Closer Look: WiseGuys Marketing Software

Apr 01, 2004 10:30 PM  By

When it comes to database marketing, the typical catalog company, regardless of size, talks the talk but fails to walk the walk. Oh sure, most catalogers expend considerable effort segmenting their customer file, usually by multiple takes on recency, frequency, monetary value, and product purchase (RFMP) or lifetime value (LTV) analyses. But let’s face it, few of these computations are performed in a true database marketing environment. Instead, you find a vast array of spreadsheets littered with countless iterative snapshots of customer behavior, with no elegant way to tie all this back to the actual customer file.

One reason catalogers shy away from database marketing is the expense. Data warehouses and data marts can be laborious to maintain, and database analysis tools not only carry their own substantial price tag but most often require the services of a well-paid analyst to work their magic as intended.

Bruce Gregoire of Falls Church, VA-based Desktop Marketing Solutions aims to change that, at least for the small and midsize catalogers — the ones who need it most! His WiseGuys Marketing Software is built on Microsoft Access (or on MySQL) and performs both RFMP and LTV calculations in a robust and coherent fashion, with a price tag for the “enterprise” version of abount $2,500 (without the LTV module it costs less than $2,000). If you use the Mail Order Manager (MOM) system for order processing, the software price is $1,000 cheaper.

The MOM version is virtually plug-and-play. The enterprise version requires some initial set-up from Desktop Marketing Solutions, which can run you $3,000-$5,000. Technical support on both versions is 20% of the license price, after an initial free year. The cost of the Access database is not included, but that database is included in Microsoft Office Professional Edition. The MySQL version may be of interest for larger companies or smaller service bureaus.

Core competence

The system is set up to automatically import all the data needed for analysis from the user’s order management system. This enables WiseGuys to automatically adjust for cancellations and returns on a customer’s record, for example.

WiseGuys does RFM scoring “by the book,” using fundamentals popularized in Arthur Hughes’s seminal text, Strategic Database Marketing (Gregoire uses this text as an instructor at Johns Hopkins University). Customers are scored on recency, the primary key in RFM, on a quintile basis. The most recent 20% of your customers score a 5, the next 20% score a 4, and so on.

Alternatively, you can set up five arbitrary recency “buckets” (1-6 months, 7-12 months, etc.), which is the way many catalogers prefer to do it.

Frequency, which is scored on a quintile basis as well, is considered more important than monetary value, based on the reality most customers buy once and disappear.

Monetary value is the sum of all of a customer’s purchases. Again, the system scores these on a quartile basis.

You can determine actual LTV based on gross and net sales, an approach favored by users of order management systems (including MOM) that keep a running total of gross and net sales for each customer. Net sales in this context is defined as gross sales minus cost of goods sold and acquisition costs.

Point and click

To derive an RFM score in Wise-Guys requires just a few points and clicks. It uses dependent variables, in which scoring for frequency is based on the recency score and scoring for monetary value is dependent on both the recency score and the frequency score. In other words, there is a major sort on recency, then a minor sort on frequency within recency score, and finally another minor sort on monetary value within recency score and frequency score.

The resulting matrix has 100 “cells,” each of which has roughly the same number of individuals. This is a more coherent and statistically valid approach than sorting values independently.

But wait — there’s more! WiseGuys allows you to filter the raw data for each customer in the database, to achieve more specific targeting or segmentation. You can deselect:

  • customer types that you don’t want included in your calculations (such as one-time customers with huge purchases, or wholesale vs. retail)
  • product orders that you want to exclude from the RFM calculations (orders for a particular class of items, perhaps, or free promotional items)
  • source codes for orders that you want to exclude from your marketing analysis (such as point of sale customers).

Even more important is the ability of WiseGuys to roll up customer purchases by household (for consumer catalogers) or by company (for b-to-b marketers). In those cases, however, the monetary value is classified only as low or high (two cells), so the final matrix has only 50 cells, with roughly the same number of organizational or household site records in each cell.

You can apply the same filters in calculating LTV in WiseGuys that you use with RFM. This is helpful when you are targeting either retail or catalog customers and don’t want their values averaged together.

WiseGuys provides cross-tab reports on LTV by customer type, customer’s original source code, customer’s monetary value score, and customer’s year of first order. The software distinguishes between actual LTV (currently provided) and forecasted LTV. Forecasted LTV calculations — available in an upcoming release — will be based on the average customer spending rate (if it goes up, LTV goes up), customer retention rate (if it goes up, LTV goes up), variable cost, or cost to service a customer (if it goes up, LTV goes down), acquisition cost (if it goes up, LTV goes down), and discount (present value) rate (if it goes up, LTV goes down slightly). For b-to-b, you can generate a cross-tab of average overall LTV by segment of business.

Creating a targeted mail file is one of the functions that the WiseGuys software does best. WiseGuys builds a mail file by selecting one segment at a time, allowing you to make your choice of include/exclude selection criteria (from R, F, M, P, source, and channel) with Boolean (“and/or”) logic.

The system also assures that the final file is deduplicated and lets you add a key code to the selected names. You can add in prospect records as long as they have been assigned a unique finder number. And you can add title slugs for b-to-b mailings.

You can also indicate the total number of names you want to mail and have WiseGuys pick the best-qualified names from your selections and filters for the desired count (using “weights” pre-assigned to each RFM score, with the option to display and change the weight values in the RFM filter menus). One criticism: The filters on list selection only allow you to exclude customers with returns, rather than customers with a certain percentage of returns vs. order dollars. This could result in excluding some rather profitable customers. In this case, WiseGuys is a blunt — not a precision — instrument.

Configuration

WiseGuys is meant to be used by marketers without extensive support from IT. Setting up WiseGuys, however, does require some initial technical support to configure it properly to match its database fields with the data fields in your order management system. If there is not a one-to-one mapping (for instance, if your source has city/state/zip code in one field while WiseGuys requires separate fields), then you may need to write (or have written) a custom utility.

Reporting tools within WiseGuys are extensive and configurable on both customer profiling and mailing activity (and cross-tabs of each) and include extensive response analysis capabilities. A minor quibble: In the LTV derivation, you can account for acquisition costs by source code or by customer type but not by both.

Currently under development is a data-mining function that will perform market basket analysis. It will let you correlate product purchases over the life of a customer (all customers who bought X and Y after buying Z, for example).

With this kind of bang for the buck, and for so few bucks, at that, WiseGuys is a no-brainer for companies still stuck in “Excel hell.”


Ernie Schell is president of Marketing Systems Analysis, a Southampton, PA-based database marketing solutions consultancy.