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Posts Tagged ‘profit’

The thing is: no one really cares about banking products. There’s no idolizing of the guys who started AmEx Cards or CapitalOne, no queue outside HSBC the night before a new card is launched. This is a problem because people only buy things they care about, or things they need and for which there is no alternative.

Banks used to keep outside competitors away with the huge capital and regulatory costs of setting-up a payments system but as more commerce moves online and as these other costs drop, those barriers will fall.
The problem is cards are essentially commodities. With a few exceptions, a credit card is a credit card is a debit card, even. This is especially true as the actual plastic starts to play a smaller role in the transaction. In freeing customers from location-specific branch and ATM networks, online banking has also removed the personal relationship that may once have made a bank something more than a logo on a card.
The credit card survives – and indeed still thrives – because it is the most convenient way for most people to make most payments, at the moment, but this is changing. With more and more online and mobile alternatives, banks will have to start competing with more retail-savvy competitors and to do that they need to reconsider the way they consider and market their products.
Traditionally banks spent large amounts on above the line advertising to attract customers and retain customers who they offered a suite of standard products; a one-size-fits-all model. Then, stand alone credit card issuers and other niche companies started to attack the banks’ market share with tailored products offered through direct marketing campaigns; an altered-by-the-in-store-tailor, still not 100% customized.
Direct marketing is no longer enough because it works on a some key principals which are being undermined: the contact must be made at a time and place where the customer is open to the idea of a new card, but in a flooded market the chances of your contact reaching a customer before a competitors in this window period is getting smaller and you’re almost always contacting them at home; the contact must come in a medium that is relevant to a customer, both mail and email are becoming less relevant to customers; and the offer should appeal to a particular niche, but a direct marketing campaign, even a niche one, must involve a degree of choice compromisation.
A new model is needed that can reach customers at a convenient time and place, through a relevant medium to offer products tailored to their needs, cheaply. The last word is especially important because banks have long used vague pricing structures to protect themselves from commodity prices but new laws and competition from more transparent – and even ‘no cost’ –competitors will drive prices down, making only the most efficient banks profitable.
This article is an attempt to run with that idea, sometimes beyond the limits of practicality; hopefully in doing so I will raise some interesting questions about what is and isn’t important in the modern, mass market credit card business.

That’s where the idea for the credit card vending machine took root: it is a symbol for efficient, convenient, and ‘productized’ transactional banking. Turning the credit card marketing model around to offer customized cards to customers in convenient locations, without paper work and at low cost.
I envisage a customer approaching a machine in a shopping mall, choosing a card design from the display, entering the relevant data, selecting product features, paying a fee based on the feature bundle, and then waiting while the machine embosses, encodes and produces their card.

The concept is simply an amalgamation of components that are all already available and automatable:
·        an online application form,
·        a means of automated customer verification (ID card scanning in HK and fingerprint reading in Hong Kong for example) ,
·        a secure communications channel,
·        a card embossing machine

