WO2009065231A1 - Method and system of yield optimization - Google Patents

Abstract

A method of optimizing the yield provided to a seller of a plurality of units, is provided including: having a plurality of customers associated with records in a computer database, each of the records of the customers associated with a preference for one or more of the units, each of the customers associated with one or more selection opportunities; and on a computer, selecting each of the plurality of customers in a first order, each of the customers using the selection opportunity associated with the customer to select a unit from said plurality of units, and on a selection of the elected unit, displaying on a screen associated with the computer, the customers that still have a selection opportunity and for whom the customer's associated preference is for the unit.

Description

Method and System of Yield Optimization

Related Applications

This application claims the benefit of US Provisional Patent Application No. 60/989,839 filed November 22, 2007, which is hereby incorporated by reference in its entirety.

Field of the Invention

This invention relates to methods of maximizing the yield from sales of a limited resource, and to also minimize buyer dissatisfaction in such a sale.

Background of the Invention

When selling a limited resource, such as real estate properties, to a pool of customers, there are a number of factors which make it difficult to determine the greatest possible outcome for the seller. Pricing, features, desirability, investment potential, marketing effectiveness, purchaser demographics, and risks associated with global purchasing, financing trends, or fulfillment are just some of the factors which make such sales very complex and difficult. Also, the purchase of such limited resources (often for significant sums of money) is typically an emotional process for the parties, which introduces further variability and complexity.

As an example of such a complex process, a seller typically markets and sells units in large real estate projects to customers on behalf of a developer (the maker of the resource, such as units in a condominium project in either an urban or resort setting). Each unit can only be sold once. Each customer is associated with a trusted advisor (e.g. realtor or sales specialist) to work with them through the buying process. The seller may build demand from prospective customers to purchase homes/units in developments through sales and marketing efforts. These sales may or may not be restricted within a specific timeframe, and the initial release of units for sale may or may not be delayed in order to consolidate demand to accentuate competitive pressure on customers. One consequence of restricted supply is that the seller runs a risk of losing multiple potential customers by selling a single unit that was coveted by multiple customers. If a customer's level of dissatisfaction rises above a certain threshold, they will disengage from the process, potentially causing a cascading reduction in demand. In an attempt to reduce the risk of cascading demand, it is common practice to encourage customers not to focus on one particular unit, but rather to consider buying a unit from as large a number of choices as possible (typically from a set of units with similar features, price or desirability). This way, if a particular unit the customer prefers is chosen by another customer who had an opportunity to select or purchase at an earlier time, the level of disappointment for a customer who missed the purchase will be reduced, and that customer is less likely to abandon the process altogether. Expanding a customer's focus to a large number of acceptable units helps the seller increase the likelihood of maximizing the desired total yield from the sale. A seller may use a variety of enticements, such as price changes, financing opportunities, purchasing terms, enhancements or upgrades to a unit, as well as persuasion, to influence customers to change or expand their preferences. Having a wide range of preferences is also often necessary in instances when the exact pricing of the units is not finalized until shortly before the initial sales release, or in situations when the price will not be communicated to customers until after their initial expressions of interest and communication of preferences.

Throughout the marketing and sales process, sales specialists attempt to positively influence the final sales result using several approaches, including: (1) consolidating demand in advance of the initial release of units for sale by increasing the number of prospective customers and/or increasing the number of units bought per customer; (2) increasing the demand once the units have been released for sale by creating the perception of (or communicating the reality of) a "feeding frenzy" of customers quickly buying all available units; (3) using yield management, which involves balancing customer preferences against declining availability with the objective of maximizing total revenue.

Customers do not select the unit(s) to purchase simultaneously with other customers. At the time of selection, a customer can only choose from those units that have not already been selected. As such, each customer's choice impacts the set of remaining units (sometimes called "inventory"), and thus also affects the subsequent choices that can be and are made. The sequence in which customers select their unit(s) of choice can vary (within some constraints) at the discretion of the seller, and the rationale can be specific to each sales project. Sometimes the sequence is based on a rotation amongst sales specialists, with each specialist using their own skill and judgment to determine a prioritized order of their customers for selection purposes. At other times, the sequence can be determined by a fulfilling the demand of certain discrete marketing groups before others, or by the order in which customers made their pre- sales commitments (sometimes called "reservations").

In advance of the initial release of units for sale, sales specialists may participate in a variety of scenarios or simulations of the sales process. These scenarios reveal opportunities to increase sales by redirecting excess demand for the most popular units to other units for which there is less demand and give sales specialists some indication of how their customers' preferences may need to be changed or expanded in order to satisfy each customer's desire to own one or more units. These simulations enable the sales specialists to determine improved sequences in which the customers make their selections in order to minimize customer dissatisfaction and thereby maximize yield. As well, the simulations tend to highlight situations in which competition exists amongst customers and sales specialists for a particular unit or set of units, enabling resolution of such conflicts to be planned or attempted prior to the actual time of sale. Conflicts may arise due to the competitive nature of the sales business, in which the unstated (or stated) goal of each sales specialist is likely to maximize the revenue of units purchased by their customers, which typically maximizes individual commissions to the sales specialist. This goal may conflict with the overall goal of yield maximization, and managing these conflicts is a goal of yield management. One mitigation strategy is to pool commissions amongst the team of sales specialist, reducing the competitive nature of the sales process (though it certainly continues to exist in terms of achieving non-monetary or cultural recognition and advancement).

Maximizing yield can be achieved by, but is not necessarily restricted to, minimizing the value of unsold units at the conclusion of the sales process. For example, there may be occasions when a seller prefers any unsold units to be of a certain type, price, style or location, so the seller attaches a lower value to selling those units than their normal selling price. Reducing the seller's risk (e.g. risk to financing, achievement of certain liability thresholds) and increasing overall customer satisfaction are also parameters in the calculation of optimal yield. The levers of yield management include altering the sequence in which customers get to choose their desired unit; increasing the total number of alternative choices each customer would be prepared to buy; influencing customers' order of preference of units they would buy (by logic and/or persuasion) and, altering the pricing and inducements of the units in an effort to influence customers' order of preference of units they would buy (to "move demand around").

