Major Commercial Policy Design Issues

Should a company choose a volume based, growth based, wallet-share based, or some other kind of commercial policy?  Should commercial policy be discount or rebate driven?  What should the commercial policy be?

Once firms have made the decision to shift commercial policy from being heavily dependent on customer, executives must address the question of what kind of commercial policy they want to drive their customer relationships. This has to happen after taking into consideration salesperson-specific negotiated discounts, and rebates towards a more planned commercial policy with pre-planned and calibrated commercial policy elements.

In this missive, I examine how companies selling to end customers through distributors and retailers can design their commercial policy.

Data Science AND Management Insight

One method of designing commercial policy is through data science and machine learning. With statistics (machine learning) applied to transactional pricing data (data science), one uncovers the historical pricing patterns delivered through past tactical decision-making, and uses this as the basis for designing the future strategic commercial policy. This approach is likely to deliver a commercial policy that closely matches historical reality of what a company has achieved with customers, and therefore be acceptable to both customers and salespeople alike in the future.

In using machine learning, executives address the question: “What is?” The approach however fails to address a more fundamental question: ”What should be?”

An alternative method is to design commercial policy to address specific business goals.  In this approach, executives discuss their business challenges and the desired behavior of customers.  It reframes the discussion of rebates and discounts from “How much can we get from customers?” to “What incentives should we offer to shape customer behavior towards a more desirable position?”  It moves the focal discussion from “What discounts and rebates must we offer to do to close deals?” to “What discounts and rebates can we offer customers to improve our market relationships towards a more profitable position?”  These are much more taxing questions, but they go more directly to the question of: “What should be?”

Discounts and rebates should not only be framed as customer concessions closing deals. They should also be framed as payments to customers made for behaving in a particular manner.  With this latter framing, executives must ask: “What should a company be willing to pay to incent specific behaviors of, or acknowledge differences in pricing power between customers?”

Data science can show the difference between these two different vantage points.  Statistics can also delineate the gap between them and, in many cases, be used to narrow the differences and demonstrate the size of change in customer behavior needed to drive profitability. Hence both data science and management are useful and necessary to optimize commercial policy.

Recent emphasis has been on the use of data science to drive commercial policy. While this added insight is a highly welcome development, we cannot overlook the importance of management insight to drive commercial policy decisions.  It is this latter issue, the need for management insight, which we will address in the remainder of this missive.

Discount OR Rebate

In one sense, both discounts and rebates are price variances. If a discount is given equivalent magnitude of an alternatively considered rebate, both the discount and the alternatively considered rebate will result in the same price variance between the list price and the pocket price.  From a purely mathematical or financial viewpoint, one might consider them equivalent.

A discount versus a rebate of equivalent magnitude is not the same however from a market or accounting viewpoint.  Some cultures are more oriented towards discounts and shun rebates, while others prefer the opposite.  Discounts made to distributors, being an on-invoice event, may be passed through end-customers more quickly than an equivalent rebate, being an off-invoice and period-end event.

Moreover, competitive intelligence can more easily reveal transactional prices from invoices with discounts than transactional prices from invoices with rebates paid at the end of the year.  And discounts appear directly on the invoice thus easy to manage in accounting, whereas rebates require robust provisioning, accrual, and reconciliation processes in accounting.

The tradeoffs between using a discount versus a rebate are many. I will leave that subject for another missive.

I would like to focus this missive on some of the more common major commercial polices and the tradeoffs inherent in their selection. Most of the tradeoffs examined will be prevalent regardless of whether we are talking of a rebate or a discount, hence I will consider them as rebates but encourage the reader from a discount-oriented company to temporarily ignore the difference between a discount and rebate. And in their reading, mentally replace the word “rebate” with “discount”.

Major Rebates

The rebates we explore herein are:

• Volume Based Rebate to Address Buyer Power
• Volume Based Rebate to Encourage Growth
• Growth Rebate
• Wallet Share Rebate
• Business Plan Rebate

Most companies operate with one or more of the above rebate policies, or a variation thereof, and these rebate policies tend to have a greater influence on overall price variance than other policies.  As such, I deem them important candidates to be addressed early in the design of a commercial policy.

Volume Based Rebate to Address Buyer Power

Volume Based Rebates that address buyer power tend to have deepening rebates as purchase volumes, measured in dollars, increase. They are sometimes called simply turnover rebates or revenue rebates.  An example rebate schedule would be:

With this schedule, a customer buying less than $100K annually wouldn’t be ineligible for a rebate while those buying between $500K and $999K annually would get 3% back at the end of the year. The actual purchase volume cutoffs (first column) and rebate depths (second column) should be determined through a combination of data science and managerial insight to calibrate the schedule.

