Modern definitions of retail shopping have been completely revolutionized and are constantly bringing dynamic changes in the market.  Brick-and-mortar retail can be defined as an establishment that operates from a physical storefront and differs from other common retailing methods such as mail-order catalogs and online shopping. 

Coming with a number of benefits, brick-and-mortar retailing has been present in the market for a long period of time, much before the introduction of e-commerce. However, due to rapid technological advancement and the internet making a huge inroad in this domain, brick-and-mortar retail shopping has suffered a huge setback. 

Analysis done by market experts in this context reveals that this form of retailing may eventually be phased off in favour of more technology-involving business. Even though it may not get completely wiped out, brick-and-mortar retail businesses can be propped up by data science and be brought back to the mainstream of retail business. 

Being an interdisciplinary field that encompasses scientific methods, processes, algorithms, and systems that extract knowledge and insights, data science can help to formulate a phenomenal and groundbreaking market strategy which can be infused within brick-and-mortar retail shopping to give it a complete turnaround from being a dying business model to a flourishing one. 

An understanding of the customer base will help develop an integrated approach that will give immense boost to such businesses.  One of the most important reasons that have triggered the application of data science to brick-and-mortar retail shopping is that more and more business owners are struggling against bankruptcy and trying to keep their traditional shops up and running in the face of competition from e-commerce giants. 

Thus, data science can make a valuable contribution and bring about significant change in the brick-and-mortar retail scenario.  It offers critical help in tackling unresolved issues by getting essential data or insights on what was impeding success in such business. 

Here are a few important ways in which data science can help brick-and-mortar retail shopping remain viable. 

Understanding behavior analytics with data science 

Gaining in-depth data-driven customer insights has always been a critical challenge for brick-and-mortar retail shopping in order to improvise on their conversion rates. Data science leverages in personalizing campaigns that are targeted towards increasing revenue, retaining customers and bringing down customer acquisition cost. The dramatic transformation with data engineered analytics will pull in the gears for a new start for brick-and-mortar retail shopping.

Inventory Optimization 

The ascent of data science into brick-and-mortar retail shopping has brought potential increase of operational efficiency within physical stores. Achieving inventory accuracy is now possible for retailers with the inclusion of machine learning, artificial Intelligence (AI) and other such advanced tools. Further to this, data science in inventory management has proven to bring 100% inventory accuracy through near accurate demand forecasting that has impact on escalated ROIs and reduce loss due to inventory mismanagement. Stores that faced challenges of having limited intelligence on the location and quantity of stock can now leverage the benefits of executing a competitive Omni-channel retail strategy with the help of data science. 

Data science in dynamic personalization of in-store experience 

The inclusion of data science in retail shopping has helped many retailers to understand their data in order to optimize merchandising tactics and create unique personalization of their store experiences. This encourages consumers to purchase from them resulting in increase in sales prospects.

Achieving higher conversion rates with predictive analysis 

Data science phenomenally changes the traditional concept of brick-and-mortar retail shopping with a 360-degree view of possible prospects. The co-relation of customer’s purchases in history and their profiles fetched out from data engineering helps to re-strategize business promotions, advertisements and how retailers can get higher conversion rates. 

Gaining customer journey analytics 

In the traditional brick-and-mortar retail shopping storing customer journey analytics was a big challenge. With the technology of advanced data engineering, all relevant data and analytics related to potential customers can be stored and leveraged to answer complex retail questions.  Such questions like, how the customer’s journey was, who were their high-value customers, what was their behavioral pattern and how to reach them can all be resolved with data science and analytics. 

Operation analytics of retail shopping 

Data science enlivens brick-and-mortar retail shopping by superior operational analytics. These analytics data includes trends information, patterns, and outliers that prospectively increase efficiency in operational performance. Structured data such as CRM, ERP, Mainframe, Geo location, and public data helps brick-and-mortar retail shopping to access a new phase of development. 

In conclusion, it can be optimistically said that brick-and-mortar retail shopping has not yet been thrown into oblivion – it can be revamped with the help of advanced data science. With the transformed pattern of the current market, it is always advisable to adapt to the latest technologies that not only increase business competencies but also help to shape-up the traditional and fundamental aesthetics of retail shopping.