Telecom marketing, as covered in one of our old blogs, requires in-depth insights into customer behaviour. The AI-ML capability helps campaign managers design and execute highly relevant and contextual campaigns and improves the campaign uptake rate, ARPU, retention and CLV.
How AI/ML is making Contextual Marketing in Telecom more dynamic, agile and customer-centric.
mViva- the AI/ML driven Customer Engagement Hub
Pelatro tries to follow a guideline that Analytics’ integration in mViva mesh well and unobtrusively with the client’s business-oriented perspective – these include the ability to design campaign experiments by creating different configurations of target groups, methods to sample out control groups, various statistical KPIs, preferred channel identification etc.
In addition to the above, the mViva platform can also ingest certain types of models that can then be used to make predictions. The model output can then be used in campaigning like any other KPI.
mViva also includes other modules that are more obviously AI/ML. These include a Next Best Offer module that can choose the offer that will best align to a stated and configurable business strategy; a Customer Lifetime Value model that can be used for targeting subscribers appropriately; an Analytics Workbench that has been designed to help citizen data scientists build and deploy their own models; additional segmentation methods such as RFM analysis and Segment Shattering which permit users to automatically break down and describe larger segments so that they can be targeted differently.
Want to know more about the AI/ML capabilities of mViva Customer Engagement Hub?
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As the pandemic has shifted gears on customer behaviour, people are becoming more accustomed to a new way of customer experience. Labelled as an essential service, telecom operators though already aware, realised the heat of evolving mindset during the pandemic. Today’s consumer expects and entertains only value-driven interactions from their service providers. To give you a view of the situation, the customer churn doubled for telcos in India in Feb’21. Every communication must be personalised, customised to their need. Not only this, customers expect businesses to anticipate their future needs and offer contextual, relevant and timely solutions.
This blog highlights some intriguing learning shared by the speakers.
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Scale is significant for telecom operators as it adds another dimension to the problem of hyper-personalisation in the telecom industry. Personalisation is the default standard for engagement for web, mobile and in-person interactions and telcos can’t rest on past laurels.
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Research shows that 1/3rd of consumers expect brands to deliver personalisation, and if failed, it’s easier for them to switch brands. Three-quarters of customers switched to a new store, product or buying method during the pandemic. To counter this, telcos can design multiple micro yet thoughtful customer touch points. Something as simple as the relevance of offers, post-purchase checking, and sharing ‘how-to videos to show that the brand care for its customers.
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There is already a felt need for personalisation in the telecom industry. A normal customer who is exposed to the personalised experiences delivered by the likes of Google or Amazon wonders why their telco, despite having access to all the relevant data about them, is not able to provide a hyper-personalised experience.
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Companies who opt and invest in personalisation solutions can generate 40% more revenue than others and increase the CLV by 35%. McKinsey research shows that telcos have the potential to generate around $200 billion in value from personalisation alone in the next few years. Telcos must be invested in hyper-personalisation to make the most out of this opportunity and ensure they keep benefiting from future technological innovations.
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Telcos today must live up to the expectations of dynamic and evolved individuals and plan every single interaction based on subscriber requirements and preferences. Telcos need to consider the behavioural aspect and the context at an individual level. That’s where hyper-personalisation empowers telcos to connect with subscribers in the most authentic way via a value-driven interaction.
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Things previously rendered impossible became possible during the big data era when telcos started investing in data marts and building aggregated personal customer profiles. Telcos created 100% telcos-centric customer profiles but were completely blindfolded by customer engagement with other dimensions of their life. Then followed the OTT or digital era when the likes of Netflix and YouTube started gaining a mind share. New channels like WhatsApp and Facebook began to replace long-proven and legacy channels like SMS. This was the time telcos started focusing more on AI/ML.
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Location intelligence was the buzz word during the personalisation era, and almost every telco attempted and achieved different levels of success. The focus was to reach out to the customer in the most relevant manner, but for the offer that the telco wanted to sell. Next is the hyper-personalisation or individualisation era, where telcos are reaching out for customers’ felt needs and not necessarily for what the telcos have to sell at that particular time.