Data Capture
I hate forms, especially hand written forms. Every time someone asks me to write out my name and address I immediately assume they value bureaucracy over customer service.
Instead, the data capture process should be designed to leverage stored data, focusing on verifying data rather than capturing it. In Hong Kong I can use my government-issued identity smartcard and a scan of my thumbprint to enter and leave the country, the same tools could provide my demographic which could then be supplemented by bureau and internal databases, requiring me to enter only minimal data. An ATM card and PIN code might do the same thing.
Where this is not possible, the interface would need to provide a vivid and easy means for manually capturing data.
Customer Acquisition
Credit acquisition strategies should already be automated. Very little about them will change, they’ll just be implemented closer to the customer. Hosting them in a vending machine – or doing it via secure link to the bank’s system – is also no different, just a lot of smaller machines processing the data rather than one big one. In fact if there is anything in your processes that can’t be automated in this way you should probably revaluate the cost:benefit trade-off of them anyway.
In terms of marketing, by being located closer to the point of use also makes it easier to do short-term, co-branded campaigns.
Product Selection
Once the data has been captured and the credit and profitability scores have been calculated, a list of product features can be made available, either explicitly or as shadow limits. The obvious way to do this would be to allow a customer to add features onto a low cost, low feature basic card: higher limits, a reward programme, limited edition designs, etc. all with an associated higher fee.
But I’m not threatening anyone’s job here. Any number of strategies can be implemented in the background. The product characteristics might be customer selected, but the options provided and the pricing of those options will be based on analytics-driven credit strategies.
Even target market analysis is still important. In fact, you’ll have one more important data point: the demographic data will allow you to model risk and behaviour based on home address, but you’ll now also now where they shop, allowing you to model behaviour in more detail.
Just because credit card designs don’t obviously affect the standard profit levers, it doesn’t mean they can’t be important influencers of application volumes, but most banks offer only two or three options in each product category.
In part this is because the major card companies want to protect their visual brand identities, but mainly it is because it is hard to advertise hundreds of different card designs to your customers without confusing them.
By filling each machine with a unique selection of generic and limited edition designs, though, you could offer a selection of designs to the market that is never overwhelming but which presents more opportunities for individualism across the market. You might even be able to offer an electronic display of all possible designs to be printed on white plastics.
Look, I started out managing fraud analytics on a card portfolio and I know my old boss will be fuming at this stage; there are risk involved in storing blank plastics and especially in storing the systems for encoding chips and magstripes. However, ATMs have many of the same risks and I believe that they are sufficiently controllable to support the rest of the idea at least in its intended purpose here.
Invoicing
Connecting the card to a funding account could be done offline afterwards, but I would prefer a model that had the customer link the card to their savings account by inserting their ATM card and entering the PIN; the bank could to the debit order/ standing order administration in the background.
Payment
Finally payments, I would propose a single cost model where the actual card is paid for by debiting the funding account when the invoice is created or by cash as with any other vending machine purchase; a single cost model makes the process more transparent and helps to reposition the card as a product purchased willingly.

The systems that make the credit card vending machine could also be leveraged for other, revenue generating purposes.
It provides a channel that could revitalize card upgrades. Instead of linking card upgrades to hidden product parameters, they can become customer initiated and feature driven: learning from the internet’s status-badge mindset, banks could allow customers to insert a card into the machine, pay a small upgrade fee and have it replicated on a new, limited-edition plastic made available based on longevity and spend scores for example, even linking it to retail brands so a Burberry Card might become available only if you spend $5,000 or more in a Burberry store on a vending machine card, etc. Multiple, smaller upgrades would create a new and different revenue stream.
The machines could also act as a channel for online application fulfillment. Customers who have applied online, who have a card, or who need to replace a lost card could have those printed at the most convenient vending machine rather than having to visit a branch.

The way I have spoken about the credit card vending machine is as a new and somewhat quirky sales channel for of generic cards in a generic market place – a Visa Classic Card with a choice of limits, reward programmes and designs, for example. In other words, I have positioned it as a better way to make traditional credit cards relevant in a retail environment.
But it could also offer opportunities in other ways too, for example in the unbanked sectors in places like South Africa where branch networks are prohibitively expensive to roll-out in low-income, rural areas. There customers incur significant costs to reach a bank for even simple services. Though mobile banking is making inroads, there is still room for card based transactional banking. A credit card vending machine would be more difficult to get right in this sort of environment, but if done right it would be a cheap way to expand market share for innovative lenders.

This article is not intended to stand as business proposal, but rather to highlight the parts of the traditional lending business that I feel are most at risk from competition and irrelevance. A review of your marketing efforts and team structures with this in mind might reveal functions that are no longer needed, product parameters that are too complex or attitudes to customer service that need to be improved.

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I wrote in my last article that a debt collection agency (DCA) working on a commission basis had the ability to ‘cherry pick’ the accounts that they worked, distributing their invested effort across multiple customer segments in multiple portfolios to generate significantly higher rewards.  In this article I will walk through a simple example of how a DCA could do this across three portfolios and then discuss how the same principles can be applied by primary lenders.

 

A DCA Example

A third-party DCA is collecting debts on behalf of three different clients, each of which pays the same commission rate and each of which has outsourced a portfolio of 60 000 debts. 

Half of the accounts in Portfolio A have a balance of €4 000 while the other half are split evenly between balances of €2 000 and balances of €5 500.  After running the accounts in question through a simple scorecard, the DCA was able to determine that 60% of the accounts are in the high risk group with only a 7% probability of payment, 20% are in the medium risk group with a 11% probability of payment while the remainder are in the low risk group and have a 20% probability of payment.

