using gis to enhance customer experience

Using GIS to enhance the Customer Experience

Meet your customers where they live

Gary Allemann

Why Customer Experience Management?

• New Products or Pricing models quickly nullified

Why Customer Experience Management?

• Poor service or unhappiness easily made public

Why Customer Experience Management?

Product/ Pricing

ServiceCustomer Experience

“the practice of designing and reacting to customer interactions to meet or

exceed customer expectations and, thus, increase customer satisfaction, loyalty and

advocacy”Gartner

How and where does our customer interact?

Big data promises to answer these questions

• Who is our customer?• How do they interact with

us?• Which channel do they

prefer?• How do they buy?• Where do they interact?

What is big data?

Big data provides new insights quickly by simplifying the processes of combining and analysing data of any type and any size.

Big data is characterised by any combination of:

• Variety• Volume• Velocity

Case Study: Insurance

Case Study: Fraud Detection

Geolocation

Transaction

Point of Sale

$$$$$

$$$

Case Study: Telecommunication

With the new insight generated through big data analytics, this telecommunications company gained a complete view of its customers and which mobile towers are being used by power users.

As a result they saved over hundred million dollars in network optimization, by upgrading the towers that are mostly used by power users.

Location Data

Subscriber Demographics

Network Performance

Case Study: Government

“To accelerate the eradication of poverty in South Africa through the use of enabling technologies that support the

improved planning, targeting, co-ordination, and delivery of anti-poverty services.”

What we received

Multiple spelling variations (similar sounding phrases)

English and Afrikaans Addresses

Many variations e.g.Township, T/ShipA/A, Admin AreaSq/camp, Squarter

Camp

e.g. +- 300 variations of East London

Standardised Address

Actual vs Theoretical Beneficiaries per Local Municipality

Identify areas with large disparities between theoretical and actual beneficiaries.

Nelson Mandela Bay: Theoretical 126,670 vs Actual 159,772 (METROPOLTAN)

Engcobo: Theoretical 73,221 vs Actual 45,389 (RURAL MUNICIPALITY – Chris Hani District Municipality) Camdeboo: Theoretical 14,046 vs Actual 3,472 (Cacada District Municipality)

Example 2 – Coverage of Clinics & Hospitals

Hospital / Clinic

Bridging the gap

Bridging the gap

Bridging the gap

Questions?

gary@masterdata.co.za

+27 11 485 4856

@Gary_Allemann

http://www.linkedin.com/company/master-data-management

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