Hard vs Soft Data
Every Monday, Wednesday, and Friday I rush out of the office at 5:00 pm and hurry to pick up my daughter from daycare (which closes promptly at 5:30 and charges a $1 per minute late fee). Once I've scooped her up into the car, I start a different race - the race home through Portland traffic.
Traffic is a complex creature well deserving of its own post, but one thing I've noticed is it seem to peak at 5:00 pm, then again between 5:30 and 6:15 pm. So, if I can make my way through the most congested streets before the second peak begins, my commute takes 30 minutes. Miss that window, and we can easily spend an hour getting home. Listening to "Let it Go." Over. And over.
The area of concern lies between my daughter's childcare center in SW and HWY 99 past the Ross Island Bridge. The most direct route (and shortest by distance) is to jump briefly on 405 then take Ross Island over the river, but this route is often clogged after 4 pm. So I consider alternative routes based on existing condition and adjust accordingly.
(I've become especially fond of a convoluted route in which I dip down to Water Ave past OMSI then jumping on 99 via a little road right before Ross Island.)
But what does this have to do with work?
As illustrated above, the shortest path is not always the best choice to get to your destination. Knowing possible routes is not enough - we also need to understand the current conditions and take into account previous experiences to determine which one may be the best bet. While seemingly a banal reality of commuter life, my route home is actually a perfect example of the entwined relationships of Hard and Soft data.
Let's start with Hard data: Hard data is defined as data in the form of numbers or graphs, as opposed to qualitative information.
In the world of customer service work, Hard data would be the types of data that is generated from ticket time, chat duration, customer satisfaction ratings, survey results, and time punches. This information can be measured, traced, and validated. Most companies use Hard data for analysis and metrics.
But, just as with my traffic conundrum, it is often best to look at all data for an accurate picture of what is happening and what action should be taken, which would include Soft data.
Soft data is often defined as human intelligence and insights which is not so easily measured. From a customer ops perspective, I would define Soft Data to include things such a ownership, commitment to the customer ("going above and beyond"), passion for the product, loyalty to the company, and commitment to continually learn and improve. It can also include feedback in the form of comments, both from customers as well as via internal surveys.
Because soft data can be difficult to collect, measure, and analyze, it is often used only as a supplement to hard data.
This is especially true when trying to prove the monetary value of an employee or department, which requires subjective methods and hard data. However, such as with the case of a customer service agent with less than amazing metrics (hard data), the use of soft data (such as contributions to the department or team beyond her role expectations) might be used in to justify a raise. Cough cough.
When thinking about our role as customer service agents and the stated goal of our Cust Ops departments, I believe that a narrow focus on hard data may cause us to forget those aspects of excellent customer service that caused our department to be so wonderful in the first place: real human interaction and understanding, a passion for the product and those who use it, patience and willingness to listen.