In the publishing industry, we’re constantly innovating and rethinking the way we run our businesses. Over the last couple years, we at Newsday have begun to shift the way we perceive success in the contact centre.

In the past, the contact centre was measured by agent productivity metrics such as average speed of answer, abandon rate, and cost per call. These measures led to publishers focusing on maximising operational efficiencies and cost reduction. These measures, while still important, no longer fit in our business model.

Today, with the publishing industry migrating to an audience-based business model, the contact centre plays an even larger part in helping retain and grow subscription revenue. At Newsday, we’ve shifted our focus to agent and customer satisfaction, stop-save performance, price increase realisation, and personality matching.

Before delving into each one of these metrics, let’s discuss one of our biggest struggles: technology.

Our platforms do not pass caller attributes between one another, which leads to a poor experience for both the customer and the agent. The customers continually need to provide information, while the agent is ill-prepared to answer the call. From the data analytics perspective, we are left to match the call flow ourselves, which is less than perfect. 

We’re partnering with Voiceport to upgrade our technology, which includes the automatic call distributor, interactive voice response, and call monitoring solutions. These investments will allow us to enhance experience, satisfaction, and resulting revenue. We expect to see a smoother customer experience and will be able to provide valuable customer data to the agents along with personalised call scripts. 

Behind the scenes, we’ll be able to append critical customer attributes and prioritise high-value subscribers as well as intelligently route calls based on personality types, agent skill sets, and customer attributes. 

Let’s get into how we’re measuring contact centre success. 

Agent and customer satisfaction

Measuring experience has generally been focused around customer satisfaction. The use of surveys gives us insight into the elements of the call experience that predict greater retention. It also gives our customers another channel to engage with our brand. 

This year, we made it a priority to understand our agent satisfaction. The last thing we want is a less-than-satisfied agent answering a phone call of a potentially dissatisfied customer! We kept the practice of utilising surveys and tacked on roundtable discussions. Through these discussions, we identified best practices for certain call types. We also identified areas of concern and needs. 

By giving the agents this open forum to voice their opinions, we continue to boost employee morale and, in turn, both agent and customer satisfaction. 

Stop-save performance

What is a “stop-save” call? A “stop-save” is when a customer calls with the intent to stop their subscription and an agent is successful in preventing the subscriber stop. Our focus is to drive incremental stop-saves, as it costs time and money to reacquire these same subscribers through other marketing channels, at significantly lower subscription rates. 

Industry averages a 20-30% rate, with the majority of top performers around mid-30%. We finished 2016 with a 36% save rate and are seeing a four-point improvement this year. Each one-point improvement yields multiple six figures in annualised revenue. 

It’s our agents who are the last line of defense from a customer canceling his subscription. We’ll continue to invest in training, technology, and agent staffing levels to maximise profitability on stop calls.

Price increase negotiations

Through the partnership with Mather Economics, we’ve had years of utilising its market-based pricing algorithm. As price increases are being deployed, we experience a small but increasing number of subscribers calling to negotiate their subscription rates.

Upon review of the net rate realisation realised by agent, we found that variance to be significant — US$250 thousand in annualised revenue from the top performing agent as compared to the bottom performing agent. 

Through this analysis, a negotiation tactic was established within the contact centre. We developed a model that helped identify the elements of this conversation process that yielded the greatest realisation.

The first tactic was value proposition to secure the full price increase, and our second was offering 50% of the price increase amount. We sought to maximise revenue and decided to test this interim offer. We found there was greater rate realisation by having two interim offers — 65% then 30%. 

Personality matching and attribution-based call routing

Aimed at driving improvement to our stop-save rate, our senior data analyst, Peter Long, built a characteristics model that will help identify the ideal match between customer and agent. We have defined our agents into four personality types (professional, mellow, chatty, and aggressive) and utilised nine customer attributes. 

Currently, half of our stop calls are being randomly matched to the correct agent personality. The group of calls that were successfully matched yielded a 45% save rate, whereas the calls that were not matched yielded a 37% save rate.

This suggests that, when personalities are matched, there is an opportunity to improve the stop-save rate by eight percentage points. This lift would yield US$2 million-plus in annualised revenue and US$4.5 million-plus in customer lifetime value.

Coupling our characteristic model with our planned technology upgrades via Voiceport, we will have the ability to perform the attribution-based call routing required to capitalise on this opportunity. 

We’ll continue to manage the contact centre as a revenue generator rather than as a cost centre. The fuel for revenue growth comes from bringing together contact centre technology, satisfaction, and customer data into actionable analytical models.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .