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It is estimated that the market research services industry will exceed $90 billion in 2025. Understanding the market is not only required in the establishment phase of a company or while you are launching a new product or a service but also crucial to understand the existing trends to stand out in the market. That’s why B2B and B2C companies regularly conduct market research surveys. 

 In this article, we aim to: 

Highlight the benefits of conducting market research surveys,

Provide some suggestions before investigating the market through surveys,

Review some applications of market research in different industries,

And give some general tips to improve your market research

Why should you conduct market research surveys?

To position your brand in the market.

To get customer insights and improve customer experience

To determine the optimum pricing for your products or services

To measure the effectiveness of your marketing strategies

To get a grasp of the opinions and preferences of the public

Here are some suggestions to consider before investing your resources and money into new survey research:

Conduct web and social media research to learn about the market to build your research upon the existing knowledge.

Determine your scope if you aim to focus on a specific target. Otherwise, you can randomly select the participants.

Decide when you plan to finalize the study so you can have a systematic process and know when you will be done.

List all expected outcomes, what you want to learn most, and what you expect from the study.

Three real-world use cases of market research in different industries 1. Energy Industry – Shell

Shell, one of the largest energy companies in the world, conducted market research and segmented customers based on their pain points by recording more than 500 videos of customers from the US and China. 

They have analyzed the customer’s voices and opinions to grasp their emotions, thoughts, etc. Thus, they could understand the challenges customers face through real-world examples. 

2. Food Industry – Endangered Species Chocolate (ESC)

Endangered Species Chocolate (ESC) is not a regular company that sells chocolates. Their products’ organic ingredients and efforts to protect wildlife make them stand out in the market. They donated more than $2.6 million to the farmers in Africa through fair trade sourcing.

To understand their customers better, they conducted a market research survey and asked their customers about their preferences, demographics, lifestyles, consumptions, etc. With the insights they gathered from the research, they have started to work on producing new flavors and products, positioning their brand in the market, developing new multimedia strategies, and reaching new targets. 

3. Car Industry – Mercedes Benz

Mercedes Benz is a German luxury car brand ranked 38th in Fortune 500. The company’s success mainly depends on understanding its customers well. 

The company makes a segmentation analysis based on geographic, demographic, and behavioral data of the public to understand what percentage of the population can afford to buy the cheapest and most basic vehicle of Mercedes Benz. As they can determine their target through their comprehensive market research surveys, they can identify customer groups better and develop different strategies for each group. 

Here are some general tips and best practices for your market research survey. 1. Use focus groups if the existing knowledge of the market is limited.

There are some industries newly emerging or gained popularity over the last years, such as commercial space travel or lab-grown meat. If the existing knowledge does not provide enough insights, you can start with a focus group and learn how they approach the emerging industry. Focus groups usually consist of ten people, and you ask them questions about the market, product, or service.

2. The number of respondents does not necessarily mean high-quality responses. 

You may intuitively think that the more respondents you have, the more accurate results will be gathered. However, studies show that this is not the case. Even though 150 responses would provide you with more precise results than just 30 people, as the number of respondents grows, variability in the responses decreases.

Figure 1. The graph shows how the variability of the responses changes with the sample size. 3. Apply different sampling methods depending on market types

If you are interested in the B2C market, try to be as inclusive as possible and survey a representative group of customers or potential ones. However, if you want to focus on the B2B market, then make sure you balance the number of vertical markets and include businesses of different sizes. 

You can find more tips that increase the number of responses in our recent article on online survey research. 

If interested, here is our data-driven list of survey participant recruitment services.

Reach us if you have any questions about conducting research surveys:

Begüm Yılmaz

Begüm is an Industry Analyst at AIMultiple. She holds a bachelor’s degree from Bogazici University and specializes in sentiment analysis, survey research, and content writing services.