Portfolio B ia made-up primarily of account with higher balances, with half of the accounts carry a balance of €13 500 and the remainder are equally split between balances of €7 500 and €5 000.  Unfortunately, the risk of this portfolio is also higher and, after also putting this portfolio through the scorecard, the DCA was able to determine that 50% of the accounts were in the highest risk group with an associated probability of payment of just 2% while 30% of the accounts were in the medium risk group with a probability of payment of 5% and 20% of the accounts were in the lowest risk group with a probability of payment of 13%.

In portfolio C the accounts are evenly split across three balances: €4 500, €8 000 and €10 000.  After a similar scorecard exercise it was also shown that 70% of accounts are in the highest risk group with a 7.5% probability of payment, 30% of accounts are in the medium risk group with a probability of payment of 14% and the final 10% in the low risk group have an 18% probability of payment.

The DCA now has a few options when assigning work to its staff.  It could assign accounts randomly from across all three portfolios to the next available staff member, it could assign accounts from the highest balance to the lowest balance or it could assign specific portfolios to specific teams, prioritising work within each portfolio but not across them.  Some of these approaches are better than others but neither will deliver the optimal results.  To achieve optimal results, the DCA needs to break each portfolio into customer segments and then prioritise each of those segments; working the highest yielding segment first and the lowest yielding one last.

Using the balance and probability of payment information we have, it is possible to calculate a recovery yield for each of the nine segments in each portfolio; the recovery yield being simply the balance multiplied by the probability of recovering that balance.  Once the recovery yield has been determined for each of the nine segments in each of the three portfolios it is possible to prioritise them against each other as shown below.

With the order of priority determined, it is possible now to assign effort in the most lucrative way.  For example, if the DCA in question only had enough staff to work 50 000 accounts they would expect to collect balances of approximately €27.7 million if they worked the accounts randomly, approximately €40.4 million if they prioritised their effort based on balance but as much €53.3 million if they followed the recommended approach – an uplift of 92%.  As more staff become available so the less the apparent uplift decreases but there is still a 44% improvement in recoveries if 100 000 accounts can be worked.

If all accounts can be worked then, at least if we keep our assumptions simple, there is no uplift in recoveries to be gained by working the accounts in any particular order. 

 

Ideal Staff Numbers

However, that is not to say that the model becomes insignificant.  While the yield changes based on which segment an account is in, the cost of working each of those accounts remains the same.  Since profitability is the difference between yield and cost and since cost remains steady, a drop in yield is also a drop in profit.  So, continuing along that line of reasoning, there will be a level of yield below which a DCA is making a loss by collecting on an account.

So, it stands to reason then that a DCA working all accounts is unlikely to be making as much profit as they would be if they were to use the ‘cheery picking’ model to determine their staffing needs.  New staff should be added to the team for as long as they will add more value than they will cost.  As each new member of staff will be working on lower yield accounts there are diminishing marginal returns on staff until the point that a new member of staff will be actually value destroying.

Assume it costs €30 000 to employ and equip one collector and that that collector can work 1 500 accounts in a year.  To be value adding then, that collector must be assigned to work only accounts with a net yield of more than €20. 

Up to now, I simply referred to recovery yield as the total expected recovery from a segment.  That was possible at the time as we had made the simplifying assumption that each portfolio earned the same commission and were only looking to prioritise the accounts.  However, once we start to look at the DCA’s profit, we need to look at net yield – or the commission earned by the DCA from the recovery. 

If we assume a 10% commission is earned on all recoveries then for the yield of €1 800 in highest yielding segment becomes a net yield of €180.  Using that assumption we are able to see that the ideal staffing contingent for the example DCA is 104: allowing the DCA to work the 156 000 accounts in segment 24 and better. 

At this level the DCA will collect approximately €96 million earning themselves €9.6 million in commission and paying out €3.1 million in staff costs in the process; this would leave them with a profit of €6.4 million.  If they lay off two members of staff and work one less segment their profit would decrease by €6 000.  If, instead, they hired 5 more members of staff and worked one more segment their profits would be reduced by nearly €40 000.

Commission Rate Changes

Having just introduced the role of commission, it makes sense to consider how changes in commission rates might impact on what we have already discussed. 

