Data Science

Re-strategize AI for business

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Use AI for Accelerated Business Outcomes

The global pandemic of COVID-19 led several customer-centric organizations to accelerate their AI/ML initiatives. If data was the new “oil” in the last decade, then AI emerges as the new “nuke” required to boost revenue by advancing customer operations and controlling risks/costs.

7 Ways to Apply Data Science to Your Business

ML helps in driving competitive advantages by generating predictive insights on products and services. Inferences obtained from behavioral ML models improve shopper’s experience and service quality.

Here are a few use cases that data-driven organization deploy to strategize AI/ML and create opportunities for sustainable business growth:

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Hyperpersonalization at Scale

With cloud infrastructure and customer 360-degree data, DS initiatives generate deep purchasing behavior insights to target your audience. Scalable hyper-personalization is the key to driving enhanced and profitable customer experiences.

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Proactive Churn Prediction

Customer retention is the foundation for business growth. Understanding customer behavior that alerts the risk and time of a customer’s churn, strategizes retention-focused initiatives for proactive market penetration.

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Uplift Modeling

“How likely are customers to purchase the product in the future?” Answering questions as these, businesses can use AI/ML to determine the best possible course of action to up-sell or cross-sell to customers.

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Look-alike Modeling

Identifying new customers’ traits that are identical to the existing loyal customers using predictive analytics and deep learning.

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Demand Forecasting

Estimating future customer demand for a product or service using historical data. Identifying seasons, trend and levels for critical business data and making decisions to optimize operations. Accordingly, tailor price, business growth strategies and market positioning for best results.

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Data-driven Risk Assessment

Analyzing historical trends and patterns allows experts to detect fraudulent (or high-risk) transactions. The right AI/ML models can alert decision making authorities regularly to intervene on anomalies that are difficult to detect using traditional methods.

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Automation of Tasks Using AI

AI-powered automated software and chatbots can accelerate machine-human interaction. Video analytics drastically cuts down the manual surveillance efforts and low code technologies bring about smart workflows, enabling business users to focus on outcomes instead of worrying about technology implementation.

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How to Set Your AI Project Up for Success

Organizations that are data-driven invest valuable time and resources on AI/ML initiatives. With scalable cloud architecture, digital solutions such as IoT, chatbots, and next-generation data ecosystems, ML can help organizations compete with the technology leaders among the industry.

When it comes to data science initiatives, benchmarking against project success criteria and user adoption are the two most critical outcomes taken in the first step.

AI/ML initiatives can be successful in driving business growth if you address the following:

  • What are business concerns and opportunities a ML model can solve?
  • How to measure model accuracy and create measurable inferences?
  • What’s the correct methodology to scale an AI model aligning with business needs?
  • How to arrive at a ROI framework by improving profitability?
  • How to integrate AI model within business applications ecosystem?

While AI models may be challenging to scale, a seasoned implementation partner can help you achieve the tactical business goals for repeatable success.

Onboard an A-star Team to Scale AI & ML

Tezo uses cross industry reference execution methodology (CRISP) to deliver advanced AI/ML goals by processing complex data and generating in-depth insights on business processes.

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Identify Business Problems

Focusing on different business issues and challenges and translating it to well-defined ML problems. This is the key to discovering business strategies that can uplift performance over time.

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Compile Relevant Data

Collecting all relevant data from multiple sources such as websites, surveys, questionnaires, internal and external databases and converting that into practical uses.

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Deep Data Exploration

Evaluating data to understanding data arrangement, identifying, and removing missing values and errors. Inspecting the data to recognize any trend/pattern to extract useful features.

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Model Build & Evaluation

SSelecting a model based on performance needs, complexity and maintainability for ML implementation. Feature engineering and Data quality holds the key to having an optimized model performance.

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Communicating the Outcomes

Identifying business requirements in the context of pre-defined problems. Visualizing key findings in a simpler way and interpreting your insights to technical and non-technical audiences.

Our Core Service Offerings

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AI/ML Environment Assessment

ML projects vary in scale and complexity that require data science expertise, knowledge of statistical techniques and a clear understanding of the business success criteria. Here, we outline strategic goals to assess client’s AI/ML environment and leverage people, process, and technology aspects. According to the business needs, we have created a unique roadmap for best implementation.

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Data Science as a Service

AI/ML projects come with implementation nuances. We integrate technical and business acumen to facilitate the delivery of projects across the industry and business infrastructure. Designing ML production system with end-to-end under automated environment for model development is revolutionary. ML model selection and evaluation techniques are “secret sauce” to drive valuable knowledge for constant business growth. We leverage different treatments and techniques to align your project with specific business goals.

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ML Ops & Engineering

By increasing automation, we improve the production scalability and quality of production models by following the structural procedure as model versioning, monitoring, retraining, automation, and scalability. Technology enables transparency to access the applicability of AI/ML models.

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Model Maintenance

Recognizing the baseline of a model to prototype its statistical properties, we apply best data practice and techniques to maintain and monitor a productive system. We focus on how to develop, deploy, and steadily improve a conducive AI/ML model.


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