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3 Use Cases & Benefits Of Digital Twins In Healthcare For 2023

It’s been reported that 66% of healthcare executives expect increasing investment in digital twins over the next three years. This is because digital twins improve healthcare organization performance, discover areas for improvements, provide customization and personalization of medicine and diagnosis, and enable the development of new medicines and devices.

In this article, we explore digital twin benefits, use cases and challenges in healthcare.

How is digital twin technology used in healthcare?

A digital twin is a digital replica of the tools, people, processes, and systems that businesses employ. In healthcare systems, digital twins are utilized to build digital representations of healthcare data, such as hospital environments, lab results, human physiology, etc. through computer models. To construct virtual twins, data that covers the individual, population traits, and environment are used. 

Digital twin of a healthcare facility

Digital twin technology can be used to generate a virtual twin of a hospital to review operational strategies, capacities, staffing, and care models to identify areas of improvement, predict future challenges, and optimize organizational strategies. Therefore, digital twins of hospitals can be used for generating facility replicas, and in turn this enables:

Resource optimization: Leveraging historical and real-time data of hospital operations and surrounding environment (e.g. COVID-19 cases, car crashes, etc.) to create digital twins enables hospital management to detect bed shortages, optimize staff schedules, and help operate rooms. Such information increases the efficiency of resources and optimized the hospital’s and staff’s performance, while decreasing costs. For example, a review study has shown that utilizing digital twins to manage the smooth coordination of several processes enabled a hospital to reduce the time in treatment of stroke patients by.

Risk management: Digital twins provide a safe environment to test the changes in system performance (staff numbers, operation room vacancies, device maintenance, etc.) which enables implementing data-driven strategic decisions in a complex and sensitive environment.

Digital twin of the human body

Digital twins are also applied for modeling organs and single cells or an individual’s genetic makeup, physiological characteristics, and lifestyle habits to create personalized medicine and treatment plans. These replicas of the human body’s internal systems improve medical care and patient treatment by:

Digital twin in healthcare can improve the design, development, testing, and monitoring of new drugs and medical devices. For example:

Drugs: Digital twins of drugs and chemical substances enable scientists to modify or redesign drugs considering particle size and composition characteristics to improve delivery efficiency.

Devices: A digital twins of a medical device enables developers to test the characteristics or uses of a device, make alterations in design or materials, and test the success or failure of the modifications in a virtual environment before manufacturing. This significantly reduces the costs of failures, and enhances the performance and safety of the final product.

Figure 2: Digital twin for medication

What are the digital twin challenges in healthcare?

Some of the challenges that face digital twin implementation in healthcare include:

Limited adoption

Digital twin technology is not widely adopted in the clinical routine. Healthcare units (e.g., hospitals and labs) should increase the impact of technology on digital simulations, vital clinical processes, and overall improvement of medical care.

On the other hand, even though healthcare system uses digital twins increase, it is argued that it will remain expensive and not accessible for everyone. Digital twin technology will become a benefit reserved for people with higher financial capabilities, which would generate inequality in healthcare system.

Data quality

Artificial intelligence system in digital twins learn from the available biomedical data but as the data is gathered through private companies, the data quality might turn out bad. Consequently, the analysis and representation of such data becomes problematic. That eventually affects the models negatively, which also affects the reliability of the models in the diagnosis and treatment processes.

Check our article on data-centric AI to learn more about how you can improve the quality of your data in AI systems.

Data privacy

The applications of digital twins require gathering more and more individual level data by healthcare organizations and insurance companies. Over time, these health organizations grasp a detailed portrait of a biological, genetic, physical, and lifestyle related information of a person. Such personalized data might be in use benefitting the company’s interest instead of the individuals. One example would be that insurance company might leverage the data to increase precise distinctions significant to personal identity.

Feel free to explore data security best practices.

Further reading

To learn more about digital twin technology and discover its use cases and applications in other industries, you can read our in-depth articles:

If you believe your business will benefit from a digital twin, feel free to check our data-driven list of digital twin software.

