Vladimir Samokhvalov
This report by SBS Consulting analyses the use of neural networks in various business processes. It analyses examples of successful implementation of AI technologies in marketing, customer service and HR, as well as their impact on efficiency and productivity. The report presents specific cases and assesses the potential for increasing effiiency through the use of neural network solutions.
Marketing
Use cases of neural networks:
- WriteMail.ai
Process: Generating text content for marketing purposes
Scope: Writing template texts for social networks and mailing lists, event announcements, standard posts, internal and external marketing mailings
Estimated efficiency gains: 30%
- ChatGPT
Process: Paragraph-by-paragraph text generation for websites, keyword-based writing of long-form articles/p>
Scope: Content generation for websites, text improvement
Estimated efficiency gains: 40%
- Deepl
Process: Synchronising the English version of the website, improving texts
Scope: Checking English texts, creating engaging images
Estimated efficiency gains: 40%
- Midjourney
Process: Generating background images for posts and corporate presentations
Scope: Icon and image creation
Estimated efficiency gains: 30%
- Namecheap and Namelix
Process: Generating ideas for naming and logos
Scope: Creation of corporate identity, including business cards and brand books
Estimated efficiency gains: 30-40%
Impact on marketing:
- Improved quality of texts and images
- Reduced cost of content creation
- Increased customer engagement through high-quality and personalised content
Customer service and sales
Use cases of neural networks:
- EBI.AI
Process: Automating responses to customer enquiries
Scope: Responses to simple queries, with complex cases left to the operators
Estimated efficiency gains: 80%
- DigitalGenius
Process: Generating templates for email responses
Scope: Incoming message processing
Estimated efficiency gains: 20%
- LivePerson and Botmother
Process: Automating customer support via chat
Scope: SIM card activation, account management, call handling
Estimated efficiency gains: 34-40%
- Conversational AI (neuro.net)
Process: Processing orders without operator involvement
Scope: Sale products and services, complaints handling
Estimated efficiency gains: 34%
- Haptik
Process: Automating calls to customers
Scope: Provision of information, sale of services
Estimated efficiency gains: 60%
Impact on customer service and sales:
- Reduced customer support costs by 20-40%
- Increased productivity of customer service departments by 30-50%
- Increased number of customers and potential sales by 50%
HR
Use cases of neural networks:
- AI4HR and ChatGPT
Process: Generating job descriptions and postings
Scope: Job descriptions, job postings, interview recording, meeting transcription
Estimated efficiency gains: 50%
- Chatbots
Process: Interviewing and evaluating candidates
Scope: Testing, interviewing
Estimated efficiency gains: 25%
- Nanonets and Gamma
Process: Processing of single-type documents, designing presentations
Scope: Training and seminar preparation, test and quiz design
Estimated efficiency gains: 20-25%
- Quizgecko
Process: Creating tests on a given topic
Increased productivity of the HR department
Improved quality of candidate selection and adaptation
Impact on HR:
- Reduced recruitment and training costs
- Increased productivity of the HR department
- Improved quality of candidate selection and adaptation
Conclusion
Implementing neural networks in marketing, customer service and HR can significantly increase efficiency and reduce the cost of routine tasks. Neural networks help improve content quality, automate customer service and optimise HR processes. The examples of successful application of AI technologies show that companies implementing such solutions gain competitive advantages and improve operational performance.
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