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Telecom analytics brochure
- 1. Traversing Telecom with Data Analytics www.positivenaick.com Navigating the complex landscape of the telecom industry to discover new value streams for our clients.
- 2. Need for Analytics In this age of hyper-connectivity, the telecom industry is one of the most pervasive industries on the planet. A vast majority of the people in the world rely on the telecom industry to communicate with people as well as access the vast resources on the Internet. With such a large sphere of influence, plus millions of customers paying into a company, and hundreds of competitors all around the globe, the telecom industry has its own share of problems as well. With so many customers and so many players, there’s no shortage of competition. To make things even more difficult, the telecom industry is also the place where some of the fastest technological advances take place, such as fiber optics, 4G and 5G technologies. If a telecom provider does not keep up with the trends and install expensive new infrastructure to enable and support these new technologies, they’ll find themselves overtaken and decimated by their other competitors. Using the insights derived from data analytics, telecom companies can stay ahead of the curve when it comes to providing customers service and keeping revenue costs down. By anticipating demand from customers and supply from technological providers, telecom providers can effectively plan for new infrastructure upgrades at the time when those upgrades are the cheapest as well as demanded by customers. 1 Prospecting Support/Service Acquistion Cross-sell/upsell Reporting Retention Payment/ collections Order processing Potential area of focus in Telecom
- 3. 2 Next Best Offer Making customers purchase services higher up the ladder helps improve revenue. Next Best Offer would need to dynamically upsell and cross-sell products, packages, and services to the customer, by intelligently analyzing their purchasing habits. Business Need With Next Best Offer, telecom companies can provide targeted and personalized product recommendations to customers. Additionally, this can be used to improve customer loyalty and satisfaction, by providing and improving services customers use and love. OutcomeApproach Using a collaborative filtering model, automatic predictions about a customer’s interests are made. This is done by collecting individual customer preferences from customers who have similar behavior. Next Best Offer is a form of predictive analytics-based marketing strategy that deals with improving customer experience while helping telecom providers close a deal. This method is used to help companies and their marketers accurately understand customer purchase habits and streamline marketing towards efforts that have a higher chance of ending in a successful financial transaction. Next Best Offer Offer Unlimited Internet Recommend Movie Tickets Netflix Subscription change plan long lasting customer new customer
- 4. 3 Affinity Analysis Relationships between different products are analyzed to help two major goals; To develop suitable marketing strategies to cross-sell and upsell different products to customers, and to improve customer experience by identifying and offering services that are truly relevant to individual customers. Business Need Once the products and services are identified and classified into proper buckets, they can be incorporated into different loyalty programmes and discounts. This can be used to create optimized promotional campaigns tailored for each customer. This has the added advantage of of improving customer experience and reducing churn. OutcomeApproach An Association Algorithm is used to identify services that are often purchased together by customers. This algorithm is then also used to find out the various purchases made by the entire customer population. Affinity analysis is a data analysis method that finds relationships between different service offerings, such as how if they’re sold together, and in what specific bundles different services are sold together. learning the affinity of each product based on the purchase patterns
- 5. 4 Customer Lifetime Value Customer Lifetime Value (CLV), or the amount of monetary value a customer brings to the table throughout the lifetime of their relationship with the telecom provider, is a number of paramount importance to the customer. CLV is used to make internal revenue predictions, based on the value from each customer. Additionally, CLV is used to allocate marketing budgets on the basis of the relative value of each customer. Business Need In addition to calculating the customer’s past and current value to the company, the customer’s expected future value can also be predicted. The data can be used to optimize how marketing expenditure is managed. OutcomeApproach A survival model is used to predict the overall lifespan of a customer, ie, how long a customer will continue to do business with the telecom provider. Using a regression model, the customer’s lifetime value score is calculated. 20% CLV1 CLV2 CLV3 60% 20%Inactive nonprofitable customers Active profitable customers Very active very profitable customers Lifetime Value = (Average order value) * (Number of Repeat sales) * (Average Retention Time) identifying profitable vs. non-profitable customers through clv
