Will You Pay for a List of 100 Customers Ready to Buy Now?
And how artificial intelligence and predictive maintenance are the keys to turn that list into 100 million customers.
A few years ago, I went to speak to CEOs of major home appliance companies. I was asking questions and looking to understand their world and what they cared about.
In the beginning, I got the obvious answers: “We care about our customers.,” “The customer is in the center,” “Fair competition,” etc. I found no concrete insights worth mentioning.
So, I went on and asked during these meetings, “How do you gain revenue?”
The answer was simple:
“We sell home appliances, and we sell service and maintenance to these appliances. We do it either directly or through our vendors and distribution channels.”
“And how do you actually get to your customers?” I asked.
“We have a strong reputation, and customers like us.”
When I dug further and asked them for their actual attrition rate… I didn’t get any answer. They didn’t know.
When I dug further and asked them for their actual attrition rate or how many newly acquired customers they have annually vs. their returning customers, I didn’t get any answer.
They didn’t know.
The fact was that the percentage of returning customers was very low. They were bleeding with a very high attrition rate, which was difficult to quantify.
The reason for that was extremely important yet elusive to understand. They sold their home appliances, and then they lost contact with the customer. So, unless the customer chose to contact them or their distributors again at some point, the connection was cut off, and the customer was lost.
Then I did something that surprised them. I pulled out a list and asked this question:
If I gave you a list of 100 customers who have a home appliance broken at this very moment, would you pay for it?
They were quiet. Every single CEO I met took a few seconds, looked at this list I gave them, lifted their eyes at me, and responded with one word:
Yes.

The first time, I was overwhelmed by the definitive response, but then it became a custom of mine to ask this question. The more I got their excited yes, the more I realized that there was a crazy opportunity there.
But there was one more thing that astonished me even more during these meetings. Some of them shared with me their constant struggle to acquire customers and told me that they often pay field agents to tip them off if there is maintenance or replacement need at an address.
For example, whenever a real estate agent called and let their employees/vendors/distributors know that there was a failing appliance (broken AC/boiler/refrigerator) at an address along with the customer’s name, the company turned that information into dollars by selling a replacement or a service call to that customer.
After having a sufficient number of meetings with these CEO’s, the following became clear:
1. There was a huge industry of major home appliance companies with a high average selling price (ASP) that struggled to acquire and keep their customers every year.
2. Home appliance companies already used a mechanism that generated real-time alerts that were translated into sales leads, but it was very random and inefficient, based on human agents that were only generating leads every now and then.
what if I could take their strong need to acquire customers and the willingness to pay for the information and base it on IoT sensors and artificial intelligence advanced analysis capabilities? Would I be able to generate 100 million leads?
So, I thought, what if I could take their strong need to acquire customers and the willingness to pay for the information and base it on IoT sensors and artificial intelligence advanced analysis capabilities? Would I be able to generate 100 million leads?
Kaboom! This was a huge opportunity. This was taking an old and primitive lead generation mechanism and scaling it exponentially.
Technology is a gateway to make it happen:
Artificial intelligence and predictive maintenance are the keys for this mechanism.
A highly scalable and reliable mechanism ought to be based on predictive maintenance (PM) capabilities. PM is the ability to predict that an asset, device, or anything else that is being monitored is due for maintenance
I realized that in order to scale up the existing leads mechanism and be able to generate 100 million leads for these CEO’, I should start by generating the following:
1. Just happened alerts: Alerts when a failure has occurred. The goal is to shorten the “downtime” and send a service professional to fix the problem ASAP.
2. About to happen alerts: Alerts regarding functionality deterioration, efficiency degradation, and the prediction of a potential failure that can shut down the appliance before it happens.
Predictive maintenance is based on two other disciplines, which are artificial intelligence and machine learning. It required data gathered from the home appliances and later their analysis.
Using sensors to collect data such as temperature, current, voltage, flow, humidity, pressure, etc., can create a three-dimensional picture of the monitored system. Then, after collecting the data and defining the alert threshold, the algorithms can pinpoint and detect upcoming failure through constant comparison between standard patterns of a working machine to “unusual patterns” of a machine that is about to fail. Once the algorithm detects a failure, it will immediately alert a technician.
Of course, this mechanism also depends on the number of home appliances that are actually monitored. Estimations are that in the coming years, billions of home appliances will be connected to the internet (Gartner estimated twenty billion connected devices by 2020).
This is how a single question I asked in a meeting made me see a huge opportunity.
See an example of how this business opportunity is turning into reality in the use case of the water heating industry: watch the movie.
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Please write me with any feedback at yossi.azulay@sindesy.com.