Lessons Learned

  • Lead scoring is not a technology problem. It's a business problem.
  • Use the 90% rule with third-party leads to achieve breakthrough results
  • Position solution as a way for aggregators to increase lead prices
  • Automate sub-channel lead analysis and predictive analytics to make results actionable

Emergence Of Data-Driven Marketing

These days everyone is talking about big data technology in online marketing. There is a lot to be excited about. We have access to more data on individual consumers than ever before. As marketers, this means we have an incredible opportunity to personalize the customer experience with an entirely new level of precision. When done properly, this personalization will unlock new levels of marketing efficiency, allowing us to grow our businesses faster and more profitably. What is not exciting about that?

Let's be honest. Some of us are wondering if all this big data talk is just hype. Is this for real or is it just the latest marketing fad?

Ultimately, you will have to answer this question for yourself in the marketing plan you are executing for your business. What I can tell you is that the technology exists, and it works. We have seen it first hand with our clients, the vast majority of whom are innovative, data-driven marketers that continuously push the envelope to achieve breakthrough results. 

Across fifteen recent case studies with real customer data, our technology improved customer acquisition efficiency from 20 to 80%. The average across these data sets is 42%. When you think about it, these results are significant. If you have a $100 annual marketing budget, our technology unlocks $42 million of extra budget in your marketing plan. That's a meaningful impact. In addition to our proprietary technology, some competitive solutions have demonstrated good results as well, especially when compared to a non-optimized base case that most marketers are in today.

So if the technology works, why are so many lead buyers experiencing a decrease in customer acquisition efficiency? Why are costs per new customer going up? If lead scoring technology works, why aren't marketers realizing better results?


Why Lead Scoring Isn't Working In Online Lead Generation

After countless conversations and multiple implementations with customers, we've uncovered the real issue that is preventing lead buyers from achieving better performance. And here's the rub. It's not a technology problem. It's a business problem. 

As you know, there is a shocking lack of trust between lead aggreators who sell leads and lead buyers who buy leads and try to convert them into paying customers. This lack of trust is understandable and it's well deserved. The problem is this toxic relationship continues to harm both parties, to the point where the viability of the entire channel is called into question. There is simply no technology that can overcome the negative affects of this entirely dysfunctional relationship.

When buyers have implemented lead scoring in the past, lead aggregators refused to cooperate. The reasons why are straight forward, if you consider the situation from an aggregator's perspective.

Let's imagine you are a lead buyer. You just implemented a predictive analytics / lead scoring solution. The solution worked and it showed that 50% your leads aren't converting to paying customers. More specifically, the solution can tell you exactly which leads will have near zero conversion. The question is, what will you do with that information?

Most marketers look at these results and simply increase their scrub, effectively throwing out the bottom 50% of leads. They go to their channel partner and say, "We only want to keep the best leads, and we want to pay the same price per lead that we are paying now." What they want looks like this:

1,000 leads * $100 per lead = $100,000

1,000 leads * $100 per lead * 50% scrub = $50,000

In effect, the lead buyers use the scoring results to "cherry pick" the best leads while asking for a 50% price cut. Considering lead aggregators operate at 14-15% margin, they simply can't accept those terms. If they did, the aggregators would go out of business.

So, if the lead buyer takes this approach, the lead aggregator simply increases the price. They have no other choice.

After Aggregator Adjustment
1,000 leads * $200 per lead * 50% scrub = $100,000

After the adjustment, the average customer acqusition cost is exactly the same as it was before they purchased and implemented lead scoring. As a result of the price increase, the lead buyer is unable to capture the efficiency benefit of higher quality, more productive leads. 

This isn't a technology problem. It's a business problem. The key to solving this problem lies in business and technology working together.


The Secret Solution To Making Lead Scoring Work

After trying a bunch of different approaches to this problem, we finally figured out how to improve efficiency for both sides of the ecosystem, increase lead quality, and turn the race to the bottom into a face to the top.  

Since the buyers are paying for the leads, they really need to drive the process forward to make positive improvements. Here's how to do it if you are a lead buyer.


Step 1. Implement a 90% rule based on lead-level performance, aggregated at the sub-channel level.

The best way to break the price-performance stalemate between lead buyers and aggregators is to implement the 90% rule. This rule dynamically adjusts lead prices at the subid level by tying price to conversion performance. The following chart illustrates how this simple formula works. 

Assume your target CAC is $2000. Your conversion rate hypothesis was 5%. Therefore, you initially priced the deal at $10 CPL to hit your CAC target. 

Subid performance should be calculated on a weekly basis, and no longer than on a monthly basis. 

Here are the results after the first sub-channel calculation. 

During each analysis period, simply calculate actual conversion rate results at the sub-channel level. Then, make adjustments to the price paid using the New CPL formula shown above. This formula will send a powerful signal to lead aggregators, telling them exactly what to do to adjust their lead acquisition tactics.


Primary Benefits:

1. With more precise lead pricing at the sub-channel level, buyers are providing aggregators with a powerful, specific and actionable signal of the relative value of different lead segments to their business. Aggregators can easily intepret these signals to make appropriate changes.  