And let us help you choose the right tool for your business:

Hazal Şimşek

Hazal is an industry analyst in AIMultiple. She is experienced in market research, quantitative research and data analytics. She received her master’s degree in Social Sciences from the University of Carlos III of Madrid and her bachelor’s degree in International Relations from Bilkent University.





7 Use Cases Of Chatgpt In Marketing For 2023

The share of artificial intelligence in the marketing industry is rapidly increasing (see Figure 1). However, the use of relatively new tools, such as generative AI and, in particular, ChatGPT in marketing is not widely known. 

Figure 1. Market value of artificial intelligence (AI) in marketing worldwide from 2023 to 2028

In this article, we will explain 7 use cases of ChatGPT to help digital marketers have an effective marketing strategy. 

1- Content creation

Content creation, text generation in specific, using ChatGPT can be a powerful tool for marketing. These AI-generated texts can be used for a variety of purposes other than generating ideas, such as:

Contents generated by ChatGPT can be integrated with other marketing strategies and channels like:

Creating various contents for digital marketing campaigns

Preparing posts for social media platforms

Generating personalized, attractive and persuasive emails for email marketing.

For more on the use cases of generative AI in copywriting, check our comprehensive article.

2- Personalized customer experience

ChatGPT with its natural language processing (NLP) can generate personalized content for your customers based on their preferences, past behavior, and demographics. This can help you create targeted content that resonates with your audience, which can lead to higher engagement and conversion rates.

3- Audience research

Audience research involves gathering data and insights about your target audience to better understand their interests, preferences, behaviors, and needs. This information can help you create more effective marketing strategies, including content creation, ad targeting, and product development.

ChatGPT can be used to analyze customer data such as: 

Search queries

Social media interactions

Past purchases to identify patterns and trends in customer behavior. 

By analyzing this data, ChatGPT can help you identify your target audience’s preferences, interests, and pain points, which can inform your marketing messaging, content, and product development.

4- SEO optimization

ChatGPT can be a valuable tool for SEO in marketing. SEO, or search engine optimization, involves optimizing your website and content to rank higher in search engine results pages (SERPs) for relevant keywords and phrases. Here are some ways that ChatGPT can help with SEO:

Generate attractive topic ideas for content marketing

Make keyword research

Find the right and attractive titles

Group search intent

Create content structure

Generate meta descriptions

Figure 3. ChatGPT SEO-friendly title suggestions for contents in B2B marketing

5- Writing product descriptions

Product descriptions are a crucial part of marketing, as they provide potential customers with information about the features, benefits, and value of a product. ChatGPT can help create compelling and informative product descriptions that resonate with your target audience.

6- Chatbot for customer support

ChatGPT can be integrated into a chatbot to provide instant and personalized customer support. Chatbots can help customers with frequently asked questions, provide technical support, and even troubleshoot issues. Chatbots in marketing can help: 

Improve customer satisfaction

Reduce response times

Decrease the workload of customer service representatives.

7- Creating customer surveys

Surveys are an effective way to gather feedback and insights from customers, which can help marketers improve their products, services, and marketing strategies. Here are some ways that ChatGPT can help with creating customer surveys:

Question generation

Organizing survey structure

Making surveys multilingual with its translation ability

Survey analysis

If you have questions or need help in finding vendors, please contact:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





Top 13 Use Cases / Applications Of Ai In Manufacturing In 2023

The industrial manufacturing industry is the top adopter of artificial intelligence, with 93 percent of leaders stating their organizations are at least moderately using AI.

Manufacturers are frequently facing different challenges such as unexpected machinery failure or defective product delivery. Leveraging AI and machine learning, manufacturers can improve operational efficiency, launch new products, customize product designs, and plan future financial actions to progress on their digital transformation.

Why is AI important in the manufacturing industry?

Implementing AI in manufacturing facilities is getting popular among manufacturers. According to Capgemini’s research, more than half of the European manufacturers (51%) are implementing AI solutions, with Japan (30%) and the US (28%) following in second and third.

