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Enabling Telco Transformation

WHITEPAPER


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Telco Digital Transformation through the Power of Real-time Decisioning

The Telco Industry: In the Midst of a Transformation The telecommunications industry is continuing to change at breakneck speed. In the past five years, the Telco business has entered a period of slow decline, with revenue growth down 4%, EBITDA margins down from 25% to 17%, and cash-flow margins halved to 8%1. Competitive boundaries are shifting as core voice and messaging businesses continue to shrink, partly under regulatory pressures, but also because digital transformation and social media are opening up new communications channels. Among US service providers (SPs) for instance, landline and mobile voice now account for less than a third of total access, down from 55% in 2010, while data revenue has risen from 25% of total revenues in 2010 to 65% today.

Figure 1: The Telco Industry in 5 years

But digital transformation is not just a threat; it also offers SPs an opportunity to rebuild their market positions, reimagine their business systems, and create innovative offerings for customers. Faced with ongoing disruption from every side, SPs have recognized—and are taking advantage of—their pivotal role in a digital society. Mounting pressure on the bottom line means the emphasis is still on driving efficiencies into the business, yet many SPs are also exploring new opportunities in a rapidly widening digital ecosystem as they look to meet a new wave of customer demands.

1 Source:McKinsey&Co.

7XMobile Traffic Growth 2016-21Source: Cisco VNI

20%of all IP data traffic is Mobile by 2021Source: MGI

30GbAverage 5G data consumption Per User per monthSource: Cisco VNI

5.5BMobile Users by 2021Source: Cisco VNI

63%increase in time spent in communicating over next 10 yrSource: McKinsey

75%of Mobile traffic will be Video by 2020Source: Deloitte

400%YoY increase of M-payment Source: Euro IT

26BIoT devices by 2021Source: US Census Bureau


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SP are well placed to act as catalysts of transformation in a fast-changing digital society. But before they can take advantage of changing customer and industry stakeholder expectations, they must decide where they want to play-and then focus rigorously on developing the strategies, technologies and talent that will be prerequisites for success in that area. The telecommunications industry has changed radically in the past 10 years as data-hungry customers with smart devices consume ever more bandwidth. Over the next 5 years the mobile traffic is expected to grow 7x2 with almost 20 (or more)% of IP traffic being carried over mobile networks. Meanwhile, the SPs have expanded their service portfolios and overhauled their price plans to meet explosive demand driven by almost 5.5B consumers, while rising capital expenditures underline the ongoing imperatives to upgrade network capabilities. While many players are diversifying their revenue streams, ensuring that new services deliver healthy margins remains challenging. At the same time, over-the-top (OTT) players are also expanding the scope of their offerings, disrupting different industry verticals in the process. All entities in the digital ecosystem are now seeking new points of differentiation in order to maximize their share of customer spend. As a result, issues of competition and collaboration have never been more pronounced. While service providers still enjoy a majority of ecosystem revenues, OTTs have grown their share to 10% in the space of a few years, fueled by IoT (forecast to be 26B devices by 20213) and Internet wherein the bulk of mobile content is Video. Moreover, the competition in retail and distribution is becoming more intense.

What is Driving Usage, Perception and Value Creation? For years, SPs—both fixed and mobile—have embraced a technology-driven, one-size-fits-all approach. The fundamental strategy has been to: increase speed, and bandwidth, and increase it everywhere. It’s an approach that has worked well—until now. Consumer demands for data is soaring. Data is expected to grow 7x by 2021, with an average user consuming 30gb/month. Streaming video, popularized by the likes of services like Netflix and YouTube, is driving more and more traffic into mobile networks. It is also expected that a full 3/4th of overall mobile traffic will be video by 2020. Moreover, consumers are also expecting to seamlessly transform from one channel to another and maintain their service experience. Most are access network agnostic and want things to just work. Businesses on the other hand are also driving demands in Telco networks. Enterprises want hence, to work with SPs to zero-rate (i.e. no data caps apply) their specific content so as to drive consumption. The SPs are driving strategic partnerships as content consumption shifts. This has the added burden of more traffic on their network that they are not monetizing directly. SPs are also looking less at how they can boost pure speed and more on how they can handle many connections within the same cell while keeping latency low. Hence the ties to content delivery networks (CDNs)—distributed arrays of servers run by companies like Akamai Technologies, Amazon, and Netflix.

