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Better Inventory Management by eliminating Data Silos

Blog | October 4, 2022 | By Mohamed Anas, Anish Bhola

The proliferation of digital technologies world over has lowered the barrier of entering newer markets or launching new & differentiated products for companies the world over. This opens an entirely new channel of revenue to brick ‘n’ mortar stores. More often than not, such companies continue to rely on traditional supply chains to procure, produce and ship goods to stores/customers. Depending on the nature of products and volumes this may take anywhere between a few weeks to many months from ideation to product launch. This poses a challenge in crafting effective, timely marketing campaigns to drive more inward traffic and improve conversions at both the store front & web front.   

Our customer, a premium lifestyle accessories and personal fitness equipment retailer of repute with global operations has observed a marked shift towards online purchases by their customers. This switch in customer behavior mandated new software applications, some easy to ingrate with existing infrastructure, but others working in silos to solve very specific business needs.

However, as operations grew manifold, a level of disjointedness crept in as these tools & utilities couldn’t scale up fast enough, leading to poor governance, data quality issues and a general lack of trust in data. Business teams worked in silos which reduced real-time collaboration and/or poor decision making.  

A specific case to this point was lack of alignment between sales & marketing. With siloed data from different sources, analysts couldn’t map product searches with order/purchase. Supply chain, logistics teams were unable to get timely alerts/inputs to manage requisite inventory.

This hurts in multiple ways – lost business opportunity, unhappy customers, also big pile up of unwanted/unsellable inventory.

Realizing that this issue has the potential to adversely affect not just existing business, but future growth of company, our customer requisitioned consultants from USEReady to find an effective, scalable & future proof solution(s).

What followed was a process of interviews with different stakeholders to understand the process in depth, identify bottlenecks.

A bird’s eye view of the business problem mentioned below:

  • Click stream data is used to track webstore marketing effectiveness.
  • Inventory, orders and purchase information stored on a traditional RDBMS hosted on public cloud.
  • Product catalog used by marketing team to track marketing campaign effectiveness on clickstream feeds is in divergence to purchase information.
  • Marketing teams track product categories while inventory is stored as SKUs.
Fig 1: Pre-existing Architecture  

Consultants at USEReady realized the impact of siloed data and limitations it posed on real time collaboration, the ability to generate meaningful & actionable insights. While crafting a solution, care was taken to ensure the solution is future proof and able to handle data manifold as compared to the existing volumes with minimal or no changes to architecture, the insights generated will enable real time collaboration between the various stakeholders via a simple & easy to consume interface.

Proposed Solution:

The key to tracking of marketing initiatives’ effectiveness is to understand customer journey, what products were customers looking for, what’s the click to conversion ratio, the time spend during each session so on.

Our consultants opted for Snowflake data platform, given its benefits over on-prem or other cloud databases. It’s near infinite storage & compute makes it the natural choice to support our customer’s fast paced growth.

Matillion for Snowflake enabled rapid data ingestion to make near real time collaboration between different teams possible.

Visualizations with self-service capabilities designed in Tableau empowered analysts with deep insights.

Fig 2: Solution Implemented

Benefits:

  • Future proof, cloud native solution offering near infinite storage & with near zero maintenance.
  • Reduction in time taken for data ingestion resulting in significant savings in both cost & person hours on recuring basis – what took days earlier now takes just a few hours.
  • Additional data sources could be provisioned which would not have been possible otherwise.
  • Combining different data sources led to robust data prep and enrichment at scale, better data governance & cataloging capabilities.
  • Intuitive visualizations helped understand customer journey with just a few clicks.

Business Outcome:

  • Supply chain teams could better forecast demand & right stock products at warehouses, stores to cater to both walk-in customers, online visitors.
  • Sales & marketing teams can reconcile numbers and craft effective marking campaigns.
  • Optimized UI design to improve click to conversions.
  • Improved user experience of customers.
  • Greater brand recall, improved reach amongst target audience.
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About the Author
Senior Data Engineer. Data Engineer thru the week and wanderlust during weekends. A techie at heart, he loves building open source tech stacks.
Mohamed Anas | Senior Data Engineer | USEReady
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About the Author
Anish Bhola is a Senior Full Stack Data Engineer at USEReady with rich experience in solving business problems leveraging modern data stack tools like Snowflake, Matillion, dbt (data build tool), Tableau, & PowerBI.
Anish Bhola | Senior Full Stack Data Engineer | USEReady
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