At present, most yield management is done manually, a data intensive process that often breaks down for even a moderately sized project and is therefore most often poorly or incompletely executed. Typically, sales specialists manage their own lists of customer preferences and available inventory using paper-based processes. During the simulation sessions, they will attempt to manipulate the sequence of customer selections or the units selected by request of a colleague whose customer is ahead of them in the selection sequence to "steer their customers away from" certain units in an attempt to avoid losing a sale. This is done by "open outcry" during the dry run. Sales specialists may or may not be conflicted in meeting this request because being flexible, and steering their customers towards other units, may increase total yield but might also reduce their personal commissions. In large projects (e.g. greater than 150 units), the possible number of combinations is extremely high, and the traditional paper based process is overwhelming, as it is almost impossible to explore all possible scenarios manually.

A simple spread sheet model exists in the prior art for use in real estate which looks at the mix of sold and unsold inventory by type of unit and suggests which unit should "ideally" be chosen next. The sales team then identifies a customer most likely to buy that unit and so builds a customer selection sequence. However this model simply chooses the unit type which is proportionately "least sold out" as the next "ideal" pick, based on the total inventory and units sold so far. This results in an "even" drawdown of inventory by type, so the remaining unsold inventory shows the same mix as the total for the building. There is little reason to believe this achieves the potential customer's goals, nor can it be assumed to achieve the goal of minimizing the value of unsold inventory, and it is clearly not designed to take into account buyer dissatisfaction, or to maximize yield based on other parameters.

Related prior art includes USP 7171667 for a "system and method for allocating resources based on locally and globally determined priorities"; USP 6990460 for "dynamic demand management"; USP 6895381 for a "method and system for management of a wait list for reserved purchases"; USP 6253187 for an "integrated inventory management system"; USP 6826538 for a "method for planning key component purchases to optimize revenue"; USP 6134534 for a "conditional purchase offer management system for cruises"; and USP 5148365 for "scenario optimization".

Summary of the Invention

The present invention relates to a method of optimizing the theoretical yield achieved in the sale of a scarce or limited resource, while simultaneously reducing the risk of failing to achieve that yield by reducing the level of dissatisfaction amongst a group of competing customers. The present invention provides a system and method for predicting the highest theoretical yield, and for predicting and minimizing the overall level of dissatisfaction, from a given list of reservations for the purchase of unique and distinct units that are limited in supply (for example, real estate units, works of art, or antiquities). The invention includes a method for listing customer preferences and for quantifying the relative customer disappointment (or dissatisfaction) corresponding to lower priority offerings and estimating the corresponding level of customer defection. As customer dissatisfaction increases, the likelihood of customer defection increases, the result of which is an increased financial risk for the seller. The current invention provides a system and method: for quantifying the dissatisfaction and corresponding reduction in commitment; for extrapolating the impact on dissatisfaction that can be made through adjustments in price and inducements; and for estimating the subsequent probability of defection; and ultimately, for estimating total yield for a given set of assumptions. Based on the analysis of these factors, along with other price discovery and preference related metrics, the present invention provides a system and method for estimating and optimizing the yield for any given set of customer preference assumptions, and for calculating and comparing any number of scenarios based on alternate sets of assumptions in an effort to ascertain the optimum outcome on a combination of factors including revenue, risk, buyer satisfaction, remaining inventory number or value, or any combination thereof.

The system, according to the invention, offers users of the system:

1. the ability to list preferences of customers in relative order;

2. the ability to estimate dissatisfaction levels of customers as the number of units available matching ordered customer preferences decreases;

3. the ability to estimate the risk of defection by a prospective customer as their dissatisfaction level increases;

4. the ability to estimate the relative impact of price adjustments on any of the parameters of yield management identified above (e.g. risk, revenue, dissatisfaction);

5. the ability to estimate the relative impact of non-price inducements on any of the parameters of yield management identified above (e.g. risk, revenue, dissatisfaction);

6. the ability to estimate revenue, yield, dissatisfaction, or risk for a given set of assumptions;

7. the ability to simulate combinations of assumptions and to recalculate outcomes quickly based on changes;

8. the ability to track and report the details of simulated or live sales events related to the yield management process;

9. the ability to calculate the optimum set of assumptions for a given objective (highest yield, lowest dissatisfaction etc.) or for a combination of objectives; and

10. the ability to share reports and interactive models of the calculations, simulations and actual sale events through various forms of representation (e.g. visual, audible, three-dimensional) in real-time or non-real-time. A method of optimizing the yield provided to a seller of a plurality of units, is provided including the steps of: (a) providing a plurality of customers associated with records in a computer database, each of said records of the customers associated with a preference for one or more of the units, each of the customers associated with one or more selection opportunities; and (b) on a computer, selecting each of the plurality of customers in a first order, each of the customers using the selection opportunity associated with the customer to select a unit from the plurality of units, and on a selection of the selected unit, displaying on a screen associated with the computer, the customers that still have a selection opportunity and for whom the customer's associated preference is for the unit.

The selection order may be stored in a database, and on the computer, wherein when selecting each of said plurality of customers in a second order, each of the customers using the selection opportunity associated with the customer to select a unit from said plurality of units, and on a selection of the selected unit, displaying on a screen associated with said computer, said customers that still have a selection opportunity and for whom the customer's associated preference is for the unit; and determining if said first order produces a higher yield to the seller than the second order.

A maximum price and minimum price may be associated with each of the preferences associated with a customer. On selection of a customer, units meeting the preference of the customer may be displayed on a screen associated with the computer. If, on selection of a unit associated with the preferences of a customer prevents a second customer from selecting a unit satisfying the second customer's preferences, the computer displays an alert. At least one of the preferences for the units are based on a zone including the unit. The zone may be based on the geographical facing of said units within the zone, or based on the floor of the units within the zone.

A system for optimizing the yield provided to a seller of a plurality of units, is provided including: (a) a server having a database with records about a plurality of customers each of the records of the customers associated with a preference for one or more of the units, each of the customers associated with one or more selection opportunities; and (b) on a computer, selecting each of the plurality of customers in a first order, each of the customers using the selection opportunity associated with the customer to select a unit from the plurality of units, and on a selection of the selected unit, displaying on a screen associated with the computer, the customers that still have a selection opportunity and for whom the customer's associated preference is for the unit.

