Fully Automated Real-Time Analytics to Action
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1 Real-Time Contextual Marketing Fully Automated Real-Time Analytics to Action Verita Poerbasari Nathan Rae 2017 TM Forum 1
2 Customer Perspective Why did I receive this offer which is not relevant? Why did I receive this offer if I m not able to purchase it? I just recharged more than I normally do so why is my service provider pushing me to recharge even more again? Do you have any special offers for me today? CVM Team Perspective How do I match the thousands of BTL offers I have to each possible customer situation? How should I balance incremental revenue demands with customer experience? How do I ensure there is no revenue cannibalisation? How do I successfully retain a risky customer at the lowest possible cost? How do I ensure the customer experience is consistent across all inbound and outbound channels? 2017 TM Forum 2
3 TIMING TARGET SEGMENT OFFERING CUSTOMER PROFILE REACHABILITY FULFILMENT CONTACT CHANNEL REPORTING Still suitable for campaigns where timeliness doesn t have much impact. Manual batch (campaigns only run when scheduled) Customers addressed in segments Defined offers per campaign and segment Uses historical profile insights/attributes e.g. balance can t really be used Lower reachability rates Issues around fulfilment failures e.g. package class conflicts, insufficient balance Treats each channel independently A Lot of Manual Intervention What We Had (Batch Campaigns) Best for campaigns where you want to respond immediately to target subs behaviour Fully automated 24x7 (responds to customer behavioural activities or channel interactions) Customers addressed one-by-one Only limited by the size of the offer catalogue being made available Decisions based on a combination of both historical and real time attributes Higher reachability rates What We Wanted (Real-Time Contextual NBO Campaigns) Improved fulfilment success and customer experience by making accurate decisions by having the current customer state Real Time Omni-Channel Policy Management across inbound and outbound channels Automated: Live & Historical Performance Reporting Intelligent the machine automatically decide the best offer from the catalogue. Increase incremental revenue more personalised and relevant offers 2017 TM Forum 3
4 Our Approach To Contextual Real Time Next Best Offers Ensure No Revenue Cannibalisation Ensure Offer(s) Presented Are The Most Relevant Omni-Channel Consistency & Personalisation Determine Minimum Offer Pricing Rank Packages By Relevancy Channel Specific Offer Presentment 2017 TM Forum 4
5 Solution Details Outbound Contact Window Speed Layer Offline Layer Campaigns seamlessly executing across outbound and inbound channels Inbound Channel Requests 24x7 (for Inbound Offers) USSD IVR Request Offer/Action Return Offer/Action Initiating Activities / Moments (for Outbound Offers) Real Time NBO Brain (Real Time Decisioning) 07:00 John: Data 0701 Sarah: Sends Check Customer State Richard s Sarah s John s Richard attaches to the network at 18:00 2. Check Offer Catalogue 3. Calculate Next Best Offer 4. Send Offer Pro-Active Upsell: 7 Retention: Day Combo Recharge Offer D+2 $4 Discount Discounted = 30% and get Data with either: Extra higher Offer quotas < $5 bonus 2GB Data than her current pack. $5 bonus 5GB Data 22: Real Time Customer Profiles (Real Time Analytics) 24x7 nonstop Richard s Sarah s Customer Customer State Sarah Richard currently has John s no active has Customer a 7 pack day (PPU/PAYG). combo pack 0701 expiring at midnight ARPU = Medium John primarily today. Value uses data but never buys data extras Weekly Balance John is near ARPU = Low the is $3. package quota and with 2 days until Tenure Service it expires. is Preference 70 days and = Data churn risk is low. Tenure is 47 days and churn risk is high. Data Lake Offline Analytics CUSTOMER BEHAVIOUR & NETWORK DATA FEEDS Usage, Subscription & Rechage CDRs, Probe xdrs, LBS, VLR Attach, etc 2017 TM Forum 5
6 Our Journey Go-Live: End July Month Implementation Nov Jan 2017 Campaign Launch Ramp Up Continual Optimisations Met Incremental Revenue Objective for the year May Present Design and launch of second NBO Campaign Continual campaign optimisation and offer catalogue refinement Aug 2016 Oct 2017: Finding our feet launching real time contextual campaigns and adjusting from the way we used to design batch campaigns. Feb April 2017 Design of first NBO Campaign (Inbound and Outbound) and Launch 2017 TM Forum 6
7 The Benefits from the NBO Approach We Have Adopted Traditional Approach Next Best Offer/Action Approach I have the segments in my campaign identified. Which offer should I apply for each segment? I have a large catalogue of offers. Let the machine automatically determine the best offer for every customer in real time during a channel interaction or upon a key behavioural moment. Limited number of offers from the catalogue used. Trade off between offer personalisation (number of segments) and campaign complexity and testing effort. Personalisation is only limited by the number and granularity of the offers available. Unlimited number of offers can be used. Adding or removing offers requires no changes except for an update to the offer catalogue table. The number of offers doesn t affect the testing and complexity. Easy to see where the product gaps exist in the offer catalogue. Easy to extend and apply self learning. Simulation and Pre-Analysis 2017 TM Forum 7
8 Result Highlights The Impact of 24 x 7 Next Best Offer Decisioning The number of subscribers qualifying for offers increased 89% The number of successful fulfilments increased 299% Incremental revenue increased 421% 2017 TM Forum 8
9 Key Learnings Mindset Shift Analytics Offer Catalogue Omni-Channel Measurement Batch Behavioral Triggered Propensity Models Will Grow in Importance and It will take time to think about Live Performance Monitoring Next Best Offer Decisioning Offline vs. Online Focus and design the policy: Historical Performance Next Best Offer Decisioning The more granular the catalogue Outbound Outbound Customer State Treatment the greater the personalisation Outbound Inbound Simulation Inbound Outbound Inbound Inbound Accuracy requires real time contact and response tracking 2017 TM Forum 9
10 Knowesis Overview What We Provide: Delivering insights to action Analytics applied for meaningful outcomes Campaign Management Customer Experience Management Fraud Management Knowesis Sift Applications / Use Cases Lead Generation (Micro-Loans, Credit Card Acquisition, Mobile App Acquisition and Upsell, etc.) Founded: April 2013 Headquarters: Singapore Regional Presence: Malaysia, Thailand, Indonesia, India & Australia DATA Loyalty Complex Event Processing Real Time Big Data Analysis Ingest Normalise Analyse Predict Profile For an individual customer, network element or entity Airtime Advance, Prepaid to Postpaid, Handset Financing Real Time Decisioning Decide what to do Take action Outbound Channels SMS, , Mobile App, Telesales, etc. Inbound Channels USSD, Web, CRM, IVR, Mobile App, etc. Action End Points Charging, Loyalty Mgmt., Coupon Mgmt., Fault Mgmt., etc. Clients: 2017 TM Forum 10
11 Verita Poerbasari Nathan Rae 2017 TM Forum 11
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