Smart Technology Transforms Retail Operations

Retail operations are shifting to include AI/ML among other technology.
Application Innovation / Automation - AI, ML, & RPA / Digital Optimization Strategy / IT Consulting / Software Development / Technology

Smart Technology Transforms Retail Operations

Over the past few years, CIOs in retail have been shifting their organizations more online. The main focus of retail operations during these online transitions has been on e-commerce and AI-enabled personalization for customers. Moreover, these retailers’ adoption of technology has grown rapidly throughout the COVID-19 pandemic.

While AI transformation sometimes brings thoughts of robots or large machines interacting with customers that is not the reality in the retail space.  AI and automation adoption has been more subtle but still successful.


Retail Operations Going Digital

While many retail businesses already had online offerings, the COVID-19 pandemic accelerated the rate at which retailers moved online by about five years, according to one study. The report forecasts a 20 percent growth in e-commerce this year all due to the global pandemic. Other statistics from the beginning of this year saw retail e-commerce numbers rise over 30 percent between the first and second quarters and nearly 45 percent over the previous year. It is obvious that retailers have been going digital at a rapid pace.

For retailers looking to move operations online, the key is looking for omnichannel opportunities. This means expanding online offerings to include alternatives like buy online and pick up in-store (BOPIS) and ship from store services. A report by McKinsey found that customers’ demands for these types of services will grow. Fifty-six percent of customers indicated their intention to use BOPIS post-pandemic.

The introduction of AI into retail operations brought customers more personalized online shopping experiences that used predictive analytics and suggested purchases. These tools will continue to be a major focus for those in the retail industry as retail businesses find their new normal. McKinsey suggests that retail businesses focus more on digital offers than they previously have. Some of the ways that retailers can do this include acquisition and driving traffic online through digital marketing efforts. Other considerations are to build branded apps and ensure web pages are optimized for digital shopping.


The Addition of AI in E-commerce

Among the biggest impact that AI has had on retail operations are personalization and predictive analytics. This includes customer tools like visual search, customized emails, and purchase suggestions. With AI many brands have been able to improve their conversion rates, boost their sales, and build customer loyalty.

Personalization in retail has moved beyond just adding in a customer’s name to a generic email. By using AI capability, businesses are able to customize email offers and content that will appeal specifically to different customers. That means that brands may be sending out many different email offers to their customers rather than a general promotional offer for everyone. This marketing technique can help build customer loyalty and improve sales by offering people items or services they are more likely to buy.

Predictive analytics in retail operations has also become one of the industry’s most valuable tools.  This AI tool is able to gather information and identify patterns. From there, it is used to forecast upcoming trends. With these predictions, marketers are better able to keep up with changing customer demands, which positions their retail operations to be more successful.

Some of the data gathered for analytics purposes come from various sources such as smartphone apps, retailers’ websites, customer loyalty programs, point-of-sale systems, and social media. Putting all this data together can help build an accurate customer profile. This can help the company personalize offers to customers or be used to upsell or cross-sell items.


Extending Machine Learning

Machine learning is a valuable tool for retail operations as it can be used to build models that define how to automate and optimize tasks. It can also be a powerful asset when it comes to risk assessment and predictive tasks. One of the biggest benefits of machine learning is that it is able to improve over time. Forecasting using machine learning vs traditional predictive processes can provide an advantage for retailers as machine learning has more advanced algorithms and can plow through lots of information quickly.

Here are some of the ways that machine learning is beneficial to retail operations:

  • Chatbots – Many customers begin interacting with brands through one of the omnichannels. This often takes them to the brand’s website where a chatbot can enhance customer communication. Chatbots are able to answer common questions, recommend products or solutions, and collect valuable information from consumers. These AI-powered tools have the capability to learn from past data and interactions. This can make them more powerful over time.
  • Pricing – Developing a pricing strategy is easier with AI. Retailers are able to analyze the different advantages of different pricing models before deciding on the best pricing model. Prices can also be adjusted from season to season or changing customer demand all by using AI algorithms. For example, Amazon has used AI to bring a higher level of sophistication to its retail operations online. The e-commerce giant has an algorithm that can understand the opportune time to reduce the price of items so as to attract customers. It is also capable of increasing prices when customer demand is higher, thus, maximizing profits for sellers.
  • Flexibility – By employing machine learning in retail operations, businesses will be better equipped to weather changes either locally, regionally, or even globally. Using machine learning capabilities alongside predictive analytics will help retailers stay flexible in a changing marketplace.
  • Inventory – COVID-19 presented a unique challenge to e-commerce businesses as many ran out of in-demand items and were unable to re-stock quickly enough. Machine learning and predictive tools can help retailers prevent this in the future by refining their inventory and in-stock levels.
  • Fraud detection – Machine learning is able to reduce credit card fraud when it comes to online shopping. It is also capable of reducing customer fraud through coupons or discounts by tracking behavior from a specific IP address.

AI and machine learning will continue to play a large role when it comes to improving retail operations online. The e-commerce industry has the tools at hand that can expand the personalization and predictive analytics that have become a center point of digital retail operations.