The U.S. research and consulting company specializing in information technology markets – Gartner came to the conclusion that by 2025, 80% of customer interactions will be automated using bots.

Why is this happening?

Bots can process requests faster than people, work 24/7 and not get tired. They not only save time and money, but also improve the quality of service, making it more personalized.

Let’s look at how bots help businesses:

1. Increase efficiency by automating routine tasks.
2. Reduce costs by replacing human labor in key processes.
3. Increase customer loyalty by offering personalized service.

 

How Bots Increase Efficiency

Automation of routine tasks

Bots take over repetitive tasks that previously required human intervention.

For example:

• Processing applications and requests.
• Managing schedules and reminders.
• Collecting and analyzing data.

Example:
At Bank of America, Erica, a virtual assistant, processes 50+ million requests per month, from money transfers to financial advice. This has reduced the workload of employees by 25% and accelerated customer service.

Examples of application in other industries

• Medicine: Chatbots are used for initial consultations with patients and for scheduling appointments. For example, the virtual assistant Florence reminds patients to take medications and provides health recommendations. This reduces the workload of clinic reception desks by 30%.
• Education: At universities, bots help students with schedules, submitting assignments, and finding materials. In 2023, the University of Arizona reported that implementing an assistant bot reduced the number of requests for student support by 40%.
• Logistics: Bots help track shipments, update delivery status, and optimize routes. For example, Amazon uses bots to analyze supply chains, which has reduced order processing time by 15%.

Integration with IT systems

 

 

Modern bots easily connect to CRM, ERP and other platforms.

This allows:

• Synchronize data in real time.
• Automate reporting.
• Optimize logistics.

Additional examples

• HR processes: At Unilever, a bot helps automate the initial CV screening stage by assessing candidates’ skills. This has reduced recruitment time by 50%.
• Supply chains: Coca-Cola integrated bots into its ERP system to analyze warehouse needs and manage inventory. This helped avoid stockouts and save up to 20% on logistics costs.

Table 1: Comparison of task processing time between bots and humans

Results for Business

According to the international consulting company McKinsey, process automation increases productivity by 20-30%. In addition, companies that actively use bots achieve savings on operating costs of up to 15%, which confirms the need for their implementation.

How Bots Reduce Costs

 

 

Reducing personnel costs

Bots replace manual labor in areas that require minimal creativity:

• Customer support.
• Order processing.
• Accounting and document management.

Example:
Pizza chain Domino’s has implemented a bot called Dom to accept orders via messengers. This has reduced call center costs by 15%, and 60% of orders are now received through digital channels.

Cases of other companies

• Amazon: The company actively uses bots in customer support. Chatbots process 50% of requests without the participation of operators, which saves about $100 million annually.
• Shopify: In an e-commerce platform, the implementation of bots to support sellers helped reduce customer service costs by 30%.

Reducing errors

The human factor inevitably leads to errors, especially in managing large volumes of data.

Advantages of bots:

• They work with an accuracy of up to 99.9% (Deloitte study).
• They save companies money on fixing deficiencies.

Additional data

• In banking, the use of automation bots has reduced transaction processing errors by 25%, helping to reduce losses by $1.2 billion globally.
• In healthcare, bots help with filling out medical records, reducing errors by 40% and saving doctors time.

 

Saving on infrastructure

 

 

Cloud bots do not require expensive hardware.

Example:
Chatbots in messengers (Facebook Messenger, WhatsApp) use existing platforms, minimizing development and support costs.

Example from e-commerce:

At Zalando, bots handle customer returns and complaints by integrating with messengers. This allowed them to eliminate a significant portion of their server infrastructure and save up to 20% on IT maintenance costs.

Table 2: Distribution of savings by industry

How Bots Increase Customer Loyalty

 

Interaction Personalization

Bots analyze customer purchase history, preferences, and behavior to offer relevant products and services.

Example:
Cosmetics brand Sephora uses a Facebook Messenger bot to help curate products. It increased conversions by 11%, and 30% of customers said they returned to the store more often.

Additional examples:

• Netflix: Uses automated movie and TV show recommendations based on user preferences. This increased customer engagement by 25%.
• IKEA: Chatbots help users find the right home products based on style and budget. The result is an 18% increase in loyalty.

24/7 support

Bots work 24/7, answering questions, solving problems and taking orders. This is especially important for international companies.

Example:
TD Bank has introduced a bot to process requests in TD Bank Online. Clients can transfer money, pay bills and receive consultations at any time, which has increased satisfaction with the service by 20%.

Other examples:

• Uber: Automated support for drivers and passengers solves problems with payment, navigation and reviews without the involvement of operators. As a result, the average response time to requests has been reduced by 40%.
• Airbnb: Bots help guests and hosts with booking questions, reducing customer support requests by 25%.