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Personalisation was essentially a way to be more relevant in customers’ minds. But telcos have their own challenges, starting points, contexts and possibly different literacy levels on their customers. So, if telcos have to take on a sustainable approach to hyper-personalisation, they need to stay invested in it, take the best practices and mould them as per their needs. Success calls for a personalised approach to personalisation, and the prescription varies from one telco to another.
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Almost 25% of telcos globally are just starting their journey of personalisation. Even within the operators who have initiated pilot projects, many are yet to scale their projects to realise the full potential of hyper-personalisation. In fact, very few telcos are using data analytics and AI/ML to its full potential.
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Telcos are still experimenting with the idea of hyper-personalisation and are inspired by the success it has delivered for other industry verticals. It has increased the experiment appetite for telco marketers, but to achieve the full potential of hyper-personalisation, telcos need to do the following with agility and speed-
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]]>The blog also highlights how Pelatro uses data and analytics to create more value for its customers. Data-driven decisioning is at the core of Pelatro’s mViva customer engagement hub and enables telcos to explore new revenue streams successfully. Check out the infographic for more details.
Data-and-Analytics-The-silver-bullet-for-telcos-1
With the enormous growth in internet data usage over the past few years, modern telecom marketing analytics systems need to munch real-time datasets such as deep packet inspection for browsing alongside ever-changing locations and other streaming information at rates northwards of 1 million events per second. Telcos need to reach out to customers on the right channel at the right time using the right offers before the context is lost, as relevance holds the key to achieving the desired outcomes. Typically, promotional messages are piggybacked to events like post-call notifications, balance checks and other informational API accesses initiated by the end customer, and in large telcos, this could easily translate to an ask for over 30,000 to 40,000 decisions per second.
Business intelligence systems that operate at such scale are distributed and replicated by design with decisioning logic evaluated at one of the many peers based on load balancing algorithms with continuous state synchronization between the peers. However, given practical considerations around network latencies, system faults, inherent concurrency among different events, data streaming challenges and asynchronous transfers, decisions taken for a subscriber at two different peers can, at times, end up being different owing to different states, and there may not be a canonically correct way for the conflicting peers to arrive at a common plane using well-understood CRDT (conflict-free/convergent/commutative replicated data types) techniques.
Consider that Bob, who has a $2 balance on his sim card while surfing Facebook in the background, drops a message to Dick to recharge for $25 and also makes a call to Alice requesting her to top up $15, and they both oblige nearly instantly. Let’s assume there are two event interceptors, call termination and Facebook browsing at low balance, and the former sees $15 recharge first while the latter sees $25, and they both send two different offers to Bob reflecting the best recommendation from their standpoints — only to realize in a while on receiving the next recharge that they both are wrong and when they try to sync up with each other, they can neither defend their recommendation nor accept the one received from the peer. An engineering solution can help them reconcile on the balance, and a technique like “last writer wins” may crudely choose one of the two offers, but a business solution needs more than that as it puts the end customer at the center of focus.
A business-centric system needs to work a way out in the interest of the end customer, and in line with “N=1, R=G,” the calls taken may be different from case to case. It may involve soliciting both offers with no recall so as to not confuse the customer, or it may be to retain better of the two offers than going for an altogether new one, or it may even be to send a polite on-demand explanation revoking both of the offers in lieu of another more personalized offer. This decision should be taken at the level of the individual customer.
The business end of real-time personalization is where context-aware, user-centric resolution takes precedence over preset mechanized norms that may be acceptable in certain areas of data engineering but fall short in realizing the objective of retaining the best interests of every single customer even at scale. Ensuring accuracy and eventual consistency of data by itself is not sufficient in meeting the goal. An enterprise has to go beyond that to stay relevant and offer the highest value to its end customers.
Telcos need to rise above the personalization limits of classical data engineering and work toward sustained customer-centricity even beyond the moments of intervention by staying open to real-time course corrections at scale in order to preserve the best interests of end users.
