Straight Through Processing

This article has been written by Supriya Naidu pursuing a Startup Generalist & Virtual Assistant Training Program from Skill Arbitrage.

This article has been edited and published by Shashwat Kaushik.


As humanity transitions from counting stars on fingers to managing gigabytes of cosmic data, unlocking the stories of cosmic secrets and making it a possibility to venture into planets far beyond, as humanity wants to expand and embrace exponential growth and abundance, as we try to understand the data at an astronomical scale and architect the bridge to the future, the science of data at scale arrives at the centre of all disciplines. Data science is a reflection of the innermost desire of humanity to expand and achieve unprecedented growth. Early written inferences about the attempt to understand this science point to five decades ago by John Tuckey. 

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Deciphering the story of growth

Data science is deciphering the story of growth, overcoming obstacles, and unifying the objectives across the differences. With the exponential advancements in the last three hundred thousand years in all the spheres we have stepped upon, we are looking at a horizon of unimaginable size of opportunities glaring in our eyes.

A billion dots on earth are trying to get connected via different paths, exploding into billions of pieces of data and leaving a trail of gazillions of interactions behind for willing minds to decode. This is the challenge of this century for any business. For any business to unlock its growth potential, the capability to unlock the story behind human interaction sitting behind closed numbers is crucial. Be it the customer/potential customer/competitor/employee/employer humanity is at the centre of every interaction and opportunity. When any business, however small it might be, steps onto the internet, which is the web of exponential connections, you become a tiny dot that has access to infinity, making this one of the most interesting times to exist. It also amplifies the challenges you are exposed to, as you are on the narrow edge of innovation and obsolescence. Augmented with the advancement of artificial intelligence, the technological wall of obsolescence has become even more leaner but nevertheless, the basic principles of getting answers to your questions by analysing the data are eternal. Cloud computing has democratised data, and the accessibility of data is no longer limited to data scientists. Let’s explore how this science can positively impact some of the most critical business functions.

Sales and marketing

Businesses usually spend 5–20% of their revenue on marketing, depending on the strategy, which is significant. The exploding number of touchpoints that can help you key in your next customer is growing at a rapid scale, making it crucial to understand the effectiveness of the different channels and the plethora of media and content spread across the channels to optimise your marketing budget by doing the right split and derive the maximum value. Between offline and online campaigns, understanding which segment holds your best customer and doing the right segmentation often requires a deep understanding of your current customer base and the effectiveness of your past campaigns. This often requires the humongous task of collating the interaction data, which is often unstructured across all the different touch points, decoding what is generating the interest of the potential customer,

Customer segmentation

Creating effective campaigns requires identifying your target audience across the different segments and creating specialised content across the different channels personalised to the human at the centre of interaction requires complex analysis of your history as well as the current customer landscape.

Personalisation: The amount of data available across social media for an individual outlining not only the demographic details but also the likings and interests and the ability to capture what is spurring the customer to like your product/service, buy your product/service and remain a loyal customer requires a deep analysis of human behaviour, which needs to be tied to external events to hook your customer.

Churn prediction and retention

Data plays a pivotal role in identifying the customers at risk early enough and creating the right preventive measures and interactions to reduce the churn and enhance the overall customer retention, which is crucial in increasing the lifetime of your customer. Eg: coming up with the right offers during the expiration of a current subscription pushes the customer to renew the subscription and reduces the churn.

Branding strategy

Branding strategy influences the perception of your product/service and creates/enhances the connection between your potential/existing customers. Data driven personalisation helps create that personal connection, which can give you a competitive edge and help you create a feedback loop for continuous improvement.

Cross selling and upselling

Identifying the right offer to be made to the right customer at the right time requires you to create a 360 degree view of the customer across different business functions. Unifying the customer data across the different functions as well as the different channels of interaction to extract the complete story of an individual helps you create the right cross selling and upselling strategy.

Competitor analysis

With the democratisation of opportunities, it’s more important than ever before to stay updated with the competitive landscape. Knowing your competitors, monitoring their digital activities and their strategies across sales and marketing, which are giving them an edge in the market, is a key for businesses to stay ahead of the competition.

Optimising the pricing strategy

Breaking even the pricing and discount strategy, adjusting the prices in real time proactively and dynamically to maximise revenue and growth requires collating vast amounts of Sales/Service data and analysing them to extract actionable insights. It’s one of the most complex metrics which requires deep analysis in line with the business value proposition to drive Profitability/growth.

Customer relationship management

Creating a unified view of the customer from the beginning of the interaction during the sales and marketing lifecycle to the service and operating lifecycle, understanding the demographics/likes/interests of the users, and bringing it all together in ways not possible earlier requires capturing the data across the timeline of a customer to derive actionable insight that will not only nurture long term relationships but also increase customer value and profitability.

Recently, Salesforce has launched Data Cloud, which enables unifying the customer view across the Sales, Marketing, and Service clouds across all the integration touchpoints (mobile/email/social media/IOT to create a 360 degree view of any persona or any transaction from initiation until analytics challenges human intelligence, stretching the threads of imagination like never before.

Supply chain management

Delivery within 6-10 minutes is what the delivery aggregators are targeting. This requires massive optimisation of logistics and routing, the creation of micro warehouses strategically located at the right distances and the analysis of historical customer orders, leading to more accurate inventory management. large scale automations of all processes across all touchpoints, including GPS tracking and real time integrations, have disrupted the supply chain management landscape forever.

Application of data science at each integration point, identifying the patterns which can lead to optimisations, and also identifying the problems before they create losses, is crucial in managing the Supply chain optimally.

Operations and service management

Customer expectation has grown manifold with the personalisation applied across the various channels of customer touch points, which have been integrated to provide customers with a seamless experience. CRM with the integrated knowledge base between the online/field service/Sales/Service sales and service centres has enabled the service industry to offer extremely fast/reliable service, reduce the overall turn around time, increase customer satisfaction and customer lifetime value and open up opportunities to increase sales and profitability. Opportunities for cross selling and upselling, pitching the right product to the customer during service operations and interaction have never been this seamless before. Analysing the sentiments of the customer and reacting proactively is helping organisations increase their loyalty and brand value.

Human resources

Human experience is at the heart of all the innovation and progress we are making. And measuring human behaviour is a very complex process. Identifying the metrics that motivate an individual to outperform his past performances, how to offer competitive benefits that reduce attrition rates, measuring the factors causing attrition, and identifying and retaining the best talent are some of the areas where data science has seen progress by leaps and bounds. From processes which were mainly human centric, it has become more data centric which is helping organisations create a 360 degree view of the employee, capture his skills, experiences over the course of time to provide growth and opportunities in line with the talent of the people.

Performance management, employee satisfaction or dissatisfaction are extremely complex metrics that can be measured by data, but as the human resources sector continues to evolve and it becomes more tangible to record the experiences/skills/interests of an individual in a searchable database, it becomes easier to find the right people for the right opportunities at the right time to improve the human experience of the employee.


Data science plays a huge role in innovating a product and applying continuous improvement. Right from initiation till delivery in the various legs of product development, it becomes critical to analyse the data that gets generated and take the right decisions to optimise the product and reduce the risks. The exponential growth in IoT has made it possible to capture data at every interaction/ integration, which also provides an opportunity to create intelligent products closer to customer expectations.



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