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Analytics Magazine

Customer Relationship Management: Cognitive computing for automating customer knowledge

September/October 2015

Guy MounierBy Guy Mounier

If your customer relationship management (CRM) system could actually think, would Elon Musk and other AI detractors want to kill it?

Much has been made lately of the fearful admonitions about machine learning or AI by technology luminaries such as Musk, Stephen Hawking and Bill Gates. But would a CRM system – employing cognitive computing to do a better job helping customers get what they need faster – be dangerous? Would a CRM work flow that tells the advisor that a customer needs to adjust his portfolio to avoid a coming risk be a candidate to become emperor of the world? Of course not. Businesses are already using cognitive computing to make the bond with their customers stronger by making themselves more valuable, and they are gaining new revenue with that strengthened bond.

Now to add some perspective, a cognitive computing platform is not a sentient being, but it is two generations beyond business analytics, and even one generation beyond machine learning. So what is it that cognitive computing delivers that makes it superior to previous generations of analytics and why should we embrace it? In three words, automated customer knowledge.

In today’s big data environment, companies are gathering and storing vast amounts of information about their customers, but it is rarely translated into actionable customer knowledge. Businesses know this information can help them better understand their customer, innovate products and services, and improve revenues, but they have not been able to accomplish that with previous technologies. What they require is a technology smart enough to make sense of all that data and transform it into actionable and measureable customer outcomes.

Cognitive computing does just that automatically, providing a clear means to make the most of a company’s proprietary data. Some innovators are already disrupting their industries with this technology. They use it to intuit what customers need and to enhance customer insights in order to hone their cross-sell offers and increase revenues. At a time when corporations are trying harder than ever to keep and grow their customer base, this technology can offer a sustainable competitive advantage by capitalizing on existing relationships.

Cognitive Computing Implications for CRM

There are many applications for cognitive computing, but one of the most compelling is its ability to make sense of the great volumes of customer data – both static and dynamic – to learn, think and recommend. Global banks are using the technology to connect external and internal information to accelerate revenue across corporate accounts managed by a diminishing number of bankers. Wealth management organizations are enriching their internal data with information about a customer’s key life events obtained from external public social and professional data sources, helping their advisors proactively up-sell to existing clients.

For customer care operations, cognitive computing platforms help companies substantially reduce the time and resources they spend achieving customer issue resolution. Even more interesting is that these organizations are finding that the technology can be used to convert their customer care centers into profit centers. This nifty trick is accomplished by resolving customer issues quickly and proactively, making the customer feel known and appreciated, and then using the resulting “wow” moments to make up-sell recommendations during the very same client interaction.

The Cognitive Computing Advantage

Automated data science is the key to winning the battle for customer loyalty at scale. Companies can learn from their data and enrich their learnings with information from external sources. Unlike traditional business intelligence or big data projects that take months or years to execute, cognitive computing helps companies attain payback within weeks. Businesses are using this technology to leverage existing human and technology investments in CRM, customer experience tools, big data analytics and more. In essence they paid for all that IT infrastructure, and cognitive computing allows them to fully capitalize on it.

Global corporations took some time to understand the benefits of cognitive computing, but that is now changing. Technology giants like IBM have used cognitive computing to help companies in a number of ways, e.g., the new “Chef Watson” app for Bon Appétit, utilization management decisions in lung cancer treatment and developmental assistance in Africa. Companies that are truly innovative don’t throw away or waste the vast amounts of data that they already have; instead, they are using cognitive computing to extract predictive value, i.e., answers to important business questions, and leverage their data for business gains in a more rapid fashion.

Not only does cognitive computing offer a competitive advantage, it also helps unlock the value of data to counter emerging threats from non-traditional market entrants and competitors. It is one of the key technology solutions used today by businesses across the world to fend off competition, increase revenue from existing customers and thrive in fast-changing market conditions.

The Technology

Customer-facing employees often have to resolve issues in split seconds. They might struggle to identify the best course of action, which impacts customer experience. Cognitive computing can help these front-line professionals by providing suggested actions or best practice recommendations. The technology does this by extracting actionable insights from structured and unstructured customer data. Sales agents and customer relationship professionals can use these best practice recommendations to identify up-sell and cross-sell opportunities. With this technology, companies can get a 360-degree view of their customer.

Cognitive computing technology can be used to create a customer knowledge layer that enriches data collected over years of customer interaction and domain experience. The platform combines data (which resides in the CRM, care or account management system) with files from various internal and external sources. Once the information has been enriched, the technology continuously applies data enrichments, predictive recommendation algorithms and unsupervised semantic learning. The process is both continuous and dynamic. Unlike other predictive analytics that are rules-based and static, this technology is self-learning, real-time and contextual. Every interaction and result educates the platform, helping it become even more effective over time.

Since the technology is lightweight and quick to deploy, cognitive computing can impact business revenues within weeks. This is achieved using a compressed platform-based methodology. By choosing a technology partner that has relationships with key consulting firms and systems integrators, businesses can potentially get first use cases developed in weeks. Also, the cognitive computing technology is offered via a private cloud-based solution, making it easy for companies to deploy on their internal cloud or an industry standard external offering.

The Potential

We have just begun addressing the potential use cases of cognitive computing in the business world. Those leading the way know this to be true because they are continually discovering new revenue-generating applications with customers across multiple industries. In an environment where businesses are finding it hard to gain new customers and even harder to retain existing customers, cognitive computing offers a chance to differentiate. Innovative companies understand that deeper customer knowledge is the key to surviving and thriving in this competitive landscape, and that cognitive computing not only enhances customer experience, but also delivers new revenue.


Guy Mounier is co-founder and CEO at CustomerMatrix. Before that, he co-founded and ran BA Insight – a leader in agile information integration. Mounier holds a master’s degree in mathematics from Harvard University and a master’s degree in computer science and electrical engineering from École Centrale Paris.

 

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