With advances in Big Data, Internet of Things and other technologies contributing to an ever-increasing deluge of information in today’s digital world, 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 action that starts to resemble hair splitting. (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 not to the 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 this pass.
But what really got my goat was the article’s assertion that averages don’t tell the real story of checking accounts.
Well, isn’t that true for everything? If averages told the whole story, who’d read the rest of the story?
Perhaps the best illustration of the misleading nature of averages can be found in this episode where a mathematically-obsessed but swimming-challenged guy drowned in a lake thinking he could comfortably wade across it because he was six foot tall and the lake was only four feet deep – on an average.
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, this is when analytics could morph into hair splitting.
Because the next question is, at what level should an FI carry out profitability analysis and recommend 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 rural areas, shut down all branches in villages.
- Account level, as recommended in this article? e.g. Close all unprofitable checking accounts.
To use consultant-speak to answer the above question, each bank should decide the optimum level for itself by evaluating the cost versus benefit of each additional level of analysis and the corresponding action it’d take from it.
Unfortunately, the classical business case approach is challenged by several qualitative factors – business ethics, reputation damage and regulatory rap on the knuckles, to name a few – inherent in this context.
For example, what happens if customers whose checking accounts are terminated unilaterally by banks because they’re unprofitable take to social media to vent their fury against the bank, the way Brett King did in a recent incident when HSBC USA canceled his account? The resultant public backlash could snowball into a PR crisis that could stop other – p0tentially profitable – customers shunning the said bank.
That said, I love analytics and, as I’d hinted at the beginning of this post, I’m somewhat ambivalent about hair-splitting myself. Therefore, I can’t resist the temptation of drilling down even one level below and proposing analysis and action at the sub-account level. Let me illustrate this approach 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 adding more operating costs 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, slapping excess-ATM-usage fees might appear to the most natural option but such a tactic might run afoul of the regulator.
Therefore, we 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.
Jokes apart, how do you draw the line between analytics and hair-splitting? Please share your thoughts in the comments below.