The selection order is stored in a database, and on said computer, selection pf each of said plurality of customers in a second order, each of the customers using the selection opportunity associated with said customer to select a unit from said plurality of units, and on a selection of the selected unit, displaying on a screen associated with the computer, the customers that still have a selection opportunity and for whom the customer's associated preference is for the unit; and determining if the first order produces a higher yield to the seller than the second order.

Description of the Figures

Figure 1 is a block diagram of a system according to the invention;

Figures 2a and 2b are respective top and bottom views of an embodiment of a web page according to the invention showing the summary of the results of a run;

Figure 3 is a view of a web page according to the invention showing a summary of inventory control;

Figure 4 is a view of a web page according to the invention allowing a user to indicate purchase of a unit;

Figure 5 is a view of a web page according to the invention allowing a user to add a reservation holder;

Figure 6 is a view of a web page according to the invention allowing a user to select a run;

Figure 7 is a view of a web page according to the invention showing the summary of the yield management of a run;

Figure 8 is a view of a web page according to the invention allowing a user to select a unit, and display the unit's matches to preferences; Figure 9 is a view of a web page according to the invention confirming the selection of the unit and includes confidence parameters;

Figure 10 is a view of a web page according to the invention allowing a user to decline a pick from a reservation holder and provide reasons why the pick was declined;

Figure 11 is a view of a web page according to the invention allowing a user to add or edit information about a reservation holder;

Figure 12 is a view of a web page according to the invention allowing users to edit a selection preference of a reservation holder;

Figure 13 is a view of a web page according to the invention allowing users to edit the selection preference of a reservation holder by type;

Figure 14 is a view of a web page according to the invention allowing users to edit a selection preference of a reservation holder using a reason and a price range;

Figure 15 is a view of a web page according to the invention allowing users to edit or add information about units;

Figure 16 is a view of a web page according to the invention allowing an administrator to adjust values;

Figure 17 is a view of a web page according to the invention allowing an administrator to manage multiple phases of a project;

Figure 18 is a view of a web page according to the invention allowing users to access reports; and

Figure 19 is a block diagram showing a data model according to the invention.

Detailed Description of the Invention

Throughout the following description specific details are set forth in order to provide a more thorough understanding to persons skilled in the art. However, well known elements may not have been shown or described in detail to avoid unnecessarily obscuring the disclosure. Accordingly, the description and drawings are to be regarded in an illustrative, rather than a restrictive, sense.

Definitions

In this document the following terms will have the following meanings:

"availability display" means a matrix of available units that can be displayed, filtered, and/or electronically or physically represented to sales specialists and customers. As units are selected the availability display is updated to reflect the change of status of that unit;

"customer" means a prospective or actual purchaser of unit; a customer is also referred to herein as a "buyer";

"developer" means an entity that has prepared the units for sale. An example of a developer is a property developer that has built a number of resort homes for sale. Other developers may include artists, event managers, dealers of fine art or antiquities, etc.;

"group" means a set of customers sharing one or more characteristics in common;

"preference" means, for a customer, their preferred characteristics of a unit. For example, a preference, when used with real estate, may include a view, or a certain floor of a high rise, a price range, a floor plan, or a specific unit (or range of units or addresses);

"reservation holder" means a customer that has stated preferences, and has been granted a selection opportunity due to a certain level of commitment or engagement in the sales process;

"run" means a simulated or live session using the system in which selections of units for purchase are made for customers until all of the units are gone, or all customers have exhausted their selection opportunities. For example, a run may be a yield management simulation or it may be representing activity during an actual real estate selection event. Runs may be stored in the system and have a variety of data (e.g. name, date) associated with the run to assist with modeling, reporting and analysis; "sales specialist" means an entity engaged in the selling of units to customers. An example of a sales specialist is a realtor;

"selection opportunity" means a reservation or opportunity for a customer to purchase or select one or more units;

"seller" means an entity that is engaged in the sale of units to customers. The seller may engage or use sales specialists to assist in selling the units. In the example of a sale of resort real estate homes, the seller may be, but need not be, a developer as defined above;

"unit" means a distinctive article for sale, of which a limited quantity is available. When used with property, a unit can be an apartment, condominium, a house, land, or a fractional ownership or time share of any of the preceding. Units may also be art, antiquities, vehicles, seating at certain events or any other physical, digital or virtual asset that is of limited availability and has value. In the example of real estate, each home occupies a unique physical space, and has distinct characteristics such as views, location within the property development, or desirable times of possession (in the case of fractional ownership); and

"zone" means a set of units sharing one or more characteristics in common. For example, in real estate this could be all units with a north-facing balcony, or on a particular range of floors, or of a particular floor plan or price range.

Description

The present invention relates to a method of optimizing the yield out of the sale of a limited commodity, such as units, to a group of competing customers while simultaneously reducing the level of customer dissatisfaction and managing the seller's risk. The present invention provides a system and method for predicting the highest theoretical yield, lowest overall level of customer dissatisfaction, and expected level of risk of the sale of a limited supply of units, particularly unique and distinct units (for example real estate units, works of art or antiquities) to customers having reserved the right to purchase. The process includes a method for listing customer preferences and for quantifying the relative customer disappointment (dissatisfaction), corresponding to less preferred units, and the corresponding level of customer defection and seller risk. As dissatisfaction increases, the likelihood of closing a sale of a unit diminishes due to increases in customer defection rates. The present invention provides a system and method for quantifying the reduction in commitment, for extrapolating the impact on dissatisfaction that can be made through adjustments in price, and for estimating the subsequent probability of defection and estimating total yield and seller risk for a given set of assumptions. Based on the analysis of these factors, along with other price discovery and preference related metrics, the present invention provides a system and method for estimating yield for any given set of assumptions, and for calculating the optimum set of assumptions required to achieve the highest yield, lowest dissatisfaction, lowest risk and/or any combination thereof.

The invention below will be described in terms of real estate, wherein the units represent condominiums, homes or the like. However, the system and method are applicable to any situation in which a limited supply of unique and differentiable units is available to a multiplicity of customers.

