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Blog / Big Data Analytics Holds the Key to Reducing Churn for Telecoms

Big Data Analytics Holds the Key to Reducing Churn for Telecoms

Analyzing customer data in an attempt to understand and prevent user churn is one of the top-level priorities of telecommunication companies. In such a tough market, having the right tool to execute this analysis and get ideas on how to reduce churn could be a huge asset.

Customer churn is a key topic in the telecoms industry. With a constantly increasing competition from a wide range of industry players, it has become extremely difficult to secure customer loyalty. Traditional wireline telcos are facing increasing competition from cable operators, as well as innovative OTT businesses that stream low-cost audio, video, and comms services over IP.

On top of that, it’s getting harder to differentiate products and services based on network quality or handset options, and the rapid rate of product refreshes only helps to encourage consumer churn. Telecoms firms have one big advantage, though, and that’s the huge volume of customer data they’ve collected over the years and continue to gather. The secret to fighting churn lies in the ability to dive deep into that data, discover trends and use the information to understand the customers and try to prevent them from moving to the competition.

Data can help in reducing churn by 15%

It’s widely known that it is a lot more expensive to acquire new customers than to retain existing ones. So, if you can keep hold of your customers through a combination of excellent customer service, timely offers, effective personalization, rewards for loyalty, and predicting and anticipating future behavior, you will reduce churn and continue to grow your base.

In fact, telecom companies can reduce churn by as much as 15% by applying a comprehensive, analytics-driven approach to managing their customer base, according to analyst firm McKinsey & Company.

Intelligence gathering through Big Data

It’s now possible to create the comprehensive view of your customer that marketers could only dream of in the past. And this should be their goal: to have a way of aggregating all the varied datasets you have for individual customers, and using these to their advantage. This includes the transaction data from your data warehouse, which tells you how much each customer is spending on your services and when. It also includes service call information that lets you know how well or badly services are going for individual customers; and data that relates to network performance or web logs, which can tell you about delays and downtime.

You should also look at historical CRM-related contact center data regarding products, offers and promos, usage, and rebates; as well as social media intel that communicates how your customer feels about your brand from their likes and comments. In addition, it’s possible to add into the mix information from online chats, plus website cookies that can tell you a lot about your customer and his habits. All this data can help you build a rounded picture of each customer, as you work towards the goal of keeping your customers delighted and lowering your attrition rate.

The power of micro-segmentation

McKinsey advises telcos to use all this data to create customer micro-segments, which can help you personalize your offers and services to particular groups of customers that have a high likelihood of leaving. One leading telco developed a library of 50+ offers, targeted such a micro-segment with marketing offers, and slashed their churn rate by 10-15% over 18 months. The trick to changing customer churn behavior, says McKinsey, is to be able to identify and quickly test new offers on individual micro-segments, learn, and adjust various aspects such as value, messaging, and mode of delivery.

Take a data deep-dive

Alongside this, telecoms firms should look to adopt cutting-edge analytical techniques that apply advanced algorithms to their aggregated data sets, to uncover hidden trends and better understand customer behavior. This is particularly useful in predicting why customers might choose to leave. According to McKinsey, one leading operator used an analytical technique called ‘feature discovery’ to identify over 50 variables that contributed to customer churn, as well as their relative importance. Among these variables were specific factors such as combinations of types of phone, data usage, and call-center interaction history. If a customer hit on any of these combinations, the software could reliably predict that the customer was on their way out.

Control churn through data analytics

There are several key data analytics techniques you can apply to control churn. One of these is customer Lifetime Value (LTV) analytics. This estimates how much value a customer is likely to bring to your business over their lifecycle and helps you to prioritize who you need to target. LTV can indicate who’s most likely to move away, who might need special attention, for example through personalized sales, and what kind of plan might be attractive to wavering customers.

Another weapon to combat customer attrition is traditional A/B campaign management analytics, which can observe pre- and post-campaign sales and responses for a particular timeframe and cohort and work out the promotional benefit. As in the previous technique, data visualization really helps to bring clarity to this important information.

The rise of the robots

Advanced analytics also enables you to carry out AI-driven predictive analytics through sophisticated techniques such as predictive behavior modelling, which is a mathematically intensive technique that can accurately predict churn in a specific customer micro-segment. Another is customer retention analysis (aka ‘survival analysis’), which can indicate how many new users will remain customers over time.

Thirdly, Next Best Offer (NBO) recommendation can predict what your customers want before they do. NBO can help you put together a highly customized offer and guide your customer to it at the right moment in time, using the most convenient channel for them, and at a price they will find most appealing.

Lastly, you can apply sentiment analysis to text - such as social media comments, reviews, emails, and webchats - via machine learning-based Natural Language Processing (NLP). This has the power to identify the positive, negative, or neutral emotions customers feel towards your products and services. Sentiment analysis is growing more and more sophisticated by the day, and in terms of addressing customer churn, it can rapidly detect disgruntled customers, give you deep insights into what they’re thinking, and help you win them over before they disappear.

Race ahead of the competition

Telcos are in a unique position to understand their customers and reduce customer churn, due to the large amounts of information about customers they are collecting. Analyzing this data can build a complete picture of each customer through their current and historical transaction, social and usage data, and can assist in building customer micro-segments. Then, by applying advanced analytics techniques, not only can you drive down your churn rate, but you can gain a significant competitive edge in a very crowded marketplace.

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