BIG DATA AND MACHINE LEARNING: WHY IT’S TIME FOR MARKETING TO ACT
CSP Marketing: Opportunities for Progress?
Communications Service Provider marketers operate in a challenging commercial landscape hallmarked by constant change. To remain at the front of highly competitive markets, they need to master the latest technologies as they become available, implement the innovative use cases that unlock their value and correctly judge the impact progressive programs can have on their businesses.
Among the technologies foremost in the marketing arsenal at present are Big Data Analytics, Artificial Intelligence, Machine Learning and the latter’s sub-types; Deep and Neural Networking, deep Web, BTC, Intelligent Chat-bots, Self-Healing networks, IoT (intelligence of Things) and many more.
Sorting The Wheat from the Chaff
Given the variety of options that can be leveraged to enhance marketing program-mes, the CSP marketers first take is identify the potential impact of innovation. To do this, two separate considerations must be taken into account:
· Commercial/Business — Can innovations help to resolve a particular problem more effectively that it has been addressed in the past. As we the industry becomes more technologically advanced, progressive technology must be able to resolve business problems in a more sophisticated way and provide outcomes that have not been possible before.
· Technology — The business impact noted above will need to be embodied as 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. Can this be done?
From a Business Managers Perspective, it is vital to understand both the terminologies and technologies if underlying business problems are to be addressed in a more effective way.
CSPs are Well Placed to innovate
The progressive business manager does not need to be a Zen master in writing code but they should be able to understand the concept, requirements, and Use Cases that relate to any new technology in order to use them effectively.
The telecommunications and Internet industries have historically been among the pioneers in embracing and deploying innovative technologies, especially those that leverage the large volumes of unstructured data they generate. In particular, the telecommunication industry 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 has constantly evolved, driven by new technologies and infrastructure which continue to filter into the market. The statistics underlining this are impressive — in 2020 there are around 7.7 billion active mobile broadband subscriptions worldwide, an enormous rise from 3.3 billion just 5 years ago, thanks in part to the deployment of 4G LTE*(EY). With mobile technologies in a state of constant evolution, there is little doubt that the intense focus on the potential opportunities now offered by 5G in particular will continue to foster progress.
As a result, as the industry continues to grow more and more complex unstructured data will be created. It will be imperative to comprehend these mammoth volumes of data if CSOs want to lead their markets by delivering better customer services, identifying needs and offering solutions based on effectively utilizing what initially is little more than a Big Data repository.
Big Data Matters: The Four “Vs”
Big data should enable organizations to store, manage, and manipulate vast amounts of disparate data 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; the four “Vs”:
1. Volume: How much data is there?
2. Velocity: How fast data is processed
3. Variety: The various types of data
4. Veracity: accuracy of data
It is convenient to assess big data via the four Vs ( in fact there is a fifth characteristic as well — Value) but it can be misleading and overly simplistic to do so. Why? For example, you may be managing a relatively small amount of very disparate, complex data or you may be processing a huge volume of very simple data. That simple data may be all structured or unstructured.
In short, Big Data is confusing 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 created increase. With the arrival of an operator like Jio (https://www.jio.com/) global analysts forecast that the demand for mobile data at 500–600 million GB / month in India only. Thus CDR’s (Call Detail Records) are increasing and the resulting volumes of data cannot fit an SQL or Excel query anymore. Big Data analytics must be used to derive more meaningful information from the humongous amount of raw data being generated.
· Velocity: How fast data is processed? — A CSP customer may call his service provider’s customer care helpline multiple times, for instance to activate a service or to connect or disconnect an add-on. When this happens, the marketing or customer service team must be able to act immediately. With customers having so many options available, he or she may be rapidly lost if response is slow and it is leveraging big data that can ensure this doesn’t happen. Big Date helps to generate meaningful information which can lead to action quickly, for instance through a map-reduce format.
· Variety:- Composing useful data — A CSP customer my order a third-party or partner product (for instance, from Amazon, us 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 a favorite show on mobile on Netflix. Besides this, the customer also sends occasional SMS’, calls and receive calls from a single number and has subscribed to a caller tune. How can meaning and direction be gleaned from these seemingly disparate activities?
· Gone are the days when a CSP could simply analyze the number and type of calls and offer a talk time voucher in response. The new varieties of data must be converted from unstructured to structured in near real-time, used to inform a persona for each customer and then 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 customer satisfaction. There is a gold mine of information at hand that can deliver commercial success, but only if the data right monetization processes are in place.
· Veracity: — The accuracy of data is critical. The wrong data means the wrong insights and selling the wrong products. Big data and machine learning algorithms can easily detect anomalies in data points for a course correction at the earliest opportunity.
Next Steps
To summarize, the amount of data telecommunication service providers produce is far higher than in past and still increasing rapidly. CSPs must urgently start using Big Data and Machine Learning technologies to simplify and derive “usable” (meaningful) information from the new data points to achieve superior decision making that positively impacts revenues and customer satisfaction.
Machine Learning 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.
The Key Question for CSP Marketers: When is the Right Time to Act?
The CSP business manager today faces this question: At the current scale, should my organization be using Machine Learning and Big Data analytics?
This can be resolved by answering:
1. Is the prediction you or the organization trying to make (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 data 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 allowance of error?
For marketers in the telecommunication and the internet industries the answer to the above is almost always “yes”.
Using Big Data analytics and ML to deliver the best-fit offer to customers for reasons means increases in customer satisfaction and spend; table 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 arrives 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. Analyst predict that by 2022, 75% of all the new data will be processed by machines.