2 Source:CiscoVNI2016 3 Source:USCensusBureau


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CDNs bring content physically closer to users, so prioritizing their effectiveness can be a better investment than adding capacity in the access network. Another key driver is mobile location and advertisements. Mobile devices are inherently bad for display and banner ads. But SPs are beginning to monetize location by aggregating, anonymizing and participating in data curation networks. Their insight into consumer location and preferences gives them great advantages on predicting behaviors.

Figure 2: Confluence of Forces

Standards & Regulations are at the forefront of Telco disruption. The data revolution started with IMS (IP Multimedia subsystem), an architectural framework to deliver multi-media services over IP networks. Further advancements in smart phones drove adoption of LTE (Long Term Evolution) that blends voice and data traffic into IP. The latest torchbearer is 5G, or the 5th generation of standards, which allows for higher bandwidth and lower latency, is ultrareliable, and support machine to machine connectivity. Though standards like 5G are few years away from mass deployments, various spectrum frameworks, roaming and termination rules are already being adopted. Video services are also evolving (UHD 4K-8K video, Virtual/Augmented Reality) and drive future 5G networks design to match mobile usages, very-high bandwidth requirements and ultra-low latency constraints. In parallel, video consumption is changing as follows: less TV and more connected devices

More Smart devicesMore VideoService ExperienceIoTMulti-ChannelAutonomous

Telecom Industry

5GSDN/NFV

Sensors & DevicesAdaptive Streaming

AR/VRAutonomous Vehicles

Advanced Devices

Zero RatingMulti-PlayMobile AdsLocation-Enabled FraudConsumer InsightsSmart City & Public Infrastructure

5GLTE-A, LTE-M, LTE-UGDPRNet NeutralitySpectrum FrameworksRoaming & Termination Rules


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usage, less broadcasting, more streaming, all with a larger impact on the network end-to-end, from the access to the core. As SPs support customers’ day to day digital footprint, they are also bound by laws on data privacy and protection including the GDPR (General Data Protection Regulation) from EU and PII rules. Technology, has been the lynchpin of innovation in Telco. One area technology focusses on improving is the transmission and access. Docsis 3.1, XG-FAST in fixed networks and 5G, LTE in unlicensed spectrum, License Assisted Access, and next-generation Wi-Fi are all becoming part of mobile communications. And in the transit area, advances in microwave and multiplexing technologies are occurring rapidly. The other area of technological focus is the way networks operate. Together, software-defined networks (SDN) and Network Function Virtualization (NFV) are fundamentally changing network operations. They are leveraging advanced software technique (including virtualization) and applying it to Telco networks. SDN and NFV can not only reduce networks’ capital and operating costs but also improve their flexibility, scalability and allows SPs to operate with greater agility. Given these advancements, speed and capacity alone will not aid in the SPs transformation. Data and the ability to harness it is a key step to digitize the value chain.

Telco data at the core of transformation Telco customers’ aka subscribers constantly connect to their networks through voice, text, and other smartphone interactions, therefore Telco companies have access to huge quantities of data. Yet relatively few have adopted big data architectures and analytics technologies to profit from them significantly.

Figure 3: Data Galore SPs have traditionally used network data to drive operational decisions. Telco networks by definition are real-time with a high availability rate and hence most standardizations put a lot of emphasis on availability, redundancy, and scale. Telco networks data can be broadly classified into 5 categories:

1. Network Infrastructure. This includes network speed, latency type, signaling information, location information, faults and outages, traffic and congestion, and cell tower information etc. Most of the

Analytics to link and

transform all

Speed, Latency, Type, Signalling, faults, GPS location, wifi, traffic

Network Infra

CDR, EDR, tariff, usage, discounts, promotions

Billing

Network, Voice, Support,Orders, Contracts,fault

Support

Data, Call, SMS, Apps, Web,Phone, Screen, OS, Chipset,

Usage

Demographic, Age, Income, Brand, Preferences, Profile

Customer


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data here is currently used by SPs to make day-to-day operational decisions on routing, traffic management and access.