The invention provides for a method and system of filtering a list of customer preferences to exclude already sold inventory and showing a list of remaining units satisfying those preferences. If there are no remaining preferences that result in units being available, there is an option to filter remaining inventory by various criteria (e.g. price) to identify units which may be satisfactory to the customer. The system according to the invention speeds up the selection/optimization process; creates a positive customer experience; and can assist the seller in helping customers to focus on what they can buy, not what they cannot.

When the list of units meeting the customer's preferences is completely exhausted the system allows a seller to quickly propose available units to the customer that are similar to those that meet their preferences in the hope the customer will make a purchase. The system also allows the seller to track and analyze data on what customers have actually bought rather than what the customer said they would purchase and thereby provides a better measure of true demand for units by type. Assessment of demand is normally based on reservations or sales. Reservations are generally not unit-type specific and sales are capped by available inventory and do not show excess demand for a certain unit type. Furthermore, should a customer with a reservation fail to purchase highly preferred units - even if inventory matching the customer's preferences exists - the system and method allows the seller to track these non-purchase decisions and thus provide highly valuable infoπnation on customer actions for future analysis. With this information, and with wider sets of data, the system and method provide insight to steer future phases and projects for sellers.

In an embodiment of the invention, customers can be bundled into one or more groups. These groups may be used for a variety of purposes, such as to determine the sequence of selections, or to categorize customers according to a discrete marketing category. The groups may be few and large (e.g. seller's friends, owners of existing units, the general public) or many and small. These groups may be nested within other groups, and the number, nature and structure of groups can be individually determined for each sales project.

Prior to the time when customers purchase units, customers have the opportunity to express, for each selection opportunity, an ordered list of preferences. The list of preferences may be one or more units in the inventory, one or more combinations of zones of units (explained below) within an optional price range, or any unit within an optional price range. The seller can attempt to influence the number of preferences or the sequence in advance.

For each preference in order, the system can apply a probability of buying from a set of predefined probability models. Also, the system can assume that a customer will purchase anything on their corresponding preference list regardless of where it is in their preference order, as long as it is still available, and can assume there is no chance of the customer buying a unit not on the list.

Units having similar characteristics can be bundled into zones (e.g. by floor plan; by price range; by floor of building; by views/orientation; by associated amenities/privileges; or by macro location). These zones are known in advance and typically do not vary during the life of a sales project.

Usually the priority for the seller to sell each unit is equal to the sale price of each unit. In some cases the seller will wish to identify certain units that are a lower priority to sell. For example, a property developer may wish to stimulate demand for less desirable units so that they are sold early (e.g. units overlooking the car park), knowing that a more desirable unit (e.g. a penthouse with a great view) will be easier to sell - and thus poses a lower risk for the developer. Therefore the system allows the ability to "overwrite" a value measure that is lower or higher than the dollar price for any given unit to reflect this preference. For example, if every home gets a number of value "points" equal to the priority to sell that home (e.g. more points = higher priority to sell), then the model should minimize the points value of the unsold inventory (for example the default number of points may equal dollar revenue which can be overridden manually to reflect developer or seller wishes). In many projects, but not all, by the time yield management commences, inventory levels are set and are not varied for the phase of a project. Results of yield management simulations can influence the inventory, including the number, nature and pricing of units, which in turn can influence unit demand. Optimally, successive yield management simulations will result in a unit inventory that features "charmed" prices, delivers total required revenue, reduces the risk of sales not completing, and results of the highest customer satisfaction possible.

The System

The system provides a visual and numeric data analysis tool for use with yield management and inventory control. It is preferably implemented in computer executable software code, for execution on a server having input means, output means, a display, a memory and a processor and accessible via a networked computer, also having input means, output means, a display, a memory and a processor.

As seen in Figure 1, the system 10, implemented as a computer application 20 operating on server 30, is preferably executed within a network so there need not be firewalls involved in communications between servers and clients 40. Users of the application 20 may be accessing server 30 through clients 40, which are typically computers operating within the network. Application 20 may use active directory for authenticating clients 40. PASS or the .NET membership API may be used to manage user rights on the system. System 10 can be deployed in different network or computing environments without significant re-configuration. If there is no network available, system 10 can be used as an application hosted on a single machine. If a network is available, users can interact with application 20 over the network using conventional means. Figures 2 through 18 show an embodiment of the system according to the invention. Further detail is provided below.

Preparing for a Run

The system uses, as input, information about the phase under consideration. This will include information about each unit in a phase, such as: number (to identify the unit); unit layout (two bedroom, three bedroom, etc.); floor; facing (north, south, etc.); price; and other pertinent characteristics, such as water view, penthouse, etc.

The system also requests, and obtains as input, information about reservation holders, which represent customers, and their preferences. The preferences correspond to fields within the unit records, such as unit layouts, floors, a price range, etc. This information may include ratings related to the likelihood of the reservation holder actually making purchases and the willingness of a reservation holder to accept units outside one or more of the reservation holder's preferences. Each reservation holder has the option of storing a client ID associated with them, through which the reservation holder obtains updates of available projects, etc. Reservation holder records may be imported from other customer lists. Each reservation holder preferably also has a list of confidence questions has been answered. These questions may be used by the system to define the level of confidence that a reservation holder will actually purchase a unit.

Information about sales specialists are also included, including which customers each sales specialist is responsible for. The sales specialists are also able to prioritize their customers.

System 10 should authenticate users before allowing access to application 20, and may use an active directory for doing so. When a user first accesses the system they may be prompted with a windows authentication box asking for their username/domain and password. Once logged in, the user may access to the system. Application 20 displays a list of phases that the user can work with, as seen in Figure 17. If the user has previously accessed the system, the system defaults to the user's last selected phase from a cookie stored on the user's computer. If the user hasn't accessed the system before, the user is prompted to select a phase. A user may add selection preferences to a phase by adding or amending a customer record to show that customer's preferences with respect to the phase. A preference may be as broad as "Any unit", as narrow as a specific unit, but more typically, may be a zone of units, for example units with a certain price range, or on a certain floor. A selection preference provides a reservation holder the opportunity to purchase a unit in a run. By default all reservation holders may be assigned a run with the preference type set to 'Any' and the reason set to 'Unknown'. This can be done to speed up data entry for new reservation holders.

