"The probability of selling to an existing customer is 60–70%. The probability of selling to a new prospect is 5-20%"*
Many companies are reconsidering the management of their customer relations in order to gain a better understanding of expectations and to offer customers a unique experience, by putting them at the center of their strategies.
Traditionally, business leaders and marketers built an understanding of the customer through 'on the ground' market research. These customer analytics relied on gathering data about the customer from customer-facing systems and some historical and structured data of customer transactions and digital activity. This gave marketers some understanding of a customer's needs and preferences.
With the advent of the online, connected customer, business leaders and marketers have had the rug pulled from under their feet. Traditional ways of gathering insight and building and understanding the customer better are showing serious limitations. Not only has the customer become a digital denizen, she has also become empowered. Now you not only have to understand her but engage with her, pre-empt her and prompt her to buy.
For this, those in mar-tech need to be able to get handle, not only on her historical or transactional information, but the big, unstructured data that provides clues about her behavior and mind-set, while also keeping up with her changing preferences and buying decision moments.
For those mar-tech and business leaders, this is a nightmare. They turn to data scientists to get this data, this picture of the customer and tell them what to do. Data scientists, on their part, with a limited and limiting data framework and strategies, find it impossible to view the activity and track the behavior of every customer, across multiple channels and data points.
In fact, it would take a group of mathematicians, data technologists, information technologists, marketers and graphic artists, with advanced cognitive computing tools and lots of effort to make any sense of it. By the time they got an answer, the situation would have changed. It would appear as if business leaders and marketers are asking for the moon.
A decision-making platform that brings together data science, information technology and people taking business decisions, in a synchronized way, is the solution to the challenge of intelligent and personalized customer engagement.
Statistics provide the capability to sift through tons of customer data, both structured as well as big data, and turn it into usable and predictable insights about customer sentiment, preference and behavior.
Information Technology provides the capability of acquiring data from various channels, applications and touch points and delivering analysis and insight to the right people, in the right place and at the right time.
People Taking Decisions provide the decision-making at the right time, to shape campaigns, offers and communications and refine customer strategies for better returns.
AI Machine Learning : provides the capability to track results and outcomes of the predictions and make suggestions to adapt to the customer’s mindset. These are then fed back into cognitive engines. This learning feedback loop ensures that the solution itself 'learns' from its customer interactions and improves its capabilities for more accurate insights, predictions and suggestions. Over time, the ‘machine’ is able to provide better decisions.