Framework to power customer loyalty with AI-driven Customer Profiles, Behavioral Analytics, and Predictive insights
Knowing the customer and building Customer loyalty are the two most important success factors for a retail business. Both these ideals can only be executed well with the technology and data infrastructure that supports a deep understanding of the customer combined with responsive communications, personalized experiences, seamless multi-channel interactions, and effective returns services.
While data technology is expensive to create and maintain, modern AI/ML and Big Data technologies are making it easier to organize data, discover patterns, and derive intelligence that can revolutionize operations and sales.
However, harnessing AI/ML, Big Data and Cloud computing can be complicated. The technologies and tools are rapidly evolving and skills shortage can hamstring your organization’s ability to develop and maintain analytical and operational systems.
Enter Datamorph - Kwartile’s self-serve data transformation platform. Datamorph is a suite of tools that optimizes development of modern data architectures. It shields developers from the complexities of the implementation, reduces development time, and allows engineers to focus on uncovering intelligence within the data. The platform abstracts out the complexities of two critical areas – distributed computing and deployment environments – that represent the most complex and time-consuming tasks in the development pipeline.
Datamorph makes it vastly more convenient to develop and maintain powerful data and analytical applications such as Customer Identity and Profile Management systems, Omnichannel Behavior Analytics, Helpdesk Analytics, and Returns Analytics. Additionally, the platform is customizable and extensible, allowing engineers to implement any kind of complex business logic required for building custom business applications. With Datamorph, a retail business can create streamlined applications to obtain deeper insights into customer behavior, create highly relevant product lines, optimize customer service, vastly improve customer experience, and achieve high-ROI on marketing campaigns – all with significantly reduced spending on IT skills and infrastructure.
The retail industry is no stranger to headwinds. Even then, the year 2020 witnessed previously unseen challenges with the near collapse of the restaurant and travel industries caused by theCovid-19 pandemic. The retail industry experienced an unprecedented dichotomy as some retailers went bankrupt and others struggled to meet demand. As consumers stayed indoors, online commerce surged and convenience became the mantra of retail.
There is no question that in terms of knowing the customer and anticipating customer needs, the retail industry has crossed the Rubicon. The gap between leaders who understand how consumer expectations are evolving, and the rest of the industry has continued to grow.
Consider the following statistics:
Predictably, leaders are pulling away from the pack with increasing revenues while laggards are going out of business. Further strengthening this trend is the rise of digital adoption. Since the Pandemic, digital sales have skyrocketed and retailers that doubled down on digital have done much better than their peers.
So, what should a retail business do in order to survive and outperform in the near future as retail is turned on its head?
Apart from the core supply-chain capabilities and operational systems, the four fundamental capabilities that need to be built are:
From a systems standpoint, all of the above capabilities require mature data integration and management practices supported by the latest data processing software and infrastructure. These include:
More specifically, retailers need to embrace the paradigm shifts happening in data processing technologies, as illustrated below:
The above defined technology stack has great data manipulation and processing capabilities, but requires a highly skilled data team to organize the data, create processing workflows, and build operational data systems.
Add to this, advances in AI/ML systems which require complex training systems and a multitude of data stores for scenarios, and IT development operations can become unwieldy.
It is well known that about 85% of a data scientist’s time is spent on just identifying data sources and pre-processing steps. Similarly, application developers spend much of their time in dealing with the complexities of distributed computing environments (i.e., the multi-cloud),and the complexities of deployment environments.
Kwartile’s DataMorph platform is a Low/No-Code Data transformation platform that reduces the hurdles to developing and maintaining modern data applications. The platform offers a powerful set of tools to create and maintain a modern data processing framework (see Figure 2):
While the Modern Data Architecture solves many problems of data organization, data processing, data accessibility, and data governance, it creates a new set of challenges for developing data applications, including the following:
Due to all these challenges, the data applications programmer is faced with a steep learning curve. The underlying technologies are also advancing rapidly, making it even more challenging for developers to keep up.
What is needed is a way of reducing the quantity of coding required for developing and maintaining data applications, and managing DevOps, cloud deployments, and security frameworks.
Datamorph is a low/no-code platform that is optimized for developing data applications. By using the platform, developers can improve their productivity by 50-90% while reducing operational costs between 20-60%. In the long term, Datamorph provides many other benefits for software maintenance including automated version control, dependency management, and application security management. This leads to high code maintainability and improves the long-term economics of application development even further for companies.
At a high-level, the specific features and benefits of the Datamorph platform include the following:
Also, the platform provides many productivity enhancing tools including the following:
While the Datamorph platform is appropriate for almost any possible data application development scenario, some more representative retail use cases include the following:
Below are three representative case studies that the Datamorph platform has been implemented at retail customer sites.
The rollout of the projects provided multiple benefits and capabilities to the marketing, sales, and supply-chain business units, and operational organizations including IT and Customer Service.
Kwartile offers a robust set of solution and support services for the Datamorph platform. Our Product Support services offer multiple plans for Enterprises, Software Development professionals, and Partners. The support services include technical help during the warranty period, bug fixes, and regular version upgrades.
In addition, our professional services team offers data strategy, design, and application development services. Kwartile Consultants and Data Engineers possess a combination of deep and practical technology expertise, industry expertise, and strategic product management experience.
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