The same study also reveals that the most popular AI use cases in manufacturing are improving:

maintenance (29% of manufacturing AI use cases)

quality (27%)

This popularity is driven by the fact that manufacturing data is a good fit for AI/machine learning. Manufacturing is full of analytical data which is easier for machines to analyze. Hundreds of variables impact the production process and while these are very hard to analyze for humans, machine learning models can easily predict the impact of individual variables in such complex situations. In other industries involving language or emotions, machines are still operating at below human capabilities, slowing down their adoption.

The COVID-19 pandemic also increased the interest of manufacturers in AI applications. As seen on Google Trends graph below, the panic due to lockdowns may have forced manufacturers to shift their focus to artificial intelligence.

What are the common AI use cases in manufacturing? 1. Predictive maintenance

Manufacturers leverage AI technology to identify potential downtime and accidents by analyzing sensor data. AI systems help manufacturers forecast when or if functional equipment will fail so its maintenance and repair can be scheduled before the failure occurs. Thanks to AI-powered predictive maintenance, manufacturers can improve efficiency while reducing the cost of machine failure.

2. Generative design

Generative design uses machine learning algorithms to mimic an engineer’s approach to design. Designers or engineers enter parameters of design (such as materials, size, weight, strength, manufacturing methods, and cost constraints) into generative design software and the software provides all the possible outcomes that can be created with those parameters. With this method, manufacturers quickly generate thousands of design options for one product.

3. Price forecasting of raw material

The extreme price volatility of raw materials has always been a challenge for manufacturers. Businesses have to adapt to the unstable price of raw materials to remain competitive in the market. AI-powered software like can predict materials prices more accurately than humans and it learns from its mistakes.

4. Robotics

Industrial robots, also referred to as manufacturing robots, automate repetitive tasks, prevent or reduce human error to a negligible rate, and shift human workers’ focus to more productive areas of the operation. Applications of robots in plants vary. Applications include assembly, welding, painting, product inspection, picking and placing, die casting, drilling, glass making, and grinding.

Industrial robots have been in manufacturing plants since the late 1970s. With the addition of artificial intelligence, an industrial robot can monitor its own accuracy and performance, and train itself to get better. Some manufacturing robots are equipped with machine vision that helps the robot achieve precise mobility in complex and random environments.

Cobots are another robotics application that uses machine vision to work safely alongside human workers to complete a task that cannot be fully automated. Feel free to learn more about cobots with our comprehensive guide.

5. Edge analytics

Edge analytics provides fast and decentralized insights from data sets collected from sensors on machines. Manufacturers collect and analyze data on edge to reduce time to insight. Edge analytics has three use cases in manufacturing:

Improving production quality and yield

Detecting early signs of deteriorating performance and risk of failure

Tracking worker health and safety by using wearables

To learn more about analytics in manufacturing, feel free to read our in-depth article about the top 10 manufacturing analytics use cases.

6. Quality assurance

Quality assurance is the maintenance of a desired level of quality in a service or product. Assembly lines are data-driven, interconnected, and autonomous networks. These assembly lines work based on a set of parameters and algorithms that provide guidelines to produce the best possible end-products. AI systems can detect the differences from the usual outputs by using machine vision technology since most defects are visible. When an end-product is of lower quality than expected, AI systems trigger an alert to users so that they can react to make adjustments.

You can also check the lists of data annotation and AI/ML tools and services to find the option that best suits your project needs:

7. Inventory management

Machine learning solutions can promote inventory planning activities as they are good at dealing with demand forecasting and supply planning.  AI-powered demand forecasting tools provide more accurate results than traditional demand forecasting methods (ARIMA, exponential smoothing, etc) engineers use in manufacturing facilities. These tools enable businesses to manage inventory levels better so that cash-in-stock and out-of-stock scenarios are less likely to happen.