This article originally appeared here
]]>In our last article, we told you about how the mighty duo is giving a new meaning to Contextual Marketing by empowering Campaign Managers.
Let’s see how the new-age technologies are nurturing CVM by adding a layer of dynamism to Telecom Marketing.
CLM teams drive hundreds and thousands of multi-channel campaigns with specific intent and goals. This calls for a high degree of automation to ensure the entire lifecycle of each campaign passes without any bottlenecks. Furthermore, dispatching offers, provisioning of rewards and fulfilment requires machine guided assistance for seamless execution.
In new-age campaign management solutions, automation is driven by machine learning algorithms which have great ability fulfil automation goals.
Segmentation of customers based on Mass Marketing principles is a thing of past. Today’s performance-oriented CVM teams demand micro-segmentation based on intrinsic parameters – deep insights into customer behaviour including preferences and sentiments. “Clustering” in Machine Learning is driving segmentation at a granular level.
Intelligent segmentation and targeting customer profiles based on similar exhibits have proven results in increased adoption and uptake rates.
Campaign Managers often require access to real-time data to evaluate campaign performance. This is made possible by an intuitive dashboard and reports (slice and dice of data) on trends, impact and other KPIs. Data Insights becomes the basis for managers and machines to tune the campaigns at different junctures to maximise outcomes.
Campaign communication is often relayed across multiple channels – SMS, USSD, Email, Web, App and others. A smart unified communication manager does the job of connecting and disseminating communication to and from multiple systems. With the help of Neural Networks and automation tools, sending out campaign communication with a pre-defined set of rules can be done without intervention.
Ready to transform the Campaign Management practice at your Telco? Using AI-ML techniques, the Multi-Channel Marketing Hub suite of products from Pelatro offers cutting edge solutions for global telecom companies. To know more about Pelatro’s solution can boost campaign management efforts in your organisation, visit pelatro.com/products
To request a demo/discussion, email hello@pelatro.com
]]>The need for speed in resolutions is linked to the need for effectively handling queries and issues. And this is all about supporting the customer service representative with crucial tidbits of information. Getting the larger picture often hinges on identifying the little cues, the finer nuances. And Artificial Intelligence in telecom has been effectively doing it for companies that have identified the need to differentiate or disappear from the preferences of customers.
One of the many aspects of AI-assisted customer service in telecom includes collation of information through linguistic and statistical algorithms. Keyword-based processing of issues has become a reality thanks to AI that offers suggestions and effective responses to the queries of customers.
Customer care functions work best when post-call analytics help teams understand the needs of the customer and the best way to handle dissatisfaction. This is achieved through insights that indicate customer preferences and issues faced in a given scenario. Such insights, available on simple and uncomplicated dashboards, help executives adopt a proactive approach during interactions. The conventional reactive approaches are unlikely to help improve customer satisfaction levels as the need is for swift and effective pre-empted resolutions.
Pelatro’s Next Best Action (NBA) Analytics leverages the power of Machine Learning to offer deep insights that lead to problem resolution at the first touchpoint. Transaction history, interaction records, and customer’s user preferences are just some of the key inputs that go into advanced algorithms and filters to suggest a summary that offers a recommendation which suits the needs of the customer. This helps to reduce the threat of customer churn and also offers the possibility for upsell and cross-sell. The module offers the flexibility of making appropriate changes to the recommendations, based on the latest inputs from the customer while on the call. The GUI-based module, empowers CX in telecom and marketing personnel to roll out the offers directly, cutting down the need for a separate process.
A handful of telecom majors have plunged right into AI. They rely on it to pair inbound calls in a jiffy with customer care executives, best equipped to handle them. For instance, the transaction history combined with the nature of queries makes certain calls unique. This demands the handling of such calls by customer care executives armed with the required information and skills.
