“Data really powers everything we do.” (Jeff Weiner, CEO, LinkedIn)
“The telecom industry is one which not only sees a large customer base, but a customer base who’s needs and desires are constantly evolving and/or shifting. On top of this, telecom firms face cut throat competition, making it a highly dynamic and challenging industry”(Bhandari, 2018). The customers are looking for better quality services, reliability, and uptime. Therefore, to stay relevant in the market, “telecom companies are relying on marketing analytics to evaluate their marketing initiatives.” (Quantzig,2018)
According to an article by Deloitte (issue 1, 2013) telecom companies face challenges, by the communication tools available on the internet such as weibo and twitter, it has highly reduced the profits of telecom operators for SMS and voice calls. The article further suggests that Mobile Internet and big data will create tremendous opportunities for telecom operators. Mobile internet is expected to be booming in the following years. With such intense competition, every decision becomes important, this is where data analytics helps.
“The unprecedented volume, velocity, and variety of primary data available from individual consumers, resulting in the so-called Big Data revolution; potentially, a revolution that will lead to entirely new ways of understanding consumer behaviour and formulating marketing strategy”(Erevelles, Fukawa and Swayne, 2016). Big data allows an ability to capture and process the data in an efficient manner(Yazti and Krishnaswamy, 2014).
Marketing analytics is a set of practices and tools that empower marketers to evaluate the attainment of their marketing resourcefulness. “The evaluation can be done by determining business effectiveness. Marketing analytics works on business-critical facts, like return on investment, marketing acceptance by end customer and global marketing success”(Rishi, 2017).
EY (2014) defines “descriptive analysis as a set of techniques used to describe or explore any kind of data and Diagnostic analysis as the next level of analysis where the data elements are further analysed to get to the root cause of the problem.” There are two more types of analytics, Predictive analysis- which is the practice of using extracted information from big data to predict future results and patterns and Prescriptive analysis-which uses optimization and stimulation of big data to give out recommendations to make better decisions.
Although analytics can be instrumental in the telecommunication industry, some of the major applications of big data are:
1- Customer retention and improving customer loyalty.
2- Strengthening current services and launch new products.
3- Targeted marketing
These applications will be discussed further in the essay. This essay will critically examine the marketing analytics used in the telecommunication industry. This study starts with an example of Analytics being used, followed by the advantages of using analytics in telecom industry and how it helps in defining a marketing mix. Third, it will talk about some limitations of using analytics in telecom. At the end, the essay will give recommendations and throw some light on the future of Marketing in telecommunication using big data and give a path for future research.
Let us look at the role of Marketing analytics in telecommunication industry with an example of Sprint-
“Pinsight media (subsidiary of Sprint) used network-authenticated first party data to build up more accurate and reliable ( and therefore more valuable) profiles of consumer behaviour, which allows them to offer more precisely targeted audiences to advertisers. This means there’s less chance of putting an advert they will find boring or irrelevant in front of them, and a higher chance they’ll see something they will consider spending money on.” (Marr, 2016, p.170)
In a telecommunication organisation, there is a lot of data available through ‘Internet of things’ and analytics helps in using that data to the best of the organisation’s advantage. Jony et al. (2016) argue that Big data technology provides companies, such as telecom operators with an ideal platform for centralizing and storing and analysing their structured, unstructured and semi-structured data.
Telecommunication firms operates in an environment which is very dynamic, and customers might shift to other operator if they find a better deal elsewhere, to maintain a competitive advantage, organizations need to analyse big data and use them in their marketing strategies.
Following are a few of many advantages of marketing analytics in telecommunication industry.
1- Real time analytics for client activities hence better service – Through big data analysis it is possible to keep a tab on how a customer or a potential customer feels about the product. Marketers can use these feedbacks and negative comments to improve their customer services. ‘Using deep data analytics, you can optimize your customer service resulting in happier customers. Some customers may not only complain of products or services through the official channels offered by website, but may also go social about their groups.’ (le and liaw, 2017)
2- Key Performance Indicators for marketing and customer experience- KPIs helps marketers understand their current position in the market. Analysing big data and using that in these instruments will help organisations make better marketing plans, as they will know what the customer demands. One such KPI that can be analysed is online selling, which helps to know- which services are generating how much income.
3- Investment planning – To stay relevant an organisation needs to keep on investing in new projects which will make life easier for the customers. Such huge investments require a lot of resources and time. Through analysing the data available, telecom companies can predict whether the investment is worth the risk or not.
A report by Tata tele business services (2017) says ‘the need to be able to effectively combine network traffic data, customer experience metrics, revenue potential and location data along with customer value data to ensure they are investing their resources in the right spots.’
4- Predicting churn and customer lifecycle- Given the competition in the market and rationality of the consumer, it is very difficult to predict how long a customer is going to be loyal to one telecom operator. Through big data, organisations bring together various points including – performance, billing information, calls to the customer care, quality of service and social media sentiment analysis to build a model to predict and prevent churn. Through data analytics it is also possible to find out about the service which is no longer liked by consumers, it can then be modified or replaced, to ensure a longer customer lifecycle.