2. Usage. This includes voice call information, data and SMS usage, application behavior and use, types of devices in the network, type of operating systems, congestion information, and bandwidth. Though usage data has been ever expanding, there is little evidence that SPs leverage the full power of the information contained to predict and act on usage.

3. Customer. This includes demographics information including age, income, preferences, broader subscriber profile, location, billing and payment information, and renewals. SPs were the traditional storehouses of subscriber “golden” data. Today they have been surpassed by the variety of social networks and Internet firms in their ability to reach the end customer. SPs still can drive more adoption and experience if they leverage their data set.

4. Billing. This includes call detail records (CDR), event data records (EDR), tariff plans, usage, adoption, pricing buckets, discounts, promotions and adoptions, and marketing data. SPs have leveraged data from their billing systems to further proactive marketing and targeting of customers but most are primarily log scraps and offline and less dynamic. They are also mostly siloed in their decision analysis.

5. Support. This includes network issues, support calls, orders, contracts, fault information, fraud and anomalies and so on. One of the fundamental issues with SPs is the lack of understanding and analysis on customer loss and targeted strategies to mitigate them. Analysis of support data in real-time could lead to greater understanding of problems and drive decisive actions in real-time.

SPs have tremendous breadth and depth of data across various facets of their business. The potential for SPs to apply data science effectively is substantial. SPs should start by mapping out the wealth of data at their disposal and their opportunities to exploit it. Impact of Real-time Decision Analysis

Figure 4: Telco SP transformation axes

Transform Network Embrace Customer Centricity

Dig

ita

l In

no

va

tio

n

Dynamically Manage and Optimize Network usage and control to

drive best Experience at Lowest Cost

Keep Customer at the Core of the best Digital

Experience (Churn, Rev Assurance, Support,

Marketing)

Innovate across Connected Ecosystems & drive Hyper-

Personalization for new revenue streams

R-TDATA


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SPs today are focused on alleviating the squeeze on margins and creating more value. Major advances in data analytics, artificial intelligence, network equipment, and other technologies have rewritten the winning formula. One such aspect is their ability to leverage the data for dynamic and real-time decision making. If SPs can allow their transactions to drive decisions they can achieve breakthrough cost savings and capital intensity while maintaining or even increasing their scale. The transformation of SPs through real-time analytics cuts across three core areas:

1. Transforming their network 2. Embracing customer centricity 3. Digital innovation

Network transformation involves dynamically managing and optimizing network usage and control to improve transmission, flexibility, and capacity planning and scalability. As a function that allocates resources, builds and maintains networks, works with unstructured data sets such as voice and text - all the while anticipating behavior it seems a natural beneficiary. One example of network transformation is mobile self-optimizing-networks (SON) - a dynamic automation technology that is designed to make the planning, configuration, management, optimization and healing of mobile radio access networks simpler and faster. Another example is analytics models that predict the periods of heaviest network usage arising from video streaming. This allows SPs to take targeted steps in real-time to relieve congestion during those times, reducing their CAPEX (capital expenditures). Customer Centricity embraces the notion that customers are at the core of the transformative experience. SPs will drive real-time decisions and analysis to reduce churn, provide the best support team, marketing the right products and services and foster revenue assurance. As user acquisition costs continue to grow, SPs are managing their cost to serve to improve customer satisfaction (CSAT). One example of customer centricity is using machine-learning models that combine sociodemographic data, information from customer touchpoints (such as call centers and social media), and data on network usage to identify, in real time, the customers most likely to defect or have trouble paying their bills, as well as to cut churn and improve the recovery of payments. Digital Innovation allows for SPs to look for new revenue models. Whether it is driving connected automotive or building new sensor/IoT networks or allowing urban center to become smarter, it involves managing data in real time and driving connected decisions across a multitude of channels. Hyper-personalization based on real customer insights and usage will help drive new revenue models. IoT network sensors need faster processing of information as decisions need to be made before the data becomes stale. Advanced real-time decisions thus offer SPs a fundamental way to reshape their business.


