The following is a guest blog by Mandeep Singh Kwatra, VP, Solutions and Capabilities at Hinduja Global Solutions (HGS). For more information about HGS, visit their website.
As companies gain a deeper understanding of customers through research and predictive analytics, they will use that information to develop more individualized customer experiences. Today, it’s not enough to have a knowledge base. The best, most insightful business intelligence is developed from a contextual knowledge base that can be used by both agents and customers to predict the right answer based on that particular customer’s data, situation, location, and needs. The end result is faster responses, increased issue resolution, reduced customer effort, and happier customers.
Internet and digital channels have shrunk the world of customer information, and now everything is available at the click of a button. Customer loyalty is as fragile as that button click. Customers want brands to understand what they really need. They want everything personalized for them, at every step of the customer journey—from prepurchase research to complaints or when they are contemplating whether to jump ship. Today’s customers want personalized sales cycle, service queues, and complaints management. This means a knowledge base that employs two key elements:
– First, brand knowledge cannot be dated. It cannot be robotic or a copy paste of a manual or process. Knowledge has to be contextual. It has to be natural and language driven. It has to be how customers like to talk.
– Second, today’s predictive analytics answer to contemporary customer expectations, which are: to get the right answer fast, while also meeting sophisticated personalization criteria.
Contextualized Knowledge Bases
This shift from “content” to “context” requires companies to understand and categorize customers in personalized slots. According to a recent survey by Adobe, more than 60% of online users wanted to know why, what, and how web sites select content personalized for them. Companies need to have their personalization strategies backed by solid, accurate customer data. This is made easy by customers leaving a virtual bread crumb trail on their online journeys for brands to follow. But, the first order of business is to build a contextual knowledge base template that can support personalized customer data. Because customer behavior changes from one situation to another, a contextual database should include customer reactions across a broad spectrum of situations to be comprehensive and accurate. A recent study by Forrester Consulting on behalf of EverString found that marketers most often face the two challenges of ensuring data quality and managing data from a variety of sources (both 47%) in attempting to gain greater insights about customers and prospects.
In terms of reducing customer effort, contextual knowledge base helps with predicting the right answer based on that customer’s data, situation, location, and needs. Customer service can provide faster responses, increased rate of issue resolution, reduced customer effort, and happier customers.
Role of Contextualized Knowledge Bases in Personalization
Contextual knowledge bases support brand efforts for personalization by supplying crucial individual customer data for ad and campaign targeting. The same survey by Adobe found that for 33% of marketers, personalization is the one capability that will be most important to marketing in the future.
When implementing personalization, possessing the right kind of customer data is the most important necessity. You need to know the customer’s:
– Preferred communication platform
– Best time to contact
– Spending patterns
– Purchase plans
– Significant life situations (that can affect spending and purchasing patterns)
– Product preferences
Regular customers expect companies to know their preferences and even new ones think that companies should have some idea of what they’re interested in. Keeping up with these expectations means that companies need to build contextual knowledge bases that are capable of recording and linking intricate customer preferences to their contact database. Knowing the advances in technology, customers have come to expect personalized treatment and customer service by their favored brands, and rightly so. Customers expect brands to understand their need or issue and provide personalized solutions for them. However, data given to them in trust by customers, companies have to ensure it is used responsibly and in a transparent manner.
Contextualized Knowledge Bases and Predictive Analytics
According to the same EverString study, predictive marketers are 2.9x more likely to report revenue growth at rates higher than the industry average and 2.1x more likely to occupy a commanding leadership position in the product/service markets they serve. Predictive analytics is a crucial requirement to ensure that you get the best out of your contextual knowledge base and your personalization efforts are optimized for best results. Use of predictive analytics needs accurate data to plot patterns and predict customer behavior to tell you when a customer will indulge in a certain purchase or when they will begin to research products. Your contextual knowledge base should consist of sufficient customer data from different scenarios for analytics to be fairly accurate in predicting customer shopping patterns.
Also, ensure that your predictive analytics software is processing all available and relevant customer data from all sources. In today’s multi-channel environment, customer data is sourced from multiple contact platforms and so counting in every source is important.