8. Process optimization

AI-powered software can help organizations optimize processes to achieve sustainable production levels. Manufacturers can prefer AI-powered process mining tools to identify and eliminate bottlenecks in the organization’s processes. For instance, timely and accurate delivery to a customer is the ultimate goal in the manufacturing industry. However, if the company has several factories in different regions, building a consistent delivery system is difficult.

By using a process mining tool, manufacturers can compare the performance of different regions down to individual process steps, including duration, cost, and the person performing the step. These insights help streamline processes and identify bottlenecks so that manufacturers can take action.

For example, a manufacturer that employed a process mining tool in their procure-to-pay processes decreased deviations and maverick buying worth to $60,000.

9. AI-Powered digital twin use cases

A digital twin is a virtual representation of a real-world product or asset. By combining AI techniques with digital twins, manufacturers can improve their understanding of the product and allow businesses to experiment in future actions that may enhance asset performance. There are typically 4 applications of digital twins in manufacturing:

10. Product development

Manufacturers can use digital twins before a product’s physical counterpart is manufactured. This application enables businesses to collect data from the virtual twin and improve the original product based on data.

11. Design customization

Due to the shift toward personalization in consumer demand, manufacturers can leverage digital twins to design various permutations of the product. This allows customers to purchase the product based on performance metrics rather than its design.

12. Shop floor performance improvement

A digital twin can be used to monitor and analyze the production process to identify where quality issues may occur or where the performance of the product is lower than intended.

13. Logistics optimization

Digital twins allow manufacturers to gain a clear view of the materials used and provide the opportunity to automate the replenishment process.

What are the benefits of AI in manufacturing? Safety

Manufacturing is one of the highest-risk industrial sectors to be working in with more than 3,000 major injuries and nine fatalities occurring each year. The involvement of robots in high-risk jobs can help manufacturers reduce unwanted accidents.

Cost Reduction

AI technologies can reduce the operation costs of manufacturers due to several applications:

Leveraging AI technologies can enhance organizations’ analytics capability so that they can use their resources more efficiently, make better forecasts, and reduce inventory costs. Thanks to better analytics capabilities, companies can also switch to predictive maintenance leading to eliminating downtime costs and reducing maintenance costs.

This one is obvious but manufacturers don’t need to pay monthly salaries to robots. However, robots require CAPEX which needs to be weighed against the recurring cost of labor.

Faster decision making

Thanks to IoT sensors, manufacturers can collect large volumes of data and switch to real-time analytics. This allows manufacturers to reach insights sooner so that they can make operational, real-time data-driven decisions.

24/7 production in dark factories

Factories without any human labor are called dark factories since light may not be necessary for robots to function. This is a relatively new concept with only a few experimental 100% dark factories currently operating. However, dark factories will increase over time with the application of AI and other automation technologies since they have the potential to unleash significant savings, end workplace accidents and expand their production capacity.

Read more on AI applications in different industries:

If you still have questions on how AI revolutionizing the manufacturing industry, don’t hesitate to contact us:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





Top 12 Use Cases Of Rpa In Procurement Process In 2023

Chief Procurement Officers (CPOs) are pessimistic: 66% of them surveyed in 2023 believe that supply chain volatility will persist in 2023.

A remedy is a more robust procurement process that keeps the business ahead of the market and geopolitical dynamics. For instance, paying vendors on time ensures timely delivery of goods which sustains the manufacturing cycle. This at least ensures that whatever supply chain issues the company is facing, it’s not procurement-related.

Robotic process automation (RPA) can assist the procurement department in managing their procurement tasks better. In this article, we will explain the top 12 use cases of RPA in procurement.

1. Input identification

RPA’s first procurement application is input identification. RPA can retrieve each product’s input list from the bill of materials (BOM) and store it in a hub.

2. Contract management

Robotic process automation can automate contract management. Use cases would include:

Drafting B2B contracts by automatically extracting the vendors’ info and putting it on the draft

Sending notification to the procurement teams whenever a contract is reaching the expiry date

Archiving each contract in each vendor’s dedicated database

Using OCR and NLP to review contracts and ensure the SLA terms comply against company policy

3. Purchase request & purchase order submission

Purchase requests and purchase orders are submitted to inform the company’s decision-makers and the vendor of the type and quantity of the needed items.

