Last week, when my car insurance policy came up for renewal, being somewhat indifferent towards the brand
since there had been no engagement with it anyways over the past three years, I decided to go in for an alternative provider with a better market standing and that was more convenient to purchase. Upon calling the new provider who had no data about me or my records whatsoever (being a new customer), within minutes
they had all my details and with impeccable speed and efficiency processed my application until the final stages, when it required the earlier provider to release no-claim discounts. I feared an aggressive bid from my previous provider at this stage to retain my business, even though the previous three years had seen no attempts whatsoever towards trying to create a brand-customer relationship or reach out to me, not even to remind me that my renewal was due. Thus the indifference had been mutual.
Lo and behold and to my pleasant surprise, there was no such resistance on the part of the phone desk. Little did I expect that I’d be let off the hook so easily, with no effort to even enquire why I was exiting to another provider, leave alone trying to retain me with any kind of bait. What is interesting amidst this experience is that this same provider has invested in the most sought-after breed i.e. data scientists, with a view towards
more accurately predicting churn, among other things.
The above incident ought to make us sit back and wonder: on the one hand there is little doubt that with the ‘data revolution’, there is an urgent need to exploit the opportunity and harness data to optimise returns in the form of insights, ultimately translating to revenue. However, on the other hand, it throws up a myriad of
questions: Is there a common purpose or shared objective across departments (towards preventing churn in this case)? Or is money being invested in data science purely because it’s become the buzz word, with
no attention being paid to more fundamental aspects like having the frontline staff aligned with the organisation’s goals? Above is a case where investments are being made in technology and resources at one level to predict and prevent churn, and on the other hand there is no effort being put into building a
relationship between brand and consumer and even letting the customer slip without a whimper. Or perhaps, where human interface failed, possibly even a chatbot may have done better at retention, if humans have indeed become slaves of machines.
MACHINES VS HUMANS:
Much has been written about machine learning and artificial intelligence and these new phenomena leading to significant reduction in human resource requirements. However, these phenomena have brought about requirements for new skillsets at a higher level – data scientists and engineers (and more). At another level these disciplines are creating redundancies with chatbots, voice, facial and image recognition, natural language processing and more such technologies. Ultimately with machine learning, even as per its classical definition, humans are only training our machines to master certain specific tasks through a repetitive process, and given the inherent strengths of speed and efficiency, continue to thereafter do it in a way whereby
it displaces humans.
Besides, and most importantly, machines come with no ‘attitude’, ‘pre-conceived notions’ or ‘reservations in the mindset which unfortunately play a huge role and often come in the way of human efficiency and output.
…IS MONEY BEING INVESTED IN DATA SCIENCE PURELY BECAUSE IT’S BECOME THE BUZZ WORD, WITH NO ATTENTION BEING PAID TO MORE FUNDAMENTAL ASPECTS LIKE HAVING THE FRONTLINE STAFF ALIGNED WITH THE ORGANISATION’S GOALS?
KEY INGREDIENTS:
At the crux therefore, this leads to highlighting the following mandatories when it comes to becoming truly data-driven, which is way beyond paying lip-service through hiring data scientists or any kind of data professionals for that matter:
- Make it all-pervasive and be known across the organisation. This applies more so to areas involved like
customer service, marketing, sales and even human resource, to ensure everyone is striving towards the same goal, be it preventing churn or attrition, growing revenue, developing new products and markets or anything at all. - Encourage ownership of data across stakeholders for different purposes but a common goal. Often sales
and marketing focus lies so much on acquiring customers and growing the numbers, with data being seen as best left to the geeks in the back office, or data lying in silos. Data for the geeks to crunch and analyse is ultimately captured through sales and marketing activities, both online and offline. - Foster attitude to succeed, brought in through creation of the feeling “if this meant me retaining my job”. By this we’re not suggesting creating any kind of a fear psychosis, but a survival instinct which is inherent in humans and traits we are known for.
- Empower employees to deviate from the script. In the above anecdote, my behaviour as a customer opting to discontinue services with a brand wasn’t radical, but clearly if life-time value of a customer matters, there is a need to encourage and empower the frontline to prevent attrition, rather than the often-prevailing mindset of ‘have to ask my supervisor’.
- Expectations from data (and the data scientist) : Let data (and the data analyst) not be seen as a magician with a magic wand but an enabler of insights and data-driven activities.
In the ultimate analysis therefore, we may be tempted to ask ourselves whether this cannibalisation has been selfinflicted on each other.
Are we rendering ourselves whereby we are heading soon towards a day when there will be Michelin chefs in the kitchen, serving gourmet meals through robots in the service area?
More importantly, in the process of trying to keep up with the Jones’ and quickly adopt state-of-the-art technologies which have become buzzwords, are we missing out on the fundamentals.
There is a famous inspirational quote that goes: “Always aim for the stars, and you’ll reach the sky”, which is very well. But then there’s also a pertinent quote which goes “Keep your eyes on the stars, and feet on the ground”.
