Data importer for a sales prospector

A data importer for a sales prospecting system imports one or more data tables that each may include one or more records. The data importer first (a) imports a data table into an intermediate table. The data importer then (b) determines if the imported data table depends on another data table and moves one or more records from the imported data table that have no missing dependencies to a corresponding working table; and (c) determines a set of previously imported data tables that refer to the imported data table. The data importer then, for each previously imported data table, repeats (b) and (c) above.

FIELD OF THE INVENTION

One embodiment is directed generally to customer sales, and more particularly to importing data into a customer sales system.

BACKGROUND INFORMATION

A sales lead includes a name or other indicia of identity (e.g., a phone number, mailing address or email address) of a person or business that may have an interest in purchasing a product or service. A sales lead provides a starting point for a salesperson to further develop the lead by marketing a specific vendor's product or service. The salesperson develops the sales lead by gathering information about the potential customer and providing the customer with information about the vendor. This mutual exchange of information helps the salesperson to persuade the potential customer to purchase a product or service from the vendor. If the customer makes a purchase, the salesperson has converted the sales lead into a sales transaction. A sales lead with a high probability of being converted into a sales transaction is considered a good lead.

Data mining can assist in generating sales leads by finding patterns in information gathered about customers. In the past, vendors have used data mining to match potential customers to products. To do so, data about customers, products, product categories, and previous purchases are imported into an application to generate the sales lead. Components or tools that import data from one system to another are often expected to ensure that the imported data has referential integrity for the receiving application to work. For example, if both customers and orders are imported from an external system, it is expected that the order records refer to customer records that actually exist.

A conventional import implementation ensures integrity by rejecting “bad” records and only accepting records for which all related records exist. For this reason, this implementation also requires that different types of records are imported in a certain order. For example, if order records depend on customers, then the customer records must be imported before the order records are imported. In some cases, these dependencies can be complex and hard to follow. Consider the following example: “order lines” refers to orders and products; “orders” refers to customers; and “products” refers to product categories. Using this example, the conventional implementation must import the record in the following order: product categories, products, customers, orders, order lines. If this order is not followed, some imported records will be rejected and the user will have to begin again.

SUMMARY OF THE INVENTION

One embodiment is a data importer for a sales prospecting system that imports one or more data tables that each may include one or more records. The data importer first (a) imports a data table into an intermediate table. The data importer then (b) determines if the imported data table depends on another data table and moves one or more records from the imported data table that have no missing dependencies to a corresponding working table; and (c) determines a set of previously imported data tables that refer to the imported data table. The data importer then, for each previously imported data table, repeats (b) and (c) above.

DETAILED DESCRIPTION

One embodiment is a sales prospector that identifies potential sales prospects and analyzes the past purchasing patterns of customers (throughout this specification, “customer” may refer to existing or potential customers). In one embodiment, a “prospect” is a combination of a customer and a specific product, which may be used to prepare for a sales call. One embodiment predicts which customers in a sales representative's territory will be interested in purchasing certain products, based on an analysis of the buying patterns of similar customers. A sales representative can see what the next purchases are likely to be for their customers, as well as the probability, revenue potential, and time to close for each prediction. In addition, they can drill down into the details to do further analysis.

Generally, an administrator first uploads customer data tables with which to train a prediction model. The customer data tables imported to train the model, such as product categories, products, customers, orders, and order lines, should be imported with full referential integrity, though tolerating a small number of missing records.

For example, a customer data table may include a list of customer accounts. A products data table may include a list of products available for sale. An orders data table may include the order header information that identifies an order. An order lines data table may comprise order detail information, such as the products ordered, the product quantity, the product price, and the date of the sales lead that led to this order. Even if certain elements of order lines data are missing, the sales prospector can still use the information to make predictions. For example, the presence of an order lines record itself can affect the probability of purchase; products already owned by a customer would not be recommended. The presence of an order amount record can help predict revenue even if other elements are left blank. The presence of an order date and lead date help predict time-to-close even if other elements are left blank. The presence of an order date and amount exhibit the customers purchasing history, even if other elements are left blank.

One embodiment ensures that all the references among the tables exist, while not limiting the order in which the tables must be imported. For example, order lines can be imported before customers and products without being rejected. The import processor in accordance with one embodiment achieves this by importing records in two stages. In the first stage, all imported records are accepted into an intermediate holding table where referential integrity is not yet checked. Once all of the related records have arrived, the “clean” records (i.e., records whose referential integrity has been determined) are moved into a working table.

FIG. 1is a block diagram of a system10that can implement an embodiment of an optimized sales prospector. System10includes a bus12or other communication mechanism for communicating information, and a processor22coupled to bus12for processing information. Processor22may be any type of general or specific purpose processor. System10further includes a memory14for storing information and instructions to be executed by processor22. Memory14can be comprised of any combination of random access memory (“RAM”), read only memory (“ROM”), static storage such as a magnetic or optical disk, or any other type of computer readable media. System10further includes a communication device20, such as a network interface card, to provide access to a network. Therefore, a user may interface with system10directly, or remotely through a network (such as the Internet) or any other method.