Simplifying communication

Users prefer to solve issues via messengers rather than calling support. Bots make this process fast and intuitive.

Additional Examples:

• Burger King: Customers can place orders through WhatsApp bots, which has increased repeat purchases by 15%.
• Lufthansa: The chatbot helps passengers check flight schedules and find the best ticket prices, which has increased customer satisfaction by 12%.

Trends

Modern bots are becoming smarter thanks to the integration of neural networks and voice assistants.

• Voice assistants: Alexa, Google Assistant, and Siri provide personalized recommendations and control over smart devices, which forms customer loyalty. According to Voicebot.ai, 70% of smart speaker owners actively use them for everyday tasks.
• Neural networks: AI-powered bots analyze behavior in real time, adapting to changing customer needs. This increases user engagement by 15-20%.

Cases of Successful Implementation of Bots

Bank of America 

Task: Improve financial management for clients.
Solution: “Erica” is a virtual assistant with features for analyzing expenses, credit rating and providing financial advice. Erica is integrated with the bank’s mobile application and is powered by artificial intelligence, analyzing customer transactions and providing personalized recommendations.

Results:

• 15 million active users.
• Saving $1 billion on operating expenses.
• Reduce the time it takes to complete standard transactions, such as fund transfers and balance checks, by 30%.

Challenges:

• Ensuring the security of user data.
• Configuring algorithms to take into account multiple scenarios of customer interactions with the bank.

Delivery Club 

Task: Speed up the processing of food orders.
Solution: Development of a bot for arranging delivery via Telegram. The bot is integrated with the internal order and logistics management system, and also uses notifications to inform customers about the delivery status.

Results:

• 40% of orders are automated.
• Order processing time has been reduced by 2 times.
• Reduction of the workload on call center operators by 35%.

Challenges:

• Integration with a large number of restaurants and their accounting systems.
• Ensuring correct operation of the bot under high load during peak hours.

Leroy Merlin

Task: Simplify the selection of tools for repairs.
Solution: A WhatsApp chatbot that helps customers find suitable products, provides recommendations, and sends video instructions on how to use them. The bot is integrated with the online store catalog and CRM system, which allows synchronizing data on product availability.

Results:

• Conversion increased by 18%.
• Average check increased by 12%.
• Increased customer satisfaction by 20% due to fast and convenient interaction.

Challenges:

• Updating information on product availability in real time.
• Creating training materials (videos, instructions) that meet user needs.

PayPal 

Task: Automate transaction processing and reduce fraud.
Solution: Integration of bots that analyze transactions in real time, identify suspicious activity, and block potentially fraudulent transactions. Bots operate on the basis of neural networks trained on large volumes of transaction data.

Result:

• Reduced fraud by 30%.
• Accelerated transaction processing by 40%.
• Increased user trust in the platform.

Challenges:

• Setting up algorithms to take into account regional transaction characteristics.
• Combating false positives to avoid blocking legitimate transactions.

Other international examples

1. H&M (Sweden):
   • Task: Повысить качество обслуживания клиентов в онлайн-магазине.
   • Solution: Чат-бот с рекомендациями по стилю, основанными на предыдущих покупках и текущих трендах.
   • Result: Конверсия в покупки увеличилась на 20%, а время обработки запросов сократилось в 3 раза.
2. Unilever (United Kingdom):
   • Task: Optimize personnel selection.
   • Solution: A bot for the initial selection of candidates that conducts testing and analyzes resumes.
   • Result: Reducing the time for recruiting personnel by 50% and reducing costs for HR services by 35%.

How to Implement a Bot into Your Business: 5 Steps

 

Define the goal

Clearly formulate what the bot should do: increase sales, reduce costs, or improve service.

Example:

• Small business: The coffee shop is launching a bot to automatically accept pre-orders. The goal is to reduce the workload on employees during peak hours and increase throughput.
• Corporation: Retail company develops chatbot to automate returns and exchanges to improve customer experience.

Possible risks:

• A vague task assignment can lead to the creation of a bot that does not solve specific business problems.
• Solution: Conduct an analysis of key customer pain points and agree on goals with key stakeholders.

Select the platform

For simple tasks: builders like Chatfuel or ManyChat.
For complex solutions: custom development in Python with AI integration.

Example:

• Simple solution: A small language school is creating a ManyChat bot to sign up students for trial lessons.
• Complex solution: The bank is developing an AI bot in Python that analyzes clients’ financial behavior and offers personalized products.

Possible risks:

• Limited capabilities of designers for specific tasks.
• Solution: Assess the scale of the project, the planned load, and the need for integrations before choosing a platform.

Test the MVP

Launch a trial version of the bot and collect feedback from customers.

Example:

• A pizza chain is testing a chatbot for accepting orders in one region, collecting data on the accuracy of request recognition and processing speed.
• After testing, improvements are made: a function for choosing a delivery time and notifications about the end of a promotion is added.

