Raise 70% Doing Only 30% Of The Calls
(1 – 2 minute read)
Situation:
One of the main focuses for NGOs is to convert one-time donors to regular monthly donors and to do this, one of the most efficient channels used is telemarketing (TM).
Meanwhile, it is no secret that NGOs have a high focus on minimizing administration costs, with the obvious goal of securing as much funding for the cause as possible.
This is also a metric that is closely monitored by donors and has a direct impact on the donors’ trust in the NGO.
This raises a question: Is it effective to call every single lead and how do you know?
Complication:
Sort of a double-bladed sword wouldn’t you say? On the one hand, NGOs need to spend money, in order to fundraise via TM. On the other, they need the cost to be as small as possible while achieving the highest return.
Quite the predicament as these two often go hand in hand. You spend more, you make more, right? So how do you, as an NGO, find the perfect balance between the two?
As mentioned, when it comes to Telemarketing initiatives, the case is often focused on converting one-time donors to regular, monthly donors. However, the status quo has been the only option for many years.
The only real option for optimization was either to negotiate a better deal with the same or a new telemarketing partner or to move it in-house in order to control the full funnel. Meanwhile, this only unlocks marginal changes, if any at all.
Opportunity:
What it really comes down to, is using data in a smarter way.
Machine Learning isn’t a new thing anymore, it is however rarely used to predict the outcome of outbound activities – why is that? Well, the main reason (our best guess) is that the entry point, until now, came at the high cost and commitment of hiring one or more data scientists, who would easily spend upwards of a year building and perfecting the models.
And even while this might make sense in some cases, it doesn’t come without risk. With models being built manually, you rarely get automation “included” and you are highly reliant on the data scientists to retrain and maintain the models. If one decides to resign, is the organization then still fully covered to do business as usual?
A faster and cheaper option is to outsource the modeling to an Allyy with experience. So far we have a lot of cases that prove that Allyy is a more viable option with quite amazing results.
Resolution:
Adding Machine Learning to the mix with Allyy.io, results in a hyper-focused approach to outbound activities. Directly integrated into current systems and based on data from previous campaigns, we can with high precision predict who is most likely to pick up, as well as who is most likely to convert/react positively to a call.
Scoring individual leads and ranking them into “A, B, C & D” leads, ensures the spending of your valuable resources only on the right leads. In a specific case, with a 5% hit rate baseline across all leads, we saw a great elevation on A and B leads. Allyy was able to precisely predict that A leads would have a hit rate of +25% and B leads 7-8%, while C and D leads were well below the previous 5% average.
Don’t take our word for it. If you want to hear more specifics on the how what and why we’d be happy to talk you through specific use cases and see if they align with your situation.
