As business leaders expand their horizons across industries and verticals, data science and AI are increasingly vital to driving digital transformation and delivering exceptional customer experience. Tackling these opportunities head-on, eClerx supports global leaders and pioneers through the enablement of customer-centric digital transformations to solve critical problems.
Prior to the explosion of digital touchpoints, interactions with customers were purely offline. But, as technology progressed, eCommerce blossomed to connect businesses with customers directly. From the development of chatbots all the way to the metaverse, this has completely changed the landscape of engagement.
Leading the charge with data.
The richness of customer data has skyrocketed, and the intelligence of available insights has really unlocked value. This means that businesses can no longer rely on outdated ERP systems or click stream data–understanding a customer’s pain points, high-effort touchpoints, and detractors requires deeper and more nuanced analysis across images, videos, AR, and more.
Yet, a common challenge that arises from this progress is the fact that the volumes of customer data available are indescribably greater than ever before. Big data is now a comical understatement. To meet this challenge, parallel processing and high-performance computing are a must. This led to the development of tools such as deep learning modules, computer vision, natural language processing, and the expansion of scale and efficiency.
However, despite developing the building blocks, many firms still struggle to deploy data science and AI effectively. Why?
That is a question we face time and time again from our global clients across industries. And driven by our expertise and experience, our answer is that there are critical guiding principles to account for when building any data or AI solution.
Taking the structured approach.
First, you must define the use case. What problem are you trying to solve–what is the objective you are trying to achieve? Are you looking to expand your customer base? Are you trying to provide a better experience? The purpose drives the foundation for your solution. Once defined, you need to ensure that you are sourcing accurate, quality data. Any machine learning or AI tool that is provided poor data will provide poor output. And it is important to remember that there is no one solution that fits all. It often takes multiple tools and experimentation to find your optimal process.
After these considerations, you must then decide on your tech stack. How do you develop the solution? Addressing this question helps to convert a blueprint design into an operational solution. Then comes the testing and validation phase. Think hard about which guard rails need to be put up to ensure that a solution works for all applicable use cases. A single mistake here can be extremely costly, whether that’s brand or monetary damage. To conclude, you need to focus on scale and monitoring to ensure efficiency, consistency, and constant improvement.
In no way are the above steps linear. They could operate in parallel, cyclic loops, or even jump from one to the other to ensure a continuously learning and improving process.
Driving results that matter.
Our focus has always been to leverage data and AI as a means to achieve business outcomes and build transformative solutions for business problems.
We have helped countless firms to build impactful analytics, data science, and AI solutions, such as:
- Real-time customer journey personalization and optimization through persona profiling, path analysis/optimization models, driver analysis, and a next-best action recommendation engine across digital channels for higher engagement, conversion rate, and average order value (applicable across eCommerce, CPG, B2B/B2C technology, and travel and telecom clients with digital operations)
- AI-driven one-stop customer support ecosystem with first-of-its-kind modules, such as issue prediction, guided resolution, eServices content and journey optimization, failure prediction, on-the-box proactive support, and virtual agents powering omnichannel support intelligence for higher first-touch resolution and CSAT and reduced support costs due to self-serve enablement (customer support functions across industry verticals)
Through proven business problem-solving frameworks, best-in-class solutions, advanced technology, and transformative data science, ML, and AI algorithms, we solve niche use cases that drive impact and results.
With a global client base of 200+ across industries from retail, eCommerce, B2B/B2C banking, technology, manufacturing, CPG, ed tech, software/app tech services, and more, we are problem solvers with cross-functional expertise ready to help you navigate the pitfalls and challenges of data science and achieve real transformation.
Contact us to learn more or partner on a maturity assessment and strategic roadmap development workshop for your critical use cases.