“A lot of Millennials don’t necessarily even have a history. If they’re an unknown, we go ahead and let them purchase, which can open us up a bit to fraud at that step,” says a regional account director for a cable TV services provider. “And then we wait to determine that on the back end, after a month, once we see the data, to say, ‘Alright, what happened?’”

The marketing director for a telecoms services provider notes, “What we need is information on prospects so that we didn’t have to just utilize a current customer model to project on prospects. Then the other is just having a more detailed credit profile on prospects – we have their credit profiles, but that’s all. And, ‘How do Millennials look differently than the older target?’ Unfortunately, we’re only looking at prospects from a general level, not an individual one, because we don’t have that kind of data.”

Going Beyond the Credit Score

In making a decision about consumer risk, credit scores are just one part of the picture. Media and communications service companies use a range of information to assess consumer risk from aggregate-level socio-economic data, such as census data and tax maps, to predictive modeling and segmenting based on consumer demographics and behavior.

At the street and household level, media and communications service providers look for key indicators of whether a neighborhood represents a higher or lower risk. How many occupied residences are there? What are the number of residences with bank account? What are the average incomes of the inhabitants? These qualitative factors can help companies make more accurate guesses on which consumers to serve.

On a demographic level, media and communications service providers make assumptions based on media consumption behaviors, churn profiles, credit status, age and occupation.

“We do internal segmentation of our customers, so that we know what attributes make a higher value customer who will pay and stay with us. We can also determine if a new customer is extremely high risk based on our current customer data, who will probably just use us for six months until we have to cut them off and might be a bad credit risk,” says a strategic and analytics decision maker at a telecom services provider.

The downside to this approach is that it gives an insulated view of customer attributes, risks and churn triggers that could ostracize valuable prospects, such as Millennials, who differ to older demographics in terms of consumer behavior. 

Some media and communications service providers have even incorporated social media data into their analysis of a consumer’s risk profile. This can include their purchase preferences and behaviors, which filters prospecting lists on a more individual level in terms of risk as well as marketing channels. 

Multi-layered Approach to Credit Risk Decisioning

LexisNexis Risk Solutions found that those using individual-level prospect data or customer data to predict prospects’ involuntary churn are nearly as challenged with credit risk as those using less detailed types of data and approaches. While information on individuals is invaluable, credit ratings don’t always align with consumers’ behavior once they have opened an account. Excluded from those demographics are Millennials, who do not fulfill the criteria, as they lack adequate credit history. Further, consumer situations can change – losing a job, buying or selling a house or filing for bankruptcy might go undetected in a credit report.

This is exemplified by the vice president of a wireless service provider, who states, “We do a lot of modeling and look at lifetime value, but really need behavioral data about prospects instead of relying on our own customer data.”

Combining credit data with behavioral information provides a fuller picture of a prospect’s attitude, purchasing patterns and decisions. By using alternative data and predictive analytics to augment this data, a media and communications service provider can build a profile around a prospect’s household, relationships and demographics. This negates the risk of changes in a credit report that might go unnoticed, and develops a fuller picture of how a consumer might behave once they are on-boarded. This also increases the field of prospects from thin-file consumers, such as Millennials, who constitute an untapped lower-risk consumer base.

In doing so, media and communications service providers can feel more confident in moving away from mass marketing approaches, which have the tendency to attract fraudsters and low-credit consumers, while shifting towards targeted invitation-to-apply marketing campaigns. This is far more cost effective, reducing both marketing budget spend and the cost of weeding out involuntary churn consumers, collections on bad debts and fraud losses at a later stage.