AI into apps and capture micro& macro expressions. Through image and voice data processing, software can now read human emotions through the capturing of subtle variations, body language cues and vocal inflection. Companies can use these analytics to enhance consumer experiences by identifying the need for a product or service or to get new ideas for new product.

Real-time translation: There is a vast array of translation apps available. However, the majority of these apps are inoperable without access to the internet. AI could allow smartphones to translate & transliterate different languages in real-time without requiring an internet connection.

AI can provide a language instruction tool that allows sentences and phrases to be translated almost instantly without a time lag, similar to how interpreters work. The translation tool can be adjusted for latency using AI. This means that a user can specify the amount of time between a spoken word and its translation. This would be incredibly beneficial for languages that require a longer time lag for accurate translation.

Advantages of implementing AI

1. AI assists you in completing monotonous tasks quickly
2. Accuracy and completeness
3. Enhanced customer experiences
4. Intelligent interactions with users
5. User retainment

Personalized user experiences

The advancement of AI technology has enabled mobile users to completely redesign the value benchmark of existing user experience. Users are starting to demand more detailed and personalised mobile app performance.

Retail brands such as Tommy Hilfiger, Starbucks, Nike, etc can deliver personalised experiences that include recommendations unique to each user by collecting and analysing customer data based on purchases and locations.

In reference to Tommy Hilfiger’s chatbot, users can use the chatbot to browse their most recent collections or get a behind-the-scenes look at the most recent fashion show. The chatbot also employs natural language processing to provide style advice and product recommendations in addition to responding to customer inquiries. The bot gathers information about the user’s style preferences by asking a series of questions and then suggests an outfit based on the information gathered.

Smartphones have GPS tracking capabilities, as well as microphone and camera features, making them an ideal platform for AI applications. Furthermore, Apple revealed that the iPhone XR, 11, and 12 will include an A12 Bionic chip with a neural engine designed to use AI hardware in previously unimaginable ways.

When AI technology is combined with these built-in features, apps become more relevant and personalised. Using artificial intelligence to contextualise user behaviour will make each app session better than the previous one.

Wind-Up

AI opens up a plethora of opportunities for innovation in the mobile app industry. AI is the wave of the future in mobile app development. Users’ interactions with app services and products are changing as a result of artificial intelligence. Users of mobile apps will also be linked to an ecosystem of intelligent applications that will collaborate to provide a personalised user experience.

The greater role of AI in mobile apps has demonstrated its value in terms of business growth and user engagement. Here are some examples of how AI can help you understand your users:

• AI can collect and store user data by analysing user behaviour and interactions with the app.
• AI collects essential data such as location, contacts, and daily actions to better serve users.
• AI products level up the user experience.

A smart ecosystem will collect a large amount of social data and behavioural interest, which can be used to boost revenue and improve user experience. It is not an exaggeration to say that AI is reshaping the smartphone industry. As a result, it is critical to include AI in your business and mobile applications.

The post Artificial Intelligence And Its Impact On Mobile Applications appeared first on Indium.

The phrase augmented analytics refers to the use of machine learning (ML) and natural language processing (NLP) to enhance data analytics, data sharing and business intelligence.

According to the report, businesses will increasingly implement augmented analytics where automation will help them find the most important insights – both opportunities and risks. It’ll optimize the entire process of decision making, bringing in efficiency into several key processes that may have been manual in the past.

Advantage Indium: Expertise + Experience

Indium Software is a cutting-edge Advanced Analytics solutions provider leveraging machine learning and artificial intelligence to automate data-centric applications. It offers clustering, regression and classification services not just extracting insights from numeric data but also from text, audio-visual and image inputs.

Indium also has cross-domain experience, having served clients across sectors including banking and financial services, retail, e-commerce, FMCG and next-generation technology companies. Indium’s current clients include a Fortune 500 FMCG company and one of the world’s leading online cab operators.

The company has built AI/ML and NLP-based solutions for different functional teams including those in marketing, finance and operations.

The team uses Open Source tool sets such as Python, R, or proprietary tools such as STSS for data management and dashboarding tools such as Qlikview, Tableau, Power BI and R Shiny. The team’s expertise in machine text analytics has also led to the development of a proprietary, patent-pending tool teX.ai.

Moreover, Indium provides end-to-end services including Big Data Analytics, Pricing Analytics, Customer Analytics and Social Media analytics. The company is well-positioned to serve as a pure-play analytics partner or an end-to-end solutions provider delivering a range of analytics and digital services including product development.

Considering most modern applications (apps) incorporate data engineering and analytical models, it makes sense to work with a single partner, who can integrate big data solutions with product development.

In the last 20 years, Indium has worked on 350+ client engagements, serving a range of customers from startups to mid-size growth companies to Fortune 500 clients. From undertaking complete projects to augmenting resources, Indium Software has had the opportunity to display its capabilities and provide customer delight on every occasion.

Customer Success Story #1: Working with a leading price comparison platform for e-commerce

A leading online price comparison platform  helps consumers with quick comparative information on various products from a large number of third-party online shopping players. The platform combines data collection from 1500+ online retailers. The key challenge in the workflow was to enable end-users with real time visibility of products (including availability, description, etc.) and their dynamic changing prices.

However, each site would have its own breadcrumb trail, making it difficult to identify and process all products that belonged to the same sub-category. Indium was able to use text analytics to process data in real time with minimal lag and effectively crawl dozens of e-commerce sites to return only the most relevant information with the correct taxonomy.

Using batch processing, the team was able to:

–         Reduce the process time from 25 hours to 7 hours

–         Reduce the data refresh and response time from 2 seconds to 1 second providing real time price information

–         Reduced cost of operations (including the need for manual tasks) by 35 percent

Indium’s text analytics capabilities delivered value for money for a tough problem with limited resources and in a short duration of time.

Subsequently the client has engaged Indium for two of its other projects: one in the areas of web analytics and another in data-driven campaign management.

Customer Success Story #2: Working with a fintech company specializing in payday advances

A Singapore-based financial services company offering a streamlined mobile app that gives payday advances was embarking on a technology modernization program to improve business workflow, increase revenue and decrease costs.

Apart from improving the performance of several business operations and legacy systems, the company was looking to derive insights from advanced analytics in two specific areas: Churn Analysis and User Profiling.

Using tool sets such as Elasticsearch, R, Python, Logistic Regression, XGBoost and K-means Clustering for Descriptive and Predictive Analytics, Indium was able to increase customer retention, lower costs through loan losses and increase ‘tips’, which was its main source of revenue.

·          Effective user profiling led to an increase in the user acquisition rate by 10 per cent

·          Identifying and addressing the underlying reasons led to a churn reduction of more than 20 per cent

·          The combined power of an operationally and intuitive interface resulted in the surge in tip collections in the range of 6.5 per cent

·          Potentially bad clients were weeded out

Our approach to a client engagement

Indium is highly process-driven and follows a three-step process:

·          Step 1 revolves around Data Preparation.

·          Step 2: Data exploratory analysis is like a preliminary investigation to assess the data and correlate it with the final output.

·          Step 3: Models are built and fine-tuned following testing and training of datasets. Once it has been tuned to the optimum extent, it is then deployed and integrated with the client’s system.

In some projects involving text analytics, multiple models are explored before arriving at the optimal solution keeping in mind the client’s context.

As mentioned before, Indium Software is a trusted partner for fast growing organizations around the world. We bring to the fore our deep expertise in advanced analytics, to formulate data-driven solutions for a multitude of business situations.

The post Why Indium is the Right Partner for Advanced Analytics appeared first on Indium.