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Data - and the History of Analysis, Decision making and Action In order to strengthen their existing businesses, SPs should digitize their value chains end to end. In the past, there was a strong need for operational visibility and businesses required strong reporting tools for feedback on their operational decisions. They traditionally employed batch analysis on static big data which was mostly historical. The outcome was primarily an indicator of “what is happening.” Businesses then had to drive the decision-making process manually.

The next phase leveraged Business Insights systems that employed a combination of online transaction processing (OLTP) to facilitate and manage transaction-oriented applications, and online analytical processing (OLAP) to perform analysis on the data and provide means for trend analysis, data modeling and interactive dashboards. Naturally, both the transaction and analytics functions were separate and operated in silos.

Figure 5: Different levels of analytics The notion of Intelligent Business evolved with advancements in stream processing and the ability to deal with near real-time data. Enterprises used massive data lakes to store, massage, and dynamically predict outcomes. Even though advancements in big data technology allowed for faster processing and for storing more structured and unstructured data sets, most of these systems still remained as decision support systems. Also, decisions only took enterprises part of the way. Most still needed to take prescriptive actions separate from the decision process. The simple fact is that the modus operandus of most enterprise, even for the diagnostics and predictive analytics, has been the traditional data warehouse method in which all the data is stored, and

INSIGHT

DECISION

VALUE

ACTION

Reporting- Staticreports- Batchoriented- Historical

BusinessInsights

- Decision Support- Consolidated Reports- Static,Batch oriented- OLAP,OLTP- Transaction &Analyticssilos- Interactivedashboards

BusinessIntelligence- Rulebased- Micro-batch- Staticandnear-RT- Datalake- Moresilos- Hadoop/Spark- Reactive

BusinessMoments

- Transactional Systems- Intelligent Decisioning

Algorithms- AnalysisofTransactional Data

inReal-time- AI/ML- Proactive&Predictive- Automated

TRANSACTIONALPREDICTIVEDIAGNOSTICDESCRIPTIVE


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manipulated and patterns learned. It is possible to store all this data in a data lake and analyze it later. Combined with some near real-time and diagnostic analysis, they could drive some level of predictive outcomes. The obsession with the volume of data and with mining large databases of data searching for the proverbial needle in the haystack are anchored in a world where data has enough longevity to make historical analysis relevant. When the richness is defined by instantaneous data, this approach fails.

Transactional Analytics Creates New Value Transactional Analytics fills the rest of the journey. It consists in analyzing real-time data, decisioning and actioning at the same instance. Advancements in artificial intelligence (AI) and machine learning (ML) technology drives the convergence of transactions and analytics. The algorithms are also codified to dynamically adapt to the data. This proactive approach drives more autonomous decision making and allows enterprises to act on their decisions and drive towards “Business Moments” – the ability to drive business decisions at the same moment while the transactions and data are still being processed. Recent advancements in analytics, AI and ML represent a generational opportunity to move Telco beyond being simply convenient and cost-effective. By integrating analytics in real time, each transaction has the potential to produce better business outcomes. Personalized products and services, more engaging customer experiences, threat detection, and fraud prevention are among the transactional outcomes that can ultimately grow revenues, increase customer loyalty, and reduce risk.

Figure 6: Transactional Analytics


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Systems of Engagement and Systems of Record To fulfill the promise of leveraging transactional analytics to achieve more profitable and rewarding business outcomes, an entirely new architecture is required—one that combines the virtues of transactional systems of record and analytics systems. The term "system of engagement" (SoE) describes a new generation of dynamic, personalized and interactive applications. In contrast to traditional systems of record, SoEs are ubiquitous, contextual, mobile, location-aware, and embedded in the way consumers and businesses interact.