The simplest change to consider is an across-the-board change in commission rates.  This doesn’t change the order in which accounts are worked as it affects all yields equally.  It does, however, change the optimal staff levels.  In the above example an across-the-board decrease in commission from 10% to 5% would halve the yields of each block meaning to still achieve a net yield of €20 a segment would have to have a gross yield of €400.  In turn this would mean that staff numbers would need to be cut back to 59: now working 88 000 accounts and generating a total profit of €2 million.

A more common scenario is that commissions are fixed over the term of the contract but that these commissions vary from portfolio to portfolio. 

Most DCAs will charge baseline commission rates which vary with the age of the debt at the time it is taken on.  For example, a DCA may charge a client 5% of all recoveries made on accounts handed over at 60 days in arrears but 10% of all recoveries made on accounts handed over at 120 days in arrears.  This compensates the DCA for the lower recovery rates expected on older debt and encourages primary lenders to outsource more debts to the DCA.

When a DCA is operating across portfolios which each earn different commission rates it should use the net yield in the prioritisation exercise described above rather than the gross yield.  Assume that the DCA from our earlier example actually earns a commission of 5% for all recoveries made from Portfolio A, 7.5% on all recoveries made from Portfolio B and 10% on all recoveries made from Portfolio C. 

Now, the higher rewards offered in Portfolio C change the order in which accounts should be worked.  The DCA no longer concentrates on the largest recovery yield but rather the largest net yield. 

Primary Lenders

Of course, the concept and models described here are not unique to the world of DCAs, primary lenders should structure their debt management efforts around similar concepts.  The only major difference during the earlier stages of the debt management cycle is that there tends to be more strategic options, more scenarios and a wider diversity of accounts.

This leads to a more complex model but one that ultimately aims to achieve the same end result: the optimal mix between cost and reward.  Again a scorecard forms the basis for the model and creates the customer segments mentioned above.  Again the size of the balance can be used as a proxy for the expected benefit.   There is of course no longer a commission but there are new complexities, including the need to cost multiple strategy paths and the need to calculate the recovery rate as the recovery rate of the strategy only – i.e. net of any recoveries that would have happened regardless.  For more on this you can read my articles on risk based collections and on self-cure models.

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Probably the most common credit card business model is for customers to be charged a small annual fee in return for which they are able to make purchases using their card and to only pay for those purchases after some interest-free period – often up to 55 days.  At the end of this period, the customer can choose to pay the full amount outstanding (transactors) in which case no interest accrues or to pay down only a portion of the amount outstanding (revolvers) in which case interest charges do accrue.  Rather than charging its customer a usage fee, the card issuer also earns a secondary revenue stream by charging merchants a small commission on all purchases made in their stores by the issuer’s customers.

So, although credit cards are similar to other unsecured lending products in many ways, there enough important differences that are not catered for in the generic profit model for banks (described here and drawn here) to warrant an article specifically focusing on the credit card profit modelNote: In this article I will only look at the profit model from an issuer’s point of view, not from an acquirer’s.

* * * 

We started the banking profit model by saying that profit was equal to total revenue less bad debts, less capital holding costs and less fixed costs.  This remains largely true.  What changes is the way in which we arrive at the total revenue, the way in which we calculate the cost of interest and the addition of a two new costs – loyalty programmes and fraud.  Although in reality there may also be some small changes to the calculation of bad debts and to fixed costs, for the sake of simplicity, I am going to assume that these are calculated in the same way as in the previous models.

 

Revenue

Unlike a traditional lender, a card issuer has the potential to earn revenue from two sources: interest from customers and commission from merchants.  The profit model must therefore be adjusted to cater for each of these revenue streams as well as annual fees. 

Total Revenue  = Fees + Interest Revenue + Commission Revenue

                                = Fees + (Revolving Balances x Interest Margin x Repayment Rate) + (Total Spend x Commission)

                                = (AF x CH) + (T x ATV) x ((RR x PR x i) + CR)

Where              AF = Annual Fee                                               CH = Number of Card Holders  

                           T = Number of Transactions                          PR = Repayment Rate

                           ATV = Average Transaction Value              i = Interest Rate

                           RR = Revolve Rate                                              CR = Commission Rate

Customers usually fall into one of two groups and so revenue strategies tend to conform to these same splits.  Revolvers are usually the more profitable of the two groups as they can generate revenue in both streams.  However, as balances increase and approach the limit the capacity to continue spending decreases.  Transactors, on the other hand, seldom carry a balance on which an issuer can earn interest but they have more freedom to spend.