This rebate schedule effectively gives larger customers a lower price than smaller customers.  As such, it is best to think of and calibrate this as a rebate schedule that acknowledges and addresses the greater buyer-power of larger customers over smaller customers.

The Volume Based Rebate schedule above, with such large differences in required purchases to achieve a larger rebate, is unlikely to drive much change in customer behavior.  For instance, it is unlikely that a customer that was purchasing $150K will suddenly increase purchases to $250K just to increase their rebate percentage.  Hence, I wouldn’t suggest this particular schedule of rebates would be effective in driving growth.

Accepting that a rebate schedule with large increments between purchase volume requirements addresses buyer power rather than growth desires. This drives the calibration method for the rebate schedule.  From a calibration viewpoint, the size of the rebate should reflect the average pocket price difference between large customers and small customers. In our example, the average price difference between a million-dollar customer and very small customer would have been revealed to be near 4%.

Volume Based Rebates to Address Buyer Power have both virtues and short-comings.

On the virtuous side:

• The rebate schedule can be kept relatively simple with just a few tiers (usually under five) and is therefore easy to communicate and implement.
• Larger buyers do typically have more supply options available to them and therefore are able to extract lower prices than smaller buyers. Acknowledging this and removing this issue from price negotiations is a good means to reduce the potential for small customers to get a lower price than a large customer through a poorly managed negotiation, and therefore improve price fairness and reduce the risk of price contagion.

On the shortcomings side:

• The rebate schedule won’t particularly incent growth, and therefore if growth is a goal, this may not help much.
• The rebate schedule does reward larger customers over smaller customers simply for the attribute of being large. Companies may neither want to reward size alone nor reduce the sell-through power of smaller customers. More of this subject will be discussed in looking at Growth Rebate schedules.

Volume Based Rebate to Encourage Growth

Volume-based Rebates to encourage growth, like those that address buyer power, tend to have deepening rebates as purchase volumes, as measured in dollars, increase.  These too are sometimes called simply turnover rebates or revenue rebates.  Yet, they differ from those used to acknowledge buyer-power in a specific manner: the increments are smaller. An example portion of the rebate schedule would be:

The actual rebate schedule is likely to have many more tiers extending downwards to $20,000 and upwards to above $1 million.

With this schedule, a customer buying $250K annually would be eligible for a 2.0% rebate while those buying $300K annually would get 2.2% back at the end of the year.  The actual purchase volume cutoffs (first column) and rebate depths (second column) should be determined through a combination of data science and managerial insight to calibrate the schedule.

Like those designed to acknowledge buyer power, this rebate schedule effectively gives larger customers a lower price than smaller customers.  Unlike that purely designed to address buyer-power, it can also encourage growth.

The volume-based rebate schedule above, with relatively smaller differences in required purchases to achieve a larger rebate, is more likely to change customer behavior.  Specifically, it is reasonable to expect a a customer that was purchasing $295K to incrementally increase purchases by 2% to above $300K in order to increase their rebate percentage by 20 basis points. It may even be possible to increase sales to a $272K customer to above $300K for the same reason.  Hence, this particular schedule of rebates could be good at driving growth.

As such, it is best to think of and calibrate this as a rebate schedule that both acknowledges and addresses the greater buyer-power of larger customers over smaller customers, and shifts customer behavior towards higher purchase volumes.

The calibration of this rebate schedule will need to consider both the current real differences in price between large and small customers and a scenario analysis. In the scenario analysis, one can pose questions like “If customers with 2% of the next rebate tier increase their purchases to achieve the next higher rebate tier, what would be the overall change in volume, and what rebate increment can be extended to drive this volume change profitably? What if the rebate policy only influences customers within 1% of the next rebate tier? 5%?  How about 10%?”

Volume-Based Rebates to Encourage Growth have both virtues and shortcomings.

On the virtuous side:

• The rebate schedule should drive growth (through the rebate incentives), and most every company would like to see growth.
• Larger buyers do typically have more supply options available to them and therefore are able to extract lower prices than smaller buyers. Acknowledging this and removing this issue from price negotiations is a good means to reduce the potential for small customers to get a lower price than a large customer through a poorly managed negotiation—therefore improve price fairness and reduce the risk of price contagion.

On the shortcomings side:

• The rebate schedule will quickly become complex with multiple tiers (sometimes up to 100) and therefore may be difficult to communicate and implement without a proper quoting system. Moreover, the growth will come only if customers know that their rebate will increase if they purchase more offerings. So salespeople will need to track and communicate the progress of their specific customers regularly.
• Like the prior rebate, this also rewards larger customers over smaller customers simply for the attribute of being large. More of this subject will be discussed in looking at Growth Rebate schedules.

