Advances in Big Data, Internet of Things and other technologies are contributing to an ever-increasing deluge of information in today’s digital world. Not surprisingly, analytics has once again become a hot topic.
But I sometimes wonder if there’s such a thing as “too much analytics”.
I’m not referring to “analysis paralysis” where no action results from all the analysis.
I’m thinking of analysis that does lead to action but of hair splitting nature (Not that there’s anything wrong with hair splitting per se).
I recently went through this “analysis or hair splitting?” debate in my mind when I read the following average figures for a checking account in this Bank Director article:
- Balance: US$ 5600
- Cost: US$ 250
- Revenue: US$ 413
It doesn’t take a PhD in mathematics to calculate that the average profit is US$ 163. Therefore, it should be obvious to everyone that an average checking account is profitable for banks.
But it is not so obvious to the authors of this article. They claimed that the average checking account “doesn’t pay for itself”. I pointed out their erroneous conclusion in my comment below the article. One of the authors emailed me to admit the faux paux, so I’ll let that pass.
But there’s another line in the article that asserts that averages don’t tell the whole story of checking accounts.
Duh, thank you Captain Obvious.
Isn’t that true for everything? There’s nothing special about checking accounts.
If averages told the whole story, who’d read the rest of the story, huh?
Perhaps the best illustration of the misleading nature of averages can be found in this episode. A mathematically-obsessed but swimming-challenged guy drowned in a lake. He thought he could comfortably wade across it because he was six foot tall and the lake’s average depth was only four feet.
Anyway, proceeding with that platitude, the authors urge banks to take “action to cure the unprofitables and protect the profitables” by going beyond averages and drilling down to a more granular level.
There’s nothing wrong about this advice since FIs are under constant pressure from Wall Street / Dalal Street to trim their unprofitable businesses while simultaneously bolstering their profitable franchises. But, it’s only while trying to execute this strategy that analytics risks morphing into hair splitting.
Let’s see how.
The next question would be, at what level should an FI conduct profitability analysis and carry out remedial action?
- Product level? e.g. Be happy if the overall checking account business is profitable.
- Geography level? e.g. Since checking accounts are unprofitable in villages, shut down all rural branches.
- Account level, as recommended in this article? e.g. Close all unprofitable checking accounts.
The classical consultant-speak answer to the above question would be, each bank should decide the optimum level of analysis and action for itself by assessing the cost of conducting each additional level of analysis versus the benefit of carrying out the action therefrom.
Unfortunately, the classical business case approach is challenged by business ethics, reputation damage, regulatory rap on the knuckles and many other qualitative factors inherent in this context.
Suppose a bank terminates a customer’s checking account unilaterally since it’s unprofitable and the customer took to social media to vent their fury against the bank à la Brett King did in a recent incident when HSBC USA canceled his account? What’d happen?
The public backlash could snowball into a PR crisis that could deter other p0tentially profitable customers from signing up with the said bank.
But challenging does not mean impossible.
I love analytics. As I’d hinted at the beginning of this post, I’m somewhat ambivalent about hair-splitting. Therefore, I don’t feel like giving up.
I’d like to propose analysis and action at the sub-account level. Let me illustrate my proposal with the following example:
On the whole, John Doe’s checking account is profitable but deeper analysis reveals that he uses ATMs a lot more than the average checking account customer, thereby increasing the operating cost for the bank to service his account. Therefore, the bank should take remedial action to boost profits by weaning John Doe away from ATMs (while “doing nothing” in the case of other customers).
In this pursuit, the bank might want to slap excess-ATM-usage fees on John Doe (but do nothing in the case of other customers). While this is a highly personalized and data-driven strategy, it might run afoul of the regulator.
Therefore, I suggest a different approach: Display a ghost-like image on the screen as soon as John Doe inserts his debit card into the ATM slot. Undeterred, if John Doe proceeds to enter his PIN number, replace the customary ATM usage instructions piped through the ATM speakers by blood-curdling screams. If John Doe still stands his ground, threaten to play a video on how to avoid cash and make an online payment.
Jokes apart, how do you draw the line between analytics and hair-splitting? Please share your thoughts in the comments below.