Analyst $'s or Rich experience!

So a data scientist was “hot IT job No. 2” on a list of the six hottest new jobs in IT, according to CIO Magazine (@ciomagazine). The essential skill set needed for the enterprise of the 21st century is that of the “data scientist,” a role dedicated to understanding and making use of data to help a business or other organization.

A recent Forbes article describes Data scientists as “They are equal parts engineer, statistician and investigative journalist / forensic reporter”. Are these requirements unreal? I sure think they are! And Business analyst was possibly the hot job in earlier years!  

And, what’s the difference between the Business analysts who actually have IT aspirations & those who want to actually do more “consulting”.  And where do you slot the Web analysts, so they are yet another breed, right!!  So you have Web analytics, Business Intelligence & good old analytics! Wow, it doesn’t get more interesting than this!

Isn’t it time analysts got their act together & thought about professional growth over a longer period! Should they be thinking about the quality of exposure they get along with what they are paid?

How do analysts grow professionally?  Is the community getting "out priced"? Are they getting paid more without the requisite "richness in exposure"? End of the year, I see this ritual where I see junior analysts chasing “money” vs a strong “robust learning experience”.

I saw this interesting study on what motivates analysts. I have worked with quite a few over the last decade. I feel this is a community in "transition". At one end, technology is getting smarter & doing what an analyst could do earlier! At another end, analysts who work in pure “outsourcing” roles are not getting exposed to “what makes business tick?” But are analysts thinking 5 to 10 years ahead or is it only the $ that they are chasing!!

Your customers have forgotten you?

Often I see headlines in business publications, which scream about “so & so loyalty program having got 2 million customers” or “so & so Credit card company having 6 million Cards in force now & taking pole position in the market share game”. But what % of this customer base has actually forgotten you & does not transact with you after the first fling!

Many loyalty programs or transactional businesses (credit & debit cards) have a large base but also have a huge percentage of that base having transacted only once.  These are customers who have only “dipped their toes” into your business & then they never come back! This is what I call, the problem of the “Single visit” customer! This also makes me wonder “How much of your customer base is junk”?

Imagine the Revenue loss because of this & don’t even think about the net loss because of the high customer acquisition costs. In businesses like Credit cards these customers probably didn’t want your card to begin with & so obviously are not using it. Roughly 40% customers of most credit card issuers are inactive or nearly inactive, and almost 60% customers do not generate any profits. The credit bureau TransUnion recently conducted a study and found there are eight million inactive credit card accounts in the U.S. today.

Even One time buyers do leave a trail of data behind & the marketer needs to learn to use that information effectively.  While you don’t have the rich data that enables you to accurately predict next purchases or possible attrition for customers, but you do know enough to build intelligent campaigns for the one-timers. You can build a program that wakes up this “sleeping giant” of a segment!

There is quite a lot of information that gets captured for each one-time buyer. Learn to use it well & you can have a powerful program for reactivating customers. Here are some examples of data that can be used:

1.    The purchaser’s contact information. This lets us reach out in a personalized Manner. For businesses like Banking, there is anyway the need for establishing contact, thanks to the Know your customer norms(KYC).

2.     How many items were in the first order? This often correlates with likelihood of a second purchase.

3.    How much money was spent on the initial order? This too can correlate with second order probabilities.

4.    What was the tender type in the first purchase-cash or credit card? In the case of banks, customers who make their first deposits in the savings account by cash are more likely to keep low balances then those that made a cheque deposit! So this data is important & can allow you to predict who will deplete balances!

5.    What merchandise or services were purchased? This lets us predict the set of logical next products to offer.