© 2011 IBM Corporation A few slides on IBM Unica Interact Presenter name Date

© 2011 IBM Corporation Introducing IBM Unica Interact 2 IBM Unica Interact is REAL-TIME MARKETING software  Determines, in real-time, the best personalized message for each “inbound interaction”  Can connect with any inbound touch-point: Web site, call center, point-of-sale, etc.  Personalization based on historical data and new information gained during the interaction  Connects and coordinates inbound marketing with outbound campaigns  Designed for easy usage and management (“so easy a marketer can do it”)  Meets the most rigorous performance requirements: –Up to 1,000s of transactions per second –Response times as low as < 0.5 sec

© 2011 IBM Corporation IBM Unica Interact enables “inbound marketing” 3 ? CALL CENTER WWW “Greetings” reason for call resolution authentication hold time… ? ? ? OFFER Inbound marketing: the presentation of personalized marketing messages during “inbound interactions” – when the customer or prospect chooses to contact you Turns every customer touch-point into a channel for the kind of personalized marketing messages previously only delivered through outbound campaigns

© 2011 IBM Corporation Inbound marketing is a growing trend 4 Survey question: Is your company delivering or planning to deliver targeted/personalized messages in customer-initiated interactions (e.g., website, physical store/branches, call center)? Source: The State of Marketing: Annual Survey of Marketers, 2011 conducted by Unica, an IBM Company

© 2011 IBM Corporation Interact’s real-time decision process for inbound marketing – “how it works” (simple overview) 5 ? OFFER(S) Takes relevant info: Who is this person? (if known) What are they doing right now? (session context) What else? (optional call-out to any web service) Picks best offer using: Self-learning algorithms and marketer-defined logic/rules Identifies all possible offers: Offers relevant to this person’s behavior Offers relevant to this person’s segment Global offers relevant to everyone 1-to-1 segment global

© 2011 IBM Corporation Interact’s real-time decision process for inbound marketing – “how it works” (more detail) 6 ? OFFER(S) OUTPUT: Winning offer(s) ELIGIBILITY Identify which offers that segment is eligible for OFFER DELIVER Select & pass the required number of offers from scored list SEGMENTATION Put the customer into a segment OUTPUT: Offers with scores SUPPRESSION & INCLUSION Consults “black lists” & “white lists” to remove or add candidate offers OUTPUT: Preliminary list of candidate offers SCORING Score each offer for that person (mix of manual and algorithmic scoring) OUTPUT: Segment Assigned INPUT: Customer profile Records offer presentation and disposition Can be factored into self- learning for future scoring INPUT: Session data OUTPUT: Final list of candidate offers Advantages to this approach: Stepwise approach increases control over the process and visibility into likely outcomes and impact of changes Mix of algorithms and manually-built logic blends both to maximum effect Suppression & inclusion step allows for coordination with other marketing channels (e.g., outbound) Advantages to this approach: Stepwise approach increases control over the process and visibility into likely outcomes and impact of changes Mix of algorithms and manually-built logic blends both to maximum effect Suppression & inclusion step allows for coordination with other marketing channels (e.g., outbound)

© 2011 IBM Corporation Interact’s real-time decision process for inbound marketing – illustrative example Self-learning, suppressions, and arbitration rank the offers by scores that represent business goals to make the final recommendation Real-time flowcharts are run to classify the visitor in to a list of segments each time they do something significant When a spot on the channel needs an offer, the list of candidate offers is retrieved for the applicable segments Welcome sample content for this goes here sample cont ? Marketing AnalystMarketing Manager Eligible Segment Recommended Offer On-Line Banking Offer A (100) Investment Products Offer B (75) CD Rates Interest Offer C (80) Offer D (55) Eligible Segment Recommended Offer On-Line Banking Offer A (100) Investment Products Offer B (75) CD Rates Interest Offer C (80) Offer D (55) On-Line Banking Investment Products CD Rates Interest After Arbitration Offer C (89) Offer A (81) After Suppression Offer A (100) Offer C (80) All Candidates Offer A (100) Offer B (75) Offer C (80) Offer C

© 2011 IBM Corporation Examples of how marketers use Interact  Improve first time conversion and cross-sell and up-sell by using the context of the interaction to improve personalization of offers  Gather data to qualify leads and tailor messaging –Listening to the key words or virtual body language –Identify preferences based on ‘who customer is’ –Gauge readiness to convert (education level, readiness to buy)  Deliver targeted offers with appropriate calls to action –Educational content –Prospect nurturing, not always trying to sell –Gradually escalating calls to action  Follow-up on previous interactions in a timely and relevant way – “marketing so relevant it feels like a service” 8

© 2011 IBM Corporation Benefits of using Interact  Higher response and conversion rates  Increased purchased value through more targeted cross-selling & up- selling  Improved customer experience –Relevant marketing messages when customer is listening –Marketing that the customer perceives as a services 9

