Recently I read a fascinating article on WSJ.com on how crime investigators are using machine learning to find patterns and uncover insights that would otherwise be undetectable. Investigations range from criminal to cybersecurity, competitive counterintelligence, and corporate litigation. In one example, a firm shows that if this technology had been available during the Enron scandal 15 years ago, they might have proactively detected the company’s high-risk accounting practices before it snowballed into a crisis. Their demo poured through 500,000 Enron emails, and learned how to flag suspicious ones during the scanning.
It’s not news that pattern detection software works for fraud detection (for years, banks have been scanning structured credit card transaction data and flagging unusual activity). But here’s what’s novel: this same approach is extremely well-suited to the art and science of developing customer intelligence against unstructured big data. Customer experience pros can unlock new mysteries, and take appropriate actions leading to better customer experiences.
The first thing criminal investigators do is gather all the facts they can, from any available source - Emails, phone records, texts, web activity. The adage being, “Leave no stone unturned.” It’s never clear at the start of any investigation which clues might matter, and may link to others – so all are important regardless of their form. As the investigation unfolds, machine learning techniques, such as neural networks that use self-organizing cluster maps (known as SOMs), can help find patterns and eventually help the investigators form a hypothesis. Available evidence is used to test whether the facts fit the theory.
Taking this same approach with customer success, by gaining customer intelligence and using it to solve for marketing and customer experience challenges, you can benefit from this same methodology and technology because:
- Consumers leave clues about their preferences and behavior in many places; sometimes this is in unstructured forums, like social media, product reviews, and blogs.
- It’s virtually impossible to sift through this data without the aid of technology and automation.
- Machine learning can be used to find patterns in customer activity, such as what product they are most interested in buying, or that their sentiment is trending toward total dissatisfaction.
- Once patterns are detected, predictions can be made and actions triggered in efforts to anticipate needs or alleviate matters.
As a consumer, my natural reaction is that companies shouldn’t have this much info on me. Ironically, most firms simply want to use this to improve your experience with their brands since they know it’s critical to their success. Repeatedly, surveys show above price and product, people leave because they’re dissatisfied over how they were treated.
But the level of dissatisfaction is qualitative and differs by customer. One customer who experiences a single network issue may become enraged, while another may be more tolerant. Knowing this and the value of each customer helps the company treat each situation with a custom-tailored response.
That all sounds easy, right? But try scaling it to millions of global customers, with billions of bytes of unstructured data in their direct conversations and behaviors, and their indirect musings on social media, in blogs, and elsewhere. Moreover, try to learn when each customer reaches various stages of interest or displeasure, and over time, improve your ability to predict these and take timely action.
Since the dawn of time, we have learned that to survive we need help from machines. Use this newest breed of machines along with time-tested investigation techniques to crack the enigma of your customers, gauge their state of mind, and delight them with more personalized experiences.
Pegasystems’ Vince Jeffs explains why firms need a Customer Decision Hub – a customer relationship management brain with memory and predictive intelligence.