Yet research says those transformation efforts fail most of the time. Why? Perhaps there are many reasons: strategic mistakes with goal setting, tactical flaws in planning, operational errors during execution. In this article, I shall narrow down these issues, those that are largely in our control, and suggest a few ways to overcome them.

1. Don’t let FOMO be the driver.

Put your horse before the cart. A competing bank’s marketing campaign focused on NFT-backed digital artifacts for loyalty exerts a lot of pressure on all peers. But before onboarding a vendor to provide the backbone for crypto assets, the marketing team should list a handful of concrete use cases and assess their relevance/appeal to the bank’s customer base.

For instance, create a concrete use case like this:

“My Telco X shall mint 1,000 celebrity-signed SIMs accompanied by a collectible, say a coffee mug or watch, and distribute it to our most loyal customers. The SIM and the collectible are digitally coupled and tokenized using NFTs, making this a limited-count asset now. Customers (may) take pride in owning it. We may incite demand as the asset is limited. We may host an exclusive “Celebrity Collectible Owners Club” with the access key being the NFT itself.”

And if it sounds logical, then onboard a partner to realize the vision.

2. Remove self-imposed constraints.

Most enterprises lock themselves in with only one vendor for a specific function and let them handle the entire customer base. For instance, one vendor for CCCM, one for RTIM, one for digital analytics, etc. And each one of them caters to the whole base of 100 million customers.

Enterprises seek advanced capabilities like A/B testing from those products but do not themselves practice A/B testing with multiple vendors, where, say, two competitor products are pitted against half the customer base.

In today’s contemporary tech landscape, with most vendors taking the SaaS route, operational considerations of yesteryears should not be held as blockers for having multiple incumbent competing products.

3. Diffuse the tension between IT and business.

A common trait I find in market leaders is the presence of absolute synergy between IT and business. Meanwhile, I notice the opposite in laggards. But both are wrong and need a realignment in order to put the end customers ahead of their own priorities.

Having IT and business aligned on the right “customer” axis is pivotal to ensuring smooth and successful outcomes.

4. Don’t go for blind AI; seek explanations.

It is sad but true that even large enterprises fall prey to machine learning’s (ML) glamour and onboard many AI-heavy projects, allowing their systems to make a lot of decisions without sufficiently understanding the rationale behind them.

In their race against time, enterprises have a tendency to choose those vendors that ship with a lot of pre-built models, and knowing the trend, vendors have also inflated their stock model count. This is a potentially dangerous practice, one that can uproot a brand’s stated emphasis on customer centricity. Enterprises need to make sure they choose vendors that can provide a rationale, in business terms, for the recommendations and actions they undertake, and not go by arbitrary numbers emitted by mathematical models based on never-understood matrix transformations.

For example, if Alice was recommended a four-year mortgage loan while the CSR opines that a three-year unsecured loan is a better option, the CSR should be able to ask the underlying ML as to why it deemed mortgage to be a better option for Alice. In response, the ML should be able to give reasons in business terms (e.g., “Analysis of Alice’s cohorts reveals that there is a 3x increase in chances of bad debt when they consider a loan within six months of engaging with the bank” and not an apparently useless metric (e.g., “Alice’s proximity score to four of the deduced clusters is 0.3, 0.4, 0.1 and 0.2 with a noise level is 0.86—that’s my recommendation”).

5. Be rational and also understand the data limitations.

I have sat through business workshops on customer centricity where the need for brands to connect with the “whys” behind customer engagement is well understood, and then, we come up with a purpose for customer interaction like “To open a fixed deposit for 12 months.” There is an apparent disconnect here.

An end customer like Bob will possibly engage with an intention of “Putting idle money to better use” and possibly wants a safe bet. Hence, his preference for deposit over equity. Thinking from Bob’s perspective, fixed deposit is just a means, not his objective, and until you understand this subtle difference and align accordingly, you will never be able to transform your company into a truly customer-centric brand.

While there is no prescription for success, as Otto von Bismarck rightly said, the wise man learns from the mistakes of others. Not rushing into the same pitfalls others have just managed to come out of is important to stay on the right track.

Want to know how Pelatro can help you improve your customer engagement? Get in touch at hello@pelatro.com

Author

Chief Architect at Pelatro. Proud to help 40+ Telcos/BFSIs offer the finest contextual marketing experience to their 1B+ subscribers. Read Pramod Konandur Prabhakar’s full executive profile here.

This article was originally published here.

The spray-and-pray method worked fine during the initial days of digitalization, but it has drastically changed with the advent of big data and AI/ML technologies. Customers demand contextual personalisation and companies want to make it a business priority. It is significant to note, that companies that excel at personalisation generate 40% more revenue than average players.

Today, leading telcos have turned to context-driven personalisation that utilises real-time data and insights to cater to the customer’s current and latent needs with the use of advanced analytics, machine learning, and data management tools.

Dynamic and evolving customers demand contextual and relevant interactions

While earlier customers may have expected basic services such as call quality or network range to be a benchmark, this has changed drastically over the years with a significant paradigm shift happening during the COVID-19 pandemic. Being locked down and stuck indoors, customer expectations have continued to evolve and set ever higher benchmarks with a majority now considering experience to be just as important as products and services.