In the telecommunication sector it is absolutely essential to draft a marketing mix which will sustain in a dynamic business environment and give competitive advantage. While developing their marketing mix, organizations can use the knowledge gained from analysing the big data. This helps them not only to set a particular price range but helps to find the perfect target market for the service. For example Vodafone wanted to better understand winter roaming patterns for subscribers and how better to communicate with them. This required a deeper understanding of customers’ activities.
Overwater(senior information architect for BI Strategy & Architecture) said “We wanted to understand who goes skiing, so we could target a campaign and provide a roaming service that fit their needs more closely, “Once we understood who roams and why, we were able to more closely address customer needs.” (Greengard, 2014)
With big data coming in play, marketers now have access to – better customer insights, better understanding of marketing trends. Through a successful marketing mix, an organization aims to decrease the risks and optimize business process. Data mining techniques can help to accomplish such a goal by extracting or detecting patterns or forecasting customer behaviour from large databases. (Fan, Lau and Zhao, 2015)
Figure 1 – Use of data analysis in marketing mix
Following is a draft of Marketing mix-
Product :
1- After analysing data, an organisation can amend their plans according to the usage and needs of consumer(more internet, less calling).
2- By studying the behaviour of consumers towards the old product, the length of product life cycle can be increased, by making regular changes in the characteristics of the products.
3- Personalised products to target individual consumers based on multi-model data (mobile, social media, location, etc)
4- Better customer services could be promised with the products. Consumer feedback can be accessed and analysed.
Pricing:
1- On the basis of how much a consumer is spending on services (top-ups, internet packages, et al.) Marketers can devise out pricing plans ‘tailor made’ for the particular consumer.
2- Through studying the analytics thoroughly, organizations can find out about the competitors services, which will allow the marketers to develop a competitive pricing strategy.
3- Through predictive analysis, an organization knows where it will stand in the near future and where it wants to be. To make sure, the company meets the investment funds required, pricing strategy should be designed accordingly.
Place :
1- Through big data, it can be found out in what parts are the services being used the most and in which parts, the services are not available easily. The places with low market share can be targeted with new strategies.
2- By analysing data, organisations can get to know about the channels through which the customers pay for the services (retailer, internet or directly to the company), accordingly investments could be made to strengthen other channels of distribution.
3- Studying the data, relating to other countries where the services are of the same kind. Opens up a scope for internationalization.
Promotion :
1- With access to the browsing history- organizations can make partnerships for adverts with websites or applications which are most used.
2- Through analysing cookies available on internet– organizations can get to know, when a customer is looking to shift to some other service and can then promote their services to the potential customer.
3- Through feedbacks, drawbacks in the branding could be found out and help in rebranding.
Although big data is already making a huge difference in marketing all around the world and many industries are expecting great benefits out of it. However big data comes with some drawbacks as well which either slows down the process of marketing or lands the marketers in no man’s land. Biggest problems regarding big data is the infrastructure’s high costs (Wang & Wiebe, 2014). Hardware equipment is very expensive even with the availability of cloud computing technologies. (Sivarajah et al., 2015)
More limitations will be discussed in this section with context to the telecommunication industry.
1- Not enough data scientists:
Telecommunication industry because of it big size, requires a huge man power to convert all the data available and according to A 2011 McKinsey & Company’s Business Technology Office report, “By 2018, the USA alone could face a shortage of 140,000 to 190,000 people with analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.”
Figure 2: Size of Telecommunication Source– Plunkett Research
2- There is a lot of big data available today, according to Waterford technologies (2017), 2.7 zetabytes of data exist in the digital universe. Not of all this data is relevant to the telecommunication industry. Data scientists might end up analysing the data not required (noise) and delay the marketing process.
“most of it is just noise, and the noise is increasing faster than the signal.” (Silver,2017, pp 15)
3- Privacy and data security: As more and more data is becoming available, more and more hackers are getting attracted towards big data, which they might use to hurt consumers. For example – a hacker might get access to a consumers credit card information while he is buying a top-up for his mobile network online.
Le and Liaw (2017) argued that ‘A high diversity of Big Data information lead to organizations lacking the ability to manage and solve these data, and third parties have opportunities to access data. They may not comply with data protection regulations’
4- Competitive disadvantage: In such a dynamic industry, one cannot afford to miss out on data, an organization needs to act, and needs to act fast. If the competitor takes advantage of the data first, there will be a lot to pay for.
5- Data analytics sometimes focuses marketers towards one particular group of consumers and makes them biased.
On one hand analytics is easing the process of marketing in the telecommunication sector, with its numerous advantages. But on the other hand it has some significant drawbacks, which might hinder in the marketing process. I agree with many academic scholars who argue that data analytics is the future of marketing in telecom, despite its flaws. this essay recommends organisations to develop a marketing mix on the basis of multiple analytics styles, mainly predictive and prescriptive. This will not only allow organisations to stay relevant in the dynamic business environment but also will increase customer loyalty and profits. Although this essay covered many aspects of analytics in marketing, but this vast topic cannot be discussed in one study as the field of big data is still changing. This opens up the possibilities for research in this area. The findings of marketing analytics will interest the readers as big data is expected to grow even faster.
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