Complexity of credit card data

This entry is just a little bit of what I have experienced after more than one year working at a bank. By no means am I trying to imply that I am an expert in credit card data. Not even close. This is simply to shed some light or pull the curtain a little bit into the world of banking. It’s also a way to put into writing what I have learned or think I have learned so far.

Lost and stolen

One reality that makes compiling and analyzing credit card data complicated and tricky is that people lose their cards or have them stolen all the time. When that happens, banks issue new cards to customers with totally new account numbers. On the front end, customers don’t see any change. New cards just work like old cards. However, on the back end, it’s a totally different ball game. If an analyst like myself wants to analyze performance of cards, especially accounts that have a complicated history, such as spend, balance and profit history, a reconciliation of all the account numbers associated with the original account number can be daunting and easily lead to mistake.

Tricky balance between risk and profit

If you are like me who uses cards regularly and pays off balance every month to avoid financial charges, you are not a profitable customer to credit card issuers. To make money off customers, issuers usually charge interests on late payments and fees such as annual fees, foreign transaction fees, over limit fees. No late payments mean no financial charges and no profit. On the other hand, customers who are usually late on payments and accrue balance are usually the riskiest bunch. Such customers, as we call revolvers, are more likely to charge off and cost issuers money. As a result, issuers must find a sweet spot between risk and profitability. Having the majority of customers under 680 FICO can lead to higher profitability, but also higher risk at the same time. Acquiring great credit customers poses a lower risk, but also a lower chance of profitability.

One popular practice in the credit card world is balance consolidation. In a layman’s term, it is an offer allowing customers with a balance at a higher interest rate to transfer the balance to another account at a substantially lower rate which can be 0% for several months. This offer appeals to credit trapped customers who are likely to be late on payments, but want to avoid interests. By taking on more accounts and balance, issuers take on more risks, but balance consolidation may not always lead to higher revenue and profit.

Ambiguous definition

Sometimes, metrics in the credit card world may not be as straightforward as they sound. For example, if a customer calls to close a credit card, but chooses to pay balance in installments instead of one big payment, is that credit card considered closed or still open with an outstanding balance? When discussing “active” accounts, are we talking about “balance active” (having balance > 0) or “debit active” (having net purchase which is comprised of purchases minus refund > 0)? When discussing spend per account, are we talking about spend per account or spend per “debit active” account only? Those are just a few examples on top of my mind. There are plenty more cases where a question, straightforward as it may seem, is really not.

Apple to apple comparison

A performance analysis needs a benchmark to compare against. To analyze a cohort of accounts, it’s important to find another cohort that is as similar characteristically as possible to the one at hand. The challenge is that there are multiple elements to consider such as acquisition channels, vintage (when accounts were opened), cashback or rewards cards, FICO, credit bureau segments (more on this later), brands that cards are associated with (FCA, Delta Airlines, Walmart…) and so on. If the task is to perform an analysis on the same set of accounts over time to test the effect of an offer, seasonality needs to be considered. Obviously, consumers spend more during holidays. So, comparing spend in Dec to spend in Feb isn’t really a fair one.

External data

Issuers work with agencies such as Acxiom to acquire demographic data. To my best knowledge, issuers send account information such as addresses and names to Acxiom to match up with their demographic database such as household income, net worth, how likely they have a mortgage, how likely they have children, how they are etc…Such information is sent back to issuers for future marketing purposes. One of the challenges arising from this practice is that an account can have more than one user. Joint accounts can have two users whose demographic profile can be different. In that case, a decision has to be made as to which profile should be used and data needs to be handled with care. Otherwise, it would lead to double accounting.

Issuers can work with credit bureau agencies for credit score and other information. An individual banks with multiple corporations and has multiple cards with different issuers. It’s helpful to know how big of a customer’s wallet share your issuer makes up. For instance, it’s helpful to know how much a customer allegedly tends to spend in a year and the percentage of that spend goes to your issuer. The same goes for all credit card balance and revolving balance. Agencies such as Experian receive data from different issuers and return their estimated data back to issuers for marketing purposes.

Another challenge is that between the time when data is sent to external data partners and the time when data is sent back to issuers, accounts can be lost or stolen. Reconciling that change can be problematic.

I have worked intensely at my current company for the past year. It’s really satisfying to learn how data works, what goes under the hood, what the nuts and bolts are and what can be done to help the business. There are a lot to learn and do such as

  • How to optimize the acquisition process? How to get the best response rates for Direct Mail, an important practice in this industry
  • How to encourage spend?
  • How to balance risk and profitability?
  • Who are the “best” customers?
  • How to use data to improve customer experience
  • How to improve interchange?
  • How to predict charge off?
  • How digitally engaged are customers?

I hope this entry has been helpful somewhat. If I miss anything or any point is incorrect, please leave a comment. I hope I’ll learn more and be able to share with you in the near future.

Thoughts and notes on Uber’s latest earnings

On 6th February 2020, Uber announced its Q4 FY2019 earnings. Below are some of the thoughts I had from reading their press release

Take Rate

Uber defines take rate as the result of adjusted net revenue divided by gross bookings. In a layman’s terms, it is the amount of Uber takes from what riders pay for rides, after paying drivers their share. In Q4 2019, the take rate reached 20.6% compared to 18.7% in Q4 2018. It meant that out of $100 taken from riders, Uber took in more money in 2019 than in 2018

Source: Uber

However, if we look at 2019 as a whole, take rate dropped to 19.8%, compared to 20.7% in 2018, almost a full 1% lower.

