I was asked to post this to the board.

I do actually think it would be good to crowdsource everybody’s input.

Before I begin, however, major obvious caveat:

These projections could be totally wrong. The last thing I want is for a reader to make investment decisions based off this. Everyone needs to do **their own analyses** of the **business fundamentals** of a company and NOT buy/sell stock just because some random guy on the internet spouted off numbers that sounded appealing.

Plus, the last mathematics course I ever took was in high school, and I have zero tech background. That should not instill much confidence in my ‘forecasts’.

**Let’s treat this as a purely ‘fun’ exercise.**

With that out of the way, my opinion is that UPST’s management is sandbagging their Q3 estimates.

Management is guiding for 205M to 215M for Q3.

In Q2 conference call, it was stated “June was our first month with more than 100,000 loans”

So let’s assume exactly 100000 loans were transacted in June.

We know they did 286864 total loans in Q2.

This leaves 186864 for the two other months.

If we assume a “proportional” growth in loan numbers each month (5% growth each month), then we can estimate roughly 95550 in May and 91000 in April.

If we assume identical growth into Q3, at 5% increase each further month.

This would be: 105000 in July, 110250 in August, 115762 in September.

That’s 331012 total loans for Q3.

At roughly $670 revenue per loan ($670 comes from 194M of Q2 rev divided by Q2 loans of 286864), that comes to **$221 million rev.** That’s a 2.8% beat on the top end of their guidance.

Here are my projections via other estimations.

Semrush data is broken down into their estimate of upstart.com organic/paid traffic via desktop and mobile for the month of August. I have checked each day, and the numbers fluctuate, so Semrush is performing real time estimations. The month is almost over, so surely it will have a final estimation of August by Sept 1.

For today, here is what it is showing: 284864 desktop + 254235 mobile = 539099 total traffic for August.

This is in comparison to Semrush’s estimation of: June 390688, July 509122

Let us assume 100000 loans in June were transacted.

July had 30.3% increase in traffic. Let us assume the conversion rates and application rates are identical. Then we can estimate July had 130314 loans.

August would then be 37.98% increase in traffic and thus loans over June, so we estimate 137987 loans in August.

Let’s assume September is identical to June, to be extremely conservative. So assume 100000 loans in September.

**If we add this up, we project 368301 loans. That’s revenue of 246.76M in Q3, at $670 per loan.**

If we were euphoric and estimate Septmeber is identical to August at 137987 loans, then we’re looking at 406288 in Q3 with revenue of 272M. That’s 40% sequential growth from Q2. It seems too ridiculous so I’m going to go with the above conservative estimate.

**We especially have to keep in mind a lot of traffic in August may be noise from investors like us visiting the upstart website after their earnings report**

Also, hats off to Pavlos21 for introducing Semrush to the board, as I didn’t even know they existed previously, or what SEO even was. Without that post https://discussion.fool.com/semrush-semr-q2-earningsthoughts-349… I wouldn’t know to use their platform.

Moving on to Google Trends data.

“Upstart” appears to be the best predicting search term. I have tried other terms such as “upstart login”, “upstart loan” or “upstart loans” and I found the all-encompassing “upstart” was still the best at correlating with prior quarterly results.

And I’ve so far found the time series data set that seems to fit well with prior results is Q1 2020 to present (up to 8/22/2021 data).

Since Google Trends only gives relative search term popularity, I take the area under the curve to estimate that as directly proportional to absolute search volume which should correlate strongly to actual traffic and loan application numbers. From there, I adjust for conversion rate numbers depending on how I want to analyze it (I’ve created ‘coefficients’ to estimate application numbers and stuff, but I didn’t include those results in this post as they weren’t conservative appearing).

The area under curve numbers I get are:

Q1 20 323

Q2 20 230

Q3 20 331.5

Q4 20 469

Q1 21 495

Q2 21 766

Q3 21 569.5

A quick back of the napkin math shows:

The 569.5 number is then divided by (53 / 92), because 53 days had elapsed of the 92 days in Q3 (as the data was up to date to 8/22/2021). That estimates 988 for Q3.

988 is about 29% bigger than Q2’s search figure of 766.

Assuming identical conversion and application rates, then Q3 would have 1.29 x 286864 = **368940 loans**

**That’s 247.19M revenue estimate**, similar to the Semrush “conservative estimation” above.

Again, all of this should be with a HUGE grain of salt.

**There’s a lot of noise in Google Trends data for the term “upstart”, as we can see it correlating to terms “UPST”, “upstart stock” which is traffic coming from investors like us. So we definitely should remain conservative on Q3 estimates.** (See https://i.imgur.com/A9vHsqX.png )

But the underlying increase in popularity for searching “upstart” from *actual borrowers* is very much real, based on “upstart login” or “upstart loan” search trends (see https://i.imgur.com/EQ65OCW.png )

Google trends also gives me a huge range of estimations depending on the time series of data pulled and depending on how I try to account for conversion rate.

And I could not get last year 2020 Semrush data to fit very well with previous quarters data, so I really don’t know how accurate Semrush is in the long run.

I do have some other ideas, including using the exact URL that goes to Upstart’s application. (That would remove extraneous traffic from investors like us, but I think I need a full Semrush account to get that level of detail.)

Also I did try to use Trustpilot data but that was not predictive at all when ‘backtested’ on previous quarterly results.

Finally here’s a fun graph of Upstart’s google trend popularity over time, using the ‘R’ statistic program: https://i.imgur.com/Z8kajYV.png

I don’t know how to code at all, so credit goes to a friend.

**If anyone has any criticism, suggestions, tweaks, or other fun ideas to try to predict Q3’s revenue, please post! I hope it will be an entertaining discussion.**