Pure Growth Rebate

Pure growth rebates tend to have deepening rebates as purchase volumes, as measured in monetary terms, increase with a specific customer.  An example portion of the rebate schedule would be:

With this schedule, a customer buying the same amount this year as they bought last year would get no rebate. Those that grow their purchases this year by 5% would get a 1% rebate.  Those that grow their purchases this year by 10% would get a 2% rebate, and so on.  The actual growth requirements (first column) and rebate depths (second column) should be determined through a combination of data science, and managerial insight to calibrate the schedule.

This rebate schedule directly incents purchase growth to all customers.  The size of the rebate for a given growth scenario can be calibrated directly from evaluating the volume hurdle required to justify the volume growth.

Unlike volume-based rebates, growth rebates do not depend on the size of the customer. For some business this is a virtue.

Some businesses find that they are overly dependent on sales to one or a few customers, and would like to diversify their business.  Eliminating incentives that are dependent on customer size puts smaller customers on the level playing field with larger customers. For manufacturers selling to distributors, there may be a need to put smaller customers on a level competitive position. This is in respect to cost and price to reduce the power of a single or small handful of large distributors in the market.  Indeed, for some markets (food manufactures selling to grocers for example), pure volume-based rebates can be found illegal in certain circumstances.

Yet pure, growth-based rebates pose a major problem for multi-year customer relationship building.  Consider the dynamics of a good customer under this schedule. Suppose that in year one they grow their purchases by 10% from $200K to $220K to achieve the 2% rebate at the end of the year. In the next year, even if they achieve the same purchase volume, they will have no rebate. To achieve the same 2% rebate, that customer would have to purchase $242K in year two.  And in year three, they would be seeking $266K. By year five, you would be demanding the customer to purchase $300k just to achieve the same rebate as they had in year one. This is usually an unreasonable expectation.

Customers who find the rebate schedule to make unreasonable demand of their behavior are likely to dismiss the offer. If customers dismiss the offer, the rebate schedule will have no impact and the commercial policy will have failed to improve results.

Moreover, customers that are already buying a large portion of their potential spend from the company will not see a growth rebate as realistic. Further growth at these high-wallet share customers may be constrained by their own growth potential. For these customers, the growth rebate may be a disincentive, meaning the rebate schedule will have no impact and the commercial policy will have failed to improve results.

Thus, growth-based rebates alone have both virtues and shortcomings.

On the virtuous side:

• The rebate schedule should drive growth, at least in the short-run.
• The rebate schedule treats large and small customers equally.
• The rebate schedule can be kept relatively simple with just a few tiers and is therefore easy to communicate and implement.

On the shortcomings side:

• The growth requirements may become unreasonable beyond the first year, and therefore quickly deliver diminishing re.
• The growth rebate structure may be a disincentive to large wallet-share customers.
• Companies may want to address buying power through their rebate schedule.

Wallet Share Rebate

Wallet share rebates tend to have deepening rebates as the company’s wallet share with specific customers increase.  Customer wallet share is how much of a customer’s cost for a product or service goes to a specific company. It’s the purchases a customer garners from the company as percentage of the total purchases of that customer within the product category in which the company competes. For instance, when a company sold $100K in cereal to a customer that purchased $500K of cereal from a competitor, the company and its competitors would have a wallet share of 20%. An example of the rebate schedule would be:

With this schedule, a customer buying 25% of their goods in specific category from the company would get a 1% rebate.  The actual wallet share requirements (first column) and rebate depths (second column) should be determined through a combination of data science and managerial insight to calibrate the schedule.

This rebate schedule directly incents customers to buy a high proportion of their goods within any one category from a single supplier.  Many researchers have found that driving wallet share is a more cost efficient and effective means to driving growth and profit than more direct efforts at increasing market share or sales volume.

Like growth rebates and unlike volume-based rebates, wallet-share rebates do not depend on the size of the customer.  For some business this is a virtue.

For manufacturers selling to distributors or retailers, having large wallet share enables them to brand their offerings stronger to end-customers, thus driving sales overall.  Moreover, companies achieving large wallet share with customers will find their supplier power increased, thus reduce the negotiating power of their customers overtime and reduce volatility and uncertainty in the market they serve.

Yet wallet share rebates pose a very fundamental problem:  How does the company measure its wallet share objectively?  While the metric is clear, attaining the data need to apply the metric is not possible in most situations.  An accurate measurement of wallet share requires knowing exactly how much a customer purchased from the company and its competitors.  Customers are rarely willing to share this information.  One might accept this and instead estimate wallet share, but once estimates are used, the rebate program itself becomes less objective and subject to a high number of customer complaints.  Incentive programs should be designed to reward customers for good behavior, not to encourage customers to complain.

A mediating approach might be to replace a pure measurement of wallet share with a customer classification system that is highly dependent on wallet share, but this too can fall foul as salespeople and customers alike argue for a higher customer classification than their behavior warrants.  Moreover, a virtue of a good rebate program that is focused on driving customer behavior is that it can be clearly communicated to customers with clear rules regarding requirements to be met to achieve a greater rebate.  Once one moves from a clear metric to an obscuring classification system, one has decrease transparency and therefore the ability to shape customer behavior.