Figure 7: A Telco SoE These new applications focus on subscribers and devices, not processes, and harness a perfect storm of mobile, social, cloud, and big data innovation. The user experience is delivered in the context of the daily lives and real-time workflows of customers, partners, and employees Modern Telco SoEs are incorporating a new generation application architecture that eliminates the wall between transaction processing and analytics. For Telco digital transformation strategies to be successful, analytics processing must be tightly integrated with transaction processing systems. This predetermined integration enables real-time interdiction, and drives actions that are called automatically, based on policy. Automated systems can provide a more comprehensive view of customer behavior by leveraging analytics calculations and algorithms to detect and manage network actions and provide a cluster of decision points.

Figure 8: Transaction and Analytics


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Gartner refers to this as "Hybrid Transaction/Analytical Processing" (HTAP). An HTAP architecture is best enabled by in-memory computing technology for analytical processing on the same (in memory) data store that is used to perform transaction processing. By removing the latency associated with moving data from operational databases to data warehouses and data marts for analytical processing, this architecture enables real-time analytics and situation awareness on live transaction data. Aerospike: Sustained Reliability at Scale Not all hybrid architectures are the same in terms of reliability, availability, speed, and data integrity. As workloads increase, the ability to predictably process high-volume, concurrent transactions while performing complex analytics against massive data sets in milliseconds becomes increasingly difficult. Prevailing relational and NoSQL databases with caching as well as RAM-based in-memory databases often fail, particularly when unanticipated peak loads occur. Without warning, response times can become unacceptable, data can get lost, errors can occur, and systems can become unavailable. Aerospike is focused on a singular purpose. It is the industry’s first hybrid memory database, incorporating revolutionary advances in database design. Its mission is to deliver a database for transactional analytics that powers SoEs, with predictable performance, at scale, and with the lowest total cost of ownership (TCO). Aerospike is the only database that can reliably handle the demands of transactional analytics processing: Internet-scale data volumes, decisions at millisecond speeds, and operational efficiency. Aerospike’s Hybrid Memory Architecture combines solid-state drives (SSD) and DRAM to achieve the sustained performance that SoEs require–with a significantly smaller footprint than that of other NoSQL databases. Aerospike’s Smart Client™ technology handles complex database management processes automatically so developers and operations staff can focus on the business, not administration.

Figure 9: Aerospike’s predictable performance at the lowest TCO

HYBRID MEMORY ARCHITECTURE• No cache required – simpler architecture! Smaller Server Footprint• Patented Flash Optimization – Log structured File System• Record Oriented, Schema Free NoSQL KV Store

DYNAMIC CLUSTER MANAGEMENT• Highest Uptime & Availability (5 nines plus), Scalable• Automatic DB Cluster formation, self healing and dynamic sharding• Cross Data Center Replication (XDR)

INTELLIGENT CLIENTS – SMART CLIENTTM

• DB aware Clients, No load balancers required• Supports One-hop application to data • Broad language support (C/C++, Java,C#, Python, Go, Node.js,

PHP)• Rich API’s - Accelerated development

LOWER TCO • Up to 10x reduction in servers deployed• Demonstrated 10:1 price performance savings• Huge operational efficiency – “Set it and Forget it”$

REAL-TIME ENGINE• Multi threaded, massively parallel• DRAM or Hybrid DRAM/Flash for Persistence• Stable, Low Latency and high throughput under any condition• Deployable on Bare Metal, virtualized, containerized, or Cloud

RELIABILITY• Predictable performance Stable, Low Latency

and high throughput under any condition• Highest Uptime & Availability (5 nines plus),

Scalable

FASTER TTM • SmartClientTM provide DBA awareness, reduces

development time• Reduce your front-end on-boarding from months

to days


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In summary, Aerospike provides:

● High-volume automated processing of requests leveraging intelligent data assessment across multiple systems.

● Superior Scalability to process high volumes of requests simultaneously, performing

investigations, shaping customer action and contact strategies, and reaching resolution in milliseconds.

● Seamless connection between network analysis and customer behavior. This helps decide

the necessity, timing, channel, and nature of actions required to manage networks, shape traffic, and foster customer loyalty.

