The business process outsourcing (BPO) industry has undergone major changes in recent years due to emerging technologies like automation, big data analytics, and artificial intelligence. These new technologies are enabling BPO companies to dramatically improve efficiency, reduce costs, and provide better customer experiences. 

In this comprehensive blog post, we will examine the top trends in automation, big data, and AI that are transforming the BPO industry. Understanding these trends can help BPO companies stay competitive and meet the evolving needs of their clients.

Automation Trends in BPO

Automation is one of the biggest trends reshaping business process outsourcing. Repetitive, rules-based tasks like data entry, invoice processing, claims processing, etc. can now be automated using robotic process automation (RPA) and other tools. This reduces the need for large teams of agents performing mundane work.

Here are some of the notable automation trends in BPO:

Growth of RPA 

Robotic process automation (RPA) uses software bots to automate repetitive, manual tasks. RPA adoption in BPO has grown rapidly because it offers quick ROI and improves efficiency. BPO companies are using RPA bots to automate processes like extracting data from documents, moving data between systems, filling forms, etc. 

According to research firm Gartner, RPA software revenue grew over 63% in 2018 to $846 million. It is expected to reach $2.4 billion by 2022 as more companies integrate RPA.

Intelligent Process Automation

Traditional RPA automation works for structured data but struggles with unstructured data like emails or texts. This led to the rise of Intelligent Process Automation (IPA) which combines RPA with AI technologies like machine learning, natural language processing, etc.

IPA allows BPO companies to automate more complex tasks that involve judgement, natural language interactions, and unstructured data. For instance, IPA can automate processing of insurance claims by reading emails and PDFs. 

According to Accenture, 65% of BPO companies plan to implement IPA at scale within the next 2 years.

Autonomous Agents

Chatbots and virtual agents are also gaining popularity in BPO services. Using NLP and conversational AI, these bots can have natural conversations with customers via chat to resolve queries, collect information, process transactions etc.

An example is [IPsoft’s](https://www.ipsoft.com/ares/) cognitive chatbot Amelia that can act as an autonomous customer service agent. It can understand questions, have conversations, and resolve customer issues without human involvement. 

According to [IDC](https://www.idc.com/getdoc.jsp?containerId=prUS45971519), 75% of enterprises will use conversational AIs like chatbots for customer interactions by 2022.

Crowdsourced Intelligence

Companies like [CrowdFlower](http://www.crowdflower.com/) are leveraging crowdsourced teams of human workers to train AI algorithms and feed them the data needed to make them smarter. This combination of human intelligence and AI is helping automate a wider range of BPO tasks.

For instance, the company’s Custom AI offering can develop machine learning models to categorise support tickets, recognize handwriting, moderate offensive content etc.

Use of Big Data Analytics in BPO

Big data analytics is also making major inroads into BPO services. By applying data mining, predictive analytics, machine learning on large datasets, BPO companies can extract actionable insights to improve operations and create business value for clients. 

Some examples of big data analytics applications in BPO include:

Optimising Processes  

By collecting and analysing data like average handle times, call wait times, resolution rates etc. BPO companies can identify inefficiencies in processes and optimise them for better KPIs. Analytics helps create performance benchmarks and ensures processes are streamlined.

Predictive Modelling

Historical data can be used to build predictive models using machine learning algorithms. This allows BPOs to forecast metrics like customer churn, lead conversions, workload projections, etc. Clients can leverage these insights for activities like campaign management, call volume planning, resource allocation.

Sentiment Analysis

Natural language processing and text analytics techniques can determine customer sentiment from call transcripts, chat logs, social media, surveys etc. This provides a voice-of-customer view that can be used to improve products and services.

Fraud Detection

Big data analysis helps detect anomalies and patterns that indicate fraud. For instance, insurance BPOs can analyse claims data to uncover fraudulent claims and prevent losses. Financial companies can detect fraudulent transactions in real-time by analysing transaction data.

Hyperpersonalization

Detailed customer data can be used to segment audiences and customise services and offers. Agents at a travel BPO can leverage customer history and preferences to provide personalised travel recommendations. Banks can offer customised products based on a customer’s financial profile and behaviours.

Adoption of Artificial Intelligence

AI is making BPO services more efficient and intelligent. Besides automation of repetitive tasks, BPO companies are implementing AI for cognitive capabilities like speech recognition, natural language processing, deep learning and more.

Here are some examples of AI applications in BPO:

Chatbots for Customer Service  

Chatbots powered by NLP and machine learning can understand customer queries and respond with answers from the knowledge base. Over time, the chatbot’s responses get better through continuous learning. 

BPO companies are using AI chatbots as the first line of customer service, with human agents stepping in for complex issues. This improves efficiency and service quality. 

As per Gartner, 25% of customer service organisations will integrate virtual customer assistant or chatbot technology by 2020.

Call Analytics with Speech Recognition

AI-based speech recognition and natural language processing allows transcription and analysis of customer calls. The system can extract insights like caller sentiment, compliance issues, product complaints etc. These insights help improve processes and agent training.

AI-powered Knowledge Management

Knowledge bases used by call center agents are getting more intelligent. Machine learning algorithms can analyse agent-customer interactions and derive answers for the knowledge base automatically. This evolves the KB, reducing the need for manual updates.

Fraud Investigation

Unsupervised learning algorithms can detect abnormal patterns like sudden large transactions, suspicious wire transfers etc. which may indicate fraud. BPOs providing services like insurance claims investigation or financial transaction monitoring can use AI to flag potential fraud for human review. 

According to a survey by SAS, 63% of financial institutions deploy AI-based fraud detection.

Hyper-Personalization

Deep learning algorithms analyse multiple customer data points to determine their profile and preferences. BPO service agents can then leverage this model to provide personalised recommendations and deliver contextual services customised for each customer.

Conclusion

Automation, analytics and AI are truly transformative technologies for the BPO industry. They present ample opportunities for improving process efficiency, reducing costs, enhancing customer satisfaction and generating business insights. 

However, these technologies also raise concerns like loss of jobs to automation and need for reskilling workforces. Responsible implementation focusing on augmentation of human capabilities rather than pure replacement is key.

BPO companies that embrace these trends smartly by taking an innovation-focused approach will be able to achieve differentiated growth and provide better value to clients in the digital age. The future looks promising for BPO companies that leverage smart technologies to their advantage.