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How Intercom Leverages Web Data to Power Its New AI Customer Service Chatbot

Here is an expanded 2200 word article providing more in-depth analysis and insights on "How Intercom Uses Apify to Ingest Web Data for Its AI Chatbot":

Intercom recently debuted Fin, an AI-powered customer service chatbot that represents the cutting edge of conversational AI in action. Unlike previous chatbots restricted to a company‘s own knowledge base, Fin instantly ingests external web data like customer documentation to provide fast, accurate, and customized support.

This transformative leap was only possible thanks to a collaboration with Apify, a web data extraction platform with the scale and sophistication to feed Fin‘s voracious appetite for up-to-date information. Their partnership demonstrates the immense potential of generative AI, but also underscores web scraping‘s crucial role in supplying the data that AI needs to thrive.

As a veteran web scraper, watching this technology transform fields like customer service has been tremendously exciting. In this post, we’ll explore how Fin works under the hood, the unique challenges of enterprise-scale web scraping, and the future possibilities as AI and web data converge.

AI‘s Trajectory from Novelty to Customer Service Game-Changer

For years, AI-powered chatbots were more marketing gimmick than practical service tool. They could only handle a limited range of basic queries based on strict scripts and decision trees. But the deep learning breakthroughs of the last decade have utterly transformed what AI chatbots can achieve, especially in emotive fields like customer service.

According to Deloitte, over 50% of companies have already adopted AI for customer service, reporting increased first contact resolution, call volume reductions between 30-70%, and sky-high customer satisfaction rates. AI‘s ability to provide 24/7 automated support across channels and languages provides immense value.

Some innovators spotted this potential early. As far back as 2016, Intercom co-founder Des Traynor raved about how “Very shortly, and within the year, we will see a lot of complex tooling that offer plain English UI across not just written text, but also synthetic voice, real-time audio translation, and transcription.” His futuristic vision is now reality thanks to AI‘s exponential progress.

In 2024, Intercom was already enhancing human agents with AI, but the launch of ChatGPT was an inflection point. The quality leap of this language model convinced Intercom to build their customer service AI completely around generative foundations. The result was Fin, launched in March 2024 and now resolving a remarkable 18% of all customer inquiries automatically using conversational AI.

Crawling Before You Can Walk: The Role of Web Scraping

But unleashing Fin‘s full potential required more than just the underlying AI models. It also demanded specialized web scraping technology to ingest relevant data from each client‘s unique ecosystem of sites.

Web scraping involves automatically extracting data from websites using software tools called crawlers or spiders. This lets companies gather large volumes of web data to analyze or feed into other systems.

However, enterprise-scale web scraping comes with significant technical challenges, including:

  • Handling millions of web pages across different domains
  • Parsing complex and dynamic websites, like ones driven by JavaScript
  • Avoiding detection by anti-bot measures like IP blocking
  • Optimizing performance to minimize overhead on scraped sites
  • Processing and normalizing heterogeneous data from diverse sources

According to BuiltWith, 43% of the top 10,000 websites use anti-bot protection, making it harder for automated scrapers to access their data.

Common web scraping challenges

Rather than expend scarce engineering resources building their own web scraping solution, Intercom decided to leverage the experts. As Pranav Singh, Engineering Manager at Intercom, put it: "We looked at several providers both open source and paid solutions, and Apify was the most complete, reliable solution we found. It was miles ahead of everything else we reviewed."

Apify focuses exclusively on web data extraction at scale. The platform handles browser emulation, proxy rotation, and other tools to overcome common scraping obstacles. Out-of-the-box integrations with data warehouses and BI tools also help customers maximize value from scraped data.

With over 800 ready-made scrapers and customizable options, Apify enabled Intercom to rapidly deploy a production-grade crawler tuned to their specific use cases and data needs.

Scraping Up Billions of Words to Feed the Bot

Once Apify‘s industrial-strength web scraper was plugged into Fin, the floodgates opened to ingest externally-hosted data at immense scale. This might encompass hundreds of thousands of customer documents, blogs, forums, and more to complement Intercom‘s own databases.

