(US & Canada) | We Tailor Last-mile Solutions Based on Industry-first Perspectives — Tredence Co-founder and CRO

Shashank Dubey, Co-Founder and Chief Revenue Officer at Tredence, speaks with Robert Lutton, VP at Sandhill Consultants and Editorial Board Vice Chair, CDO Magazine, in a video interview about tailoring data solutions to the last mile problems, the role of AI accelerators, examples of transformations for clients, and the importance of the approach taken to solve the last mile problems.

Tredence is a data science company that provides data analytics and AI services to hyperscale enterprises.

When it comes to tailoring last-mile solutions to unique challenges, Tredence starts with an industry-first perspective, says Dubey. One of the crucial elements, he adds, is having experts in not just data, AI, and tech, but telco, CPG, and retail who run the businesses.

Dubey states that such experts bring an end-user mindset and expertise that customers love. Further, he maintains that almost all transformational projects at Tredence start with a discovery and design thinking workshop instead of data analysis and algorithms.

Taking the instance of the U.S. telco industry, Dubey notes that customers frequently switch between providers, driven by factors like customer experience and pricing. Through a design-thinking workshop, Trident aims to learn from the best-in-class customer experiences across industries like retail, fashion, and automotive.

Next, those learnings are applied to improve the telco customer experience, says Dubey. Eventually, the design thinking workshop becomes the nucleus that guides the development of downstream solutions involving AI, ML, and data.

Focusing on the role of AI accelerators, Dubey stresses ATOM.AI and one of its modules, known as Algo Works. He explains that the module has pre-built ML models, which contain almost 200 AI/ML workbooks across multiple use cases and industries.

Adding to that, Dubey says that it could be something as foundational as customer churn modeling, demand forecasting, or demand transference. From the retail point of view, trade promotion optimization, and CPG, it could be around predictive maintenance.

Further, Dubey mentions the centralized feature store of ATOM.AI, which has aggregate features across customer data, pricing, supply chain, assortment, and network data as dimensions. It also has standardized and integrated MLOps pipelines.

As a capability, ATOM.AI, through Algo Works, enables AI/ML engineers and data scientists to focus on model innovations rather than spending time on the basic build processes. When 80% of the accuracy issue is taken care of by the platform, the remaining 20% of model improvements are managed by experts for maximum accuracy.

When asked about client transformations, Dubey shares about the transformative engagements happening in South Korea, Australia, Dubai, the U.S., and Switzerland. He provides an example of a CPG manufacturer based in Lisbon that wanted Tredence to build a solution for its sales representatives.

Elaborating further, Dubey states that the sales representatives had a list of stores to visit and collect orders from. The challenge arose as the list was not optimized, leading to inefficiencies.

To address this, the team flew to Lisbon and shadowed the sales reps for a couple of days instead of analyzing data and building algorithms. In this approach, the team did hands-on tasks and built trust, which in turn, helped them understand the end-user constraints and challenges better. Consequently, it helped Tredence build a solution that truly met the needs of the sales representatives.

When it came to adopting the solution, Dubey affirms that while the sales representative brought in many orders in the first half, they were not visiting that many stores post-lunch. To resolve this issue, the team put in bar codes at the listed stores, which the sales representatives had to scan after visiting the stores.

Concluding, Dubey asserts that this problem could be spotted because the team was on the ground to deal with it. He assures that it is not a data engineering or AI fix but rather a process fix that solves the last-mile issue, ensuring a higher ROI.

CDO Magazine appreciates Shashank Dubey for sharing his insights with our global community.

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