Elsewhere, Vodafone has rolled out chatbot services, in addition to offering app-based resolutions to simple queries, thereby delivering a double impact on customer service. On the one hand, the app-based resolution offers an immediate resolution to queries, while on the other, it frees the bandwidth on regular channels to handle queries. Holding various aspects together to provide a better level of interaction and satisfaction, technology has elevated customer experience in telecom to levels unimagined earlier.
Telecom service providers must fully recognize the need for understanding, analyzing and predicting customer demands, Customer Churn, preferences and grievances. Services must be delivered across the board and silo-based relationships with one customer who opts for different products must be done away with. AI in telecom is helping service providers by routing multi-channel information, mining it and offering suggestions based on past experiences.
The standout customer service fact that emerged from the survey by the White House Office of Consumer Affairs is that it took a dozen positive experiences to compensate for one negative and unresolved experience. This makes it all the more important for service providers to offer a rich customer experience in telecom, in the face of competition that gets tougher by the day.
]]>The rise of IoT devices will result in a creation of an entire ecosystem where there will be a continuous exchange of information between devices, sensors, computers and networks. And connecting these ‘things’ with other ‘things’ and to a centralised database are telecom networks—the very same networks on which much of the internet was founded.
The surge in IoT adoption puts new demands on the telecom industry, where it’s just not enough for network providers to offer voice and data plans. Rather, they will have to deal with issues ranging from scalability and security to creating and managing new networks for specific IoT devices.
For instance, industry players will have to factor in the difference in IoT endpoint behaviours for different devices. A smart electricity meter, which automatically records and relays electricity consumption to a centralised repository, does not require 4G bandwidth. A low bandwidth channel would suffice. However, a smart car that provides in-car entertainment as well as a steady flow of diagnostic information to a centralised hub would require a higher bandwidth of 4G or even 5G. NB-IOT (Narrowband – Internet of Things), LoRa, and Sigfox are all wireless technologies seeking to address the needs of very low data rate devices.
Scalability and security are other pressing issues that telecom players will have to address. With IoT devices numbering more that the people on planet Earth, telecoms will have to seek new ways to scale to accommodate every device from light bulbs to running shoes. Plus the security of these networks will be the onus of telecom industry. Software defined networking (SDN) and network functions virtualization [NFV] can add scalability and manage security through better visibility, adaptability and programmability. Blockchain, based on the decentralised and distributed ledger system, is another viable alternative that can fill in security and scalability gaps.
On the data centre side, it would be critical for telecoms to adopt cloud technologies. A cloud infrastructure is robust, yet flexible and agile when required to scale. This will enable network service providers to maintain continuous IoT availability and even keep up with the pace of new IoT rollouts.
There is no denying that IoT will be a huge part of the telecom industry’s future. Telecoms will have to adapt to this change by adopting a robust innovation strategy through gaining insights into consumers’ ever-evolving demands and by investing in new technologies.
Contextual Marketing for Telcos, based on intelligent algorithms, can help provide deep insights on behavior of each customer. This gives telecoms the added advantage to understand customer behavior and invest in the right technology needed for the fast-expanding IoT ecosystem.
]]>Predictive models help telecom operators estimate the likelihood of its customers’ actions beforehand. For example, with the help of predictive modelling, network operators will be able to identify actions such as which customer is planning to exit the network or cancel their data plan.
To create these models, solution providers use ML techniques. These techniques include Machine Learning algorithms being trained to find natural patterns in data and generate insights that help you make predictions using regression and classification techniques when fed with a huge amount of data.
This gives insights into customer behavior and identifies preferences such as data to voice or vice versa, the percentage of customers who like or dislike offers proposed to them and can also determine which customers are happy with their network offerings. These insights when further processed can be converted into campaigns and help in targeting effectively.
These fine patterns in customer behavior will enable network operators to uncover misalignments between customer usage and service plan, and proactively suggest an appropriate product. The analysis generated in real-time about customer-specific data will also enable the network operators to precisely align promotions and campaigns along the entire customer journey matching the customer sentiment. For example, the solution can predict churn from a customer through his actions at each stage of interaction with the network. With this, if the customer sentiment will be classified as ‘unsatisfied’, churn can be predicted. Similarly, the models are created using different patterns and processes that can help network operators deepen customer relationships by providing tailor-made offers, deliver outstanding customer experience by learning customer behavior patterns and likewise maximize revenue through opening revenue streams for cross-sell and upsell.