2. Precise tiered pricing provides necessary unit economics for aggregators to acquire higher quality leads. In our experience, most aggregators know where the higher quality leads are. They just can't afford to get them. That is, until the buyers give them higher prices in order to make their margins work.

"You are bidding this segment down. They aren't working as well for you. I get it. At the same time, you've doubled or tripled the price on this other segments. That's great! Now I have more money to go get the more expensive leads that are working better for you!" says the savvy lead aggregator.

3. Bidding down low quality leads eliminates tremendous waste for both buyers and aggregators. Buyers spend a tremendous amout of time, energy and money converting leads into paying customers. Low quality leads create tremendous waste in the entire sales funnel and conversion process. These wasted dollars are tied up on low value-add activity and cannot be used to pay for higher quality leads. This waste helps perpetuate the downward pricing spiral and the death of improved conversion results.

When buyers bid down low performing segments, they send a powerful signals to lead aggregators. The signal is, "We don't value these leads as much as the others." Aggregators make the appropriate adjustments to their lead acquisition process and lower quality leads go away over time. The net effect is that the entire lead ecosystem gets stronger and healthier.

Quite simply, this is nothing more than Adam Smith's invisible hand of capitalism at work. Pricing large buckets of leads in one average price simply hides the insight that is required to make lead buying and selling more efficient for both sides of the transaction. Instead, micro analyzing and micro pricing lead segments correctly results in immediate and direct efficiency improvements for buyers and aggregators alike.

Step 2. Position the approach as a way for aggregators to increase prices.

To successfully implement this approach, use tiered pricing based on a minimum of 30 day metrics or real-time ping post. Longer analysis intervals are not actionable. Real-time is optimum for aggregators that are using paid Search (SEM) or other forms of real-time bidding like display RTB exchanges.

Aggregators are always talking about how great their lead quality is. They are always trying to raise prices. Almost all fail to raise prices because buyers don't believe them. This new pricing model gives them an opportunity to prove it and be rewarded when they deliver on their promise of better lead quality.

Here's an example conversation that has worked for us and our clients before.

"We are introducing a new pricing model for all our lead buys. This new model will allow you to increase your lead prices for those segments that are performing well for us. Where it is warranted, we expect to increase lead prices by 2 to 3 times what we are paying today. In addition, we will analyze lead quality and performance in real time and check with actual conversion results on a weekly basis. You can ping our system to reduce scrub rates and realize price increases for those leads that are working best for us."

Enlightened aggregators who are truly trying to provide higher lead quality, are absolutely exstatic about this approach. Try it and you will quickly see which aggregators are enlightened and which are still clueless.

Primary Benefits:

More accurate, more frequent performance-based lead pricing has the following impact on lead aggregators.

1. It gets aggregators excited about potential price increases.

2. It gets aggregators excited about the opportunity to finally acquire higher quality leads using more expensive acquisition methods.

3. It encourages experimentation with higher quality sources.

4. It provides frequent, actionable feedback which fosters continuous quality improvement.

5. It ensures aggregator won't be cherry picked to death.


Step 3. Gradually shift lead allocation away from adversarial aggregators and towards enlightened aggregators.

In our experience, there are two types of lead aggregators: enlightened and adversarial.

Enlightened aggregators tend to be smaller, hungier and use more technology to optimize their media mix and media spend. A good example of an enlightened aggregator is CampusExplorer in the education space. There are enlightened aggregators in every market.

Adversarial aggregators are those who resist transparency and more precise tiered pricing schemes. They do this because they think it is in their interest to confuse customers and prevent them from parsing results at the sub-channel level. While, they are legitimately concerned with cherry picking, their fear overwhelms their rational thinking to the point where their policies become self defeating. 

Primary Benefits:

1. Weeds out incompetent aggregators and replaces them with higher quality providers.

2. Improves the health of the lead supply ecosystem over time.

3. Fosters healthy competition and creates a win-win for buyers and enlightened aggregators.


Case Study Results

When you combine a smart business strategy with badass technology, the results are awesome.

We took the same approach outlined in this blog post with one of our more forward-thinking customers. We developed a good plan for dealing with each lead aggregator. We positioned this solution as described above.

After several months, the results were in. Our customer experienced improved performance across every metric. Level 1 conversion rates increased by a whopping 59%. Lifetime revenue was up an astounding 91%. Customer profitability, factoring in usage and acquisition costs, was up 33%. These results are even more impressive when you consider cost-per-lead prices went up by 25%. 

In summary, the lead buyer focused on leads that were working and paid more for them. Lead aggregators capitalized on the insight provided by the buyers adjustments to lead prices for each segment. The lead aggregators finally had the unit economics they needed to get higher quality leads. The buyer won. The aggregator won. The ecosystem was stronger and more prosperous.


Where To Go Next

To achieve these results, a true partnership is required, between the lead buyer, lead aggregator and predictive analytics platform. At A16E, we've developed a world-class platform that makes the calculations easy and automated. All you have to do to achieve these results is follow the road map we've laid out in this post.

Contact us today to get started.