Feb 22, 2013 - George Colwell Presenting

NORM for Enterprise - Banking EditionFocused Social Solutions for Financial Services

Saturday, 23 February, 13

Introduction to NORM

The basic concept that drives NORM is the ability to leverage social media to produce a closer relationship with your clients.

NORM was originally designed to provide CRM services powered by social media to industries that traditionally do not have CRM, like food service.

Social cues, like checking in at your favourite restaurant on Foursquare, trigger NORM and an aggregated social profile is created that the restaurant can use to link additional metadata to (food purchases, spend at restaurant, fav drink, allergies).

Saturday, 23 February, 13

NORM for Banking

Knowing your followers was the initial concept that has lead to the creation of the first NORM for enterprise version called NORM for Banking.

The ability to know if an individual that has liked your company on Facebook, tweeted about your company or directly messaged you on Twitter, or checked into one of your company’s locations is an existing client is key to knowing how to respond appropriately.

The possibility of making a connection between the social persona and an existing client in an enterprise CIF was the first use case that generated NORM for Banking.

Saturday, 23 February, 13

Know Your Followers

• The Opportunity• Banks, and most other business for that matter, are currently unable to know which

of their followers are also existing customers.•Knowing who your followers are would provide a distinct advantage when solving

customer service issues via social media, as well as creating relevant marketing campaigns via social media.

• Identifying key social influencers within an existing client base to provide incentives to amplify your messages

• The Solution(s)•A social profile aggregation service that aggregates social profiles, compaires key

metadata to an existing enterprise client profile to distinguish between clients, prospects, competitors, and fake profiles.

• Integration of solution with existing CRM applications, RTOM solutions, and sales and marketing processes.

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How NORM Works

Step 2. NORM applies its social media profile matching logic to link the aggregated social media profiles into a single social media profile view per user.

Step 1. Norm aggregated the social graphs from top social media sites

Step 3. NORM profile is created, social graphs are combined, a single point of view is created for each social media user

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How NORM Works

Step 4. NORM profiles are compared to a Bank’s client profiles. Links between a NORM Profile and a Bank’s client profile are made and NORM’s social metadata is consumed by existing, and new bank processes.

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NORM for Banking Widgets

Saturday, 23 February, 13

Use Case 1Social CRM• The Opportunity• 24/7 social customer care - customer outcome•Creation of integrated campaigns to drive traffic to social media channels - bank

and customer outcome• Integrated solution to allow existing channels (contact centre) to easily integrate

social customer care access, interaction, tracking with traditional contact management systems - bank and customer outcome

• The Solution(s)• Portalized widget based solutions for accessing Facebook and Twitter integrated

with standard security within banks• Integration of social customer interactions with traditional call management

systems. Service oriented and extendable• Integration of Twitter DM and Facebook messaging content into traditional contact

management solutions• Escalation processes designed with social customer care in mind

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NORM for Banking Integrated Social CRM

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Use Case 2Real Time Offer Management (RTOM)

• The Opportunity• Increase cross sell, up sell, share of wallet. • Reduce customer attrition.• Provide relevant next best offer, next best step.

• The Solution(s)• Integration of social metadata into customer segmentation logic• Build of more relevant offers based on lifestyle, life stage, likes, dislikes, derived

from social metadata.• Inclusion of RTOM with social metadata within existing product and customer

origination processes.•Combined social metadata, payment data, purchase data into enterprise data

wearhouse to derive actionable trends related to product usage, and customer attrition indicators.

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The inclusion of social metadata as source data in a real time offer management solution would provide the ability to produce extremely targeted segmentation that does not exist today.

Segmentation based on social metadata combined with existing traditional sources of data like payments, purchases, and existing CRM could produce extremely detailed client segments to power, next best offer, next best step, and merchant funded offers.

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Saturday, 23 February, 13