In a preferred embodiment of the invention, there are three types of selection preferences used by the system. These include:

• Any: With this preference, as seen in Figure 14, the reservation holder will purchase any available unit. They can optionally add a minimum and/or maximum price to their selection to filter the units returned.

• Zone: This preference type, as seen in Figure 13, allows a reservation holder to filter their preferences by zone and/or unit type. The list of preferred zone/unit type combos can be ordered. They can optionally add a minimum or maximum price to their selection to filter the units returned.

• Unit: This is the most granular type. For this option a reservation holder would have identified specific units, as seen in Figure 12, that they want to purchase. These units can be ordered to show the least and most preferred units.

Sales specialists are simply stored as a name in the system. They are then linked to a reservation holder so that the system can filter sales data based on a sales specialist.

A floor plan defines a layout for a particular unit. A floor plan record contains a name and an associated unit type.

Units may be used to define an apartment, condo, or a piece of a fractional ownership. They may include information such as: the strata lot number, zone, price, floor plan, floor, square footage, and a true/false value indicating whether the unit has been purchased (or reserved) or not. The units may be imported from another database, such as an Excel spreadsheet.

The relationship of the data, as stored in a database accessible by server 20, is shown in Figure 19.

A lookup is a name/value pair. Lookups may be used in drop down lists, radio button lists, etc, to keep data consistent. The values associated with a lookup may be edited by a user using the system interface. In a preferred embodiment of the system, the following lookup are available: Cancel purchase reason; Contract tables; Contract writers; Decline pick reasons; Floors; Reservation priority groups; Strata lots; Unit types; Zones; Any Selection Preference Reason; Log type; Log object type; Run type; and Selection preference type. As the lookups may be stored in the database instead of code, the lookup name may be used in queries when retrieving data from the database. The value of the lookup option is also previously defined, so the key in the database does not auto-increment.

If a user accesses the yield management, inventory control, or runs a dashboard, system 10 will check to see if the user has a run stored in a cookie, although other means are available to determine if the user has stored a previous run. If the user has a run selected in their cookie, the system will load the details of that run. Otherwise the page will redirect the user to the "Select Run" page, as seen in Figure 6, to select a run. Alternatively, the user can opt to being a new run, by selecting a phase and naming a run associated with the phase. The user may then make amendments to reflect any changes in circumstances according with that run (for example by adding a new customer and his or her preferences).

The logical data model as used by system 10, is shown in Figure 19.

Conducting a Run

Once a run is selected, users may work through each customer and make selections for that customer. The interface shows the units available that match the customer's preferences as well as those that match other customer's preferences (thus units for which only the current customer has a preference will be easily distinguished). To manage inventory and maximize sales, sales specialists may hold one or more dry run sessions leading up to a launch or selection event. During these sessions the sales specialists go through an allocation of units based on customer preferences. Without using the system according to the invention, the process is manually intensive and inefficient as the individual sales specialists have to check their customer's selection sheets, in rank order, against the available inventory. Such a process invites human error and takes quite a bit of time.

Prior to a dry run session, a user may manually enter the following data into the system:

System 10 allows for the entry of inventory information about the units, as seen in Figure 15. The inventory information that is entered may include: Unit Code - naming conventions for units vary from project to project, so this field may be a free text field that will allow the user to use whatever naming convention they wish; Zone; Floor Location; Floor Plan; and Unit Type (1 bed, 2 bedroom),

System 10 also allows for the entry of customer information, as seen in Figure 11 and Figure 5. The customer information that is entered may include: Client ID; Customer First Name; Customer Last Name; Max Price - the maximum price that the customer is prepared to pay for a unit; Min Price - the minimum price that the customer is prepared to pay for a unit; sales specialist information, i.e. the sales specialist associated with the customer (first and last name); and the reservation priority group that the customer belongs to. Other information that may be entered may be defined on a per project basis, such as: time in and out of the launch event; and selection opportunity preference.

A customer can be assigned more than one selection opportunity. For each selection opportunity that the customer has, the system should have a record of the customer's preference for unit(s). The customer's preference may be for one or more units, OR for one or more zones, OR for any unit, OR for a combination of the preceding. The customer's preferences can be different for each selection opportunity. If the customer wishes to purchase more than one unit, a separate selection opportunity may be created for each unit they wish to purchase. As seen in Figure 13, system 10 allows the user to specify the customer's order of preference units specified in their selection preference. When creating a selection preference based on zone, the system should allow the user to specify a unit type as well. For example, if a customer expresses a preference for Zone A, they are also able to pick a particular unit type in that zone. The customer is also able to specify the order for these types of preferences. The system also allows a customer to express a preference for one or more unit types, without specifying a zone, i.e. a customer could have a preference for a one bedroom unit or a two bedroom unit in any zone. The customer is also able to specify the order for these types of preferences.

Each selection preference has a minimum and maximum price that the customer is prepared to pay for a particular unit, e.g. if a customer has more than one selection preference, each one can have a separate price range. A selection preference may only have a price range associated with it if the preference is for zone, any, or unit type. If the customer's preference is for one or more specific units, there is no price range associated with that selection preference. When creating a selection preference, as seen in Figure 14, a user should be able to specify "Any", "Undecided" or "Don't know" as a preference. When showing available units, the system will always show all units for these preference types. The purpose of this requirement is to allow the differentiation between a customer genuinely wanting any unit, a customer being undecided about their preference, and the sales person not knowing the customer's preference.

In the dry run session, the user selects a customer. If the customer has more than one selection opportunity allotted, the user may pick one. As seen in Figure 8, system 19 then displays all the units that match the customer's preference for that selection opportunity. If there are no units left that match the customer's preference, the system shows a message informing the user that no matching units are available, and then displays all available units. The user can then assign a single unit to the customer, as seen in Figure 9. If the user wishes to buy more than one unit, the above steps would be repeated for each unit.

When a unit is assigned to a customer the unit will be removed from the pool of unassigned units. If the customer had more than one selection opportunity, only the selection opportunity that was assigned with a unit will be removed from the pool of unassigned selection opportunities. When assigning a unit to a customer in a dry run session system 10 will display the available units in the same order of preference that was specified when creating the customer's selection preference.

