“…Undoubtedly, an amateur blogger, nonetheless, a long time thinker”
Let’s assume, one stereotypical Sunday morning; you woke up. Picked your smart phone to snooze the alarm.
Spotted an email notification ‘All of that you want’; you clicked to open.
…an e-mail from Somebasket.com with deals, on what all you planned to buy in today’s grocery. Quite anticipatory, isn’t it.
That’s not all, they offered you a complimentary pass to Race Course in your city. (Coincidentally, even that was on your cards for
today’s evening). Now, that’s strange.
Strange enough for you. Does that mean a sheer coincidence?
Not exactly, but a mere excerpt of the power of Predictive Analytics using Big Data through Cloud.
It’s ‘just’ an extrapolation of your retro-records, to best cater your futuristic needs. The ‘just’ is ofcourse understated.
Let’s together explore the extended possibilities in terms of managing relations with you (as in customer). I mean, Customer Relationship Management (CRM).
Incidentally, my bread and butter hails from CRM on Cloud. Hence, braving a stance in relating these jargons.
CRM eventually constructs a holistic frame of the Customer (In this case, it’s your frame).
Building the frame requires data, which can’t simply be assumed, but need to be gathered from various sources.
So, let’s start accumulating the data. This could be achieved from Big Data sources, which could span from structured, unstructured, large and complex data sources. To name a few – web logs, social networks, Internet search history, call detail records, sale transactions and likes.
With the depreciating memory cost and faster processors, this is quite realizable.
The pattern could then be transformed into a comprehendible inference for further analysis.
Exploiting some hidden relationships in the data, which can really prove advantageous.
What all further analysis could we carry out?
We can calculatedly predict customers’ buying habits in order to promote relevant products at multiple touch points,
and proactively identify and mitigate issues that have the potential to lose customers or reduce their ability to gain new ones.
We can then focus on satisfying more customers, rewarding consumer loyalty and obviously retaining customers in this volatile market,
with many tempting options from various competitor’s.
(Remember, the complimentary passes for PlaySphere? That was nothing, but a reward towards your loyalty to SomeBasket.com).
How do SomeBasket.com come with such accurate results?
Wish, it would have been. At least, it would have saved a lot of calculations.
But, viable is predictive analytics. This helps analyze customers’ spending trend and other behavior, leading to efficient sell of products and services presicely required, even cross sell. This directly leads to higher profitability per customer and stronger customer relationships.
Through this, Direct attrition and Silent attrition, both can be curtailed. Direct attrition is self explanatory,
whereas Silent attrition is interesting, referring to the behavior of the customer reducing usage of the offered services and the products.
So, using predictive analytics, we now can identify the products and services, distribution channels, payment terms, timings,
which can best position in the market; Importantly, it would be customer specific or customer cluster specific.
Even, this predictive analytics can be extended for potential customer, which could further lead to recognize prospect and convert into Opportunity, Lead, and ultimately to a big time customer.
How can these permeate to more customers on an individual level?
Not every customer can afford humongous infrastructure. And can’t certainly be omnipresent. But a cloud can.
Over here, shared infrastructure and services would come in tremendous help, and that’s what cloud leverages.
(You have your portable mailbox, where SomeBasket.com can place a win-win email. And the mail box is on cloud).
Warning: To benefit from these, please keep your phone charged!
Note: This was just the tip of the iceberg. To avoid undue complexity and potray theinterlink, the topics have been kept simple.
Name of any organization is imaginary and snaps are reused. Any resemblance would be mere coincidence.
Each of these topics are humongous and can individually sum up to gigabytes.