No data to big data
As data grows in size, it may also be wise to
remember that not everyone in the team can look at a large database, do examine
the use of data visualization tools
There was a time, not too long ago, when we
had to go street to street looking for customer data. Honest truth. When
working on a leading automobile brand in the early 2000s, we discovered that
what the company had as customer data was in fact the data (often the registered
office address) of the car leasing company. That was of no use to us in
marketing, or at least of very limited use. We needed the names, addresses and
phone numbers of the people who drove the cars. Did the dealers have the data?
Well, we discovered that the dealers did have the data but it was on their
service records (obviously when the car came for service it did not come from
the leasing company).
This data looked rich, so for a few years, we
employed field investigators to physically visit the 100+ dealer points and
collect authentic customer data. This was revalidated by back-checks. We
managed to assemble almost 75% of authentic names, addresses, phone numbers—the
database of the direct marketing division of the agency which was entrusted with
running the customer relationship programme for the said client. Was this good
enough? A regional direct marketing expert was called to audit the programme.
He was amazed at the way the data was collected and, to our delight, told us
that 75% was indeed very high. Apparently, even in developed markets,
manufacturers struggle to get real customer data—the challenge in those markets
is that the dealer feels that the customer belongs as much to the dealership as
to the brand.
From a situation of No Data we moved towards
Some Data, but with a lot of physical effort.
The situation improved dramatically with the
automation of all dealer points and state-of-the-art point-of-sale systems
which collected not just the name of the owner (the leasing company) but also
the name and address of the primary user of the car.
But just a name, an address, a phone number,
a mobile number, an email ID, does not amount to Big Data, as we all know.
In the book Big Data in Practice,
Bernard Marr chronicles the way Big Data is being used in 45 different
companies (US-based) including the usual suspects like Google, Netflix, IBM
Watson and Amazon. The book also speaks of interesting areas where Big Data is
put to use. For example: Walmart, after analysing a humongous amount of buyer
behaviour data, discovered that before a typhoon hit a town, the two top items
bought and stocked by householders are batteries/torchlights and, wonder of
wonders, strawberry shortcakes. We could have guessed batteries and
torchlights, but not in our wildest dreams would we have cottoned on to
strawberry shortcakes, supposed to be the No. 1 comfort food consumed in big
quantities during an impending storm (wonder if Big Bazaar sells out of potato
wafers before cyclonic storms?). Or take the case of Caesar casinos in Las
Vegas; they figured out that by tracking data of regular visitors to their
casino, the real potential to sell lies not with the high rollers but the
average Joe who spends around $100-1,000 per visit. By focusing on this large
cohort, Caesar became one of the best performing casino chains in the US.
The author predicts, quite rightly I would
imagine, that the term Big Data will soon become obsolete. What we know as Data
will be Big Data. As we move from MB to GB to TB to pentabyte to exabyte (in
the year 2000, just 3 exabytes of information was created) to zettabyte, our
own definition of Big Data will keep morphing.
Where are we in India with the Big Data
revolution? Given the huge talent we have in coding and analysis, are we way
ahead of the curve?
Just last month, I got an exposure to two
very different scenarios. In one, a family-owned, family-managed small
enterprise, I discovered that the company had been collecting customer data on
a regular basis and was also able to put it to good use. In yet another
company, I was asked—‘Will customers willingly give their email id and mobile
number?’
So here are just a few key pointers to the
companies in the second bucket:
• Start collecting customer names, emails,
mobile numbers from tomorrow
• Match the customer data with the purchase
data
• Start building customer segments that can
be monitored
• Test offers against these segments; maybe
10 a month?
• By using other data collection tools, see
if you can add to the customer profile
• Target messages to them through social
media
• Keep building a learning curve
As data grows in size, it may also be wise to
remember that not everyone in the team can look at a large database, do examine
the use of data visualization tools. They help the unlettered appreciate the
finer aspects of data segmentation and analysis.
While you probably were aware of the fact
that 70% or 80% of your sales came from your regular customers, you will now
know who they are and will be able to focus a large part of your marketing
budget at them.
So, instead of just mouthing empty words
about Big Data, start building your business by using data more productively.
It is really a lot simpler than you thought. Zettabytes can wait!
Ambi M.G. Parameswaran is brand strategist
and founder Brand-Building.com. He will take stock of consumers, brands and
advertising every month. The views expressed are personal.
Regards
Pralhad Jadhav
Senior Manager @ Library
Khaitan & Co
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