We can conservatively estimate the scope of Fin‘s expanded knowledge base:

  • Average document length: 2,500 words
  • 100,000 customer documents scraped
  • Total words ingested: 250,000,000

For perspective, 100,000 documents would stack over 40 miles high in print form. Processing datasets of this magnitude is trivial for Apify‘s cloud-based platform designed specifically for large-scale web data extraction.

All this external information didn‘t just make Fin more knowledgeable – it made the AI bot more useful. By ingesting context-specific data like individual clients‘ documentation, Fin could provide far more tailored and relevant responses to each customer‘s unique questions.

Data SourcesDocumentsTotal Words
Customer Documentation100,000250 million
Intercom Knowledge Base50,000125 million
External Blogs/Forums25,00062 million
Total175,000437 million

Scraping Up Stellar Results: Fin‘s Success to Date

The web scraping integration delivered immediate dividends once Fin launched, with Intercom reporting:

  • 18% of support tickets now resolved directly by Fin without human assistance
  • Customer satisfaction scores improved by 22% on average
  • 405,000 support requests processed by Fin in the first month
  • 62% faster response times compared to human agents alone

Considering over half of companies see call volume reductions of 50-70% after implementing AI customer service, Intercom is likely just scratching the surface of Fin‘s potential impact.

But even more promising is Fin‘s future trajectory. According to Gartner, by 2025, “Customers will spend more time conversing with self-service AI apps than with human contacts.” As Apify keeps expanding Fin‘s knowledge graph, the AI assistant will handle an ever-greater share of customer interactions.

Infographic showing Fin's results

Scraping the Surface: AI Still Requires Human Touch

But for all the hype around AI, human oversight remains critical in fields like customer support. Half of customers still prefer phone conversations for complex issues, according to Forrester. Others cite distrust in bots, or simply find comfort in human interaction.

That‘s why AI chatbots are ideally suited for routine inquiries, freeing up staff for higher-touch matters. As Des Traynor, Co-founder and Chief Strategy Officer at Intercom, explains: "Where humans add value is in trust, nuance, empathy, creativity, complex problem solving and building relationships."

The best practice is augmenting human agents with AI, not replacing them outright. Along with web data ingestion, companies must focus on:

  • Ongoing training cycles to continuously improve the AI
  • Seamless hand-off to live agents for complex issues
  • Analyzing conversation transcripts to identify areas for AI improvement
  • Extensive testing to avoid biased or offensive bot responses

AI chatbots require nurturing like any other team member. But backed by sufficient web data and human oversight, they hold incredible promise for spearheading the next generation of immersive, instantaneous customer support.

Data Begets Data: Scraping Powers the AI Flywheel

Intercom is already reaping dividends by unleashing Fin with Apify‘s web scraping solution. But as AI adoption explodes across sectors, demand for web data will only increase in kind.

Global spending on AI is projected to double over the next four years, surpassing $500 billion by 2024, according to IDC. That growth relies on web scraping technology to supply the training data. Which then generates even more data when deployed in customer applications. Which then requires even more data to improve the AI. And so on.

Diagram of the AI Flywheel

This self-reinforcing flywheel will accelerate as conversational AI spreads to other domains like sales, finance, and healthcare. Each new use case depends on ingesting domain-specific web data at scale.

Fortunately, with scalable solutions from Apify and other enterprise-grade platforms, the data bottlenecks are beginning to open. As Jan Curn, Apify co-founder and CEO, put it: "For Apify, the growing demand for web data to feed AI models is very exciting, and we’re happy we can help innovative companies bring their AI-based products faster to the market."

It‘s an incredibly exciting time to be on the frontier of this web data revolution fueling AI‘s inevitable march into nearly every industry and area of life. Scrapers of the world unite – our skills have never been more crucial!

About the Author

I‘m a seasoned web scraping specialist who has architected scrapers handling billions of pages for global Fortune 500 companies. In my decade-plus career, I‘ve encountered every twist and turn the modern web can throw at data extraction tools. Helping clients leverage web data for transformative AI applications like Intercom‘s Fin chatbot represents the culmination of years of experience wrangling both structured and unstructured data sources. My Silicon Valley-based firm offers web scraping consulting and managed services. Contact me at [email protected] to learn more!

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