Data-driven methodologies such as big data and artificial intelligence (AI) are driving faster and more accurate decision-making in every step of the retail value chain. Procurement, logistics, shrink mitigation, and marketing efforts all stand to massively benefit from better data management and utilization.
AI in particular promises to reshape the shopping experience for suppliers and consumers alike, and retail executives have taken note: 65% of surveyed retail CIOs plan to increase their investment in business intelligence (BI) and data analytics in 2023, and 94% aim to have AI/machine learning systems deployed by 2025.
Potential value created by AI solutions to the retail industry, more than any other industry analyzed
Predicted of the global big data and analytics market for retail in 2028, with a CAGR of 23.1% from 2021 to 2028
Percentage increase in fraction of Top 250 retailers harnessing AI from 2016 (4%) to 2021 (28%)
Embracing a data-first approach helps retailers be proactive rather than reactive, allowing them to stay one step ahead of supply chain disruptions, market volatility, and evolving customer preferences.
The complexities of managing retail data mean that operationalizing an data initiative is notoriously difficult: barely a third of AI POCs are ever piloted, and only about 1% of overall AI projects ever reach multi-site or full-scale deployment.
Given the industry’s multi-billion dollar annual investment in data-driven strategies, this low conversion efficiency results in considerable financial and productivity losses.
Functions of a data platform
A data platform enables retailers to quickly experiment with, productionize, and scale high-impact datainitiatives like AI models.
They enable teams to easily analyze arbitrary data volumes and build performant AI systems that can meet the needs of any deployment.
They abstract away compute runtime setup andupgrades, software containerization, hardwareprovisioning, workflow orchestration, etc.
They empower teams to collaboratively iterate,validate, and communicate data strategies beforeproductionizing them.
They move data initiatives across the finish line, fromidea to full-scale, ultimately enabling an organizationto reap the benefits of its data strategy investment.
Choosing the right data platform enables your team to
focus on leveraging data, not managing it.
The Kaspian Data Platform™ is a powerful compute layer for the modern data cloud. Kaspian abstractsaway the complexities of managing compute infrastructure by exposing granular, UI-driven software andhardware control planes. The compute resources can then be consumed as standalone Jobs (ex. Sparkcluster jobs, multi-GPU deep learning training jobs), chained together in a visual workflow builder usingPipelines, and leveraged via Notebooks for iterative development and experimentation.
Jobs – standalone compute workloads with support for open source orchestrators(ex. Apache Airflow)
Pipelines – low-code,multi-stage workflow graphbuilder with a built-inorchestrator and scheduler
Notebooks – Jupyter Notebooks for prototyping data workflows and developing AI models
Kaspian’s software control plane (Environments) enables users to define the dependencies needed to run their data workflows. Environments can be defined with Docker images andDockerfiles, and Kaspian can also auto-build Environments from Pip/Conda requirements files
Kaspian’s hardware control plane (Clusters) enables users to define virtual compute groups for different workload sizes, types, and use cases. Clusters automatically autoscale within specified limits and spin down fully when not in use for maximal cost efficiency.
By facilitating the provisioning and management of multiple popular compute runtimes in a single console, Kaspian empowers scientists and analysts to frictionlessly experiment with and operationalize data technologies at scale without dealing with multiple vendors and point solutions.
Retailers can deploy Kaspian in under one hour and customize their instance to meet their specificdata workload, operations, and security requirements.
Kaspian deploys into your cloud environment as aKubernetes application. Compute and storage artifactsrelated to your deployment stay within your cloud formaximal data privacy and security
In addition to interfacing with your data lakes and warehouses, Kaspian connects to your version control system so you can maintain your GitOps and CI/CDpipelines. It also plugs into your notification system.
Leverage Kaspian’s managed hardware and software control planes to define the interoperable compute configurations upon which Kaspian Jobs, Pipelines, and Notebooks can run.
Kaspian’s core platform product empowers data scientists and analysts to prototype, productionize, and deploy data pipelines and AI solutions at any scale.
In addition to interfacing with your data lakes and warehouses, Kaspian connects to your version control system so you can maintain your GitOps and CI/CDpipelines. It also plugs into your notification system.