- 6. 5 Being one of the most competitive industries in the world, the telecom industry has no shortage of competition. Without proper service and customer experience, customers are easily motivated to take their business to the competition. It’s also much more expensive to acquire new customers compared to retaining current customers. Business Need With the churn data on hand, retention policies and incentives can be prepared beforehand to retain the customer. If there are any recent business process changes from the company that have prompted customers to opt out, they can be modified or removed altogether. OutcomeApproach Using a survival analysis model, we can find out when a particular customer might retire their relationship with the company. Additionally, using data like billing records, customer service records, usage patterns, we can identify the probability of churn for each customer. Customer Churn The challenges with churn prediction and identification lies with identifying which customer behaviors trigger churn and to predict the attrition of customers. Once the reasons for customer churn are identified, steps can be taken to address issues. Age < 60 Usual call duration< 2 Min Placed Calls > 10 Churner Non Churner no no noyes yes yes Non Churner Churner a simple churn rule for older customer segment
- 7. 6 With profit-based customer segmentation, we can identify loyal customers and provide appropriate offers to maintain their loyalty to the company. We can also identify high-value customers who aren’t loyal, but have the potential to be high-value to the company. Business Need The biggest outcome expected here is a direct increase in CLV (Customer Lifetime Value) for the telecom company. Additionally, rewards and loyalty programmes can be rolled out to loyal customers to ensure they are incentivized to stay with the telecom provider. OutcomeApproach Using segmentation techniques, customers are classified into different segments based on various factors, such as purchase behavior, media consumption habits, etc. With this segmentation, customers from high-value segments can be identified to help improve revenue targets, improve CLV of existing customers, and finally, try new methods to find customers. Profit-based Customer Segmentation Different customers have different purchasing habits. Customer segmentation classifies customers based on how much value they bring to the company. Some customers are highly profitable and they should be rewarded loyalty incentives, as well as developing new marketing strategies to point them towards products they’d also be interested in. Email address Age group Attributes for segmentation Purchasing history Profile infomation Internet usage Usage patterns Calling type Payment history 18-24 65% 77%77% 73% 72% 78% 71% 59% 26-34 85% 72%72% 70% 75% 74% 68% 70% 35-44 87% 73% 73% 73% 63% 73% 73% 45-54 60%60% 60% 63% 63% 57% 62% 64% 55-64 65% 51% 66% 63% 44% 50% 69% 65+ 53% 68% 63% 43% 45% 53% 43%
- 8. 7 Adopting Analytics into Telecom There are four major steps to be followed when adopting analytics into a telecom provider’s company. • Identifying Internal Use Cases • Measuring Analytics • Finding Required Talent • Technical Requirements Some companies already have certain resources and skills in hand to open up an internal data analytics team. Other companies might need to build one from scratch. Some others are better suited to using a third party data analytics firm, like PNA, to get their work done. Different companies have different requirements, and without the necessary research beforehand, companies can run the risk of choosing an option that isn’t suited to their needs, causing further inefficiency. Finding Required Talent When it comes to telecom, there is already a vast array of implements in place to collect and organize data. But in this phase, we can find out what other technical requirements are needed, considering the particular goals the telecom provider has. This often varies depending on the data the company already has, and the infrastructure they already have in place to collect, organize, and integrate that data into their processes. Technical Requirements Before we can implement data analytics, there needs to be an identifiable use case within the company. These are issues and problems that are clearly defined and data analytics can offer measurable results in. Once we identify these use cases, we have the foundation with which we can build analytics solutions tailored to each company’s needs. Identifying Internal Use Cases The next step is to measure the impact of those changes. This means having a conversation with all the stakeholders and asking a few key questions. What are our performance goals after deploying the solution? How are these goals going to be measured? Does our organization have the tools necessary to measure them? This will provide valuable insight into finding out whether or not the deployed solution is performing as intended or if it has any unintended consequences. Measuring Analytics
- 9. We hope this gave you better insight into how Data Analytics can help your company reach new business goals. If you have any questions, please contact us using the details below. Thank You PositiveNaick Analytics Ltd. No177,1st floor,LM Tech Park, 1st Main Rd, Nehru Nagar, Kottivakkam, Chennai, Tamil Nadu 600041. Email: [email protected] Website: www.positivenaick.com Phone: +91-44 4857 6162