Inventing e-Business

In December of 1993 Gary Green launched two web sites: GaryGreen.com  and a now-defunct site for the circus he co-owned. At the time there were only 623 web sites in the world. (As of January 2017 there were 1,129,313,038 sites.) By 1996, he was selling circus tickets online; one of less than 25 sites doing any sort of internet commerce. With more than 100,000 websites active by 1996, most retail and mail order companies realized that there was some kind of future ordering in the new medium. The mail order companies turned to their vendor, the largest provider of ERP (Enterprise Resource Planning) software for their industry. That Florida-based software company, deeply embedded in soon-to-be-archaic COBOL programming on equally-dying mainframe computers, turned to Gary Green for a marketer’s take on the new medium. Accepting the role as the Director of their new Internet Commerce Division, Gary was given the recources of a team of some of the most skilled “old school” computer programmers in the country --mostly former IBM employees from “Big Blue’s” southern campus in Boca Raton Florida. In fact, Gary’s desk was literally on the exact spot where the first IBM PC (model 5150) had been powered up 16 years earlier. Gary Green stepped into what he called a “twilight zone” between technology, marketing, business processes, sales, and visual presentation online (one of his first tasks was to design a VRML static website for the company). Taking the already-cutting-edge direct mail structure of the company’s software, Gary visualized what he believed would be the ideal consumer experience for internet shopping. The team of programmers created innovations that would not become the industry standard for another five or ten years. Among those inventions were: real time inventory; automated pick, pack, and ship; real time credit card charging; dynamic web pages; predictive selling and related product recommendations; live customer service; search engine optimization; and dozens of other features that then were revolutionary but today are industry standard. Those innovations were so market-disrupting that among the first customers to use the new software were the fulfillment operations of AOL (America On Line) and Microsoft (after Bill Gates watched Gary demonstrate the software at the COMDEX trade show in Las Vegas). In January 1999 the once almost-stagnant legacy software company filed a dot-com-bubble IPO, riding the wave of Gary’s innovations to their core offering dominating the direct marketing industry. Following the IPO, Gary became a traveling speaker at various internet and direct marketing trade shows and conventions. He spent most of 1999 to 2002 lecturing, consulting internet start-ups, and serving on boards or as President or CEO of various dot-com companies. As the dot-com bubble stabilized, Gary adapted his technological innovations for direct marketing to casino marketing, player tracking, and casino management systems. (It was that methodology that he first tested at Trump Hotels and Casinos and that led to his phenomenal casino marketing programs.) At the left is an article from Yahoo Finance, in 2002, announcing Gary’s adaptation of his e-commerce system to casino technology. Below are the “vintage” Gary Green descriptions of the innovations created in those infancy days of Internet commerce. It is presented here because, frankly, there are very few archives of early Internet history.

What A Site SHOULD DO ... personally for the visitor.

