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jerryking : call_centres   5

Citigroup CEO says machines could cut thousands of call centre jobs
February 17, 2019 | Financial Times | Laura Noonan and Patrick Jenkins in Dublin.

Citigroup chief executive Mike Corbat has suggested that “tens of thousands” of people working in the US bank’s call centres are likely to be replaced by machines that can “radically change or improve” customers’ experience while cutting costs.

Mr Corbat, who runs America’s fourth-largest bank by assets, made the comments in an interview with the Financial Times in which he also ruled out Citi’s involvement in any wave of US banking consolidation triggered by the $66bn SunTrust-BB&T merger and justified its continued presence in China.

Under pressure to bring its cost base in line with peers, Citi executives have been upfront about the impact of technology on their 209,000-strong global workforce, including last summer’s warning that as many as half of the 20,000 operations staff in its investment bank could be supplanted by machines.

Mr Corbat’s latest comments are the most explicit the company has been on how the $8bn a year Citi spends on technology could transform its vast consumer bank, which serves 100m customers across 19 markets.

“When you think of data, AI [artificial intelligence], raw digitisation of changing processes, we still have.....
artificial_intelligence  automation  call_centres  CEOs  Citigroup  layoffs  job_destruction  job_loss 
february 2019 by jerryking
When Big Data Isn’t an Option
May 19, 2014 / Summer 2014 / Strategy + Business | by David Meer
When Big Data Isn’t an Option
Companies that only have access to “little data” can still use that information to improve their business.

Many companies—probably most—work in relatively sparse data environments, without access to the abundant information needed for advanced analytics and data mining. For instance, point-of-sale register data is not standard in emerging markets. In most B2B industries, companies have access to their own sales and shipment data but have little visibility into overall market volumes or what their competitors are selling. Highly specialized or concentrated markets, such as parts suppliers to automakers, have only a handful of potential customers. These companies have to be content with what might be called little data—readily available information that companies can use to generate insights, even if it is sparse or of uneven quality....the beverage manufacturer developed an algorithm based on observable characteristics, then asked its sales professionals to classify all the bars and restaurants in their territories based on the algorithm. (This is a classic little data technique: filling in the data gaps internally.)

. Little data techniques, therefore, can include just about any method that gives a company more insight into its customers without breaking the bank. As the examples above illustrate, mining little data doesn’t mean investing in expensive data acquisition, hardware, software, or technology infrastructure. Rather, companies need three things:

• The commitment to become more fact-based in their decision making.

• The willingness to learn by doing.

• A bit of creativity. ...

The bottom line: Companies have to put in the extra effort required to capture and interpret data that is already being generated.
small_data  data  analytics  data_driven  market_segmentation  observations  call_centres  insights  data_quality  data_capture  interpretation  point-of-sale  mindsets  creativity 
september 2015 by jerryking
How should we analyse our lives? - FT.com
January 17, 2014 | FT |Gillian Tett.

“Social physics helps us understand how ideas flow from person to person . . . and ends up shaping the norms, productivity and creative output of our companies, cities and societies,” writes Pentland. “Just as the goal of traditional physics is to understand how the flow of energy translates into change in motion, social physics seems to understand how the flow of ideas and information translates into changes in behaviour.”...The only question now is whether these powerful new tools will be mostly used for good (to predict traffic queues or flu epidemics) or for more malevolent ends (to enable companies to flog needless goods, say, or for government control). Sadly, “social physics” and data crunching don’t offer any prediction on this issue, even though it is one of the dominant questions of our age......data are always organised, collected and interpreted by people. Thus if you want to analyse what our interactions mean – let alone make decisions based on this – you will invariably be grappling with cultural and power relations.
massive_data_sets  social_physics  data_scientists  quantified_self  call_centres  books  data  social_data  flu_outbreaks  Gillian_Tett  queuing 
january 2014 by jerryking
In a Routine Business, Extra Service Pays Off - WSJ.com
December 7, 2004 | WSJ |by PAULETTE THOMAS | Special to THE WALL STREET JOURNAL
call_centres  fulfillment 
june 2012 by jerryking
Using data to enhance customer experience
: January 24, 2006 | FT.com | By Ian Limbach. "“Call
centres are often seen as a way to manage costs rather than enhancing
the quality of [customer] service,” warns Wes Hayden, CEO of Alcatel’s
Genesys subsidiary. This has discouraged investments in new technology
and led management to measure efficiency with metrics such as throughput
and call duration, rather than customer-centric measures. “There needs
to be a change in C-level executives’ view of call centres,” he says.
This narrow focus has led to call centres being one of the most
under-used corporate assets today, says McKinsey. Beyond fielding
customer complaints, the call centre should be closely integrated with
other company functions such as sales & marketing.

Some leading companies are focusing on ways to turn calls from customers
into new selling opportunities, and finding that callers are more
receptive to buying after a positive service experience than they are
when reached by outbound telemarketing campaigns. "
call_centres  contact_centres  customer_experience  McKinsey  customer_centricity  CRM  data  upselling  cross-selling  unstructured_data  churn  predictive_modeling  metrics  mismanagement  underutilization  assets  cost_centers  C-suite 
august 2010 by jerryking

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