Today’s telecom providers all offer similar levels of coverage and service, which makes it difficult to prevent customers to churn for the latest competitor offer. However, it has become clear that quality of service and experience is a leading differentiator between providers. In fact, a Gartner survey found that 89% of companies believe customer experience is now the primary base for competition.
So, how do telecom providers differentiate the customer experience they offer? The key is optimisation. I’m not talking about optimisation in the loose sense of the word – just doing things better. I’m talking about mathematically optimising the analytic process of determining the best action to take to reach specific business outcomes, given data and constraints. Also known as prescriptive analytics, optimisation is really the next plateau for businesses that compete on analytics.
Here are four business areas telecoms companies should optimise to create an improved experience for customers and gain that extra competitive edge.
Identifying potential churn
Telecoms spend a significant amount of money to recruiting new customers yet churn rates for multi-play packages can still reach 20% per year – a serious drain on profitability.
Optimisation can substantially move the needle on this all-important performance metric. Successful strategies can be created based on customer profiles and activity data – for example, age of mobile device, dropped call incidents and customer support interactions – and used to segment the customer base according to the risk of account closure within specific timeframes.
Once potentially problematic accounts have been identified, optimisation tools can be applied to automate measures mitigating those risks. For example, offering a discount voucher to certain customers that rated a support interaction as poor will reduce the likelihood of them leaving for a competitor.
Investing time in the right customers
In addition to keeping customers engaged, telecoms companies should use optimisation to identify areas to cross-sell to existing accounts. This would entail incorporating various analytics – such as behaviour scores assessing the customer’s ability to pay, models predicting the likelihood of offer acceptance and take up, customer value, churn rates, individual credit risk and more – to create automated strategies that can be finessed in response to changing market demand and individual circumstances.
Reducing bad debt
Figures published in July this year by the Financial Conduct Authority (FCA) reveal approximately one in six people with consumer credit debt are in moderate to severe ‘financial distress’. If a customer is in debt on their account, it is highly probable they are also in debt elsewhere. Although collections are never enjoyable, offering good customer service throughout this process makes it more likely the customer will stay loyal.
After leveraging optimisation capabilities to identify the accounts showing the first signs of payment difficulty or increasing credit risk, it’s then possible to trigger treatment actions such as sending automated messages in a preferable format via a channel that the customer is likely to engage with, introducing temporary restrictions, or prompting a call from the helpdesk. This step not only reduces the risk exposure for telecoms organisations, but also helps customers keep their accounts open.
Furthermore, optimisation allows operational resource to be deployed to have the greatest impact, within the constraints applied (such as headcount availability).
Compliant with explainable decisions
As telecoms’ credit policies, customer management and marketing grow more complex, it can become difficult to fully grasp – much less explain – how decisions are made. While an analytics-driven strategy is essential, the old ‘black box’ approach, which offers no transparency on how data inputs are transformed into decisions, is no longer acceptable. For instance, under the General Data Protection Regulation (GDPR), coming into effect across Europe in May 2018, customers need clear-cut reasons as to why they were adversely impacted by a decision. For example, consider how offers for mobile phone service plans are calculated, and to whom they are offered. If a consumer feels they have been adversely affected by a decision model and queries the decision-making process, it would not suffice for the operating company to state ‘computer says no.’
It is therefore vital to choose optimisation tools that enable business users to easily navigate complex decision strategies and pick out the inputs used at any point. If a decision is questioned, users can immediately use them to determine the credit policy rules and models invoked. Optimisation translates existing data into actionable insight, which enables better business decisions. With it, businesses will be more equipped to harness the potential of market change and counteract competitor tactics. Given the reality of the telecoms market in 2017 – more choice for consumers, more competitive offers, and more regulatory scrutiny – it’s crucial, now more than ever, that telecoms organisations use optimisation to retain and grow that all-important customer base.