Thus, Wallet Share Rebates have both virtues and short-comings.

On the virtuous side:

• The rebate schedule should drive wallet share, and therefore growth.
• The rebate schedule treats large and small customers equally.
• The rebate schedule can be kept relatively simple with just a few tiers and is therefore easy to communicate and implement.

On the shortcomings side:

• Wallet share itself may be objectively unmeasurable, and therefore impossible to incent.
• Companies may want to address buying power through their rebate schedule.

Business Plan Rebate

Business Plan Rebates are an attempt to address the shortcomings of the prior rebate schedules, but they introduce a new set of challenges. In a business plan rebate, salespeople are charged with developing a business plan for each customer that delineates expected purchases, in revenue, for that customer to be on-target for the year.  Importantly note that the business plan is defined by the company according to its goals and expectations, not the customer.

Given that purchases go through some natural volatility, the business plan rebate may extend a rebate to customers when they are within some range of the target (90% to 105% of the target for instance).  When the customer purchases above that target, they may get a deeper rebate in a manner similar to a growth rebate schedule

An example of the rebate schedule would be:

With this schedule, a customer buying between 90% and 104% of their target business plan purchases, measured in monetary terms, will achieve a 1% rebate.  If they grow their purchases, they can achieve a higher rebate.  The actual target percentage requirements (first column) and rebate depths (second column) should be determined through a combination of data science and managerial insight to calibrate the schedule.

This rebate schedule directly incents customers to (1) buy at least as much as the business plan suggest and (2) grow their purchases where possible.   This creates for the company both greater business stability and clarity in growth.

Implementing a business plan rebate requires, a priori, the development of customer specific business plans.  Data science applied to historical purchase plans can create a baseline business plan per customer.  Usually, this baseline must be adjusted to account for changes in offering portfolios: new products being introduced and exiting products being retired.  A second order adjustment might account for planned purchase volume increases related to price increases or expected demand increases.

Customer business plans development is detailed work.  Collaboration between sales, finance, and marketing is required.  Such collaborative and detailed planning at the customer level creates a challenge of its own.

Like the growth rebate, the business plan rebate incorporates an incentive to grow.  While this may be a good incentive for one year, it can create challenges for multi-year relationships as the higher level of rebates are extended then retracted with the natural fluctuation of the business.

To mitigate the potential incentive for business to tank their purchases one year and then grow their purchases the next, which the growth portion of the business plan rebate may incent, companies often couple a business plan rebate with a multi-year customer loyalty rebate.

A multi-year customer loyalty rebate may pay a fixed amount in year 3 of the purchases in year 1, say 0.5%, if that customer has achieved at or above their business plan target for three years in a row.  Deeper loyalty programs may be associated with achieving a growth target for each year over the same three years.

The multi-year customer loyalty rebate doesn’t eliminate the shortcomings of rebates associated with growth, but they can mitigate them to a negligible level.

Thus, Business Plan Rebates have both virtues and short-comings.

On the virtuous side:

• The rebate schedule should drive business stability and growth.
• The rebate schedule treats large and small customers equally, and high and low wallet share customers equally.
• The template rebate schedule can be kept relatively simple with just a few tiers and is therefore easy to communicate and implement.

On the shortcomings side:

• It will require detailed business planning at the customer level. This may be manageable for companies with a handful of major customers but be managerially overwhelming for companies with hundreds or thousands of smaller customers.
• Financially, rebate provisions will have to made over multiple years, not just one, if multi-year customer loyalty programs are used as well. This may prove challenging.

Choosing the Best Rebate Policy

Given the choice, which is the best rebate policy?  Even a casual reader of the above will recognize that the answer is situationally dependent. Executives must choose the rebate policy that best fits their needs and goals after considering the alternatives and tradeoffs. They cannot just “implement best practices” according to the next firm over.  While I acknowledge this requires more work from executives, I will not relinquish them from their decision-making responsibilities.   But I can help clarify their decision landscape.

Commercial policy design enables companies to shift from unplanned price variances dependent on individual negotiations towards planned price variances that are rational, defensible, and potentially even attractive to customers.  The design of commercial policy should rely on a combination of machine learning, data science, and managerial insight.  By illuminating the meaning and differences of Volume Based Rebate to Address Buyer Power, Volume Based Rebate to Encourage Growth, Pure Growth Rebate, Wallet Share Rebate, and Business Plan Rebate, we hopefully have helped a few executives towards a better commercial policy.

A good commercial policy should both enhance profitability and market position, and encourage customers to behave in a desired manner.  That is, the best commercial policy design should drive customer engagement to that which should be.