While we briefly touched upon the transition to context-driven personalisation, there are a couple of specific examples of how companies have evolved with changing customer demands and reinforcing their engagement strategy to ensure they are contextual and relevant.

In Europe, for example, certain operators have been able to develop a next-best-action churn model. This provided them with the ability to measure each customer’s likelihood to leave for a competitor in addition to grading the reasons behind churn. This analysis for customer churn, therefore, helped the operator to create campaigns on a micro-segment level and ensure highly personalised, contextual, and relevant interactions to address specific pain points with particular customers.

Another approach is telecoms that use network optimization techniques and real-time data to identify any potential network issues and preemptively resolve them to avoid any impact on the customer. Take for example a customer who is regularly experiencing slow mobile internet speeds, the operator can use network optimization techniques and tools to identify the issue, find the reason behind it and resolve the pain point resulting in better customer experience in addition to reducing support costs.

Moving from telco-specific services to a more digital services business model by integrating industry-agnostic offerings.

As technology becomes a more standardized approach across the telecommunication industry, the differentiation comes from how efficiently one can utilize it and offer better services through those technologies. Moving away from a telco-only specific model to a more comprehensive digital services portfolio model by integrating with other industries allowed telcos to offer an upgraded value to their customers.

Telecos may also have to reassess their business models, backend processes, and customer care support but when executed efficiently and at scale, these services can be a game-changer. The integration of newer services and products would require telcos to be customer obsessed and aim to drive value by creating a digital ecosystem that offers personalized avatars of the customer journey. Rakuten in Japan is the best example of how an e-commerce giant is revolutionizing the telecom space.

A company may choose to approach this by tracing the customer journey to truly understand their preferences to provide their customer with a differentiated digital experience via a range of digital services, which in turn enhances customer lifetime value. From payment services to music, advertisements and partnerships with other businesses – telcos must aim to pose a unique value proposition by giving their customers an exclusive omnichannel and holistic digital experience. The advent of 5G will further empower telcos in their drive to comprehensively personalise customer journeys, thanks to the inherent advantages of the technology. Benefits such as faster speeds, more reliable connections, increased capacity, and improved analytics, will also enable newer applications such as the use of augmented reality or virtual reality for a more personalised experience for each user.

In essence, with time and appropriate resources, telcos can be a lot like SaaS companies and provide integrated systems that can expand their service portfolios and revenue streams.

Changing the Customer Engagement Process

Without a doubt, there are multiple case studies on the internet (successful and otherwise) showcasing companies starting their digitalization journey by making huge investments in marketing technology. It is imperative to note that with martech, there is hardly ever a one-size-fits-all solution, and the focus should be on drawing value from the technology being adopted.

There are multiple ways to look at this and strategy will likely differ from one company to the next. Some telcos may prefer to adopt new technologies as greenfield projects, i.e., they choose not to digitize legacy systems yet. However, the telcos that have successfully implemented these technologies were able to integrate various distribution channels and avoid data siloes. This would range from accruing data from external and internal channels to providing a holistic picture of the customer with the ability to act on behaviours and needs, thereby personalizing the engagement and touchpoints to customers.

Artificial Intelligence and Machine Learning in Customer Engagement

Maximizing value from the adoption of new technologies while controlling costs or investments is crucial. The implementation of emerging technologies such as artificial intelligence and machine learning are already on the rise by telecommunication companies and have proven to be game-changers. This AI/ML-powered revolution can be seen in the form of chatbots and virtual assistants, personalised marketing via better customer data analysis and customer segmentation, predictive maintenance to reduce disruption to customers, and fraud detection to improve the security of systems. Various large global telecom companies such as AT&T, Verizon, T-Mobile, Vodafone, and Orange have implemented these novel AI/ML-enabled technologies to bring about a paradigm shift in customer engagement.

While many are aware of the implementation of these technologies in the digital customer journey to enhance experience and engagement, it is interesting to note that AI and ML can also be used in the retail/store setting to remove bottlenecks. In addition, these technologies allow for the integration of the ‘digital’ aspects of the company with physical stores and this is combined with personalized advertisements and targeted offers that reach the customer at the right time and when it is most relevant!

Artificial Intelligence and Machine learning technologies are now being used by telecommunication operators to create holistic customer profiles. Furthermore, these operators are now able to automate tedious and repetitive tasks and thereby reduce the burden on their teams while allowing more time for innovation and brainstorming.

For example, Reliance Jio uses AI, predictive analytics and big data to create real-time profiles of its 300+ million users. This gives the operator a better understanding of the market and helped it outperform competitors.

Another large telecom operator, Airtel, in India essentially created a new revenue stream by launching an advertisement service that leveraged its data science expertise and customer data. This operator ran targeted and uber-relevant advertisements from across industries including FMCG, BFSI, Automotive etc. These are some examples of how personalisation can offer new business opportunities to telcos.

Ensuring that the customer’s journey is managed and that the contextual marketing experience is on-point, can go a long way in altering the customer engagement process resulting in greater customer loyalty, enhanced brand power, greater customer retention, higher customer activation, enlarged range of revenue streams and a higher average revenue per user. To get a better understanding of how to elevate your marketing campaign’s performance, check this article here, which showcases the use of emerging technologies like AI/ML to maximize value.