Social Media & Big Data - Declared preferences vs. Discovered preferences
Posted by Abhishek Singh (View Profile | View All Posts) at 8:02 AM
The
other day, I logged into a local eCommerce site - FlipKart.com to buy a book. In India, it's a leader in eCommerce customer experience with very high Net Promoter Scores, a well-established metric with direct correlation to
customer experience. I have not met anyone in India who has used this site and not
recommended it to others. Like any forward-looking website, it allowed me to
login using my Facebook/Gmail credentials, rather than asking me to create
another username and password, which I would obviously forget. So my experience
started on a positive note. Out of curiosity, I started to browse the
"Recommendations For You" section to try and decode their algorithms. Next I
tried the same process with Amazon, which did not allow me a Facebook login (or
did I miss it?). And it dawned upon me that there are things that these
e-tailers know about me because I told them that (the declared preferences) and
there are things that they will infer about me based on my transactions with
them (the discovered preferences). There would be two primary sources of
declared preferences - my social media profile and any additions I make on
their website to my profile like a phone number, an explicit addition to my
"wish list" etc. And there would be another two primary sources on discovered
preferences - my past transactions with them and my interactions with my social
media website (including associated clickstreams). This is the perfect marriage
of Social Media and Big Data.
Social
Media
There
are things that I do on my social media profile - let's take Facebook as an
example - where I declare my preferences of music, my date-of-birth, my
relationship status, my photos, etc. Some of this data is available to
businesses, if I give a merchant access to my profile. These are explicitly
declared by me and companies can use this data to substantially improve their
interactions with me since they know that much more about me. So having
separate logins - in my personal opinion - is useless . Whether you are a local
e-Commerce site or even as complex as a bank, if you are not exploring ways to
use social media logins (Facebook, Twitter, Gmail, LinkedIn etc.) to your site,
you are really not sincere about knowing your customers and serving them
optimally, no matter how much you harp upon "We live to serve" in your print or
TV ads. Social media sites are called that because they help customers be
social. And so should businesses - all of them, not just for lip service but
for transaction-enablement.
Big
Data
Next
comes all the status updates, 'Likes', comments, check-ins that I do on
Facebook, activities which reveal a little bit about myself every time I
interact with these sites. Facebook graph search and Facebook Home, of course,
have now opened an even bigger Pandora's Box in my opinion. Add these to the
customer transactions with your company, the clickstreams of the customer on
your website, install the processing power to do statistical analysis around
the combination of all this structured and unstructured data and you are well
on your way to your big data analytics strategy. But how much Big Data is
useful and how is it useful? What can companies do better with Big Data
Analytics that they could not before?
Analytics
Part
of the answer is in the problem itself - how
intrinsically predictable something might be?
(And
as far as human predictability is concerned, just think about your spouses,
kids and parents before answering. That should give you an idea of how
predictable your customer is going to be.)
So
what's the point?
How
can you create value for me - your customer? Big Data, Social Media, Predictive
Analytics, any technology on which a company invests money, it must create value both for the company and its customers. And ideally it must
achieve this in a way whereby it improves the quality of the overall customer
experience and reduces the cost of operations for the business. So how can you
create value for your customer and yourselves?
Firstly
the businesses must have end objectives in mind - not something as broad as
"revenue increase by 2%" but something more well-defined "revenue increase by
2% from existing customers through existing products". The key word(s) here is
"existing". If you are talking customer acquisition or new product launches,
you might need different approaches than what I am about to talk next. The
intrinsic predictability of your problem is vital to finding a solution for
that problem.
When
you have defined the problem as clearly possible with potential for
predictability - you start looking for points of commonality between your product
line and your existing customers. Now all of a sudden it begins to make sense
to learn more about your existing customers and how they consume your existing
products. To study that you start mining the Big Data of your company's
enterprise datawarehouses, transactional systems and of course the social media
profiles of your customers. And you can start the journey to discover
preferences of your customers in levels of granularity that makes it meaningful
to establish relationship between customer segments and product segments with
higher degree of correlation. An improved matching of product profile with
customer profile since you now have more data points - both about the customer
and the product. So both the declared preferences and discovered preferences
are valuable
Discovering
these preferences and patterns of your customers and products is meaningful
only if you are confident that you are leaving that 2% money on the table. A
product like a book in unlikely to be bought by the same customer again but a
perishable or fast moving consumer good (FMCG) would have a typical consumption
period after which it can be recommended yet again. So if you know when the
customer last bought it, you can recommend again after a certain period.