Possible risks:

• Negative user experience due to lack of testing.
• Solution: Use A/B testing and take customer feedback into account to make targeted improvements.

Optimize

Use analytics to improve conversation scripts and functionality.

Example:

• In an online store, the bot analyzes frequent customer requests (for example, “How do I process a return?”) and adds hints to the script.
• After implementing the FAQ search function, the response time is reduced by 25%.

Possible risks:

• Outdated algorithms can lead to a decrease in the effectiveness of the bot.
• Solution: Update scripts regularly and analyze user behavior using analytics tools.

Scaling

Add new features: voice commands, integration with social networks, multilingualism.

Example:

• The online school adds multilingual support for an international audience, increasing the number of applications by 15%.
• Electronics store integrates a bot on Instagram to automate responses to questions about product availability.

Possible risks:

• Excessive functionality can make the bot difficult to use and cause confusion for users.
• Solution: Introduce new features gradually and test them on a small group of users.

Conclusion

Bots aren’t just a trend, they’re a necessity for businesses that want to stay competitive. They save time, reduce costs, and make customers happier. As Bank of America, Sephora, and TD Bank cases show, even conservative industries like banking and retail benefit from automation.

Forecasts for the future:
The bot market will be actively developing in the next 5 years. According to Statista, by 2027 the market volume of automation technologies and virtual assistants will reach $18 billion, and the number of bot users will exceed 1.8 billion people. The main growth drivers will be:

• Development of neural networks and artificial intelligence for deep data analysis and customer behavior forecasting.
• The widespread adoption of voice assistants such as Alexa and Google Assistant in everyday life.
• The growing popularity of omnichannel solutions that combine bots with social networks, messengers and CRM systems.

Don’t be afraid to experiment. Start small — automate one process, test hypotheses, and scale up. In just a few months, you’ll see how bots change your business for the better.

If you want to stay one step ahead of your competitors, order bot development and integration from us. We are a team of professionals with experience in implementing successful business projects. We will help you automate processes, reduce costs, and increase customer loyalty. Write to us today and take the first step towards the digital transformation of your business!

Appendix 1.

Checklist 1: Implementing a Bot into Business

1. Define the goal

• Examples:
  • Increase sales → “Bot for product recommendations in messengers”.
  • Cost reduction → “Automation of support request processing”.

2. Conduct a task audit

• Template for Process Analysis:

3. Assess your budget and risks

• Examples of costs:
  • Constructor (Chatfuel) → $50–300/months.
  • Custom development → от $2000.
• Risks:
  • Low staff adaptation→ training plan.

4. Select platform

• Constructors:
  • ManyChat → for newsletters in Facebook.
  • Tilda + Telegram Bot API → landing pages with bot integration.
• Кастомные решения:
  • Python + DialogFlow → for NLP bots with deep learning.

5. Develop an MVP (2-4 weeks)

• Key Features:
  • Autoresponder for basic queries.
  • CRM integration (Salesforce, HubSpot).
• Testing:
  • Pilot group of 50 clients → collecting feedback via Google Forms.

6. Integration with IT systems

• How to sync data:
  • Use API to connect the bot with CRM → example: Zapier-scenarios.
  • Set up daily data export to Excel for reporting.

7. Training

• Staff:
  • Checklist: “How to redirect complex requests from a bot to a manager”.
• Clients:
  • Instructions in format GIF → “How to use the bot in WhatsApp”.

8. Feedback and improvements

• Metrics for analysis:
  • Bot conversion → (Number of successful tasks / Total number of requests) × 100%.
• Tools:
  • Hotjar → Bot interaction heat maps.

9. Scaling

• Growth plan:
  • Add multilingualism → integration with DeepL API.
  • Connect payment via bot → Stripe, PayPal.

 

Appendix 2.

Checklist: Assessing the effectiveness of the bot

Category 1: Finance

• Cost reduction:
  • Formula: Savings = (Employee Salaries × Number of Hours) – (Bot Cost + Support).
  • Example:

$1500/month on staff – $500/month on bot = $1000 savings.

• ROI:
  • (Bot Profit – Costs) / Costs × 100%.

Category 2: Customer experience

• NPS:
  • “On a scale of 0-10, how would you rate our service?” → segmentation into promoters and critics.
• Response time:
  • Goal: ≤ 2 minutes → monitoring through Zendesk.

Category 3: Operational efficiency

• Performance:
  • Number of tasks/day → comparison with manual processing (e.g. 200 vs 50).
• Errors:
  • 30% reduction in 3 months → audit via Jira.

Category 4: Analytics

• Instruments:
  • Google Data Studio →dashboard with real-time metrics.
  • Tableau → visualization of customer behavior patterns.

Template for report:

 

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