Kaspian offers responsive customer support for technical issues, implementation assistance, feature requests, and other account inquiries.
Kaspian engineers have extensive experience settingup and managing cloud infrastructure, data lakes and warehouses, and compute services.
Retailers are expected to spend $67B on digital advertising in 2023. But with marketing teams spending 32% of their time managing data quality and with 26% of their campaigns being hurt by suboptimal data usage, retailers are misallocating tens of billions of marketing dollars annually.
Harnessing data is the first step to optimizing marketing spend. Kaspian’s infinitely scalable compute backend allows marketing teams to crunch even the most granular and massive datasets with confidence. These capabilities are essential to leverage modern methodologies for customer preference segmentation such as hyperlocal geospatial analytics.
Proactive spend allocation
Off-the-shelf vendors for marketing spend allocation struggle to understand the nuances of specific businesses and campaigns. Developing an in-house solution not only gives marketing teams greater control over spend, but also produces a treasure trove of metadata for subsequent analyses.Kaspian seamlessly integrates with the databases, data lakes, and data warehouses retailers use to store sales, campaigns, and social media engagement data. After iterating on an allocation algorithms in code notebooks, analysts can leverage Kaspian’s low-code data pipeline builder to deploy both a monthly rebalancing workflow and a daily dashboard update.
Data-driven demand planning can improve the top and bottom lines by minimizing excess inventory while ensuring sufficient stock for customers. Forecasting with machine learning is becoming increasingly more powerful as retailers have been able to integrate multiple datasets and systems.
Estimated annual cost of outof-stock events to retailers, with additional intangible brand value losses
Percentage retailers who consider inaccurate inventory forecasting a “constant issue” for their stores
Percentage of retail supply chain executives who plan to increase their investment in productive (demand) planning
By facilitating the provisioning and management of multiple popular compute runtimes in a single console, Kaspian empowers scientists and analysts to frictionlessly experiment with and operationalize data technologies at scale without dealing with multiple vendors and point solutions.
Shrink costs US retailers $100B per year and has accelerated in recent years. 60% of retailers are increasing their investment in technology to combat shrink; this investment is vitally important as the diversity and sophistication of shrink’s causal factors are increasing.
Develop a single source of truth
Consolidate data from disparate data silos such as purchase systems,
supply chain managers, and in-store inventory records to guard against
accounting errors, delivery fraud, and other logistical problems.
Analysts can schedule Kaspian pipelines to load data from APIs
into a data warehouse for further processing.
Manage inventory quality
By harnessing Kaspian’s deep learning capabilities, specifically its managed cloud GPU offering, data scientists can develop and deploy advanced computer vision models for assessing produce quality from camera feeds or other IoT sensors located in stores.
Tackle organized retail crime (ORC)ORC
mitigation is a priority for all brick-and-mortar retailers.Whether an organization is looking to deploy fraud detection systems for self-checkout or point-of-sale devices, computer vision models for store shelves, or license plate readers in parking lots, Kaspian’s comprehensive data platform has the solutions data teams require to combat shrink with AI.
Although 80% of consumers expect personalized retail experiences, only 23% believe retailers are doing a good job at providing them. 67% of surveyed retailers claimed they do not have the correct tools in place to gather and integrate customer data and therefore cannot deliver these experiences at scale, resulting in only 15% of retailers fully implementing a personalization strategy.
Percentage of online shoppers (96% of Gen Z and 97% of millennials) more likely to continue shopping on retailer websites that offer personalized experiences
Percentage of additional revenue from experience personalization captured by companies that excel at it compared to companies who are only average
Percentage consumers more likely to purchase, repurchase, and recommend brands as a result of personalization, resulting in a virtuous flywheel of LTV and loyalty
Always-on, always improving AI
Data scientists can utilize Kaspian’s low-code pipeline builder to easily architect training workflows their personalization AI models.
Kaspian’s flexible and fully-managed compute layer enables model training pipelines to leverage technologies such as big data (ex. Spark) and deep learning (ex. Tensorflow) with zero setup or maintenance.
When instrumented correctly, these training workflows form positive feedback loops: customer engagement with the personalized experiences produces metadata that can then be used to improve the underlying AI models and consequently the quality of subsequent engagements.
