The retail industry witnessed a significant setback due to the emergence of the Covid-19 pandemic and consequent restrictions and curfews. In turn, this increases the pressure on retail leaders to provide better services, maintain their customer base, and expand their market reach. RPA offers retailers numerous benefits ranging from cost savings to improving customer service, which is why retail was estimated to be one of the top 5 industries to leverage RPA in 2021.
In this article, we explore the top use cases of RPA in retail, and provide recommendations to business leaders.
1. Register / Cashier reporting
Challenge: Register or cashier reporting is the practice of aggregating customer data passing through the store register to understand the hourly/daily/monthly purchases. In small stores, doing this process may take a few hours, however, in large retail stores, manually creating cashier reports is a time-consuming and error-prone tasks.
Solution: RPA bots can create cashier reports by automating the processes of:
- Data extraction from each register ID database
- Entering the data to the correct field in the report form (e.g. product number, quantity, price)
- Classifying payment methods (e.g. cash, credit card)
- Matching the data of inflow vs. outflow
2. Invoice automation
Challenge: It’s been reported that manually processing invoices takes between 4 and 16 days, as it includes repetitive tasks such as:
- Skimming through the order data
- Entering data to relevant systems
- Generating an invoice PDF
- Sending the invoice to designated email
Invoice processing also includes finding mismatches between orders and generated invoices to avoid compliance issues.
Solution: RPA bots can significantly reduce the time and cost of manual invoice processing as they can:
- Leverage OCR and NLP to read incoming invoices in different formats (e.g. PDF, images, paper)
- Transfer the extracted information to the organization’s database
- Update invoicing data (e.g. date of last invoice received, latest payment amount)
- Generate a new invoice and send it to the designated vendor or customer
See our article on invoice automation for more details on how it works and alternative tools.
3. Return processing
Challenge: Processing customer returns requires several data manipulation processes across different platforms such as CRM, ERP, and inventory. In addition, for each return, the employee needs to issue a return invoice, and initiate a refund process for the customer.
Solution: Instead of manually updating return and refund relevant databases, an employee can initiate a return workflow at the customer’s request, initiating an RPA bot to perform the repetitive tasks included in a return process, including:
- Updating relevant databases (e.g. inventory, CRM, finance)
- Collecting customer data for refund issuing (e.g. credit card number, billing address)
- Sending notifications to both customers and finance employees regarding the refund process.
4. Inventory management
Challenge: Inventory management requires accurate data about the retailer’s warehouse including the number of products in stock, product location, and order schedules. This data is used by the retailer to track warehouse movements, forecast demands, and avoid warehouse discrepancies.
Solution: RPA bots can manage inventory data by:
- Reporting inventory flow on a regular basis
- Sending alerts and notifications to relevant employees about stock shortages
- Automating restocking orders from vendors when a shortage is detected
- Creating an accurate audit trail of all inventory movements
Learn more about inventory management automation.
5. CRM automation
Challenge: CRM software market is witnessing an increasing growth, and is expected to reach ~$45B by 2025. CRM users claim that leveraging CRM software helps them have better visibility to their customers’ data, and improve marketing and sales strategy accordingly.
However, it’s been estimated that 91% of data in CRM systems is incomplete, stale, or duplicated each year. As a result, 80% of companies believe that dirty data damages their sales pipelines, and 25% face reputation-related challenges due to data errors.
Solution: Leveraging RPA to automate repetitive CRM tasks increases data quality by automating data updates, cross-checking for data errors and duplicates, and ensures customer data privacy by preventing unauthorized access to privileged data. RPA can also be used to:
- Automate scheduling sales meetings and sending notifications to employees and customers
- Generating lead scores based on the retailer’s policies.
6. ERP automation
Challenge: Retail ERP solutions are connected to numerous business databases, including sales, supply chain, HR, and CRM. Managing ERP systems requires repetitive data entry, update, and cleaning, which is time consuming and error-prone.
Solution: RPA solutions have integration capabilities enabling them to collect data from various business platforms, standardize data collection processes, update existing databases, detect data duplicates and mismatches, as well as keep an audit trail of all data transfers.
See our article on the top 20 RPA use cases in ERP for more details.
7. Customized marketing
Challenge: Marketing teams spend a lot of time sending emails and messages to current customers when the retailer wants to announce a new product or a sales campaign. This includes creating a customized email based on the customer’s purchase history, and sending numerous emails to individual customers in the sales database.
Solution: Bots can be programmed to extract data about the marketing campaign (e.g. promotion code, current and promotion price, features of new product) and generate a campaign email using a built-in template. The bot can send a customized email or text using the customer’s name, address, and previous purchases data extracted from the sales database, to announce the retailer’s new campaign.
Explore marketing automation in more detail.
For more on RPA
To explore RPA and its use cases and benefits in detail, download our in-depth whitepaper on the topic:
And reach out to us to guide you through the process:
This article was drafted by former AIMultiple industry analyst Alamira Jouman Hajjar.
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
To stay up-to-date on B2B tech & accelerate your enterprise:Follow on
Next to Read
Your email address will not be published. All fields are required.