How Telco SPs are Applying Transactional Analytics The Telco industry is in the very early stages of the advanced analytics revolution. Today Telco SPs recognize that transactional analytics is no longer optional. They need to drive more efficiency in their operations and maintain the highest level of convenience and ease. SPs that are able to make the leap to a more digital, analytics-driven business model will be in a better position to retain their customer relationships and their current stature.

Figure 10: Aerospike Hybrid Memory Database

High velocity of transactionsTargeted engagement requires large numbers of transactions per second

Handle huge volumes of dataMust hold 100s of TBs per use case.

Non Volatile Memory (NVM)Allows you to store large volumes of data

with extremely high performance & consistency. Caching strategies routinely

fail with heavy write use cases.

Lower latencyResponses to queries must consistently

be less than 5 milliseconds.

ReliabilityHigh availability – must be up 24 x 7. No single point of failure.

Easy to scale & manageDatabase must not require high amounts of manual intervention.

Low cost of ownershipNeed to reduce server “footprint” while supporting many new use cases.

Open & interoperableNeed to leverage existing infrastructure & solutions.


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Figure 11: Transaction Analytics in Telco To drive real-time decisions and leverage the power of data, Telcos need the ability to:

• continuously ingest, correlate and analyze data (both structured and unstructured) in real-time; • analyze the data in real time and act on it instantaneously during the course of the transaction; • move the data and decision points to the edge of the network for faster, better, and cheaper

execution; and • scale to millions of transactions across millions of subscribers.

Customer Sales Marketing Network & IT Finance Control & Planning HR

Customer Base Traffic & Roaming Customer Profiling Network Elements Billing &

Accounts Budgeting Human Resources

Products & Services

Customer Care

Campaigns on care

R-T Sales performance

Big data & Stream processing

E-E Exec visibility

E-E R-T decisioning

Active User

Electronic recharge

Customer Segmentation

Predict ChurnRecharges & Consumption

Campaign Management

Active User Cost & Revenue

AMPU (profitability)

Campaign impact on Network

Campaign impact on Revenue

R-T Fraud Monitoring & Revenue Assurance

R-T decisions


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Figure 12: Telco Areas of Impact for real-time Transactional Analytics and Decisions While the biggest opportunities for technology with strong predictive methods will be found in the marketing organization, in fact is that advanced analytics can be used to address challenges across the entire Telco value chain. The biggest opportunities for real-time decisions are in operations and strategy. Examples include:

• Optimizing routing and quality of service immediately by analyzing network traffic in real time; • Analyzing call data records in real time to identify fraudulent behavior; • Allowing call center reps to flexibly and profitably modify subscriber calling plans immediately; • Tailoring marketing campaigns to individual customers using location-based and social

networking technologies; and • Using insights into customer behavior and usage to develop new products and services.

Dynamic Network control allows for automagically adjusting the network capacity based on demand, usage, traffic, weather and other event-driven patterns. This drives better quality of service (QoS) and allows for optimal use of resources. One such use is called SON, an automation technology that is designed to make the planning, configuration, management, optimization and healing of mobile radio access networks during congestion. Adaptive Steering & Caching becomes key as video services are also evolving (e.g.: UHD 4K-8K video, Virtual/Augmented Reality) and drive future 5G networks design to match mobile usages, very-high bandwidth requirements and ultra-low latency constraints. In parallel, video consumption is changing to: less TV and more connected devices usage, less broadcast and more streaming, with a larger impact on network end-to-end from the access to the core. Dynamic adaptive steering interacts with the network and to move caching and computing capabilities to the network edge in order to enable efficient mobile video delivery. These decisions have to be made during the content being delivered and usually calls for very low latency and high performance analysis.

