Dec 19 | 7 min read

Is Your Retail Store Working as Hard as It Could Be? How Data Can Unlock Greater In-Store Efficiency

In-store data holds unrealized insights that can transform how a retail store designs its layout and operates. Discover how it can maximize retail store value.

Aila Staff

It’s no secret that data is playing an increasingly central role in retail enterprise growth and development. In recent years, retailers have been forced to fast-track their digitalization strategies to adapt to a rapidly changing world and consumer purchasing habits.

Combining digital and in-store retail experiences with online and offline omnichannel services are vital foundation for long-term growth and success. But, these capabilities won’t thrive in isolation – they need to be nurtured and managed by data-backed development strategies.

Naturally, retailers that have successfully pivoted in a more “phygital” direction that engages with both on and offline shoppers have amassed a mountain of data, particularly from in-store customer transactions and engagements. However, compounding time, expertise, and infrastructure challenges mean that most retail organizations aren’t able to unlock and capitalize on the invaluable insights this data carries for in-store optimization.

Outdated hardware, fixed terminals and POS systems, and disconnected inventory and CRM systems all create efficiency gaps, preventing bigger-picture data visualization and strategic development. 

In short, success as a retail enterprise doesn’t hinge on data aggregation alone, but on how you access and utilize it to enhance the operability and performance of your stores.

Following the data: what are the benefits for in-store retailers?

By breaking down the infrastructure silos housing separated data, the potential for in-store optimization is virtually limitless. For instance, by combining inventory and sales data within one store. This enhances on-site stock allocation and ensures your store is housing stock according to demand – a process you can replicate at scale across all stores.

Identifying high-volume periods of foot traffic within a store improves employee scheduling and rotation, ensuring there are never too many or too few staff on hand at any time. 

In-store data can even be harnessed to introduce more localization and personalization within each retail store. It can power mass personalization in an accessible, cost-effective manner to large retail enterprises.

Customer demand for personalization is well established, with research finding that at least 71% of consumers expect brands to offer personalization in their engagements and service offerings. Sales data, logged inventory queries, click-and-collect requests and other data can help retailers build more agile, tailored brick-and-mortar stores, catering to local consumer preferences. 

In essence, two retail stores in different locations could offer entirely unique in-store experiences, while still maintaining the brand’s established quality of service. 

Data can help to design planograms and customize store layouts, pinpoint ideal sales and service points based on customer “hotspots” identified, and even place employees in optimal operating locations. Even in-store marketing and upsell campaigns can benefit from a data-driven approach by analyzing lead and conversion data generated from placement and positioning. 

The benefits of doing this are multifold: using data to guide in-store design, layout, and strategic decision-making creates a more customer-centric retail store, tailoring the customer experience offered between stores at separate locations. This, in turn, will contribute to higher customer spending, greater in-store earnings, and increased customer loyalty and retention. 

In the long term, this powers operational cost-efficiency and maximizes the value retailers can derive from their physical space. Staff are also released from low-value, menial work and placed in more strategic roles around a store, improving employee satisfaction and reducing attrition.

The challenges of gathering the data necessary

This, of course, all sounds terrific in theory – but the reality of immediately putting data at the center of your strategic and operational development is not that simple. There are two primary reasons for this. 

The first is the current tech and infrastructure limitations most retail enterprises are facing. At ground level, most retail stores still operate disparate in-store technologies to automate multiple processes and experiences, from POS systems and cashier tills to inventory software and CRM platforms. These static, disconnected systems don’t talk to each other, creating inevitable data silos that prevent a larger operational picture from forming. 

Retailers that do want a more visualized understanding of a store’s performance have to assign employees to manually track down and compile various datasets across systems, which is a tedious process.

CRM software that houses customer data, can prevent stores from identifying customer purchasing and engagement trends if it can’t connect with corresponding inventory and sales data as needed. 

Likewise, housing all sales data on one system at a fixed location, like a checkout counter, may contribute to an unpleasant customer experience from waiting in line, impacting upsell and targeted engagement opportunities. 

Disparate tools are the first reason. The second reason stems from the first: disconnected systems house disconnected software. This makes it all the more challenging to aggregate, centralize, and analyze in-store transactional and performance data to generate key insights. Retailers may struggle to integrate their existing analytics platform with the other tools in their tech stack, leaving data sitting underutilized with its value unrealized. 

Unlocking your data begins with “unlocking” your services

Solving the data challenge starts with solving the service and infrastructure challenges. Traditionally, retail stores have relied on disparate, single-purpose systems that separate their key services and, by extension, the data collected from them. The hardware housing these systems is also separate and generally fixed to a single location.

It’s only through “unlocking” these services from fixed locations and making them easily accessible across a retail store floor, that retailers can begin to better utilize their data.

Self-service kiosks positioned across retail floor space simplify and decentralize customer services, giving them more autonomy and freedom to access them whenever they choose to. 

Aila’s self-service platform for retail provides self-checkout with cashless payments, scanning, queries, smart fitting room, drop-off and returns, product discovery, and gifting and loyalty program sign-ups. They act as counters but are mobile, more efficient, and completely autonomous, optimizing store layout and floor space use.

While self-service kiosks themselves don’t collect data, they provide the infrastructure necessary to centralize all software and applications powering key services and offerings.

Using Aila, retailers can unify these experiences on one technology platform, making it easier for customers to engage with their preferred services and retailers to leverage the data gathered from all connected kiosks.

From here, retailers can collect data every time a shopper makes a purchase, performs a stock inquiry, signs up for a gifting or loyalty program, collects or returns an item, or views online inventory catalogs. Data gathered from multiple transactions can be seamlessly extracted and integrated between software and aggregated onto back-end analytics programs.  

The free availability to access these services across the store gives customers more flexibility in choosing the goods and services they want, giving stores a clearer, more accurate picture of shopper trends and behaviors. This enables them to adjust the store’s layout, product and service offerings, inventory, stock on sale, and staff scheduling and positioning based on “hotspots” identified for purchasing, inquiries, sign-ups, etc. 

Freeing up previously fixed services also frees up employees’ time and productivity. According to findings from PWC, checkout and cashier labor adds up to between 30% and 40% of retailers’ labor costs. 

Self-service checkouts eliminate the need for manual checking out and packing, allowing staff to refocus their time and energy on higher-value activities, like building better customer relationships, providing tailored assistance, and engaging in value-added services like agile fulfillment. 

Tap into the value of your in-store data with Aila

Data is the key to unlocking the inherent value hidden within your retail stores. But, before you can begin to reap the benefits of a data-driven approach to in-store optimization, you need the foundation to make it possible. Implementing adaptable, software-agnostic hardware that can easily move and be adjusted with layout changes is the simplest, fastest solution. 

Aila lets you roll out self-service capabilities instantly, centralizing your data across services on one single platform, all while offering your shoppers a seamless front-end, self-service experience.


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