iTech uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognizing you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.
Read Time: 3 minutes
Machine learning and AI have transformed retail spaces in a number of ways as AI tools have grown in popularity. Many retail businesses, though, have only explored the most popular uses, which are largely limited to marketing and customer service tools. Chatbots and email generators are convenient, but they don’t come close to maximizing the value of AI and machine learning in retail.
Retail is a complex industry, and successful retail businesses involve complex purchasing, selling, and logistics operations that comprise a significant share of their overhead. While AI marketing tools are nice, they do more for industries with higher average customer value. Machine learning can provide much more value for retailers when used to automate and simplify backend processes to reduce overhead.
In this guide, we will cover:
- Applications for machine learning in retail
- Benefits of utilizing AI and ML tools
- The best machine learning deployment strategy
Let’s dive into each, and provide a comprehensive overview of how best to apply machine learning and AI in retail industries.
Applications for Machine Learning in Retail
As we mentioned, backend implementations of machine learning provide the best value proposition for retail. While there are some benefits to more commonplace applications like customer service bots or AI web design tools, those don’t have as direct an impact on retail businesses’ bottom lines. We believe that if retailers are going to invest in new technology, that technology should be truly transformative, providing several significant benefits that open up new opportunities for investment going forward.
The following applications are those that our retail clients have identified as most valuable to them:
| Accounts Payable Automation | Machine learning can be used to digitize and automate invoice payment and data collection from payments and invoices, ensuring better data accuracy at a faster speed than manual entry. Any scanned document can be indexed and have its data collected automatically, regardless of format. |
| Accounts Receivable Automation | ML can also be used to automate the process of generating and sending invoices, payment collection, and applying payments to open receivables. |
| Logistics Data Management | ML tools can automate the extraction of data from logistics documents like freight invoices, organizing and generating reports and projections based on historical shipments and prices. Users can simply scan the documents, and the tool takes care of the rest. |
| Data Auditing | All of the above data is automatically checked for accuracy based on historical context, but machine learning tools also learn exactly where to organize data and how to generate easily readable reports to ensure easier human auditing so that every bit of vital data can be double-checked by experienced staff. |
All of these applications can alleviate the strain on existing back office staff and allow the business to direct more attention and investment to operations that rely more on human interaction. These include customer service, marketing, and product development.
Benefits of AI and ML Retail Tools
Firms unfamiliar with data automation may be unsure of how valuable quality machine learning data software would be to their business. With processing speed, data accuracy, security, and cost being the most important issues to our clients, it is important to understand the effect of machine learning in retail on each of them.
The benefits of implementing ML tools in retail include the following:
| Lower Costs | By reducing administrative workloads, labor costs can be reduced in kind. This not only provides an immediate benefit but also a scaling benefit as companies grow, making it far cheaper to expand. |
| Better Data Accuracy | Machine learning tools that have been taught with the proper context are far more accurate than manual data entry staff, reducing the number of costly mistakes. This saves resources needed for data entry and correction and reduces the risk of exposure to legal liability due to faulty financial data. |
| Increased Data Capture Efficiency | Not only are ML tools more accurate than manual entry staff, but they are also far faster. This means that data will be indexed and organized faster, making it easier for businesses to make reactive decisions. |
| Improved Data Security & Regulatory Compliance | Automated tools can ensure that data practices follow all necessary regulations and data security best practices, and automatically generate warnings when something is done improperly. |
| Better Visibility | Automated tools not only get everything done faster, but they also can auto-generate reports to improve visibility into data and the accounts payable, accounts receivable, and logistics operations. |
All of these benefits make it easier to operate and grow a retail business. By saving time and money, improving data accuracy, enhancing security, and making it easier than ever to have expert staff audit every step of the process, business owners can prioritize growth without worrying about ballooning costs or mistakes from staff who are spread too thin.
More efficient back office operations also help on the customer success side of the business. Clearer and more traceable data can make it easier for support staff to answer customer concerns, and get customers their orders on time. Improved cash flow from reduced costs can also help businesses price more aggressively, or add more products to their store, increasing the number of options available to their customers.
The Best Machine Learning Deployment Strategy for Retail
As we’ve outlined above, the main goal of implementing ML tools into a retail backend is to reduce the time and labor costs needed for data management. The most effective way to reduce these costs is to partner with a data outsourcing firm that utilizes cutting-edge AI and machine learning to capture and organize your data as efficiently, accurately, and securely as possible.
Outsourcing is the best deployment option because:
- It requires little-to-no staff training and far simpler onboarding than adopting a platform in-house
- Users can simply scan and send documents, and get data back quickly
- Expert data auditors with extensive experience with AP, AR, and freight invoice auditing are available to look over every step of the process
- Infinite scalability without hiring new staff
- The best outsourcing firms have access to the most advanced technology on the market
However, not all data outsourcing partners are created equal. Choosing the right firm will determine the speed and accuracy of your output, as well as the quality of audits on important data. Many data firms simply use teams of manual entry staff with menial automation, which doesn’t provide the many benefits listed in the above sections, including speed and cost. There is also a difference in quality between different AI software tools, which directly affects the reliability of the data captured by them.
It is paramount to choose a partner that not only operates with the best possible machine learning-paired OCR software but also combines technology with knowledgable staff with decades of experience auditing the documents that your company will be sending in. This ensures that businesses don’t spend excess resources dealing with mistakes made by the very partner that was supposed to optimize their backend operations.
This is why our clients choose iTech for their retail data outsourcing.
Deploy Machine Learning in Retail with iTech
We at iTech pride ourselves on our cutting-edge machine learning OCR software, top-of-the-line onboarding experience, and ongoing support. We also offer 24/7 access to support personnel and senior account managers to maximize visibility and peace of mind, eliminating the “black box” approach to outsourcing. To learn more about machine learning in retail, fill out the contact form below.
Subscribe to our blog for the latest industry trends
IDS Commander iTech2021
No Comments