System 10 may provide an interface that automatically refreshes when a unit is purchased or a purchase is cancelled or edited. When a user first begins the run interface, system 10 stores the current run version in a hidden field on the page. When an availability board flash file loads it queries the system to get the most recent unit information, including the unit code, unit ID and a true/false value indicating whether a unit was sold or not. This information is stored so that if it the web service is re- queried, it can tell which units have sold and then highlights those accordingly so the user can quickly determine which units remain available. Once the interface is fully loaded a timer starts and every two seconds the system calls a function to see if the current version has been changed. If there has been no change then there is no reaction. If a change has been made, the web page is reloaded and the various elements of the page that need to be updated are refreshed. This also allows users to quickly know the value of the units sold in the current run.

In a yield management run, a reserved unit is flagged as being reserved, but a reservation holder may still select the unit for purchase. This allows users to recognize when they should attempt to steer the reservation holder towards a different unit. In a launch event run, a reserved unit may not be purchased by another reservation holder.

During a launch event, i.e. the actual sales of units to customers, if a purchase is edited, the user preferably cannot change the selected unit or reservation holder for the purchase. If the purchase is to be assigned to another customer, that purchase must first be cancelled.

System 10 operates by selecting one Reservation Holder at a time. After making a purchase, if the current reservation holder does not have any selection preferences left, the system loads the next reservation holder with available selection preferences. The user may override the selected Reservation Holder and select a different Reservation Holder for the next purchase.

When making a purchase, a user may select a link above the reservation holder drop down list to add a new reservation holder, as seen in Figure 11. Clicking the link loads a new window with the add reservation holder form. After saving the reservation holder, the drop down list of reservation holders on the purchase page updates and the new reservation holder is available for selection. Information about new reservation holders may be imported from other software applications, such as Excel or Outlook.

The system may have two types of runs, which include yield management (a test run) and launch events (for a real sales event). The difference between a yield management and launch event run type is the user interface used for tracking the customer unit picks. A launch event uses an inventory control page for entering a purchase and tracks additional purchase information specific to a launch event.

Each run has a name and also includes a "Selection Preference Threat Level". This is the minimum number of available preferred units a selection preference may have in a run before it is flagged as being in threat. This allows a seller to notify sales specialists that if they pick a specific unit, it might put another reservation holder's preferences in threat. When a run has no units left to purchase, or there are no selection preferences left, the run is thought to be complete. At any time during the run reports can be generated to see how the progress of the run. A selection opportunity where there are three (or less) available units matching the preference may be consider a reservation in threat. The threat threshold is definable so that it can be reset to higher or lower levels if necessary.

A purchase is used to link a reservation holder's selection preference to a unit that they want to purchase. Once a selection preference has been used in a purchase, it can no longer be used to make another purchase in the same run unless the purchase is cancelled. When creating a purchase the system user can select a unit for the purchase, or they can select to decline the purchase and then provide a reason, as seen in Figure 10. A reservation holder will generally decline to purchase a unit if their preferred units are no longer available. When picking a unit to purchase the system will highlight any selection preferences that the given purchase would put into threat and then prompt the system user to select a confidence level for the purchase. This confidence level is based on the confidence questions asked about the reservation holder. A system defined confidence level is displayed by default, but the system user has the option to override it. Both values however are stored in the database to allow consideration of the confidence level after the event launch, for example, to determine if the confidence levels were too high or low.

Examples of options available to users of system 10 in an embodiment of the invention, include::

1. Add a Selection Opportunity. As seen in Figures 12, 13 and 14, a user may add a selection opportunity by adding a Reservation Holder, or adding a Preference Type for a Reservation Holder. The selection opportunity may be "Any", in which case a price range or reason (such as "Don't Know"; "Undecided"; or "Any" may be provided. The Selection Opportunity may be a Zone, which may also have a price range. Alternatively, the Selection Opportunity may be one or more specific units. An appointment time may be added, associated with a meeting with the Reservation Holder and to predict the order of selection.

2. Choose a Run. As seen in Figure 6, the user is given a display of accessible runs. The runs may be organized by Project, Phase or a particular Run Date and Description. For example, the project may be Honua Kai, which has two Phases, Phase One and Two, and three dry runs were performed with respect to Phase Two, on May 18, entitled "First Dry Run", on May 25, entitled Second Dry Run, and June

2. entitled Maui Launch Event". The user, by selecting a current run, rather than beginning a new run, can update the run.

3. Complete a Run. A run is considered complete if, for every unit associated with the phase a selection logically exists, or; all selection opportunities for that phase have been fulfilled within the run.

4. Add a Pick. Assuming the run is not completed, , the user is presented with a list of all reservation holders having unfulfilled selection opportunities. This list shows the name (last, first), associated sales specialist, and priority group of the reservation holders. This list can be ordered by any of these fields. The user selects a reservation holder from this list, and the selection opportunities for this reservation holder are made visible, as seen in Figure 8, showing the preference type and preference data for each opportunity. The user then selects an opportunity to fulfill a selection. After selection of an opportunity, system 10 then displays two lists of units: a first list displaying all available units matching the stated preferences, in order of how well they match the preference. In absence of ordered preferences, units are ordered by unit code (ascending), but the user can order the list by any field displayed in the list. The second list displays all available units, ordered by unit code (ascending or descending).

The user can then fulfill the selection opportunity, or go back to the previous screen. A selection opportunity is fulfilled by either:

• selecting any unit from either list. When a unit has been selected, the user is shown a confirmation screen as seen in Figure 9, listing that unit's details, as well as a list of other selection opportunities that will be threatened by this selection. The user can either confirm the selection, or go back to the previous screen to select a different unit. When confirming the selection, the user is asked to specify a confidence level for the selection (e.g. high, medium or low confidence). One of these confidence levels should be the default choice (e.g. high).

• declining to pick a unit for that selection opportunity. An editable list of reasons for declining to pick is displayed, as seen in Figure 10, for selection by the user. Examples of reasons may include: No units are available that satisfy the reservation holder's preferences; the reservation holder changed their mind; financing fell through; and the sales specialist was unavailable.