Strategies aimed at each group should be carefully considered.  Balance transfers – or campaigns which encourage large, once-off purchases – create revolving balances and sometimes a large, once-off commission while generating little on-going commission income.  Strategies that encourage frequent usage don’t usually lead to increased revolving balances but do have a more consistent – and often growing – long-term impact on commission revenue..   

Variable Costs

There is also a significant difference between how card issuers and other lenders accrue variable costs. 

Firstly, unlike other loans, most credit cards have an interest free period during which the card issuer must cover the costs of the carrying the debt.

The high interest margin charged by card issuers aims to compensate them for this cost but it is important to model it separately as not all customers end up revolving and hence, not all customers pay that interest at a later stage.  In these cases, it is important for an issuer to understand whether the commission earnings alone are sufficient to compensate for these interest costs.

Secondly, most card issuers accrue costs for a customer loyalty programme.  It is common for card issuers to provide their customers with rewards for each Euro of spend they put on their cards.  The rate at which these rewards accrue varies by card issuer but is commonly related in some way to the commission that the issuer earns.  It is therefore possible to account for this by simply using a net commission rate.  However, since loyalty programmes are an important tool in many markets I prefer to keep it out as a specific profit lever.

Finally, credit card issuers also run the risk of incurring transactional fraud –  lost, stolen or counterfeited cards.  There are many cases in which the card issuer will need to carry the cost of fraudulent spend that has occurred on their cards.  This is not a cost common to other lenders, at least not after the application stage.

Variable Costs = (T x ATV) x ((CoC x IFP) + L + FR)

Where            T = Number of Transactions                         IFP = Interest Free Period Adjustment

                         ATV = Average Transaction Value             CoC = Cost of Capital

                         FR = Fraud Rate

Shorter interest free periods and cheaper loyalty programmes will result in lower costs but will also likely result in lower response rates to marketing efforts, lower card usage and higher attrition among existing customers.

 

The Credit Card Profit Model                   

Profit is simply what is left of revenue once all costs have been paid; in this case after variable costs, bad debt costs, capital holding costs and fixed costs have been paid.

I have decided to model revenue and variable costs as functions of total spend while modelling bad debt and capital costs as a function of total balances and total limits. 

The difference between the two arises from the interaction of the interest free period and the revolve rate over time.  When a customer first uses their card their spend increases and so does the commission earned and loyalty fees and interest costs accrued by the card issuer.  Once the interest free period ends and the payment falls due, some customers (transactors) will pay their full balance outstanding and thus have a zero balance while others will pay the minimum due (revolve) and thus create a balance equal to 100% less the minimum repayment percentage of that spend. 

Over time, total spend increase in both customer groups but balances only increase among the group of customers that are revolving.  It is these longer-term balances on which capital costs accrue and which are ultimately at risk of being written-off.  In reality, the interaction between spend and risk is not this ‘clean’ but this captures the essence of the situation.

Profit = Revenue – Variable Costs – Bad Debt – Capital Holding Costs – Fixed Costs

= (AF x CH) + (T x ATV) x ((RR x PR x i) + CR) – (T x ATV) x (L + (CoC x IFP)) – (TL x U x BR) – (TL x U x CoC +   TL x   (1 – U) x BHR x CoC) – FC

= (T x ATV) x (CR – L – (CoC x IFP) -FR) – (TL x U x BR) – ((TL x U x CoC) + (TL x (1 – U) x BHR x CoC)) – FC

Where        AF = Annual Fee                                               CH = Number of Card Holders          

                      T = Number of Transactions                         i = Interest Rate

                      ATV = Average Transaction Value               TL = Total Limits

                      RR = Revolve Rate                                                U = Av. Utilisation

                      PR = Repayment Rate                                          BR = Bad Rate

                      CR = Commission Rate                                        CoC = Cost of Capital

                      L = Loyalty Programme Costs                          BHR = Basel Holding Rate

                      IFP = Interest Free Period Adjustment        FC = Fixed Costs

                      FR = Fraud Rate

 

Visualising the Credit Card Profit Model  

Like with the banking profit model, it is also possible to create a visual profit model.  This model communicates the links between key ratios and teams in a user-friendly manner but does so at the cost of lost accuracy.

