Collecting and analysing data about customer usage is becoming the standard for operators in all markets, as it allows them to follow and respond to customer habits. They now have access to a lot of customer information, although it may not be as complete as they’d like – particularly in emerging markets where there are a lot of prepaid customers so there aren’t personally identifiable usage patterns as there are in developed markets.
However, the data is there, and the more the operators think about uses for this data the better they will get at collecting it. So, what can they use it for?
Ultimately there are two kinds of uses for customer data. The first could be termed as traditional analytics or CRM, where the historical data is analysed for segmenting or campaign management purposes. Then, there is an operational side emerging where the data is offered to the active touch points: customer care, agents, self-care etc. This can then be used in real-time to make recommendations, either to the end user themselves or to a customer care agent.
Timo Ahomäki of systems developer Tecnotree notes: “Real-time is not the be-all-and-end-all, but some elements such as balance enquiries certainly benefit from it. In a customer care environment, it’s highly advantageous to have usable information concerning network outages or other problems in real-time.”
If for example a customer is calling from an area where there is an identified network issue, a customer care agent can reasonably assume that they may be calling in relation to this problem, allowing the complaint to be addressed more efficiently.
However, it can be somewhat risky to have certain network elements running in real time. Sometimes there are problems receiving data – it is aggregated from a number of sources and every so often a chunk of data is ‘lost in transit’. If data is fed in real time into the decision-making process, missing data can be a major headache. Many existing CRMs assume that if there is no data there’s no traffic, so using data in this way can lead to false conclusions.
“Aggregate behaviour doesn’t change much within 24 hours. If an operator launches a campaign in the morning they’ll want to follow it up during the day, but even after a day or two, the interest on an hour-by-hour basis fades. For aggregate behaviour, 24 hours is a perfectly acceptable interval”, concludes Ahomäki.
This view is not shared by all. Stuart Eveleigh of Convergys is of the opinion that being able to respond to customer activity in real-time is essential for customer retention, as pushing offers at the opportune moment is crucial to inspiring loyalty.
Simply reducing the number of user complaints is a good start to retaining customers, and this can be done simply by using real-time monitoring to keep them informed. Eveleigh cites the example of over-plan up-sell: when users are approaching their data allowance, they receive an alert to prevent them from exceeding it as well as the option of a supplementary data package – which is discounted if they buy it as soon as they receive the alert. Users who receive a warning at 90% will be unlikely to use 100% of their data allowance, which prevents complaints.
By tracking customer profiles, it’s possible to offer packages targeted at specific users. The operator can suggest packages that are more suited to the usage habits of – for example – a customer who consumes a lot of data. The real-time element allows the package to feel relevant, and this in turn inspires loyalty as the user feels that the operator is responding to their needs.
Assessing customer usage habits creates opportunities that are beneficial to both operator and subscriber. Eveleigh cites the example of airtime vouchers, which create a discrepancy between the credit sold and the operator revenue.
“People have a lot of credit cash but the operator can’t recognise this until it’s used – if a subscriber uses one dollar of a ten-dollar voucher, the operator cannot revenue the ten dollars”, says Eveleigh. “With a one-dollar bundle, subscribers can speak within the network as much as they’d like. This not only creates instant revenue for the operator, but it provides subscribers with very low-cost airtime. This inspires strong subscriber loyalty.”
The appeal is obvious for operators –100,000 subscribers would immediately avail them of $100,000 revenue, plus the plan discourages subscribers from swapping SIMs for cheaper rates on a different network. In addition, users are encouraging customers from other networks to switch over so that they can save money.
Although in the context of roaming such network-swapping is often involuntary, it is still an issue for operators in terms of revenue loss. Real-time monitoring can allow operators to be more agile in their response – if a rival can easily capture subscribers when they arrive in a new market, they essentially churn out while they’re abroad and only churn in when they return home, meaning that the operator loses the roaming revenue.
However, with real-time monitoring a subscriber’s presence on a different network can act as a trigger, allowing the operator to contact them with roaming information, perhaps offering them a cheap price on a bundle to encourage use. Multinational operators can bundle roaming into pricing plans and the convenience factor means that customers will stay with them.
While being able to respond immediately to network events has obvious applications, it’s interesting that it also presents problems in terms of ‘lost data’. In our next feature, we’ll continue to look at the importance of real-time monitoring, in terms of both efficient B2B service delivery and customer profiling.