© 2011 IBM Corporation Results from real Interact users 10 IndustryCustomer’s results Banking 4x revenue from Web site offers selected in real-time, not pre- calculated Banking 5% increase in sales volume from Web personalization Travel & hospitality 250% increase in clicks per day 400% increase in revenue per impression from Web and transactional personalization Telecom Cross sell success rate increased from under 10% to more than 40% in call centers and retail shops

© 2011 IBM Corporation Industry benchmarks for real-time personalization 11 (Association of National Advertisers) “The lift in conversion rates from even the most basic…targeting approaches averages from 10% to 50% -- and is often much higher in previously ‘neglected” segments.’” ANA 101: Website Personalization Inbound/real-time marketing response rates are often 10x traditional outbound marketing response rates. (Commonly cited by Gartner analysts)

© 2011 IBM Corporation12 extra slides

© 2011 IBM Corporation Relating the illustrative example to the “how it works” slide Marketing Analyst Real-time flowcharts are run to classify the visitor in to a list of segments each time they do something significant On-Line Banking Investment Products CD Rates Interest Self-learning, suppressions, and arbitration rank the offers by scores that represent business goals to make the final recommendation After Arbitration Offer C (89) Offer A (81) After Suppression Offer A (100) Offer C (80) All Candidates Offer A (100) Offer B (75) Offer C (80) Offer C Marketing Manager Eligible Segment Recommended Offer On-Line Banking Offer A (100) Investment Products Offer B (75) CD Rates Interest Offer C (80) Offer D (55) Eligible Segment Recommended Offer On-Line Banking Offer A (100) Investment Products Offer B (75) CD Rates Interest Offer C (80) Offer D (55) Welcome sample content for this goes here sample cont ? When a spot on the channel needs an offer, the list of candidate offers is retrieved for the applicable segments Offer C ELIGIBILITY Identify which offers that segment is eligible for OUTPUT: Segment Assigned OUTPUT: Preliminary list of candidate offers OUTPUT: Final list of candidate offers SCORING Score each offer for that person (mix of manual and algorithmic scoring) OFFER DELIVERY Selects & passes the required number of offers from scored list Records offer presentation and disposition Can be factored in as self- learning for future scoring SEGMENTATION Put the customer into a segment SUPPRESSION & INCLUSION Consults “black lists” & “white lists” to remove or add candidate offers OUTPUT: Offers with scores OUTPUT: Winning offer(s) INPUT: Customer profile Offer C INPUT: Session data

© 2011 IBM Corporation Relating the illustrative example to the “how it works” slide Offer C Welcome sample content for this goes here sample cont ? ELIGIBILITY Identify which offers that segment is eligible for OUTPUT: Segment Assigned OUTPUT: Preliminary list of candidate offers OUTPUT: Final list of candidate offers SCORING Score each offer for that person (mix of manual and algorithmic scoring) OFFER DELIVERY Selects & passes the required number of offers from scored list Records offer presentation and disposition Can be factored in as self- learning for future scoring SEGMENTATION Put the customer into a segment SUPPRESSION & INCLUSION Consults “black lists” & “white lists” to remove or add candidate offers OUTPUT: Offers with scores OUTPUT: Winning offer(s) INPUT: Customer profile INPUT: Session data

© 2011 IBM Corporation Relating the illustrative example to the “how it works” slide ELIGIBILITY Identify which offers that segment is eligible for SCORING Score each offer for that person (mix of manual and algorithmic scoring) OFFER DELIVERY Selects & passes the required number of offers from scored list SEGMENTATION Put the customer into a segment SUPPRESSION & INCLUSION Consults “black lists” & “white lists” to remove or add candidate offers OUTPUT: Segment Assigned OUTPUT: Preliminary list of candidate offers Welcome sample content for this goes here sample cont ? OUTPUT: Final list of candidate offers Records offer presentation and disposition Can be factored in as self- learning for future scoring OUTPUT: Offers with scores OUTPUT: Winning offer(s) INPUT: Customer profile INPUT: Session data

© 2011 IBM Corporation ? OFFER(S) 16 ELIGIBILITY Identify which offers that segment is eligible for OFFER DELIVER Select & pass the required number of offers from scored list SEGMENTATION Put the customer into a segment SUPPRESSION & INCLUSION Consults “black lists” & “white lists” to remove or add candidate offers SCORING Score each offer for that person (mix of manual and algorithmic scoring) OUTPUT: Winning offer(s) OUTPUT: Offers with scores OUTPUT: Segment Assigned INPUT: Customer profile INPUT: Session data OUTPUT: Preliminary list of candidate offers OUTPUT: Final list of candidate offers Relating the illustrative example to the “how it works” slide