Processor22is further coupled via bus12to a display24, such as a Liquid Crystal Display (“LCD”), for displaying information to a user. A keyboard26and a cursor control device28, such as a computer mouse, is further coupled to bus12to enable a user to interface with system10.

In one embodiment, memory14stores software modules that provide functionality when executed by processor22. The modules include an operating system15that provides operating system functionality for system10. The modules further include a sales prospector module120and an import processor130, which are described in greater detail below. The modules may further include enterprise resource planning (“ERP”) modules18of an ERP system that may interact with sales prospector module120. An ERP system is a computer system that integrates several data sources and processes of an organization into a unified system. A typical ERP system uses multiple components of computer software and hardware to achieve the integration. A unified ERP database17, coupled to bus12, is used to store data for the various system modules. In one embodiment, ERP modules18are part of the “Oracle E-Business Suite Release 12” ERP system from Oracle Corp. In other embodiments, sales prospector module120and import processor130may be a stand-alone system and not integrated with an ERP system, or may be part of any other integrated system. In some embodiments, the functions of sales prospector module120and import processor130, described below, are directed and utilized remotely from a user's computer50through communication device20. In one embodiment, the functionality disclosed below may be accessed remotely by a user as a software as a service (“SAAS”).

FIG. 2illustrates a logical diagram of a customer relationship management (“CRM”) system200in which sales prospector module120and import processor130may be used in accordance with an embodiment. CRM module220is an ERP module for managing customer information, including demographic data about a customer, purchasing patterns, assets owned, etc. CRM module220includes schemas for the customer data, and interacts with database17to store the customer data in database17in accordance with these schemas. Sales prospector module120includes user interfaces (described below) for viewing customer data, for entering queries for sales prospects, and for viewing the results of those queries. Sales prospector120imports customer data using import processor130from database17. Import processor130includes intermediate tables250and260, and corresponding working tables255and265. When a table of sales data is imported, it is first stored as unchecked records in an intermediate table, for example, intermediate table250. After referential integrity has been determined for the imported sales data (i.e., the records in the data refer to existing tables), the data is moved as clean records to the corresponding working table, which is working table255in this example. One of ordinary skill in the art will recognize that import processor130may include as many intermediate and working tables as needed.

FIG. 3illustrates a flow diagram of the functionality of a sales prospector module120in accordance with an embodiment. In one embodiment, the functionality of the flow diagram ofFIG. 3, andFIG. 4described below, is implemented by software stored in memory and executed by a processor. In other embodiments, the functionality may be performed by hardware (e.g., through the use of an application specific integrated circuit (“ASIC”), a programmable gate array (“PGA”), a field programmable gate array (“FPGA”), etc.), or any combination of hardware and software.

An administrator of the system10first imports customer data into sales prospector module120in the form of a Comma Separated Value (“CSV”) file (310). The customer data may be imported by import processor130, for example, from database17, and includes both purchasing pattern attributes and demographic attributes. The CSV files are a fixed file format that include five record types: customers, products, orders, order lines, and target customers by user. The CSV file formats follow a specific format, described below, in an embodiment. The CSV files may be imported into sales prospector module120in any order. For sales prospector module120to make a prediction, at least the following record types should be present: customers, products, orders, and order lines. In the CSV file, each column is separated by a comma, and each record starts on a new line.

A sample customers.csv file is now described. Table 1 illustrates the data types for customer records:

A sample products.csv file is now described. Table 2 illustrates the data types for product records:

A sample orders.csv file is now described. Table 3 illustrates the data types for order records:

A sample order_lines.csv file is now described. Table 4 illustrates the data types for order line records:

A sample member_customers.csv file is now described. Table 5 illustrates the data types for user-to-customer mapping records:

TABLE 5DataMax.ColumnTypeLengthRequiredDescriptionUser_EmailString100 Char.YesThe email address of acommunity memberCustomer_IDString30 Char.YesThe customeridentifierOperationString2 Char.YesIndicates whether thecurrent recod shouldbe inserted or deletedin the database, asfollows:“I” indicates an insert.The insert does notsucceed if the recordalready exists“UC” indicates anupdate by customer“UM” indicates anupdate by member (thesales representative)“D” indicates delete
An example member_customers.csv file is presented below:

The administrator, when selecting data to import (310), need not import the customer data in a specific order to maintain referential integrity because of a two-stage process. In the first stage, all imported records are accepted into the intermediate holding table where referential integrity is not yet checked. Then, once all the related records arrive (which may happen at a later point in time) the “clean” records are moved into the production tables.FIG. 4illustrates a flow diagram of the two-stage functionality of import processor130in accordance with an embodiment. For each table selected for import, import processor130accepts all records and stores the records in a corresponding intermediate table (410). No integrity checking is performed yet, thus a products table can be imported and referred to a products categories table that does not yet exist. Import processor130then determines if the imported table depends upon other tables (420). For example, a products table may depend on a products categories table because product records depend on product category records. Likewise, an order lines table may depend on the products table.FIG. 5illustrates an example dependency tree500in accordance with an embodiment. Order lines table510depends on both products table520and orders table530. In turn, products table520depends on product categories table540, and orders table530depends on customers table550.

