How to achieve best practice for dialer use (V2)

An automated dialer, like all powerful tools, must be used carefully. This article focusses on avoiding issues with dropped (silent, lost, abandoned ...) calls and related issues in connection with answering machine detection. We'll dig into the metrics and the thresholds applied in guidelines and by some regulatory bodies.

The undesirable things to avoid

The particular dangers of using a dialer are:

  • connecting calls to people and having no employee to take the call
  • connecting calls to people and incorrectly automatically categorizing as an answering machine

Both cases result in the call connecting briefly to a real person before it automatically ends. In the call center, direct marketing and sales areas, these undesirable occurrences are sometimes called "dropped", "abandoned" or "lost" calls. The term "silent call" is also used, since the person who takes the call hears nothing and then the call ends.

We usually use the term "dropped calls". We shall refer to them as "dropped live answered calls" when we want to differentiate them from dropped calls where an answering machine is correctly detected. So "live answered" means that a real person actually accepted the call.

How tolerances are laid down in regulation and guidelines

Regulations in many territories aim to limit the occurrence of dropped calls and the negative impact on consumers. Also, there are best practices recommended by industry bodies and consumer organizations among others.

In general, regulatory bodies like Ofcom in the UK, FCC in the USA and similar organizations publish research on the topic and outline the measurements that have to be taken by a company using a dialer and the tolerances allowed for the frequency of undesirable dropped calls.

These tolerances and the related regulations vary quite a lot. For example, in the UK Ofcom determined that out of 100 live answered calls, not more than 3 abandoned or falsely detected answering machine calls should occur. A similar measure in the USA had an acceptable tolerance of 2% but did not originally include falsely detected answering machine cases.

The babelforce position and goal

Our aim of babelforce is that the occurrences should be even lower than these tolerances. We consider it essential to good practice to reduce these undesirable side effects as much as possible.

Therefore, we consider it part of our mission to help businesses to achieve best possible metrics for these numbers.

How to measure the correct things

Actually, it is not that trivial to measure all aspects accurately by means of the available dialer data. Firstly, it is vital to understand what exactly needs to be measured to correctly evaluate the data.

One of the more complex problems is related to the use of Answering Machine Detection (AMD). Therefore, we will look at these two cases (AMD on and of) separately.

Answering machine detection is OFF

In this case, one of the accepted directly measurable metrics is defined as follows

biglatexfrac_droppedrate1.png 

Since these are determined based on the outcome label the agent gives the call and since these may vary, babelforce delivers the following standard metric which we call "ratio of dropped to live answered":

biglatexfrac_droppedrate2.png

In order to understand how this ratio works, let's do an example calculation. Let's say we had 100 calls so far today where an agent labelled the outcome so that we know they talked to a live person. So [livePersonToAgent] = 100. Since AMD is OFF, the number [allCallsDropped] should be small: We can only have a dropped call, if an agent is not available for an outbound call that is answered. Let's assume, we had 2 so far. Then the automatic babelforce metric will be 2%.

If we ignore the number of calls an agent is connected to an answering machine for a moment, the first metric above gives us 2 divided by ( 2 + 100 ), which is pretty close to 2% (1.96..% to be precise).

Let's say, agents connected to 20 answering machines so far. Then the first metric gives us 2 / (2 + 100 + 20) = 1.6%

Hence, the babelforce standard ratio of "ratio of dropped to live answered" always overestimates one of the standard metrics. So attempting to minimise the babelforce ratio will definitely keep you on the safe side.

 

Answering machine detection is ON

Answering machine detection (AMD) will definitely have "false positives". For example, in some cases the AMD algorithm will determine that the call was connected to a machine but in fact a real live person took the call.

The number of these occurrences are likely to be greater than the normal dropped calls, because as we noted above, a properly operated campaign and proper personnel planning can avoid connecting to a customer if no employee is available.

Therefore, regulators and industry representatives often recommend a way to factor in these false positives. Here is a common way to calculate an estimate of the needed rate 

biglatexfrac_droppedrate3.png

The formula looks complicated, right? Only the top has changed though. Basically the frequency of occurrence of false detections of answering machines is multipled by the calls connected to agents.

Here is the tricky part. The FalseDetectRate can only be determined by sampling the data for your campaign with your audience. Hence, it is not possible to give a standard calculation of this just from data available directly from the way you use a campaign.

IMPORTANT: if you use answering machine detection, in many territories it is your responsibility as the business using the dialer to assess that false positive rate for answering machine detection as well as the dropped rates above. Penalities can be assigned to businesses who cause silent, dropped or abandoned calls and you are required to be able to provide data on how you operate campaigns with answering machine detection.

Recommendations

DO NOT USE AMD in any situation where it can reasonably be avoided.

For example, if your employees are detecting and labelling answering machine connections within a few seconds, the impact of that is far lower than the impact of a number of your customers waiting for a few seconds on a silent call that then ends. 

Also note that AMD will always introduce a delay in the call connecting to an agent, even if everything goes well. So even in the best cases it will affect customer experience.

The vast majority of the benefit of using a dialer is not achieved by using AMD, but rather by the automation of integrated processes, the reduction of manual steps, the standardization of status codes and recycling/rescheduling rules, the reduction in data entry and the automatic discovery of unclean data.

Secondly, if you use AMD in your campaigns, make sure to tests beforehand to detect the false positive rate. This will allow you to determine the impact of AMD more completely and is the only way to ensure you are within reasonable bounds or within acceptable thresholds for territories where they apply.

In short, consider the perceived pros of using AMD compared to the likely cons very carefully.

 

Some links to other resources

You should consult the relevant latest regulations and guidelines depending on the country you use the dialer and the country your customers live.

Here are a few organizations and links that might be useful to you:

Ofcom - UK communications regulatory body: https://www.ofcom.org.uk/about-ofcom 

German Call Center Verband Branchenkodex: https://callcenter-verband.de/verband/branchenkodex/   

Bundesnetzagentur - German communications regulatory body: https://www.bundesnetzagentur.de

Information to misuse from the German Bundesnetzagentur: https://www.bundesnetzagentur.de/cln_1421/DE/Sachgebiete/Telekommunikation/Verbraucher/Rufnummernmissbrauch/Missbrauchsfaelle/missbrauchsfaelle-node.html 

FCC Federal Communications Commission - USA regulatory body:  https://www.fcc.gov/general/telemarketing-and-robocalls 

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