Gary Green, in developing the e-commerce methodology used by more than 200 top on-line retailers, identified nine key things an e-commerce catalog or retail web site SHOULD do. The examples cited below are all from existing customers who switched to our software for their online presence. 1.        Customer Personalization & Merchandising; 2.        An Order Taking & Shopping Cart system; 3.        Methodologies for Automated Payment & Security; 4.        Order Management; 5.        Warehousing (pick-pack-ship) 6.        TRUE Customer Service; 7.        Inventory Accounting real time with each transaction; 8.        System Interoperability with other business systems; 9.        Business processes and marketing the site.   These 9 steps are not simple processes. Look at the first one: So you want to bring more customers to your site. You want to convert more of those visitors to sales. You want to increase the number of returning customers or additional purchases by existing customers. You want to increase the average size of a purchase. Then you need to personalize your web site. This isn’t an armchair suggestion; this is a fact of web stores. To achieve those goals you must personalize. Period. A leading  business analytics company found that: “suggestive selling should be able to contribute as much as 39 percent of a commerce site’s transaction revenues and increase the number of acquired customers by 28 percent in the first 12 months of deployment.” Personalization or suggestive selling takes many forms on many different web sites, but most often appears as a personal greeting to the customer when he or she arrives at the web site and then offers either an instantly promoted product matched to purchase history, or in some cases, a behavioral based suggestion as the customer cruises through the site. In its most basic form (cookie or java driven), the first thing one might see on a site would  say “Welcome Back Gary Green” if he is a returning customer. A more data-driven version might say, “Since you bought a really ugly green tie from us last month, we’d like to show you an even uglier green tie this month.” A behavior-tracking site might note that as either a returning customer or a new customer, traveled through the site by clicking to browse travel-related items, perhaps a suit case, a pair of hiking boots, a travel coat, and an umbrella. In such case, the site might then show a page selling various maps, or maybe wrinkle-free travel pants. Suggestive selling requires on-the-fly analysis of explicit user ratings, observed behavior, purchase history, and information about existing inventory. A web offer is your advertisement to sell...your on-line catalog. You should have the capability of generating many simultaneous offers on the Internet for personalization or suggestive selling….not just the ability to up sell and cross-sell products within a single offer. There should, in fact, be no limit to the number DIFFERENT of on-the-fly offers your system can generate. Moreover, a customer of a software company who always buys PC products does not need a catalog of Apple products. The offer for this buyer should be different from the Apple product buyer. Likewise, customers who buy refrigerators from you should probably not have FedEx Overnight as a shipping option for them; while purchasers of books probably do want that option. Keep in mind we are talking about not just different looking web pages, but a different set of products, prices, shipping methods, and associated information…a whole different offer…an entirely different (web) catalog. The web site should be “smart” enough to determine which potential buyer is a PC product user and which is a MAC user, simply by tracking the activities (or behavior) of the shopper as they cruise through the web site. In short, the software powering the web site should capture demand of product inquiries for analytical purposes. But, even more complex, that analysis should be made instantly and the web pages served up should be based on that demand capture. This is the observed behavior data. For returning customers there are two other powerful suggestive selling methods: customer-driven questionnaire shopping, and customer history offers. Another really simple technique is the customer driven questionnaire typically is a “personal shopping module.” A customer must specifically and explicitly respond to questions concerning products or preferences. In a practical example, the customer might be given an on-line questionnaire concerning gift selections for a relative’s birthday. The questions would include the relative’s name, relationship, birthday, what kind of gift categories to select from, style preferences, and some general size or demographic information. On a preset number of days before the birthday, the customer would receive an e-mail reminding him of the upcoming birthday. The e-mail would contain a live link to open the web browser to a page that is personalized with specific gift suggestions for that relative, based on the data entered months before in the questionnaire. Or, a gift registry…a list of products a customer would like as gifts from this store. Friends and relatives can visit the store, access my registry, and choose gifts. (Ideally, the software would then delete or at least flag the gifts that had been purchased so that the next relative would not pick the same item.) Either of these two examples his is the explicit user ratings data. A customer visits a web site and in one corner of the page is a “web special” price…one that clearly is a different product and/or a different price that was there as a “web special” yesterday. The store, in fact, had an overstock of a particular item, or had manufacturer’s promotional advertising dollars, and posted the product as a daily special. But even more interestingly, this supply-side data was matched to customer purchasing history and when the customer visited the web site the home page offered an up-sell of (for example) a Star Trek DVD, because (a) the customer bought a different Star Trek DVD last time she purchased and (b) there is currently a deep promotional purchase discount from the distributor for Star Trek DVDs. Most amazingly, the software was (again) “smart” enough to automatically track purchase history AND the inventory considerations, to generate the up sell and the personalized offer. This is an example of both purchase history and information about inventory. The customer enters a web store by linking to it from an ad the company has purchased on a portal or on a search engine; you enter the web store by typing in the URL (uniform resource locator) directly; and our friend Sharon enters the site by clicking on a link from a different ad or affinity program. All three visitors see different looking pages…but even more importantly, all three see different products, different prices, different shipping methods, different discounts, and a host of other personalized features. Or consider a student on the campus of the University of California who enters a software store on line and another student from the University of Georgia who enters the same on line store at the same time. The California student sees a web page that has a big bear on it and a greeting of “Welcome to the University of California software store.” The Georgia student sees a bulldog and a “Welcome to the University of Georgia software store” message. But more than just a different looking page, the California student’s web catalog focuses on graphic arts programs (because that school has a big graphics concentration) and the Georgia store focuses on engineering software. Different catalogs…same store, same inventory…and in the background, the shipping, the pay methods, the infrastructure of each store is different yet controlled from one store account. In these two examples, each visitor received a different offer based on entry point to the site. The most interesting thing about these offers is that they are not entirely different web sites and different stores. They are all from the same store, the same URL, the same database…only the offer is different. Any 14-year-old HTML designer should be able to create 200 different catalogs for you and place them at 200 different URLS. But intelligent software builds the offer on the fly from one URL and from one database, warehousing, and inventory system. There is actually a sixth element that can make personalization (or suggestive selling) not just more powerful…but your single most powerful sales tool. That is the addition of R/F/M data to the suggestive model. Recency, Frequency, and Monetary value of a customer have driven traditional paper-catalog direct marketing for years. Using that data has allowed not only the testing of new products and new pricing, but also it has allowed accurate analyses and projections of profits. Marketing is a formulaic business. It is consistently predictable, and financial results should be able to be projected with a high degree of accuracy and reliability. To understand how to harness this awesome sales power of site personalization, and to step into the other eight elements in the Gary Green methodology takes check out other sections of this site or contact Gary directly. Early on an opponent to the seeming highway-robbery of pay-to-play schemes and business models for the search engine companies, Gary pioneered (one of if not THE) first Search Engine Optimization convention/forums and created initial strategies for site, page, and product placement. Gary Green filed the patent application for the manual optimization process based on that work. With nationally-known S.E.O. “gurus," Gary created a manual method to determine search’ algorithms to modify a site's HTML code to match the points on a graphed line where he wanted any given search engine to recognize it. This methodology evaluated the top 100 pages in any given keyword search to determine how a specific search engine weighted the variable tag fields in an HTML page (ALT, Body w/tags, Body w/o tags, H1-H6, Head w/tags, Head w/o tags, HREF, Image Names, Meta Description, META HTTP-Equiv, META Keywords, Text Links, Title Tag, URL, ALT, Body w/tags, Body w/o tags, H1-H6, Head w/tags, Head w/o tags, HREF, Image Names, Meta Description, META HTTP-Equiv, META Keywords, Text Links, Title Tag, URL, and so on). They then took the keyword they had used in the search and set that as one axis of a graph. Using the variable fields as points along the other axis of a graph, they were able to chart a line showing how many times each of the fields depended on the keyword. This allowed Gary to determine which fields the search engine used to rank a sight based on a key word. This process had to be repeated for each engine.This process combined with other marketing techniques BEGINS Gary's search engine optimization process. THIS is the surface of the Gary Green methodology for e-commerce.

