The document discusses how geographic information systems (GIS) can enhance the customer experience. It provides examples of how telecommunications, insurance, and government organizations have used big data and GIS analytics to gain insights into customer interactions and preferences. This allows them to better meet customer needs, detect fraud, optimize networks in high usage areas, and improve delivery of social services. Location data, transaction records, and demographics are analyzed to understand customers and identify discrepancies or coverage gaps to target for improvement.

1. Using GIS to enhance the
Customer Experience
Meet your customers where they live
Gary Allemann

2. Why Customer Experience Management?
• New Products or Pricing models quickly nullified

3. Why Customer Experience Management?
• Poor service or unhappiness
easily made public

4. Why Customer Experience Management?
Product/
Pricing
Service
Customer
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

5. How and where does our customer interact?

6. 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?

7. 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

8. Case Study: Insurance

9. Case Study: Fraud Detection
Geolocation
Transaction
Point of
Sale
$$$$$
$
$
$

10. 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

11. 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.”

12. What we received
Multiple spelling variations
(similar sounding phrases)
English and Afrikaans
Addresses
Many variations e.g.
Township, T/Ship
A/A, Admin Area
Sq/camp, Squarter Camp

13. e.g. +- 300 variations of East London

14. Standardised Address

15. 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)

16. Example 2 – Coverage of Clinics & Hospitals
Hospital /
Clinic

17. Bridging the gap

18. Bridging the gap

19. Bridging the gap

20. Questions?
gary@masterdata.co.za
+27 11 485 4856
@Gary_Allemann
http://www.linkedin.com/company/master-data-management