Business FOMO: How Machines Will Bolster the Data Game
By Matt Matsui, senior vice president, products, markets and organizational strategy at Calabrio
Most executives already know that collecting and analyzing data is central to making operations efficient. Without that analysis, they risk missing out on valuable insights that can drive a company towards profitability. As the right technology is put in place, executives are able to glean previously unknown insights. However, organizations may soon have additional help. As executives struggle with business FOMO (fear of missing out) on where to gain the next level of analysis and insight, the rise of artificial intelligence and, subsequent machine learning, may help put those fears to rest. These capabilities are becoming an even greater reality, and they will completely change the data game. Through pattern recognition and predictive analytics, self-updating tools may soon allow executives to offload some of their FOMO onto machines - and reap even greater results. Now, what was formerly a figment of imagination is on the brink of reality, and organizations must determine where these tools fit into the overall company structure.
From Pre-Sale to Contact Center
Organizations routinely track customer data and information and teams use that technology to turn that information into insights. Every point in the customer journey, from pre-sale to post purchase, is indexed and measured. Things like customer churn are predicted and support interactions are analyzed. Companies then use that information to make decisions that impact things such as sales strategies and resource allocation. Managers and executives clamor for those insights so they can develop their strategic plans using something other than a hunch or inclination. However, data analysis will undergo a dramatic shift as artificial intelligence begins to work alongside teams to dynamically predict human behavior. Not only will artificial intelligence allow these tools to take on dynamic tendencies, machine learning will add structure to incoming data as it seeks out context and patterns and continually adapts its analysis parameters.
Where Do Machines Fit?
One of the biggest quandaries companies will face is where to best implement these tools. Some parts of the organization may not have the breadth or depth of data to warrant investment in these solutions. However, areas like the contact center, where the sheer bulk of data and the value of potential insights is crucial to the success of the organization, is where executives risk missing out on information than can potentially transform a company. Contact center technology currently provides analysis and insights about people, and some even go as far as linking that data back to existing CRM or other systems in to paint a more complete picture of the customer journey from pre-sale to post-sale support. However, as the stakes for competitive advantage get higher and machine learning tools are implemented, missed opportunities or insights from contact center data become even more costly.
Contact centers field numerous customer calls and queries all day, every day. Within that data, patterns emerge that may allow a company to minimize loss and become more profitable. For example, some customers may require additional time and needs when it comes to post-sale support. The amount of resources spent on these interactions can often make or break profit margins. In the midst of these interactions, patterns develop and traits may emerge that allow machines to identify indicative behaviors. With machine learning, these patterns can be identified by software and correlations made between the contact center data and pre-sale customer data. From there, teams can use that information create things like automated, dynamic pricing models based on customer needs that can be adjusted to ensure profitability. This can be done on an aggregate - or even individual - basis. If this becomes a reality, companies can ensure they are charging appropriately without using traditional forecasting methods.
What’s Next?
As machine learning tools may soon become available and accessible to organizations, the fear of missing out on vital insights could well be diminished. While the technology may be on the brink of disrupting entire industries, organizations must decide where and when to implement such tactics by determining where the value lies. Some parts of a company, such as the contact center, are a treasure trove of data that naturally lend themselves to this type of technology. As machine learning becomes a reality and these programs bolster customer data from insight and analysis to behavior predictions, executives will finally be able to put their FOMO to rest.