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How AI can boost customer satisfaction, retention, and loyalty in the contact center

IT Security Nachrichten cio.com 03.01.2024, 14:49 Uhr 4 Min. Lesezeit Cloud & IT-Enterprise

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Customer communication is a vital aspect of any business, especially when it comes to the contact center. The contact center is where customers interact with companies through various channels, such as phone, email, chat, or social media. The quality and efficiency of customer communication can affect customer satisfaction, retention, and loyalty.

That is why artificial intelligence (AI) is playing a key role in enhancing and personalizing customer communication in the contact center. AI can help optimize processes in many different ways, for example:

customers’ speech and text into different languages, dialects, or accents. This helps customers to communicate with companies from different countries or regions.

24/7 chatbots and virtual assistants that can answer or redirect simple and common inquiries. This reduces wait times and frees up human agents for more complex tasks.

and responding to customers’ moods and emotions. This helps to create empathetic and personalized communication that meets customers’ needs and is tailored specifically to them.

offers and recommendations based on customer profiles and preferences. This increases customer loyalty and revenue by providing added value for customers.

AI First in the Contact Center

IBM Consulting and Amazon Web Services (AWS) are working together to bring an AI-first strategy to contact center transformation. They have created a joint solution that combines Amazon Connect with additional features from IBM Consulting to optimize and personalize customer communication. This solution uses the power of AI to improve processes and operations in the contact center, launch new services, and increase customer satisfaction and loyalty. The solution is cloud based, scalable, cost-effective, and easy to implement and manage. It has already been deployed successfully by multiple clients.

One of the features of this solution came from an IBM Consulting hackathon that took place in France in March 2023. The challenge was to design new solutions for customer use cases using the AWS Cloud. One of the solutions aimed to improve customer experience and agent efficiency in insurance company contact centers by creating an automated summary of customer-agent calls and chats. This solution helps agents to quickly recall the customer’s needs in subsequent interactions, enables contact center supervisors to rapidly familiarize themselves with the objectives of each case (and intervene if necessary), and ensures that insured customers quickly receive the requested information without having to repeat details they have already provided.

Scaling AI on AWS: The Bouygues Telecom Case

Bouygues Telecom is a French communication service provider that has successfully implemented AI throughout its organization to enhance processes, launch new services, and drive improvements. To achieve this, Bouygues Telecom needed a unified, integrated, and flexible cloud strategy that supported various AI providers and solutions. With the help of IBM Consulting, Bouygues Telecom launched an initiative that empowered all business and IT functions to create, develop, and implement their own cloud-native AI applications.

The initiative used the AWS Cloud for deploying and scaling AI applications. With IBM’s assistance, Bouygues Telecom was able to scale its initial four cloud-native AI applications from concept to production in just four months. These applications – including chatbots, text analysis, B2B lead triage, and call summarization – significantly improved the customer experience, agent efficiency, cost savings, and decision-making processes.

The Future of AI in the Contact Center

AI is one of today’s – and tomorrow’s – most crucial technologies, with the power to revolutionize the contact center industry. In this environment, many companies that operate contact centers are aspiring to develop a strategy and implement generative AI use cases, all while generating business value, driving transformation, and mitigating risks. If this sounds like your company, you could benefit from the expertise and solutions provided by IBM and AWS.

If you would like to learn more about harnessing the power of AI in the contact center, please feel free to contact us.

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Syntax validiert (0 Fehler)

title: Detect Exploitation - How AI can boost customer satisfaction, retention, and loyalty in the contact center
id: 449c3735-7f5a-49f4-97f1-6118aba83b29
status: experimental
description: Automatisch generierte SIEM-Erkennungsregel basierend auf CTI Intelligence
references:
  - https://tsecurity.de/
author: iShareStuff CTI Automated Detection Engine
date: 2026-09-25
logsource:
  category: network_connection
  product: any
detection:
  selection:
      CommandLine|contains:
        - 'exploit'
  condition: selection
falsepositives:
  - Legitime administrative Zugriffe oder Penetrationstests
level: high
tags:
  - attack.initial_access

Syntax validiert (0 Fehler)

rule CTI_Threat_Indicator {
    meta:
        author = "iShareStuff CTI Automated Detection Engine"
        date = "2026-09-25"
        description = "YARA Signature for "
    strings:
        $str = "How AI can boost customer sati" ascii wide
    condition:
        any of them
}

Syntax validiert (0 Fehler)

index=security sourcetype IN ("cisco:asa", "pan:traffic", "zeek_conn", "suricata", "WinEventLog:Security")
("How AI can boost customer satisfaction r")
| stats count earliest(_time) as first_seen latest(_time) as last_seen by src_ip, dest_ip, dest_host, signature
| eval first_seen=strftime(first_seen, "%Y-%m-%d %H:%M:%S"), last_seen=strftime(last_seen, "%Y-%m-%d %H:%M:%S")
| sort - count

Syntax validiert (0 Fehler)

message: "*How AI can boost customer satisfaction r*"

Syntax validiert (0 Fehler)

CommonSecurityLog
| where Message has "How AI can boost customer satisfaction r"
| summarize EventCount = count(), FirstSeen = min(TimeGenerated), LastSeen = max(TimeGenerated) by SourceIP, DestinationIP, DestinationPort, Activity
| extend DetectionRule = "iShareStuff-CTI-Compiled"
| sort by EventCount desc

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Credential Access

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Kognitive Analyse für identifizierte Bedrohung: Erhöhte Bedrohungslage im Bereich How AI can boost customer satisfaction, .... Basierend auf 368k Vektor-Korrelationen werden sofortige Isolationsmaßnahmen für betroffene Endpunkte empfohlen.

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# Exploit Title: PoC for Target
import socket, sys
target = '192[.]168[.]1[.]10'
url = 'hxxp://target[.]local/vuln'
s = socket.socket()
s.connect((target, 80))
s.send(b'GET / HTTP/1.1\r\nHost: target.local\r\n\r\n')

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