Detailed targeting in e-commerce:

Gary Green created among the first pushed personalization (based on customer history) when there were only two commercial application for creating personalization (Broadvision and Net Perceptions) to allow cross-selling and up-selling (long before Amazon and others based business on the model).

Pages (and OFFERS) on-the-fly:

Gary Green created the first personalized-on-the-fly web sites based on IP resolution and customer data. Specifically, a client company called Journey Education, specialized in contracts with University bookstores. The company provided the school’s “on-line” store for students to order textbooks, supplies, software, etc. Resolving IP addresses (in 1997 when this was novel), Gary Green devised a marketing methodology to serve up pages based on from which school the customer was logging in. Most universities had just begun to offer in-dorm internet service and all had fixed IP addresses. For example: a student from the University of California would get a California Bear and the school’s colors on the page as well as school-specific offers pushed on the page; a student from the University of Texas would get a longhorn and a lot of orange on their page (as well as a specialized offer for their school). Generic (unresolved IP addresses) students would get a portal page that would allow them to pick their school to push the page to them. In short, the strategy showed different looking pages…even different catalogs & prices…based on how the customer got to the web site; again a revolutionary step at the time

More ways to target the customers:

Early on, Gary Green pioneered usage of “brownies” (server side cookies) after proliferation of anti-cookie software on the market and backlash against CGI scripts. Beyond the history lesson and old technologies...He was THE first to apply an 8-digit source code PLUS and 8-digit use code  for precision segmentation of customers offers.

Getting Catalogs on line:

Gary Green created methodologies  to consolidated e-business branding and technology for 30 smaller catalog companies under one umbrella and created a marketing strategy for them. He strategized multi-entrance shopping portal for more than 30 traditional catalog companies using a revolutionary (at the time search engine optimization methodology without going the pay-for-play route. In doing so, he created one of the largest affiliate programs in the country through an on-line version of the world famous “Catalog of Catalogs” brand. In the same swoop, he created a fulfillment program, auction site, e-catalog, membership sites, and genuine retailing sites in dynamically created models based on mined data about the user. Early in the space of viral marketing, he marketed continuity and membership sites through hiring dedicated staff for various chat rooms, community sites, and forums. He continually modeled opt-in strategy for email campaigns and applied catalog industry standard testing model using targeted segmentation data. With these philosophies, he successfully moved brick and mortar list brokerage business (without an IT department) into 21st Century by creating new technologies and obtaining patents for those technologies.  

Helping Retail. see also "What A Site SHOULD Do"

Getting Retail on-line is vastly different from getting a catalog on line; retail is not organized for pick-pack-and-ship and the dozens of other complexities of e-commerce. Retail lives by the shelf and dies by the shelf. Recognizing that, Gary Green help identify new technologies and helped obtain 26 patents on processes of getting retail planograms to real time broadband three-dimensional consumer interfaces deliverable via the Internet for nation’s 2nd largest shopping mall developer to create true e-retail model. At the same time he became the first  “super-affiliate” of the LinkShare business model, with a “mall” portal targeted to retirees....long before such was the fad.