Cancel a Pick. A list of picks made during the run is displayed. This list displays, in order of most recent to earliest for each pick: reservation holder; unit code; sales specialist; contract writer; selection opportunity preference type; confidence level of the selection; and how well the pick matched preferences. The user can select any of these picks to cancel. When selecting a pick to cancel, a confirmation screen will appear. The user can then either confirm the cancellation, or go back to the previous screen. When confirming the cancellation, the user must specify the reason for canceling the pick from a list of reasons. Examples of reasons include: Error; and "I changed my mind". When the pick is cancelled, the associated unit is considered to be unpicked and the reservation holder's selection opportunity unfulfilled. If a user cancels a purchase, the record is flagged as deleted, and a log entry created indicating what purchase was cancelled and why. The system may be operated by a single user, preferably not a sales specialist involved in the launch event to minimize bias. Input of customer and unit information into the system may be completed manually or by import from a data file. Wherever possible, the system should use drop down lists in data entry screens, to lower data entry time, and increase data consistency.

The system may only allow one unit to be assigned per selection opportunity. If a customer wants to buy three units, they will need three selection opportunities, and it will be up to the sales specialists to decide on the order of the selection opportunities.

In a preferred embodiment of the system, probabilities are assigned to selections based on a customer's preferences. These probabilities can be used to assess the likelihood of customer purchasing a unit not within their preferences, depending on how close the unit is (for example the likelihood of a customer purchasing a unit with a floor of their preferred floor is greater than the likelihood of them purchasing a unit several floors away). The probabilities can be determined by the system or input by a sales specialist. Using a probabilistic model, the system may determine the preferred yield optimization by determining the order of selection based on analysis of the probabilities to maximize the likelihood of a sellout.

Reporting and Review of Run Results

Users may select to view reports from a list provided by system 10, as seen in Figure 18. A summary report is available as seen in Figures 2a and 2b. A fulfillment report may be generated showing all reservation holders and all of their selection preferences. For each selection preference that has been used in a purchase, the report also shows what was purchased, for example so that if a reservation holder has three selection preferences the user sees three rows with that reservation holder's name.

A feature of the system according to the invention is that when displaying a list of picks (i.e. selections of units made by customers), the system may display how well the selected unit matches the reservation holder's original preferences, for example, by using a system of "stars" to indicate the level of preference. In an embodiment of the system, the logic may be as follows: • Three Stars indicates the unit picked was the first preference of the reservation holder meaning: for unit preferences, the unit selected was one of the first two preferred units; for zone preferences, the unit selected was in the top preferred zone-to-unit-type combination; or for an "any" preference, a three-star match if the reason for the selection is indicated as "any".

• Two Stars indicates the unit picked was a secondary preference of the reservation holder meaning: for unit or zone preferences, the unit selected was not a 3-star pick but at least matched the stated preferences.

• One Star indicates the unit picked did not match any stated unit or zone preferences.

• No Star indicates the selection opportunity was declined.

Log entries are used to track system activity. The system may track the following activities: Unit added; Unit updated; Reservation holder inserted; Reservation holder updated; and Purchase cancelled.

Examples of reports the system may generate include:

Customer Preference Report. A Customer Preference Report should provide a listing of all customers with their selection preferences, prior to the dry run session.

Demand Report. A Demand Report is a listing of demand for each unit and for each zone. The Demand Report should show how many customers the seller had in each of the ANY, Undecided or Don't Know categories. The Demand Report should show the demand for particular units, particular zones, for particular unit types, and for unit types within particular zones.

Assignment Report. When the last dry run session is completed, a user should be able to generate an Assignment Report that shows the final assignment of units to customers. The Assignment report lists assigned units at the end of the dry run session and also shows the customer's original preference, as well as the unit that was assigned to them in the dry run. The Assignment Report shows which of the unit assignments were among the first choice preference that the customer had expressed. The report may also provide revenue broken down by categories including square footage, sales specialist, zones, or customer particulars (e.g. address, economic information, etc.).

When creating a preference for zone/unit type or specific units, the system may number these preferences when displaying them on a report such as the Customer Preference Report. This is in order to provide the user with a quick and easy way to confirm that they have captured all the customer's preferences in the system. When creating a preference for a zone/unit type or specific units, the system should display the total number of preferences while the user is adding them. The Assignment Report should show the numbered preference that was assigned to the customer. For example, if Unit 101 was a customer's third choice in their selection preference, and Unit 101 was assigned to the customer in a dry run, the Dry Run report should show that the customer's third choice was assigned to them.

Uses

The system according to the invention provides many benefits, for example, the system allows a seller to choose the best inventory complexion and customer selection sequence to meet the seller's objectives for risk, revenue, and customer and stakeholder satisfaction. As an example, for a given set of customer preferences, the system could optimize the selection sequence in order to minimize the value of the unsold inventory, ideally, of course, leading to a sellout (a zero dollar value of unsold inventory). Of course, this advantage is provided only if the selection sequence is not completely constrained.

The system can also improve sales by influencing customer preferences. On occasion, the ability to determine the customer selection sequence will have little to no value as the customer selection sequence may be totally set (typically by date/time of reservation) or optimization of the customer selection sequence does not yield a sellout. In such a case, the seller may want to influence customers to expand or change their preferred sequence of selections instead (e.g. Customer A should not buy unit 7, but can be persuaded to buy unit 17, the next on his list, so that customer D can buy unit 7, which is the only unit on his list). The system allows a seller to identify changes to the sequence of customer preference that most improve the result (i.e. reduce the value of unsold units in inventory, or increase the overall customer satisfaction level). Small changes (e.g. swap a customer's 3^rd choice for 4^th choice) will have highest probability of being implemented so the system identifies such high probability swaps that result in yield improvements. Sales specialists may take the output and talk to their customers to attempt to change their preferences, hi some cases, the sales specialists will succeed in persuading the customer to change, but in other cases, they would not, so the system may need to be run several times after re- inputting successful changes. Also the optimum result may involve changing both individual customer preference and the customer sequence so that the above advantages work together and can be run repeatedly in simulations ahead of the actual sales event to seek an optimum result. For simulation purposes, the system can suggest some probability models for persuading customers to choose alternatives. These models can be based on a variety of customer parameters, but this likelihood can also be determined for each customer by the associated sales specialists as hard data is not always available to drive such determinations. Processing time to perform the calculations would not be an issue in this case given appropriate lead time.