Machine Learning provides network techniques a sure-shot means to manage and draw accurate insights into their customer behavior from a large tract of unstructured data collected every day. With this predictive knowledge, the telecom industry can transform the way it works. Decision makers will have a continuous, real-time view into the performance of campaigns, allowing them to make mid-course corrections at a much earlier stage.
The goal is to be more proactive, and to understand what action or direction is the best for a favorable outcome.
]]>Digitization offers telecom companies the opportunity to rebuild their market positions, re-imagine their business systems, and create innovative offerings for customers. To revamp digitally, operators must offer an integrated, omnichannel user experience and build a portfolio of new products and services tailored to match the requirements of each customer. Together, this will allow operators to boost value.Many telcos are taking this opportunity to reinvent themselves. SK Telecom was one of the first few to enter digital services early. Launching SK Planet in 2011, by the end of 2012, SK Planet had earned more than $1 billion in revenue from this stream. More recently, the company launched a marketing platform, offering data-driven advertising and marketing solutions.There are a few pointers that telecom operators need to dwell on to catch up with the digitization trend.
Digital touchpoints now influence customers’ preferences across the whole journey. If telecom operators should address these needs and expectations, they must rethink their marketing mix by reimagining the entire customer experience, including communications, marketing, sales, and service.
In achieving this, two main objectives are involved. Preferences should be given to the demand for more powerful devices, ubiquitous connectivity, closer interactions with friends and companies alike, at work, at home, and on the go. Secondly, websites have by far the most influence on customers’ brand preferences, with mobile apps a distant second and Social Media playing a major part. Intuitive UI, fast response, automated processes and in-store virtual experience are required to hold customer interest.
Case Study: Giffgaff a mobile virtual network operator, does not run any call centre. Instead, members help each by other answering questions in the company’s forums, thereby reducing the cost to serve. The company does not engage in any form of TV advertising, members of the network spread the word to attract new customers and receive financial incentives to do so. This business model allows the network to attract a different type of ‘free-thinking’ Generation Y customer who is disillusioned with the traditional mobile operating models. giffgaff customer satisfaction is rated at 90%. 31% of customers came from long-term mobile contracts with other providers. 63% of them recommend the service to friends or family members, and 78% among these recommendations become registered members.
The product-centric market has radically shifted to a customer-centric approach. It’s all about what your customer needs. Personalizing network products or services with targeted offers, tailoring the features of phones, smartphones, and tablets to customer needs, and most importantly providing personalized data plans will help reduce churn within the network.
Additional services like cloud storage, video, music, and games, and cross-vertical offerings in e-payments, e-health and e-wellness through the use of connected wearable devices will also boost credibility to the operators. Given the digital transformation, there are growing concerns about privacy and security. Operators willing to develop guaranteed data privacy and security services for premium customers, distinguish themselves in the market and attract more customers.
Case Study: GSMA Mobile Connect has made the effort to develop an industry-wide solution to help consumers take greater control of securing their data. The Mobile Connect authentication service is available to network operators globally and enables users to create and manage a digital universal identity via a single login.
Many operators struggle to meet current digital expectations because of slow design processes, limited customer input, and rigid legacy IT systems. To overcome these barriers, operators should invest in:
Case Study: Telefónica’s Smart Steps programme helps businesses make strategic decisions based on aggregated mobile network data across all Telefónica mobile users. The solution uses big data assets to analyse the movement patterns of millions of people, as well as their online behaviour and demographic profile. The data is then extrapolated to provide insights that are representative of the total population in each area.
Network operators that implement these strategies will be able to successfully transform their business. They will be able to open new opportunities and spearhead creative initiatives and strategies that only the digital forum can offer.
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