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I was lucky enough to receive a notice from the guys at Sparkroom that they will be announcing the introduction of a new feature at the LendingTree Partner Summit, Lead Market Analytics. I am excited about this new feature because it brings my request for industry benchmarks into reality. I believe industry benchmarks have been needed and will prove to be very helpful to those who are privy to the information.
What is Sparkrooms Lead Market Analytics? It is never before seen industry benchmarks. Virtually any benchmark you would like to compare your company against is now available within the Sparkroom platform.
How does my close rate (or contact rate or application rate) compare to industry
averages?
Which lead providers are most likely to have volume that matches my product and
production sweet spots?
What performance can my company expect if I purchase a particular type of lead from
lead provider X?
How does my cost per closed loan compare to my peer group?
What is the level of competitiveness for leads in a given geography or product segment?
What areas of opportunity exist to profitably expand my geography or product mix?
Those are only a few questions that LMA (Lead Market Analytics) will answer for you, but let me go into a little more detail.
LMA takes each source that delivers leads into Sparkroom and releases every piece of conversion data to each user. Users can view contact rates, application rates, cost per closed loan averages, average cost per lead and more. LMA breaks down the data into thirds by having a top performers group, average group and below average performing group. From there you can dissect the data by state, LTV and other filters to find niches that may help you increase your performance and revenue. This information is priceless, no longer do you have to spend thousands of dollars experimenting. Of course nothing is guaranteed, but by digging into this data there is a good chance that you will find specific LTV ranges and states that perform better then others. You may also find that you outperform the average in one state, but are struggling in others and allow you to optimize your lead buying and business. Now, the core services of Sparkroom will already give you these optimization points, but now you can view and compare those findings against the industry average.
“In talking with our customers and other industry experts over the past year, it became clear that
there was a compelling need to aggregate a data set that reveals lender performance vs. their peer
group across various business metrics,” said Sparkroom’s chief executive officer, Jamie
McDonald. “Sparkroom was founded on the concept that transparency of information is power.
Unlocking that transparency is immensely valuable to both buyers and sellers of leads. It
empowers lenders with the information required to make smarter business decisions about their
marketing spend, geographic focus and product mix.”
I am really excited it about this data and I can’t see how you couldn’t be, unless you are not a Sparkroom client. If there is a downside it has got to be the sample size.
Sparkroom Lead Market Analytics is launching with eight lead buyers in the mortgage vertical,
including five lenders regularly ranked within the Top 20 of the LendingTree lender rankings. As
additional lenders participate in Lead Market Analytics, the breadth and depth of the benchmark
data will continue to grow.
This is a good start and now doubt represents a significant amount of leads, but will absolutely benefit from a growing client base.
Anybody have a different opinion then mine?
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7 Comments
The Sparkroom guys (even Ed) are smart guys and have a good concept going. They are working hard to make the job of folks like myself easier and allowing us to be more effective.
I like what they are doing and the direction they are headed overall.
For the benchmark portion of their business to be effective they need to walk a fine line. Share information but not too much. The leaders won’t participate if they see it as a way for lesser lenders to gain an edge at their expense. From what I have seen they are on the right track with this…but its an important point I think for gaining adoption.
In addition they will need greater participation than just a few of the top 20 LT originators. They are working HARD on this no doubt.
As a side note….LMS companies should be lining up to partner with these guys. LMS companies should think of this as an add on to their service that will make them more valuable. They are doing all the data work that they seem to struggle with (per the LeadsCon discussion). I still don’t understand why the LMS companies don’t take the SOA model where they partner with the best as a reseller of products that compliment their own…but I digress.
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I really like this, as well. I think this ultimately will be a tremendous benefit to the industry, but I do have a couple of questions for which I’d love to get the Sparkroom response. Namely, how are they going to handle the disparity of metric definitions form one company to the next?
For example, one company may classify an “Application” as simply pulling credit while another company may consider it a completed 1003. So, there are clearly going to be some potentially significant discrepancies between shops with regards to metrics since they may be defined very differently. The exception, of course, will be closed loans, but the rest of the metrics may involve some grey areas. But again, overall, I am excited to see this!
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You know…I gotta say that I am not convinced that the disparity between companies is all that far off, regarding dispositions. And if it is, what the margin of error results in, if anything at all.
I heard this issue being brought up many times by leads360, but for some reason I am still not convinced.
is there an example of a company who may use the application disposition when it is only a credit pulled VS. a company who uses the application disposition when a lead is actually a fully submitted deal and how it effects conversion numbers?
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Thanks for the great feedback and words of support. We folks at Sparkroom are super-excited with the release of this new product.
SomeInsider raises a great point about the disparity of metric definitions from one company to the next. When we map in the data from a particular lender, we spend time to decompose and understand their workflow in detail. Based on a standardized definition, we then map to a corresponding Sparkroom metric for the aggregated analytics. So if for example a company considers their application milestone to be the point where a credit pull occurs, we would actually map it to a separate credit pull milestone.
Admittedly, there will always be slight nuances. Provided there is sufficient data in a given segment, the metrics will behave in a similar fashion to that of a classic bell curve based on college grades. The differences will be averaged out in aggregate to provide meaningful benchmarks across the entire market.
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Kaleidico does adhere to a SOA style of open data interfaces. We have open APIs that allow us to exchange pricing data and repricing, qualify leads with lead quality services (i.e., TargusInfo), submit offers to third-party systems (e.g., LendingTree), lead processing data back and forth (for mortgage that is LOS), and in real time provide feedback lead data to lead providers (able to be toggled on and off by lead buyer) and third party analytics systems (also can be done in batch).
Our APIs are designed to work with our clients systems. This is what we were all talking about a few weeks ago–lead management is powerful when it does the job of LEAD MANAGEMENT–have the open architecture to communicate with other systems that do what they do best. That’s the way Kaleidico believes–it’s simply a matter of what system do you want to connect to?
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What is SOA? I have been after Leads360 for years to share overall metrics for their clients for years. It would be nice to know how we stack up against the other companies because I know we can do better on every metric. What will be missing is how they do it. I am specifically interested in states that are under served to help me in the decision on where to expand next. Ed Powell give me a call on how I can participate in supplying data.
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en.wikipedia.org/wiki/Service-oriented_architecture
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