The key marketing and originations ratios remain unchanged but the model starts to diverge from the banking one when spend and balances are considered in the account management and fraud management stages.   

The first new ratio is the ‘usage rate’ which is similar to a ‘utilisation rate’ except that it looks at monthly spend rather than at carried balances.  This is done to capture information for transactors who may have a zero balance – and thus a zero balance – at each month end but who may nonetheless have been restricted by their limit at some stage during the month.

The next new ratio is the ‘fraud rate’.  The structure and work of a fraud function is often similar in design to that of a debt management team with analytical, strategic and operational roles.  I have simplified it here to a simple ratio of fraud: good spend as this is the most important from a business point-of-view, however if you are interested in more detail about the fraud function you can read this article or search in this category for others.

The third new ratio is the ‘commission rate’.  The commission rate earned by an issuer will vary by each merchant type and, even within merchant types, in many cases on a case-by-case basis depending on the relative power of each merchant.  Certain card brands will also attract different commission rates; usually coinciding with their various strategies.  So American Express and Diners Club who aim to attract wealthier transactors will charge higher commission rates to compensate for their lower revolve rates while Visa and MasterCard will charge lower rates but appeal to a broader target market more likely to revolve.

The final new ratio is the revolve rate which I have mentioned above.  This refers to the percentage of customers who pay the minimum balance – or less than their full balance – every month.  On these customers an issuer can earn both commission and interest but must also carry higher risk.  The ideal revolve rate will vary by market and depending on the issuers business objectives but should be higher when the issuer is aiming to build balances and lower when the issuer is looking to reduce risk.

 






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The goal of every business is to make a profit and so, by extension, the goal of every strategy should be to move that business towards optimal profitability.  To this end, all strategists should first identify those actions which a business undertakes in its day-to-day operations that enable to it make a profit.  These actions are called profit levers.  As with mechanical levers, an adjustment made to a profit lever will result in a change in profit.  
 

Although it is possible for profit levers to reinforce one another, it is more common for them to compete.  One can therefore not optimise total profit by optimising each profit lever in isolation.  To do so, one must take into account the nature of interactions between profit levers.  A lending business is no different.  One can not evaluate the risk of a loan portfolio without taking into account the interest rate charged; one can not adjust the bad-debt collection strategies without considering the impact they have on customer service strategies and one can not build a new scorecard without knowing the cost of business it will turn away.  

  

The relationships between each of a business’ individual profit levers can be mathematically – or graphically – represented in a profit model.  The profit model for almost any business can be started as a function of sales revenue less all variable and fixed costs.  A bank is simply another form of business – one that makes its profit by borrowing a large lump sum of money at a low rate of interest, breaking it into multiple smaller sums and lending those sums on to its customers at a higher rate of interest.  So, although terminology might change when one considers a banking example, the concepts remain identical.    

   

A bank typically earns revenue from fees and interest on outstanding balances, pays variable costs that include the cost of holding capital and the cost of bad debt as well as the various fixed costs associated with the provision of banking services.  The same simplified profit model can therefore be expresses for a bank as a function of total fee and interest revenue less bad debt write-offs, capital holding costs and the fixed costs associated with operations.  

  

Profit = Revenue – Bad Debt – Capital Holding Costs – Fixed Costs  

  

Terms such as ‘revenue’ and ‘bad debt’ are too nebulous to direct specific actions and so further deconstruction is required.  Revenue is a function of total outstanding loan balances, the ratio of customers repaying their loans to those in default and the interest rate charged.  In a similar way, it is possible to further simplify the total outstanding loan balances as a relationship between total loan balances offered to customers and the average rate at which those available balances are actually utilised by customers.  

   

Revenue = (Loan Balances Offered x Utilisation Rate) x Repayment Rate x Interest Rate  

   

Repeating this process for each of the other factors brings us to a point where we have a basic profit model for a bank – or at least for its lending operations.  

