In this world of continuously disruptive competition, ever-increasing customer expectations and shrinking margins, only unique customer experience and delight can sustain telecom operators. Customer satisfaction is no longer the key indicator of a business’s success. Instead, the focus has now shifted to ‘customer delight’. Most technology-enabled businesses (including the telecom operators) possess something that can enable them to achieve this objective if used properly; i.e. big data.
The big data analytics adoption has been steadily growing and as per a research conducted by Dresner Advisory, in 2017 53% of organizations have reported using big data for business decisions. This stat was just about 18% two years ago in 2015.
Real-time big data analytics has the potential to transform telecom customers’ experience (CX), create higher engagement, lower churn and new revenue streams for the network providers.
Real-Time Big Data Analytics and Customer Orientation (H2)
Traditional data analytics using legacy analytics systems only produced ‘descriptive analysis’. That is a trend or relation of what has already happened. Then you could put some models, hunch, intuition or whatever else (mostly with many thumb rules) to predict what may happen next.
Purpose of data analytics now is not just to show what has already happened; it is to find previously unknown relationships, predict the outcomes and prescribe ways to counter or benefit from the moves. The modern data analytics focuses on predictive and prescriptive analytics rather than descriptive analytics.
But, there is a catch, for an effective or even relevant prediction, you cannot rely on the limited structured data you can capture in a market survey. Instead, the unstructured data is more useful in this case, and as a telecom operator, you already have a huge amount coming in every day.
Dynamic User Profiling & Segmentation (H3)
Customer segmentation is the proof, how well you understand your customer. Unlike traditional static segmentation, data analytics-based profiling can create dynamic profiles by:
- Their usage of voice, data, text at different times of the day
- Their interests, for example, favourite music, gaming or video content
- Location
- Socio-economic class
- Network influence (position in the social network)
- Churn Possibility
- Relationship with other network users
Offers, Campaigns & Intelligent Network Planning (H3)
For offers and promotional campaigns to be effective, the three key characteristics are crucial:
- Timing
- Relevance
- Swiftness
Google’s micro-moments report of 2015, summarises it very well. It points out that users are not always open to all kinds of promotional messages. But, at different points in time, they may be open to specific suggestions.
For example, customised product offerings to telecom customers based on usage patterns, billing data, support requests, purchase history, service preferences demographic information, location, etc. Subscribers are provided with discounts or offer based on recent purchases or enquiries or calls and are offered top-up plans or up-sell recommendations based on data usage.
Customer engagement is a challenge even for telecom operators, especially when the market is price sensitive and competition ready to drop low. Understanding your customer and preempting their needs is only possible through big data analytics.
Some Use Cases:
- A leading telecom operator used big data analysis to improve its marketing effectiveness and ROI. It’s 250 targeted outreach campaigns, achieved a 33 per cent response rate. As a result, the network traffic increased by 64 per cent, daily active subscribers jumped by 17 per cent, and revenues grew by 2 per cent.
- Another operator used data analytics to combine socio-demographic data, data on network usage, and information from customer touchpoints (g., call centres and social media). As a result, it was able to identify, in real time, the customers most likely to have trouble paying their bills, as well as to cut churn by 3 per cent and to improve the recovery of payments by 35 per cent.
Engaging Retailers & Distribution Channel Management (H2)
However, with the real-time big data analytics on consumer data, you may offer consumers specific deals, which these retailers would have no idea about. Unless you integrate retailer and user data and use it for retailer engagement as well.
Bridging the gap between what retailer knows and what consumer wants ultimately brings more revenue to your wallet. Also, monitoring and real-time update for retail KPIs would be possible with big data analytics
Developing Partnerships & Top-Line Revenues (H2)
Big data analytics also gives you an opportunity to venture into new service possibilities. The shift to digital offers new opportunities as well as challenges to the network providers. The following trends observed in the developing markets should prompt specific VAS partnerships for telecoms:
- More than 50% social media users accessing social sites on mobile
- Video consumption on mobile is growing at the highest pace among all the content
- User-generated & local content consumption is growing at ever faster pace
- More rural and sub-urban users are going online on mobile
These trends prompt and expect following changes from telecom operators:
- Network availability in rural areas
- Content optimisation for lower bandwidth areas
- Data monetization and data revenue maximisation
- Revenue assurance and retailer management in rural and suburban areas
All this shift, if backed up by real-time data analytics, can optimise the cost of deployment and maximise the revenues from VAS, OTT, Application and service integrator partnerships.
Deployment of Big Data Analytics System – The Question of Cost (H2)
A hybrid environment does not mean that if you do not have the on-premise infrastructure now, you will need to invest in it. You can create a hybrid environment using the Public and Private cloud as well.
There are two basic modes of deployment, – On-premise and cloud. Both have their own advantages and disadvantages. However, keeping in mind your ultimate objective of maximising ROI, a mix or hybrid system is more useful.
The only thing that you may need to worry about is the ease of integration for both infrastructures. Best solutions will be those which can be applied on both on-premise and cloud systems.
Summarizing Requirements for Personalization Using Big Data (H2)
The big data analytics system you deploy should:
- Generate real-time insights
- Generate predictive analytic insights
- Give prescriptions
- Be available for on-premise as well as cloud deployment
- Support multi-tenancy
- Use consumer data analysis along with the distribution channel data
- Run real-time engagement campaigns for both end user and channel retailer
- Be scalable
Conclusion:
Presently, a defining change is seen in the telecom industry where the current emphasis is on improving customer value by engaging effectively across all possible touch points. And big data analytics is being widely used to replace bulk segment marketing with contextual offerings for improved customer experience.
Furthermore, the focus will be required on innovation and new sources of external as well internal monetization in the coming years. Operators will need to turn to optimise their processes with predictive analytics (even real-time analytics will be insufficient).
It is not wrong to say that big data and predictive analytics is the present and future for operators in an increasingly customer-centric world. Operators who can take optimised advantage would surely be able to take the edge in this.
