The ecosystem.Ai Client Pulse Responder aims to establish the optimal amount of energy expended on each customer, to nurture engagement and retention. By using a combination of data science and behavioral insights, you can determine not only the ‘how’ behind the data, but the ‘why’ too.
Relationship dynamics can be seen in two ways: either as a data science problem, or as a human-driven endeavor. But, how far do we need to go to understand the customer’s behavior in order to capture more value and keep them engaged through automated predictions?
Generic data scientists are likely to look at data, particularly in retention and attraction, as the primary field of client engagement. They will note a data point or a trend, and answer the “how”. But for the full picture of customer behavior, the “why” is also crucial. The world of Computational Science takes this field of data science further towards enriching it, extracting knowledge from it, and essentially revealing the “why” people do what they do. As soon as you have this social science view, a multitude of patterns in the data are revealed.
Using the concept of energy to optimize engagement
Computational Social Science uses a number of social constructs, including the concept of “energy” for measuring customer engagement and activity. This extra layer adds new meaning to the data that will be used to develop appropriate predictors to automate customer interactions.
One of the definitions of energy is “the strength and vitality required for sustained physical or mental activity“. But how does it relate to the work of Computational Social Science? Let’s start with the customer journey, which illustrates how varying levels of energy expended on customers affect engagement.
The customer journey is the sum of experiences customers encounter when interacting with a company — from initial contact to the establishment of a long-term relationship. The time spent in engagement to go through this journey requires different levels of energy. There will also be different rhythms for different people in the customer journey. Person A might require more energy (special offers, newsletters etc) than Person B to keep them engaged. Energy also needs to be expended at the right time — for example, an email prompt to remind them that they haven’t used their account in a month (and conversely, no email prompt if they’ve used it three times that month).
Learning your customer’s rhythm with the Client Pulse Responder
With the ‘pulse’ as the measure of energy, products such as ecosystem.Ai’s Client Pulse Responder pick up the habitual, cyclical behavior of customers and measure whether they are being engaged with the right level of energy. In essence, the Client Pulse Responder establishes the ultimate pulse of engagement, one client at a time, to ensure the company/client relationship stays safe.
The energy a company needs to apply to keep the customer relationship alive thus requires a Computational Social Science view, and not simply a data science point of view. Companies are then able to use this information to optimize the customer relationship and to stay informed about the right level of energy they should exert on gaining and retaining clients.