   

Profit = (L*U*(1-BR))*i – (L*U*BR) – (L*U*CoC + L*(1 – U)*BHR*CoC) – FC  

   

Where: L          = Loan Balances Offered            U          = Utilisation Rate  

            BR        = Bad Rate                                i           = Interest Rate  

            CoC      = Cost of Capital                        BHR     = Basel Holding Rate  

            FC        = Fixed Costs  

   

This profit model is, however, not yet complete.  We can see the directional impact that an increase in loan balances will have on each profit lever – an increase in revenue, bad debt write-offs and capital holding costs – but not by how much each factor will increase; let alone their combined impact.  This framework must still be customised from three sources – financial information, an analysis of existing data and test-and-learn analytics.  

   

Financial data is usually readily available and easy to access.  In this example, it should be possible to quickly determine the interest rate charged by the bank and the interest rate it pays its funders.  With a little more effort – and a reliable data warehouse – it should also be possible to analyse the bank’s historical data and calculate from that the total loan balances offered, the average utilisation rate and the average bad rate for this product.  So, even without sophisticated analytical capabilities, an organisation should be able to populate an ‘as-is’ view of the profit model template for each of its major products.    

   

Returning to our example, it should be easy enough to find the figures needed to determine that the product in question is generating nearly three million Euro in profit  

   

Assuming:                    L          = €100,000,000                         U          = 75%  

                                    BR        = 2%                                       i           = 17%  

                                    CoC      = 10%                                       BHR     = 20%  

                                    FC        = €300,000  

   

Profit = (100,000,000*75%*98%*17%) – (100,000,000*75%*2%) -(100,000,000*75%) *10%) – (100,000,000*25%*20%*10.5%) – 300,000 = 2,695,000  

   

Knowing the ‘as-is’ view is important to a business but not as important as having a tool to evaluate and compare the outcome of potential future actions.  This simple model can be used to determine the impact that a new strategy will have on profitability but only if we assume that all the other factors remain unchanged.  For example, increasing the interest rate by a percentage point will increase in profit by three-quarters of a million Euro.    

    

Although this assumption (ceteris paribus) is common in economics, it does not present a true reflection of reality.  We know that an increase in interest rates is likely to have a consequential impact on, among others, the utilisation rate and the risk of the portfolio.  Thus, using the profit model becomes more complex when the impact of a change is considered.  

   

What is likely to happen if a bank offers all its customers a 10% increase in available balance?  This is not a question that can be answered without an understanding of how the model performs in a changing environment.  The performance of the model in a changing environment is known as ‘marginal performance’ and can only be calculated using a forward-looking analytical technique.  Test-and-learn analytics is a technique that gathers real-time marginal performance data in a series of small and controlled experiments.  The results of these experiments can then be used to populate the profit model which can, in turn, be used to extrapolate the likely impact the new strategy will have when rolled-out on a large-scale.    

   

In our example it was easy to use historical data to calculate that, on average, seventy-five percent of the available loan balance is utilised. But this fact does not necessarily extend to say that seventy-five percent of any increased balance will also be utilised.  In fact, it is likely that the marginal utilisation will be significantly lower than that.  A test must therefore be constructed to determine, in a controlled environment, the marginal utilisation rate.    

   

A test of this sort would start with the random selection of group of customers to be tested.  Some of these customers will be contacted and reminded of their existing available balances while the others will be contacted and offered a further 10% in available balances.  By monitoring the relative performance of these two groups it would be possible to calculate both the marginal utilisation (what portion of the new balance was taken up) and marginal risk (what portion of the new balances ended in default).  The only ‘new’ cost in this scenario would be those costs directly linked to the campaign.  

   

Profit = (L*MU*(1-MBR))*i – (L*MU*MBR) – (L*MU*CoC + L*(1 – MU)*BHR*CoC) – FC  

   

Assuming:                    

L          = Loan Balances Offered            = €10,000,000  

MU       = Marginal Utilisation Rate          = 30%  

MBR     = Marginal Bad Rate                  = 35%  

FC        = Fixed Costs                            = €15,000  

   

Returning to the profit model it is now possible to calculate that this strategy, because it leads to a lower marginal utilisation and a higher marginal risk, actually leads to a decrease in overall profitability.  In this format a profit model becomes a truly useful tool.  

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The goal of every business is to make a profit and so, by extension, the goal of every strategy should be to move that business towards optimal profitability.  To this end, all strategists should first identify those actions which a business undertakes in its day-to-day operations that enable to it make a profit.  These actions are called profit levers.  As with mechanical levers, an adjustment made to a profit lever will result in a change in profit.  Profit levers are not limited to the sales process, however.  Profit is the difference between the money that comes into a business and the money that goes out of it and so, while some profit levers affect the ‘money in’ side of the business, others affect the ‘money out’ side and every business is an amalgamation of several such profit levers.