4. Category management

Different departments are in charge of purchasing their own materials. RPA, intelligent automation, and ML tools can identify and assign each product’s category with the correct procurement department and tag them. RPA also reminds procurement staff to approve delivery notices or reschedule production in case of delayed shipments.

5. Purchase request approval

RPA in procurement is useful because it can automatically approve routine purchase requests by referring to business rule engines. For example, machine learning algorithm would identify the reorders for commonly used items. The data can then be structured. RPA bots will then place reorders. So as long as the orders meet the procurement strategy, orders for current needs can be approved without human involvement.

And if the order is an exception and needs human intelligence for assessment, the request can be forwarded to the procurement manager for final approval.

6. Automated re-ordering

RPA in procurement can monitor the inventory levels on the dashboard and automatically create purchase orders for the reordering products. One of the benefits of automated re-orders is a consistent manufacturing process because the vital intermediary goods will always be in-time for the production cycle.

Automated re-orders would help bypass the need for a human to keep monitoring the inventory levels and fill out purchase orders electronically or otherwise. This ensures that the future needs of the company are tended to.

7. Inventory management

Robotic process automation (RPA) and IoT integration enables digital monitoring of inventory levels. This feature allows RPA to create automated reports and inventory audits.

Some businesses, such as restaurants, need to not overload their inventory of perishable produce. So it’s important to have a real-time Especially for businesses that rely on fresh inventory levels, such as restaurants that overload on perishable stuff, it’s important to have a real-time report of what exactly you have right now.

Automating inventory management also means products that stay in the warehouse longer can be recognized and purchased less, enabling smart procurement.

8. Three-way matching

Another use case of RPA in procurement is automated three-way matching. The RPA bots can automatically compare purchase requests, with the supplier invoices, and the delivery receipt to confirm that the ordered products are those which should’ve been ordered. Three-way matching also ensures that the goods have been delivered.

9. Automated payments

RPA bots can be scheduled to make automated payments after schedule triggers. On-time payments improve supplier relationship management and uphold the business reputation. Moreover, finance APIs allow ROA bots to make payments to the correct vendor in the right amount. That’s because they would exchange the information between the suppliers’ list and the AP automation solution. This reduces the workload of the procurement teams and makes correct, timely payments.

10. Supplier onboarding

Same as with employee onboarding, companies can leverage RPA to automate parts of their supplier onboarding. For instance, RPA bots can extract vital information from the suppliers’ websites (such as their references, prices, etc.) and put it in a report.

Moreover, RPA in procurement also means that bots can asses the suppliers through rule-based decisions. For instance, if a company wants to hire an event planner with experience in the pharmaceutical industry, and there are no case studies of that on the vendor’s website or attached to their profile, they can be ranked lowest.

These preliminary assessments can be time-consuming. By having robots completing these tasks, the employees can spend their time on higher value work.

11. Price negotiation

After receiving a vendor quote, companies can use RPA bots to automatically negotiate prices through a rules-based framework. So when it comes to approving/rejecting/negotiating a quote, intelligent automation-enabled RPA bots can compare the quoted price against the established threshold. Then following the conditional result (i.e., “if price is X% higher than Y, do Z”) the bots can send their rebuttal.

12. Digitized records For more on RPA

To learn more about RPA and its use cases, read:

Download our RPA whitepaper for in-depth look into the topic:

And if you’re ready to invest, we have a data-driven list of RPA vendors prepared.

We can help you in select the best RPA vendor according your needs:

He primarily writes about RPA and process automation, MSPs, Ordinal Inscriptions, IoT, and to jazz it up a bit, sometimes FinTech.





Retail Intelligent Automation: Use Cases & Case Studies In 2023

As competition increases, so does the value of leveraging digital technologies and automation solutions in retail. According to McKinsey, 30% to 40% of retail tasks related to processes such as merchandise planning or the supply chain can be automated.