Returning toFIG. 4, if the imported table does not depend on any other table, import processor130moves the imported table to a working table and is finished (425). Otherwise, import processor130moves just the records in the imported table with no missing dependencies into a corresponding working table (430). For example, a single products record that refers to an already existing product categories record would be moved to the working table for products. Import processor130next determines if any other tables already imported depend on the newly imported table (440). For example, an order lines table may have already been imported and is being held in an intermediate table, and order lines may depend upon the products table. If such a case exists, that depending table is selected for dependency checking (450), and the process is recursively repeated by moving records with now fulfilled dependencies into working tables (430) until the top of the dependency tree is reached with a table on which no other tables depend.

Returning toFIG. 3, a hybrid prediction model is then built from the imported data for predicting a prospect, i.e., a customer, product, and a probability that the potential transaction will materialize if pursued (320). Then, a hybrid prediction model is built to predict expected revenue and expected time to close (330). After the hybrid predictive models are built, a user can query the models to return a sales prospect (340). For example, a user can enter a customer name to get a product, probability of close, estimated time to close, and expected revenue. Or, a user can enter a product name to get a customer, probability of close, estimated time to close, and expected revenue. Alternatively, a user can search all customers and products for customer-product pairs, including probability of close, estimated time to close, and expected revenue. The user can also filter the results of the query based on factors such as, but not limited to, a minimum probability of close, minimum expected revenue, maximum time to close, partial customer name, partial product name, etc. (350).

FIG. 6illustrates an example graphical output user interface600of the sales prospector module120in accordance with an embodiment, which shows each prospect as a bubble or circle on the graph. The Y-axis represents the probability of close for that prospect, the X-axis represents the expected time to close, and the size of the bubble for the prospect represents expected revenue for that prospect (i.e., a combination of a customer and a specific product). More details of each prospect can be displayed by hovering the mouse over a bubble or otherwise selecting a bubble that represents a prospect. In the example shown, the additional information for prospect/bubble610indicates that the product is “EDI Adapter” and the customer is “Supertex Inc.,” with an expected revenue of $112,147.62, an expected time to close of 4.7 months, and probability of close of 63%.

FIG. 7illustrates an example graphical output user interface700of the sales prospector module120in a dashboard view, in accordance with an embodiment. In addition to the graph view705, similar to what is shown inFIG. 6, dashboard user interface700further shows evaluated attributes of a customer710(in this example, AST Research); a products list720listing recommended products for a sales call; a purchase history730for the particular customer, including in this example a graph of money spent on products over the past 16 quarters; and a references list740that lists customers of similar needs, demographics, and purchasing patterns that can be used for a sales call. Further, dashboard user interface700includes a projected revenue filter slider750for filtering results in graph view705based on the projected revenue of the sale if completed; and a purchase probability slider760for filtering results in graph view705based on the probability that the sale will be completed. Therefore, specific filters when searching for prospects can be used to display, for example, prospects with an expected revenue greater than $100,000; which products a specific customer is most likely to purchase; or which prospects are expected to generate the most revenue for a specific product.

As disclosed, one embodiment is a hybrid prediction model combining demographic data and purchasing pattern data with an optimized data import processor. Data tables used to train the model may be imported in any order by the data import processor without concern for referential integrity, which is ensured by a two-stage process. In the first stage, all imported records are accepted into an intermediate holding table where referential integrity is not yet checked. Then, once all the related records arrive (which may happen at a later point in time) the “clean” records are moved into the production tables. The model may then be used to understand a customer's demographic, financial and commercial profile; understand a customer's buying patterns; consider the sales recommendations that it generates based on the probability of purchase and the purchase history; estimate a time frame for sales to close and potential estimated revenue; and estimate the potential revenue from a sale. Thus, it is more effective at generating sales leads that will reduce the time investment of sales people while increasing the profitability of leads that are pursued.

Some embodiments of the invention have been described as computer-implemented processes. It is important to note, however, that those skilled in the art will appreciate that the mechanisms of the invention are capable of being distributed as a program product in a variety of forms. The foregoing description of example embodiments is provided for the purpose of illustrating the principles of the invention, and not in limitation thereof, since the scope of the invention is defined solely by the appended claims.