 

Although it is possible for profit levers to reinforce one another, it is more common for them to compete.  One can therefore not optimise total profit by optimising each profit lever in isolation.  To do so, one must take into account the nature of interactions between profit levers.  A discount that increases sales volumes does so at a cost to gross margin and a new target market that is more responsive to an offer may also be more risky.  In each case, maximum profit is achieved only when the correct balance for each profit lever is found.

 

The relationships between each of a business’ profit levers can be mathematically – or visually – represented in a profit model.  The first step in building a profit model is to deconstruct a business into its most basic profit components.  The profit model for almost any business can be begun as a function of sales revenue less all variable and fixed costs. 

 

Profit = Revenue – Variable Costs – Fixed Costs

 

The simplicity of this equation is what makes it broadly applicable but it is also what limits its usefulness.  To be useful, each of these components must first be further broken down into their more detailed constituent parts.  For example, it is clear from the equation that an increase in revenue will lead to an increase in profit.  However, ‘an increase in revenue’ is too nebulous a term to inform a specific course of action.  The ‘revenue’ component must therefore be broken further into its component parts.  In this case, revenue is equal to the number of units sold multiplied by the selling price of each unit, so revenue can be affected by adjusting one of these profit levers – by changing the selling price, etc.  The other components must also be broken down until each fragment of the equation is a fully formed profit lever.

 

Profit = No. Units Sold (Sales Price) – No. Units Sold (Cost per Unit) – Fixed Costs

 

At this stage though, the profit model is only a template that shows the directional impact that an adjustment to one profit lever might have but not the size of that impact. Without knowing the size of any one impact, it is also not possible to know the likely combined impact of those adjustments.  For example, by adding a new feature to its product – and therefore increasing the cost per unit – a company might be able to increase the number of units it sells.  The profit model shows us that an increase in the number of units sold will lead to an increase in revenue but it also shows us that an increase in the cost per unit will lead to an increase in variable costs.  What the model can’t do, therefore, is tell us if that increase in revenue will be sufficient to cover the increase in costs.  Before it can do this, the model needs to be populated with organisation-specific information.

 

Customising a profit model is a two stage process. The first stage is used to describe the organisation’s current situation and the second stage to describe the likely impact of a new strategy.  Describing the current situation should be a relatively simple process as most of the data needed is usually readily available from historical databases and published financial reports.  A good understanding of the status quo is important but profit models are most valuable when they are used as forward-looking tools.  For example, it is good to know the current profitability of a business but it is so much more valuable to be able to estimate how that profitability will be changed by a new strategy that, for example, discounts the selling price of goods or one that increases fixed production costs but lowers unit costs. 

 

Building such a forward-looking profit model requires an understanding of two important concepts – marginal performance and test-and-learn analytics.  It is important to differentiate between performance on average and performance at the margin or, in other words, to differentiate between performance in a stable environment and performance in that same environment once a change has occurred.  Because new strategies always change the existing environment, their success is determined by marginal performance.  A company that sells one thousand items at one price might sell two thousand if it drops it price by fifty percent; or it might still just sell one thousand.  Before it launches a campaign to offer discounted prices it vital to know which of these new environments is most likely.

 

Historical data sources only show average performance.  To correctly account for marginal performance, therefore, it is imperative to have access to forward-looking analytical tools that can calculate the change a strategy will have on its environment.  It is clearly not possible to know the future impact of a strategy before it is implemented so test-and-learn analytics is not, strictly speaking, a forward-looking technique.  Rather, it is a technique that gathers real-time marginal performance data in a series of small and controlled experiments.  The results of these experiments are used to populate the profit model which can then be used to extrapolate the likely impact of the strategy where it to be rolled-out on a large scale.  Rather than offering a fifty percent discount on all items, for example, the firm could randomly offer a few different discount rates to small groups of customers, measure the marginal performance of each of the groups, populate the profit model accordingly, identify the most profitable strategy and then offer that discount rate when the campaign is officially launched.  All major decisions can therefore be made using profit models populated with real marginal data.

 

 

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