By John Clarke Mills, CTO, Zenput
For retailers operating across multiple locations, maintaining consistency between stores is an ongoing — and crucial — challenge. As retailers discover and iterate on methods to better design and optimize their in-store customer experiences, ensuring the execution of those processes is uniform across storefronts can make the difference between failure and success.
The fine details of a retailer’s brand experience have a tremendous influence on customer behavior, affecting whether shoppers ultimately make a purchase and if they’ll choose to revisit that brand in the future. By tracking the right metrics, a retailer can acutely understand the anatomy of a customer’s experience and recognize any issues that impede either sales or long-term customer satisfaction. These metrics can be designed to check up on key factors:
Data has always been the most powerful tool in addressing the imperative for customer experience optimization and consistency. Traditionally, managers at each store location have done their best to achieve identical store experiences while relying on spec listings sent on paper, as well as photos and shared files available online.
However, modern storefronts need the ability to access this data and other essential metrics with real-time responsiveness. This requires implementing a database capable of storing data in any and all formats a retailer might find a use for. It also calls for a database with the performance and high-availability to ensure it can reliably deliver that information to all of a retailer’s locations.
We’ve seen firsthand how complex and diverse the data types retailers wish to collect can be. In setting out to build a task management and data collection platform to help retailers achieve consistency across their different stores, we gathered feedback from retailers to better understand their needs and discovered the high demand for data flexibility.
Retailers employ brand marketing and logistical experts who are constantly exploring ways to enhance, streamline, and otherwise improve customer in-store experiences. Ideally, it should be achievable to perceive any possible factor that influences these experiences. This means that, when it comes to data, retailers needs the ability to collect any and all data that might be (or might become) useful, regardless of type or format.
Recognizing this reality led us to understand flexible, non-relational databases are the right choice for retailers. Whereas relational databases structure data and impose limitations on the format of the data being collected (features which can be advantageous in other circumstances), non-relational (NoSQL) databases — such as MongoDB — allow for data to be stored in a variety of formats without defining each characteristic upfront.
In our case, we selected mLab as an experienced MongoDB Database-as-a-Service provider in order to facilitate database operations and make it possible to better concentrate resources on platform development. MongoDB in particular serves the needs of retailers well, providing the flexibility, performance, and availability to ensure that systems for measuring, managing, and improving customer experiences are reliable and effective.
As we’ve discovered, and can share with retailers, choosing the correct database sets the stage for establishing a deeper understanding of customer behavior and the realities in-store experiences. By easily collecting and accessing any metric that may correlate with customer sentiment, a flexible database empowers retailers to nurture customer satisfaction, and win long-term business for their brands.