The data trends Communications Service Providers can expect in 2022

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The hallmark of the landscape within which Communications Service Providers (CSP) marketers operate is constantly changing. To remain at the forefront of highly competitive markets, they need to master the latest technologies and trends as they become available. They have to implement innovative use cases that unlock value, and from here, correctly judge the impact these progressive programs can have on their businesses.

Among the technologies and the use cases for incremental value generation at the forefront of a CSP marketing arsenal at present are: big data analytics, artificial intelligence (AI), machine learning (ML) and the latter’s sub-types; deep and neural networking, intelligent chatbots, self-healing networks, omnichannel marketing, Internet of Things (IoT) and many more. But how do marketers, tech teams and business managers identify which trends are worthwhile? This requires a sequence of assessments, which we’ll discuss here.

Communications Service Providers are well-placed to innovate

Marketers and business managers don’t need to be Zen masters in coding to unlock the value of these tools – but they should be able to understand the concepts, requirements, and use cases that relate to any new technology in order to use them effectively. The telecommunications and Internet industries have historically been pioneers in embracing and deploying innovative technologies, especially those that leverage the large volumes of unstructured data they generate. In particular, telecoms has been a hub for growth reflected in the increase in manufacturing, shipment and use of smartphones and the growth of mobile Internet over the last ten years.

The fact is that the global telecom sector is a unique and vibrant industry that is constantly evolving, driven by new technologies and infrastructure. The statistics underlining this are impressive; in 2020 there were around 7.7 billion active mobile broadband subscriptions worldwide, a staggering rise from 3.3 billion just five years ago, due in part to the deployment of 4G LTE*(EY). With mobile technologies in a state of perpetual evolution, there is little doubt that the focus on the opportunities now offered by 5G will continue to foster progress.

As the industry continues to grow, more and more complex unstructured data will be created. It will be imperative to handle and analyse these mammoth volumes of data if CSPs want to deliver better customer services, identifying needs and offering solutions based on effectively utilising what initially is little more than a big data repository.

Sorting the wheat from the chaff

Given the variety of options to enhance marketing programmes, the step that a CSP’s business marketing and technical teams must first take is to identify the potential impact of each innovation. To do this, two separate considerations must be taken into account:

• Commercial/Business: Can innovations help to resolve a particular problem more effectively than in the past? As the industry becomes more technologically advanced, progressions must be able to resolve business problems in a more sophisticated way and provide outcomes that haven’t previously been possible.
• Technology: The business impact noted above will need to be proven and embodied by models, logical regression tables, rules and algorithms (like naïve Bayes rule, decision tree, support vector machine, KNN, sentiment analysis, etc.) in order to deliver a result likely to improve past performance. Marketers need to analyse whether or not this is possible.

From a business manager’s perspective, it is vital to understand both the terminologies and technologies if underlying business problems are to be addressed in a more effective way. They also need to have a handle on the available data, which we’ll move onto next. 

Big data matters: The four “Vs”

Big data should enable organisations to store, manage and manipulate vast amounts of disparate information at the right speed and at the right time. To gain the right insights, big data is typically understood in the context of four characteristics, or the four “Vs”:

• Volume: How much data there is
• Velocity: How fast it’s processed
• Variety: The various types
• Veracity: Its accuracy

It is convenient to assess big data via the four Vs (in fact, arguably, there is a fifth characteristic as well – value) but it can be misleading and overly simplistic to do so. Why? For instance, you may be managing a relatively small amount of very disparate, complex data or processing a huge volume of very simple data. That simple data may be all structured or unstructured.

