(Article)Business Analytics, what is Analytics?

​While definitions abound, and each of them captures the essence of the term, let us start  with the simple one:

Analytics is the application of mathematical and statistical techniques to  data, in order  to  discover patterns and co-relations or  to  make   models that  predict,   thereby enabling fact-based decision making  or planning  within the organisation.

Or even simply, Analytics is deriving insight from information, and using it for the benefit of the organisation. But, why has  analytics become so important today?

“The  use   of  analytics  has   pervaded  all  aspects  of business. For  business today,   analytics is  no  longer an   option  but  an   imperative.  However,  as the  ever increasing pace  of  change  drives   businesses to  be more  nimble,  the skill and  sophistication behind  an organisation’s analytical capabilities are  distinguishing the high performers from the rest. With a surge in the use of  predictive   analytics, organisations  are  increasingly buying to anticipate tomorrow rather  than explain yesterday. As  a  result,   analytics-driven solutions  are helping  to transform business across functions.  To stay competitive, companies  are  looking  at  effective  ways to infuse  analytics into every  part of their organisation". (From the article ‘Analytics Everywhere: Using numbers to drive business transformation’, published in Accenture Business Journal for India, 2016,  Issue 2, by Mr. Arnab Chakraborty and Mr. Mahesh Narayan).

As the world around us goes more and more digital and our customers interact  with us through  digital channels, and not through physical interactions      (alternate Channels as compared to branch visits to transact), we may be ‘losing’ the customer, in the sense that the one- to-one  relationship, whereby we  could  understand the customers’ need and wants  and service them across the counter, is becoming less  possible at a physical  level. For  example, nearly  80%  of customer transactions in State Bank of India happen through alternative Channels (ATM, online banking,  mobile banking  etc.) as opposed to Branch  Banking.  This means 4 out of 5 transactions are   through   digital  mode   and   you  do  not  ‘see’  the customer.  To actually  get  a view of the  customer and fulfil his needs, analytics is a must.

This  shows how  the  customer has  gone  digital.  With over  900  million mobile  phones in the  country  people are  now  comfortable transacting digitally. Around  300 million smart  phone users, given  a choice,  are  most  of the time transacting through  a digital mode.  The second aspect is the  huge quantities of data  being  generated. The   data   explosion  is  so   large   that   the   amount  of data  generated over  the  last  few  years is  more  than the  amount generated  throughout our  history.  While technology has  played  a huge  role in this massive data growth, it has  also  helped in capturing and utilising this data. The reduction in data storage and technology costs and growth of networks has  made Analytics easier.

Analytics itself has grown as a science (and art) with more skilled  people and  enhanced tools  and  models being available. The  use  of analytics has  to be  all pervasive across products, processes, Human  Resources, fraud, and  risk to name a few. It has  to be across all channels so that not only is all data captured and  updated, but is available to the employees, and, more importantly to the customer at his preferred channel.

As we live in a world facing  continuous disruption  and competition, it is necessary to understand the customer and  meet  his  needs when,  where  and  in the  manner he  wants  it. If today,  you want  a pizza,  you just call. If you want a book or music,  you order  online. If I want to see a movie,  even  my movie tickets  and  snacks are  all available for purchase online. However,  many industries and  institutions   are  still playing  catch-up. Banks, for example; If you need a loan you generally have  to end up in a branch (and mostly more than  once). 

As  Bill Gates has   famously   said,   you  need  Banking but  you  don’t  need Banks!  Disruption  is  everywhere, as shown  by Pay  Pal,  M Pesa, Airtel money  etc.  The way of doing  business is changing to mostly  serve the customers. Here are few examples:

•      Air BNB –World’s biggest hotel chain  provider does not own a single hotel.

•      Uber – World’s largest taxi operator does not own a single taxi.

•      Facebook  –  World’s  most   popular   media   owner does not own any content.

•      Alibaba.com – World’s most  valuable retailer  does not own any inventory.

To help understand Analytics in a simple but comprehensive way,  we can  do no better  than  to look at the success factors  that make  Analytics work. These have  been shown  in, ‘Analytics at Work’ a book by Mr. Thomas H. Davenport, Mr. Jeanne G.  Harris  and  Mr. Robbert Morison.  They  have   grouped the  factors   as DELTA (the  Greek  letter  Δ or δ). These factors,  using analytics,  can   change  a  business.  The   full form  of DELTA is:

D   for Data  (of good  quality  and  retrievable for use), Breadth, Integration, Quality.

E   for an Enterprise wide usage (approach to managing analytics).

L    for Leadership in Analytics (passion and Commitment).

T    for Targets (First Deep  then  Broad).

A   for Analytics (Professionals and Amateurs).

First and foremost is data. The data  comes from a huge variety  of  sources and  has   to  be  stored in  a  proper manner for analytical  usage. Data is the new oil, as they say  nowadays. It is the base for all that follows. Let us take  State Bank as an example. The Bank gets  internal structured data  from 58 different sources including customer data  (Demography, Geography, income, gender etc.),  data  from the  contact centre, Complaint Management and Lead Management Data, Transaction data   (70  million,  transactions  a  day),   Product Data, Distributer  Data,   Channel Data,   NEFT,  RTGS,  ECS, employee data, Forex Data.... the list is growing. This   data   can   be   Captured  –  to  undergo  an   ETL (Extraction,  Transform and  Load  Process). The  Bank has   nearly   300  TB  of  this  data, with  huge   amounts moving in every  day.

How is this data used for analytics?

Few  examples are  below.  The  most  easy  usage,  to get  quick wins,  is to use  it for descriptive or statistical analytics. For example, how many SME loans  are being charged at  below  the  minimum  rate  (believe  me,  this can actually happen and if corrected, can result in huge gains  immediately).

Which Home  Loans  have  not registered the  mortgage in the  system, how  many  customers (specially  senior citizens),  are  without nominations, submission of subvention claims to the Government for example, Agricultural gold loans, staff accounts without the  staff identifier   (Provident  Fund  numbers  in  State  Bank’s case) etc.

Further, analytics would help in bringing in new accounts using data  mining on the narration field of the customer, to track how many  housing loan repayments to X Bank are  going  through  customers’ accounts  maintained by A Bank  through  RTGS,  NEFT,  ECS  transactions etc., car  loan repayments to Y Bank, or Insurance Payment to  Z  Insurance  Company. These  customers  can   be contacted with a  view to see if  their  accounts can  be held by the Bank too.

A very  important  usage is to create a  Customer One View (COV). Here,  the  static  details  of a customer are captured like name, address, mobile number, PAN number, Aadhaar Number  etc. The customer’s account holding with the Bank  are  also  captured – like types  of deposits, (FD, SB, CA) or loans (Home, Car, Educational, Personal etc.),  his  transaction preferences  like using ATM, online  banking  etc.)  and  also  his  insurance (life and  general) demat holdings  etc.  A value  is derived  at, using  all this data, and  the  customer is rated  (as  say, Platinum, Gold, Silver, Bronze etc.) and a tool run to see the  next  best  course of action  vis-a-vis  the  customer. The   tool  throws   up   the   products  which   should   be offered,  considering the customer’s income,  age,  place of stay,  existing  portfolio etc.  The various  methods like e-mails,   branch interface, call  centre, online,  or  ATM etc. can be used to convey  the product  to the customer. With agile  technology available, it could  even  go  as a pre-approved loan.  SBI, for example, has  a tie up with Flipkart  to  give  EMI, to  over  a  million SBI  customers when they purchase from Flipkart, in real time.