Can A Problem Be Too Painful To Solve?

As lean practitioners, we understand the importance of validating the problem you are solving with customers by talking to them directly. We get this because we know if we aren't solving a meaningful problem for customers, we will not get traction with our startup. 

As we search for meaningful problems to solve, it's logical to conclude the more painful the problem is, the better it is for your startup. If customers are hurting in a serious way, they will be even more motivated to purchase your solution to their agonizing pain. Right?


It turns out there is a optimium point for problem serverity. I recently discovered it is possible for a problem to be too painful, too severe to be a viable problem to solve in a startup. This is because of a powerful psychological phenonenon called learned helplessnes.

Learned helplessness occurs when an animal is repeatedly subjected to an aversive stimulus that it cannot escape. Eventually, the animal will stop trying to avoid the stimulus and behave as if it is utterly helpless to change the situation. Even when opportunities to escape are presented, this learned helplessness will prevent any action.

When people feel that they have no control over their situation, they may also begin to behave in a helpless manner. This inaction can lead people to overlook opportunities for relief or change.

I learned this lesson the hard way, as I was selling our predictive analytics platform to consumer marketers using online lead generation programs.

Here's the story.

I was working in the consumer lead generation market, buying and selling leads. I kept hearing lead buyers complain about lead quality problems across the industry. I decided to take a hard look at the problem to see if I could solve it.

I was able to build a powerful, predicitve analytics platform that improved lead performance by 40 to 60%. That's the good news.

The bad news was how difficult this product was to sell. The resistance we were getting didn't make any sense. 

It's true marketers have a budget to hit. They need to acquire a certain number of customers at a target cost per customer. They were using lead generation as a technique to acquire these customers. Customer acquisition cost is a function of cost the marketer pays per lead divided by the lead conversion rate, i.e., the percentage of leads that convert into paying customers. Therefore, if we can improve lead conversion rates, then we can reduce customer acquisition costs for these marketers. The value proposition seemed pretty clear to me. So, I proceeded to build my startup. 

After lots of customer validation establishing this problem was real, we built an MVP. We showed the MVP to a small handful of lead buyers. One buyer, DeVry University, loved our smoke-and-mirrors product. They expressed their frustration with third-party leads in particular. These are leads generated by affilates and lead aggregators who are collecting signups on DeVry's behalf. Third-party leads had been a problem for DeVry for some time. It was their worst performing channel. So, they logically assigned our solution to clean up their worst channel. That made sense.

I gave them a contract and they signed it. We were in business! Yay! So far, so good.

Understandably, DeVry was skeptical our solution would actually work in a real-world situation. They were right to question this because we were a startup with no customers. So, we engaged DeVry in a paid pilot test at $10K per month. 

The pilot ran for a year. It was successful. We demonstrated cost-per-enrollment efficiency gains of 43% using real lead data from their current operations. Our product worked! I was over the moon. Our startup was going to be a huge success!

What happened next took me by surprise. DeVry stopped the pilot and decided not to roll our solution into production across their enterprise. 

What? Why? "This doesn't make any sense I thought to myself," over and over again.

As I tried to obtain more information from my contacts there, I was able to piece together what had happened. 

As our pilot was underway, DeVry's business was falling apart. Student demand was crashing at 15% per year and the decline was accelerating. The marketing team had completely blown their budget and the CMO was reassigned to a "special assignment." Most importantly, DeVry decided to give up on third-party leads alltogether and outsource their entire search marketing channel to a new agency. 

DeVry had given up on the belief that they could solve their customer acquisition problems. After repeated failures, they were beaten down to the point where they lost all confidence in their ability to solve the problem.

The problem with DeVry was not that they didn't have a meaningful problem to solve. They did. I had a hypothesis this was a problem for them. They validated that it was. First they said so and then they signed a contract to solve it. This is problem and solution validation, straight out of lean startup methodology.

As the years went on, and as their business started to decline, everyone lost confidence in the marketing team, even the marketers themselves. Instead of facing the painful thought that they were doing something wrong or that why were incompetent, they concluded the problem just wasn't solvable. Incredibly, this was in the face of hard data that demonstrated we just solved the problem! We showed them an escape and they weren't able to open the door and walk through it!

Then came the bizzaro world conversation with my contact there.

"Those results are theoretical. We can never realize these improvements in reality," my contact said.

"Why not? We are using real data from your actual leads as they are coming in. Our calculations are correct. The efficiency savings are real. There is nothing theoretical about this pilot," I retorted.

"The vendors will never agree to cooperate," said my contact.

"Why do you say that? We asked three vendors to participate in the pilot and two agreed! What we learned from these two partners was incredibly valuable. The solution works and they are participating," I pointed out.

"Quinstreet will never do it, and they account for 80% of our volume," my contact replied.

"Yes it's true Quinstreet declined to participate in the pilot. But if you turn our technology on across your business, they will have to participate. If they don't, all of the business is going to shift away from them to the other vendors as our analytics start increasing prices for the higher quality leads from their competitors. It's inevitable they will come around. If they don't, they will lose all of your business," I argued.

"They will never do it," he said.

"How are you so sure? Let's take LeadID for example. For months now Quinstreet has refused to participate with LeadID. Now that you've seen our solution in action, you know that our platform is much, much less intrusive for vendors than LeadID. So, if Quinstreet will implement LeadID, they certainly will implement LegitLead. As you know, last week Quinstreet announced support for LeadID. We should ask them again to work with us and I bet they will," I concluded.

Even after this exchange, the roll out never happened. 

DeVry's confidence was completely gone. They had allowed themselves to be beaten down by their previous failures. They were not resilient. They were in a state of learned helplessness. 

I was the only person in the room that believed our solution would work. That's not a good place for you to be with your customers. When you are in this position, you are not going to get the sale.

Optimum Problem Severity

It seems there is a optimal realtionship between Problem Severity and Solvability Confidence. In fact, it is possible for a problem to be too severe to solve. It comes down to the beliefs your customers hold. If they've allowed themselves to slide into learned helplessness, then it's hopeless for your solution and for the current iteration of your business model.

Problem Severity is simply how bad the problem is, how severe it is. Some problems grow and become bigger problems. Other problems shrink and go away.

Solvability Confidence is your level of confidence in your abilty to solve the problem or that the problem is solvable at all. It's a belief that changes over time. Normally, Solvability Confidence goes down over time and with repeated failures. The rate of change is a function of your psychological resilience.

I think the relationship looks something like this.


In the first stage, the customer has identified the problem. In the second stage, problem severity has increased enough that it has risen to become a top priority in the customer's mind. In the third stage, after a number of failed attempts, the customer gives up and believes the situation is hopeless. He has learned to be helpless about the problem.

I think the key is to find problems and customers that are in the second phase. That's your best chance for getting traction around a specific set of customers and a focused problem. Timing is everything. If you wait too long, or if the customer continues to fail to many times, he will slide into learned helplessness, which essentially takes him out of the market for your solution.

Lessons Learned

  • A problem can be too severe for a startup
  • Customers will lose faith over time and after a few failures
  • Identify which phase your customers are in for your problem, and prioritize customers that are in Phase 2. 
  • When you see evidence of learned helplessness in your customers, it's time to pivot to a new customer segment or a new problem