In short, big data is complex rather than straightforward to assess. The data that is created in real time by the users of telecommunications services inherently contains all the characteristics noted above. Some examples make this clear:

• Volume: With the concept of unlimited calling and the growth in usage of mobile data and broadband services across the industry, we have seen the volume of data increase exponentially. With the arrival of an operator like Jio, global analysts forecast that the demand for mobile data will reach 500–600 million GB per month in India alone. Thus, Call Detail Records (CDRs) are increasing and the resulting volumes of data can’t be accommodated in an SQL or Excel query anymore. Big data analytics must be used to derive more meaningful information from the enormous amount of raw data generated.
• Velocity: A Communications Service Provider’s customer may call the customer care helpline multiple times, for example, to activate a service or to connect or disconnect an add-on. When this happens, the marketing or customer success executive must be able to act immediately. As the market is so competitive, a customer may be lost if the response is slow. Leveraging big data in telecom can ensure this doesn’t happen; big data helps to generate meaningful information which can lead to quick action, e.g. through a map-reduce format.
• Variety: A CSP customer may order a third-party or partner product. For instance, they might buy something from Amazon, use a big basket website for another transaction, take an Uber to work, download the latest version of PUB-G mobile, start a Spotify premium subscription, and watch their favourite show on Netflix mobile. Besides this, the customer also sends the occasional SMS, makes and receives calls from a single number, and uses a messaging service like WhatsApp. Marketers need to ask how meaning and direction can be gleaned from these seemingly disparate activities. 

Gone are the days when Communications Service Providers could simply analyse the number and type of call and offer a talk time voucher in response. The new varieties of data must be converted from unstructured to structured format in near real-time. From here, they can be used to construct a persona for each customer and overlaid with machine-learned algorithms to adjudicate which products the customer could buy next, generating the next best offer or action most likely to lead to greater satisfaction. There is a gold mine of information at hand that can deliver commercial success, but only if the right data monetization processes are in place.

• Veracity: The accuracy of data is critical. The wrong data means the wrong insights and pursuing the wrong leads. Big data and machine learning algorithms can easily detect anomalies in data points for correction at the earliest opportunity.

Next steps for Communications Service Providers

To summarise, the amount of data telecoms produce is far higher than in the past and is still increasing rapidly. Communications Service Providers must urgently start using big data and machine learning technologies to simplify and derive “usable” (meaningful) information from the new data points. This, in turn, will achieve superior decision-making that positively impacts revenues and customer satisfaction.

Machine learning in telecom opens new horizons for sets of information, creating models that can add a new dimension to decision-making. The iterative aspect of machine learning is particularly important because as models are exposed to new data, they can independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. Doing so is a science that’s not new, but one that has gained fresh momentum. 

Thus, the question remains: when is the right time to act? This can be resolved by answering the following key questions about various technological trends:

1. Is the prediction you or the organisation wants – that is, a decision you’re trying to make – complex enough to warrant ML in the first place (i.e. is the result not good enough though a traditional, heuristic approach)?
2. Do you have new and clean data?
3. Does your data have existing labels to help a machine make sense of it?
4. Can your solution to this problem afford for some margin of error?

For CTOs and CMOs alike, the answer to the above is almost always “yes”. Using big data analytics and ML to deliver the best-fit offer to customers means increases in customer satisfaction and spend, tabling stakes for commercial success. Machine learning makes this a reality by taking various data points available through sources like CDRs, network data, DPI and DMP integration to arrive at a decision or prepare a model that predicts what the customer is most likely to want or respond to.

The AI and ML journey we are now embarking on will help to remove long-standing barriers to customer satisfaction with ease. Analysts predict that by 2022, 75% of all the new data will be processed by machines. It’ll be the early adopters of these trends that stay ahead of the game.

Get the head start your company needs

In the face of these new trends, the CMO and CTO at any type of Communications Service Provider needs to understand how to navigate the landscape. Therefore, consultancy is key; you need the right expertise at the right time to identify which of these tools will be most beneficial. As discussed here, this is a complex issue with myriad variables. How they interact with your goals will depend on the company’s unique circumstances, and therefore, there is no one-size-fits-all consultant.

Finding a big data expert with the unique experience that matches your company’s situation can seem like an insurmountable task; however, this needn’t be the case. Platforms like Outvise are specifically tailored to match projects with experts in a matter of days. Thanks to its unique project-matching algorithm, Outvise can help companies source an expert with the most relevant expertise. Click here to explore the portfolio.