Crucial but ignored mid-cycle customer intelligence for Telcos to influence broader engagement

By : Varun Talati

Sr. Manager, Analytics and Consulting

Posted on : March 28, 2019
Posted in : Blog
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Telecom service providers around the globe are now aware of the potential of data analytics for driving profitable customer engagement as shown by their increased spending in analytics tools and technologies. However, their efforts are still in silos, with marketing, sales and service teams adopting analytics to improve their own sphere of engagement. For example, marketers only use the intelligence drawn from mid-cycle (during the usage phase) customer events to improve segmented marketing programs targeted at improving upsell, cross-sell and retention. There was a very little focus around how this intelligence can probably help other teams who deal with other stages of customer engagement lifecycle like customer acquisition (sales) teams or customer service teams.

Customer intelligence through different phases

In the earliest phase of the customer journey, the customer becomes aware of the products and services that the Telco is offering and interacts with the customer acquisitions or sales teams (B2B context) to make a purchase. In the next phase, the marketing teams mostly interact with customers through various promotional campaigns to onboard them, and then to drive usage and retain them. This is what is generally termed as the mid-cycle phase. And then the customer service teams make sure that customer inquiries and concerns are properly managed and they are reminded on contract renewals, bill payment and so on. Each of these phases potentially generate intelligence on customer’s digital lifestyle, usage behavior and his preferences and contextual needs.

However, according to the recent survey done by TMForum, most of the Telcos have adopted analytics in only a few stages of the whole customer lifecycle. As per the below graph, only one-third of the telcos are using analytics for stimulating contract renewals and only half of the telcos are using analytics to influence customer service.

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Moreover, intelligence is owned and used by respective teams dealing with each stage of this lifecycle.

For example, marketers have a greater understanding of customer’s usage behavior and channel preference which can probably help a customer service agent to choose right channel and right offer while interacting with customers. Similarly, marketers know which customers or segments are consistently using a specific product or a bundle, which can probably help sales teams to look for similar consumers for customer acquisition. These opportunities are mostly ignored today.

Tapping customer analytics for broader engagement – Sample use cases

Telecom marketing teams have started deploying machine learning (ML) and Artificial Intelligence (AI) towards critical objectives such as increasing upsell/cross-sell, improving profitability and reducing the churn to enhance CVM impact. However, the marketing intelligence gained by Telcos during mid-cycle stage can benefit other teams within Telcos who are dealing with other phases of customer engagement lifecycle like acquisition (sales) and customer service teams. Some examples are provided below:

Understand channel affinity of customers to maximize reach

Gone are the days where customers only had few channels to interact with Telcos. With digital disruption and high smartphone penetration, customer interactions have shifted from traditional channels like SMS and call center to mobile app, mobile portals and so on. However, customer service inquiries are still handled to a large extent on call centers which are resource intensive and costly. Telcos wish to move these interactions to digital touch points like chatbots and self-care mobile app, reducing the cost of servicing customers. In this context, intelligence can be drawn out regarding channel preferences of each customer by tracking his interactions in the mid cycle stage. If a customer is frequently responding positively to offers extended on a specific channel, then the same can be used by customer service teams as well for their interactions like bill payment reminders, contract renewals etc. The same channel affinity patterns of customers and segments can be used by customer acquisition teams as well when they choose specific channels for reaching similar type of consumers in their lead generation campaigns.

Determine product affinity, customer sentiments and contextual needs for proactive customer service

Telcos can gain a deeper understanding of customers by analyzing their transactions and interactions in the mid cycle stage. Looking at their historical usage and contextual interactions, now analytics systems can predict a customer’s mood or sentiment and also his contextual needs better. And if a customer service agent knows a customer’s sentiments and needs in advance, he can engage with customers better and with relevance. Call centers can even predict the nature of an upcoming customer call which helps in routing the call to the right agent, who is proficient in resolving specific problems or an expert in answering specific customer inquiries on products and services. This will not only reduces call handling time but also improves customer satisfaction. Moreover if a customer is consistently using digital channels like mobile app, then telcos can even divert these customer service inquiries or call to digital touch points, further reducing the cost. Product affinity analysis can also help in extending right products/offers over customer service touch points.

Identify right contexts for contract renewals and upgrades

In contract markets, customer’s life-cycle is dependent on the length of the contract, which is usually 12 months to 24 months. Typically telcos engage with customers closer to their contract expiry with frequent reminders to continue with the service. However if intelligence drawn from a customer’s mid-cycle usage events and interactions can be tapped, telcos can short circle contract renewals and upgrades. For example, if a customer is consistently exhausting his full quote of data service by noon, then a higher postpaid plan with a higher data threshold can be offered to them.

Channel/touch-point affinity of customers can also be used to devise how to approach customers with the contract renewal reminders and upgrade offers. For example, if a customer’s contract period is expiring the following month and his/her affinity is towards self-care app, telcos can reach out to him/her through in-app notifications. Also, based on his/her usage data, telcos can also recommend a new contract which is of relevance to them.

Find right prospects for customer acquisition

The behavior of existing customers can be taken into account to identify similar consumers who can be targeted for customer acquisition. Look alike modeling will help in sales activities such as lead generation, lead segmentation and acquisition. For example, Telcos can analyze the demographic profiles and usage behavior of a group of existing foreign resident customers in a specific region (like use of specific language, country of origin, recharge frequency, etc). This, in turn, can help them in determining the right audience segment with similar profiles and needs for acquisition campaigns as well as to send them right offers.

Summing it up

Understanding customer’s need across the lifecycle is undoubtedly a challenge. However, customer intelligence that can be drawn from mid-cycle events and interactions can potentially help telcos to influence different stages of customer engagement lifecycle beyond marketing like sales and customer service provided they have right AI and analytics tools.