Product Personalization

Churn Analysis & Prediction

Proactive Customer Care

New Biz Models & Monetization

Predict Fraud & Manage

Optimize Provisioning

Predict and Manage Faults

Dynamic Capacity Management

Network Planning Resource

Service

Customer

Infrastructure & Products Telecom Operations


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Preemptive customer care is used primarily to reduce churn. Current strategies, driven by historical analysis, are inefficient and expensive. Analysis and decisions that target interventions more precisely to the right customer at the right time is seen as the next big step. SPs can find out a priori about the customers at the greatest risk of dropping the service or having a bad experience (too many dropped calls, low data bandwidth etc.) and take appropriate action ahead of time. Targeting and Tailoring allows a SP to run a more effective customer segmentation combining multiple mass market information, usage data, social and demographics profiles, activity logs, etc. They can identify high-value customers and constantly reorder their predictive models with real-time information. Network Planning & Design calls for SPs to maintain best-in-class service for their most valued customers. SPs can use data to discern not just where their customers dwell but use predictive models to drill down and uncover micro clusters based on travel patterns, peak usage, network demand etc. These are used to optimize investment decisions and expand

Figure 13: Opportunities for real-time Decisions and Impact

A Future-proof database for SoE at 1/5th of the Cost Compared to First Generation NoSQL Real-time decisioning and analytics offers Telco SPs the promise of a deep new understanding of customer behavior, as well as rapid insights on networks and procedures of all sorts that once would have required prohibitive levels of effort to achieve. The challenge in fully realizing that promise is no longer primarily technological; it is cultural and behavioral. The SPs that succeed in the near future will be those that can adapt their organizations in order to generate and act on these newly-attainable insights.

Focus Area Use case Details Outcome

Efficiency Dynamic Network Control Resource Allocation

Auto adjust based on network capacity. Use ML algorithms to adjust as demand changes or even to base adjustments based on predictions =

Network usage + traffic + weather + events should drive& predict patterns. QoS

Adaptive Steering and

Caching at edgeResource Allocation Dynamic Adaptive Streaming - Caching/compute @edge. Network that is

context aware and info centric. Better UX and lower traffic cost. UX, Traffic Cost

Experience Increase CLV Price & Product Optimizations

Predictive models for cross-selling and retention. Dynamic promotion and active engagement. Proper customer segmentations. Event based Marketing

campaignsChurn, Upsell

Preemptive Customer Support

Discover new trends/Anomalies

Digitize customer support and care. “preemptive” customer care. click-stream analytics Support Cost

Churn Management Predictive Analytics

With help of predictive models and machine learning algorithms, it is possible to accurately identify customers who are likely to lapse. Bringing together data collected on customer usage, complaints, transactions, social media, they can create factors which can identify customers at risk of moving out.

Churn

Transform New Revenue models for IOT Predictive Analytics

GPS/Sensor data consists incremental data loads which needs faster processing and involves extensive data aggregations and mining in real time processing. Revenue

Location & Insights Hyper-Personalization Product and service innovations based on real-time customer insights,

location and usage Revenue


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Aerospike provides Telco teams with a database that is capable of high volume automated processing of requests that leverage intelligent data assessment, across multiple systems to enhance decision and action. It gives Telco organizations the scalability to process high volumes of requests simultaneously, performing investigations, shaping customer action and contact strategies, and helping to reach resolution in milliseconds-all this without breaking the bank.

References 1. Whitepaper: When to use Aerospike vs. Cassandra: http://www.aerospike.com/when-to-use-

aerospike-vs-cassandra/

2. Gartner Market Guide on HTAP enabling in-memory computing technologies: http://www.aerospike.com/lp/gartner-market-guide-htap-enabling-memory-computing-technologies/?utm_ls=Website&utm_lsd=AR_GartnerMarketGuideHTAP_DigitalPayments

3. Telco Transformation eBook: http://www.aerospike.com/lp/telco-ebook

4. Forrester Study on Hybrid Memory NoSQL Architecture for Mission-Critical, Real-Time Systems of Engagement: http://www.aerospike.com/lp/forrester-study-hybrid-memory-nosql-architecture-mission-critical-real-time-systems-engagement/

5. How one of world’s largest Telco tech provider drives customer experience: https://s3-us-west-

1.amazonaws.com/aerospike-fd/wp-content/uploads/2016/09/CS04-Telco.pdf

enabling telco transformation - aerospikebecause digital transformation and social media are opening...

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