Source: Uber

Overall, in the second half of FY 2019, Uber had higher take rates overall, for Rides and for Eats individually than in the second half of FY 2018. However, the gain was sufficient to make up for the deficit of the first half of FY 2019 to the first half of FY 2018. As you can see from the graph above, Eats provided a terrible take rate, compared to Rides.

Driver Incentives and Driver Referrals

The incentives and referrals help reflect the health of the brand and business. Low payout for incentives and referrals means that Uber spent less money to recruit drivers and increase rides. Incentives and referrals are usually presented in this manner by Uber

Source: Uber

I calculated the ratio of Adjusted Net Revenue (ANR) over revenue in 2018 and 2019 for both Rides and Eats. The higher the figures, the better for Uber

Apparently, it keeps getting better and better for Rides. On the Eats side, Uber seemed to recover from the slump in Q3 and Q4 2018.

Rides

Rides continues to be the silver lining in the EBITDA area for Uber. It is the only segment with positive EBITDA in Q4 2019 or FY 2019 as a whole.

Source: Uber

It’s even better for Uber that YoY growth for Rides EBITDA (34%) is bigger than that for Rides Bookings (18%), Revenue (27%) and ANR (30%).

Eats

Eats registered the biggest loss among Uber’s segments in Q4 2019. Uber may find it encourage the fact that Eats’ Q4 loss is only 111% of ANR, compared to 168% in the same period last year.

Uber recently announced the divestiture of Uber Eats in India. Since Uber Eats was losing money and users in India, the decision looked a wise one and in line with the strategy pursued by the company.

Source: Atom Finance

CEO of Uber revealed on the earnings call that Uber Eats in the US made up almost 39% in gross bookings of the global Eats GB ($1.7 bn out of $4.374 bn). There are 400,000 active restaurants in the US on the Eats side, up by 78% YoY.

Freight

Freight’s Q4 loss was a tad more than 25% of its ANR, compared to a bit more than 18% of the same period last year. Not a trend that Uber would want in their quest to become profitable.

Cost structure

On a full year basis, only depreciation as % of revenue decreased in 2019, compared to 2018. Overall, operating cost and expenses increased significantly in 2019, reaching 161% of revenue in 2019. However, Q4 2019 provided a brighter picture for Uber. Only R&D as % of revenue went up in the quarter, compared to the same period last year, especially given that operating expenses as % of revenue in Q4 2018 were higher than those of FY2018.

Good bits of information here and there

  • Uber for Business’ Gross Bookings made up 9% of the total GB
  • In Q4 2019, Uber Rewards Program had 25 million subscribers from multiple markets, up from 18 million from the US alone reported in Q3 2019
  • Multiple-app users had almost 3 times the number of transactions as single users

Thoughts

Though challenges remain, including those posed by local authorities threatening to impose infavorable regulations, driver/rider safety and competition, Q4 2019 seemed to offer the team at Uber and bull investors something to be optimistic about.

In an ideal world, I would love to see more transparency regarding:

  • Margin of products such as Uber for Business, Airport, Helicopter, Comfort, Scooter
  • Margin of Eats in the US or products in the key market
  • More details on subscriptions
  • Engagement data regarding the use of multiple apps per user

Uber’s Earnings – Is It On The Right Track?

Uber released its first quarterly earnings as a publicly traded company. Let’s take a look how they did.

First of all, I have to say that reading Uber’s earnings isn’t a straightforward task. They make it incredibly confusing and complex. For instance, there are multiple variables concerning the company’s money-generating ability such as Bookings, Revenue, Core Platform Adjusted Net Revenue, Adjusted Net Revenue. I wish they could make it easier for the audience to absorb the information.

The company lost more than $1bn in the first quarter mainly due to bigger cost of revenue and S&M expense

Source: Uber

Bookings, revenue and net revenue increased, but at a much slower clip than Q1 2018

Source: Uber
Source: Uber
Source: Uber

Monthly trips per user are stagnant while contribution margin is negative

Source: Uber
Source: Uber

Uber Eats revenue grew by 89%, but Uber tripled the driver incentives for the segment

Source: Uber
Source: Uber

In terms of segments, Vehicle Solutions and Latin America market performed poorly compared to Q1 2018

Source: Uber

Ride-sharing’s revenue grew while incentives contracted; which is good news

Source: Uber

Thoughts

Uber seems to be a story of contradiction. While the CEO claimed that “Sometimes simplicity is a beautiful thing”, the business, by no means, is presented in a simple fashion. It’s complex and the terms used by the executives don’t necessarily facilitate easy understanding.

Uber CEO also said “Our job is to grow fast at scale and more efficiently for a long, long time.” Bookings, users and all metrics increased indeed. Yet, as presented above, they grew at a slower clip than one year ago. The tripled incentives used to fuel growth in Uber Eats aren’t exactly evidence for the efficiency he mentioned, and neither is the S&M expense.

The company lost $1bn and the business model doesn’t seem to change much. Also, I don’t believe in the short term feasibility of autonomous vehicles’ impact on Uber. It’s unclear how the company can tackle the profitability question. On the earnings call, Dara mentioned competing on brand and products instead of pricing with competitors. Well, whether that plan comes into beings still remains to be seen.