Intelligent automation, also called cognitive automation or hyperautomation, which is the combined use of automation technologies with AI methods such as ML, NLP, OCR, conversational AI, and computer vision can help retailers automate end-to-end processes through bots with decision-making capabilities.

We have listed several use cases and case studies of intelligent automation in the retail industry.

Use cases Customer service

RPA bots with conversational AI capabilities can handle repetitive customer service tasks and can enable retailers to provide a better and personalized customer experience by:

Interacting with customers throughout the shopping process, from searching for products and placing orders to tracking packages and answering FAQs,

Providing recommendations according to their browsing and purchasing history,

Collecting customer feedback,

Analyzing customer sentiment with NLP models.

These can allow retailers to:

Understand their customers better, identify pain points, and develop strategies to improve customer experience,

Improve employee productivity by automating manual customer service tasks and assisting them with more complex tasks,

Provide 7/24 customer service.

Inventory management

By leveraging ML models and historical sales data, intelligent bots can predict the optimum amount of inventory for different goods and create allocation plans for different locations and different times of the year. They can alert suppliers when stores or warehouses are running low on stock. This can help retailers:

Prevent stockouts,

Reduce waste,

Automate restocking orders.

Invoice automation

Manually processing invoices is time-consuming and costly: it costs around 10$ and takes 25 days to manually process an invoice. The process is also error-prone with repetitive tasks including:

Matching up the billed amount and the amount on purchase orders,

Resolving any discrepancy in the amounts charged,

Entering data to relevant systems,

Sending the invoice to relevant employees

Intelligent bots with OCR and NLP capabilities can:

Monitor for incoming invoices,

Extract relevant data from invoices,

Cross-check invoices against purchase orders,

Enter the extracted invoice data to the system,

Make payments and settle the invoice.

Feel free to check our article on invoice automation for a more comprehensive account.

Returns processing

“Bracketing”, or intentionally purchasing more than intended to keep, increased from 40% to around 60% after the Covid-19 pandemic. Returns are an inevitable part of online shopping, and an efficient returns management is vital for retailers as it impacts profitability and customer retention: 96% of shoppers who rated their return experience positively stated that they would shop from the retailer again.

Intelligent bots integrated with chatbots can:

Guide customers through the return process,

Collect necessary customer information,

Update the inventory database,

Send notifications to customers and employees in the finance department.

Case studies

Feel free to read our article on intelligent automation case studies. Some example case studies in retail include:


Problem: Accelirate is an RPA consultant that helps companies automate their business processes. Struggling with manually processing up to 700 invoices from gasoline and freight vendors, a major retailer consulted Accelirate to help drive invoice automation. Prior to automation, retailer’s staff had to open individual emails containing invoices, find the supplier ID, manually extract invoice data, and enter it into the internal accounting system.

Results: The retailer reduced the time to process an invoice from 3-5 minutes to 30 seconds. 93% of the invoices could be reconciled without manual review. In this way, the company saved 160 hours per month.1

Recode Solutions

Problem: A large consumer goods retailer in the U.S. had many paper-based back-office processes in its accounting, loan, and credit departments. The company also had an inefficient customer service operation that required call center staff to log into multiple systems to retrieve customer information in order to answer customers’ questions.

Solution: The retailer partnered with Recode Solutions which worked with an intelligent automation solutions provider to automate processes such as AP invoice processing, customer service tasks, and loan servicing requests. 

Results: The solution reduced the time to process an invoice from 10 minutes to 30 seconds. It processes 65,000 invoices annually. In addition, the average call handling time was reduced to 85 seconds. This helped the company save $2 million annually.2

For more on intelligent automation

If you want to explore intelligent automation use cases in your business, feel free to check our article on intelligent automation use cases & examples in different business functions and industries.

You can also check our data-driven list of intelligent automation solutions. If you need help, feel free to reach out:


1, 2

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





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