The system also allows for real time optimization. On the day of a selection event, customers will not always behave rationally and may buy a different unit than assumed by their declared preferences, or they may buy two units, not one, etc. The customer may not show up at all. The system allows for re-optimization by influencing the sequence of selection of customers who remain to make their picks, and perhaps even suggest changes to try to achieve their preferences, based on what has happened to date. This requires the system to work in real time so that processing time should be considered. Changes to the selection sequence are only applied to remaining customers who have not selected yet. There is also a lower probability of the sales specialists being able to influence a preference change by the buyer given the limited time frame.

The system allows for the process to be accelerated and can improve the buying experience of a customer by minimizing customer dissatisfaction. Another advantage of the system is to accelerate the selection process, improve the buying experience, and prompt possible units to those customers whose selection preferences are exhausted so that they will not be disappointed. The system can also be used to optimize pricing to meet the seller's objectives for risk, revenue, and customer and stakeholder satisfaction.. The system can be used to assist the seller in setting prices for all units to achieve required total revenue and to move demand around as needed for maximize sales and to minimize the value of unsold units. This model functions as a decision support tool rather than a decision making tool. The system in this case, includes all the prices by unit type and can calculate total revenue returns to the seller. The system can use an algorithm to make the initial pricing suggestions (e.g. + 5% for each floor above ground level, +10% for view, - 10% / sq foot for 3 bedrooms vs. 2 bedrooms etc.). These initial suggested prices would deliver the required target returns. The parameters of the algorithm would be user adjustable (e.g. what percentage premium to add to price per floor). The seller can overwrite the prices using subjectivity and show the impact on total returns. The system can also identify units where the yield management data shows excess demand exists and compare the amount of excess demand for a specific unit with the amount of price premium being charged against the average cost per square foot and thereby highlight high demand units that are not being priced at an equivalently high price premium. In a similar fashion, the system can be used to identify low demand units that are not being discounted enough

Certain implementations of the invention comprise computer processors which execute software instructions which cause the processors to perform a method of the invention. For example, one or more processors in a computer system connected to a network may implement the methods described herein by executing software instructions in a program memory accessible to the processors. The invention may also be provided in the form of a program product. The program product may comprise any medium which carries a set of computer-readable signals comprising instructions which, when executed by a data processor, cause the data processor to execute a method of the invention. Program products according to the invention may be in any of a wide variety of forms. The program product may comprise, for example, physical media such as magnetic data storage media including floppy diskettes, hard disk drives, optical data storage media including CD ROMs, DVDs, electronic data storage media including ROMs, flash RAM, or the like or transmission-type media such as digital or analog communication links. The instructions may optionally be compressed and/or encoded. Where a component (e.g. a server, module, assembly, application, etc.) is referred to above, unless otherwise indicated, reference to that component (including a reference to a "means") should be interpreted as including as equivalents of that component any component which performs the function of the described component (i.e., that is functionally equivalent), including components which are not structurally equivalent to the disclosed structure which performs the function in the illustrated exemplary embodiments of the invention.

While a number of exemplary aspects and embodiments have been discussed above, those of skill in the art will recognize certain modifications, permutations, additions and sub-combinations thereof. It is therefore intended that the following appended claims and claims hereafter introduced are interpreted to include all such modifications, permutations, additions and sub-combinations as are within their true spirit and scope.

Claims

Claims:

1. A method of optimizing the yield provided to a seller of a plurality of units, comprising the steps of:

(a) providing a plurality of customers associated with records in a computer database, each of said records of said customers associated with a preference for one or more of said units, each of said customers associated with one or more selection opportunities; and

(b) on a computer, selecting each of said plurality of customers in a first order, each of said customers using the selection opportunity associated with said customer to select a unit from said plurality of units, and on a selection of said selected unit, displaying on a screen associated with said computer, said customers that still have a selection opportunity and for whom said customer's associated preference is for said unit.

2. The method of claim 1, wherein said selection order is stored in a database, and on said computer, selecting each of said plurality of customers in a second order, each of said customers using the selection opportunity associated with said customer to select a unit from said plurality of units, and on a selection of said selected unit, displaying on a screen associated with said computer, said customers that still have a selection opportunity and for whom said customer's associated preference is for said unit; and determining if said first order produces a higher yield to the seller than said second order.

3. The method of claim 1 wherein a maximum price is associated with each of said preferences associated with a customer.

4. The method of claim 3 wherein a minimum price is associated with each of said preferences associated with a customer.

5. The method of claim 1 wherein on selection of a customer, units meeting the preference of said customer are displayed on a screen associated with said computer.

6. The method of claim 1 wherein, if on selection of a unit associated with the preferences of a customer will prevent a second customer from selecting a unit satisfying said second customer's preferences, said computer will display an alert.

7. The method of claim 1 wherein at least one of said preferences for said units are based on a zone including said unit.

8. The method of claim 7 wherein said zone is based on the geographical facing of said units within said zone.

9. The method of claim 7 wherein said zone is based on said on the floor of said units within said zone.

10. A system for optimizing the yield provided to a seller of a plurality of units, comprising:

(a) a server having a database with records about a plurality of customers each of said records of said customers associated with a preference for one or more of said units, each of said customers associated with one or more selection opportunities; and

(b) on a computer, on selection pg each of said plurality of customers in a first order, each of said customers using the selection opportunity associated with said customer to select a unit from said plurality of units, and on a selection of said selected unit, displaying on a screen associated with said computer, said customers that still have a selection opportunity and for whom said customer's associated preference is for said unit.

11. The system of claim 10, wherein said selection order is stored in a database, and on said computer, on selection of each of said plurality of customers in a second order, each of said customers using the selection opportunity associated with said customer to select a unit from said plurality of units, and on a selection of said selected unit, displaying on a screen associated with said computer, said customers that still have a selection opportunity and for whom said customer's associated preference is for said unit; and determining if said first order produces a higher yield to the seller than said second order.