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![World's Tallest Flagpole; see ref [9]](../wp-content/themes/ai3/images/2011Posts/110815_tallest_flagpole.jpg "World's Tallest Flagpole; see ref [9]")
The New Paradigm of ‘Substantive Marketing’ for Innovative ITThis decade has clearly marked a sea change in the move of enterprise software from proprietary to open source, as I have recently discussed [1]. It is instructive that only a mere six years ago I was in heated fights with my then Board about open source; today, that seems so quaint and dated.
Also during this period many have noted how open source has changed the capital required to begin a new software startup [2]. Open source both provides the tooling and the components for cobbling together specialty apps and extensions. Six and seven and even eight figure startup costs common just a decade ago have now dropped to four or five figures. When we see the explosion of hundreds of thousands of smartphone apps we are seeing the glowing residue of these additional sea changes. Dropping startup costs by one to three orders of magnitude is truly democratizing innovation.
But something else has been going on that is changing the face of enterprise software (besides consolidation, another factor I also recently commented on). And that factor is “marketing”. Much less commentary is made about this change, but it, too, is greatly lowering costs and fundamentally changing market penetration strategies. That topic — and my personal experience with it — is the focus of this article.
Besides the few remaining big providers of enterprise software — like IBM, Oracle, HP, SAP — most vendors have totally remade their sales practices of just a few years ago. Large sales forces with big commissions and a year to two year sales cycles can no longer be justified when software license fees and the percentage maintenance annuities that flow from them are dropping rapidly. Today’s mantras are doing more with less and doing it faster, hardly consistent with the traditional enterprise software model. Sure, big enterprises, especially big government and big business, have large sunk costs in legacy systems that will continue to be milked by existing vendors. But the flow is constricting with longer-term trends clear to see. The old enterprise software model is obsolete.
Even if it were not dying, it is hard to square huge investments in sales and marketing when product development has become inexpensive and agile. The proliferation of three-letter marketing acronyms for branding “new” product areas and standard formulas for product hype of just a few years ago also feels old and dated. Cozy relationships with conventional trade press pundits and market analysts seem to be diminishing in importance, possibly because the authoritativeness of their influence is also diminishing. It is harder to justify market firm subscription costs when priority budget items are being cut and new information outlets have emerged.
In response to this, many developers have forsaken the enterprise market for the consumer one. Indeed enterprises themselves are looking more and more to the consumer sector and commodity apps for innovation and answers. But, still, problems unique to enterprises remain and how to effectively reach them in this brave new world is today’s marketing problem for enterprise software vendors.
Most entities today, when opining about these challenges, tend to emphasize the need for “laser focus” and “rifle-shot” targeting of prospects. The advice takes the form of: 1) emphasize well-defined verticals; 2) know your market well; and 3) target and go after your likely prospects. Prospect data mining and targeted ad analysis are the proferred elixirs.
But, there is little evidence such refined methods for prospect identification and targeting are really working. Like politicians doing focus groups and opinion polling to capture the desired “message” of their potential electorates, these are all still “push” models of marketing. Yet we are swamped with pushed messages and marketing everywhere we turn. The model is failing.
Besides message overload, there are two issues with laser targeting. First, despite all that we try to know about ready buyers (for enterprise software), we really don’t know if any particular individual is truly needful, in a position to buy, has the authority to buy, or is the right advocate to make the internal sell. Second, though the idea of “laser” carries with it the image of focus and not flailing, it is in fact expensive to identify the targets and send a focused message their way. Because of these issues, decay rates for laser prospects throughout conventional sales pipelines continue to rise.
There has always been the phenomenon of the “fish jumping into the boat“; that is, the unanticipated inbound inquiry from a previously unknown prospect leading to a surprisingly swift sale. But we have seen this phenomenon increase markedly in recent years. Structured Dynamics‘ current customer base — including recurring customers — comes almost exclusively from this source. As we have noted this trend in comparison with more targeted outreach, we have spent much time trying to understand why it is occurring and how we can leverage what Peter Drucker called the “unexpected success” [3].
What we are seeing, I believe, is a shift from sales to marketing, and within marketing from direct or outbound marketing to a new paradigm of marketing. Others have likened this to inbound marketing [4] or content marketing [5] or permission marketing [6]. What we are seeing at Structured Dynamics bears many resemblances to parts of what is claimed for these other approaches, but not all. And, it is also true that what we are seeing may pertain mostly to innovative IT for emerging enterprise markets, and not a generalized paradigm suitable to other products or markets.
For lack of a better term, what we are seeing we can term “substantive marketing”. By this we mean offering valuable content and solutions-oriented systems for free and without restriction. This shares aspects with content marketing. Then, in keeping with the trend for buyers doing their own research and analysis to fulfill their own needs, similar to the premises of inbound or permission marketing, potential consumers can make their own judgments as to relevance and value of our offerings.
Sometimes, of course, some prospects find our approaches and solutions lacking. Sometimes, they may grab what we have offered for free and use them on their own without compensation to us. But where the match is right — and we need to be honest with both ourselves and the customer when it is not — we can better spend the customer’s limited time and resources to tailor our generic solutions to their specific needs. In doing so, we offer higher value (tailored services) while learning better about another spectrum of consumer need that can virtuously enhance our substantive offerings for the next prospect.
So, let’s decompose these components further to see what they can tell us about this new practice of substantive marketing and how to use it as an engine for moving forward.
The premise of substantive marketing is to offer square-deal value to the marketplace in the form of solutions-based content. Like content marketing that offers “the creation or sharing of content for the purpose of engaging current and potential consumer bases” [5], substantive marketing goes even further. The whole basis and premise of the approach is to provide substantive content, in one of more of these areas, preferably all:
Further, this substantive content is offered without strings, restrictions or customer fill-in forms. The content is not a come on or a teaser. We are not trying to gather leads or prospect names, because we have no intent to dun them with emails or follow-ups.
This substantive content is as complete as can be to enable new users to adopt the information and tools in their current state without further assistance. (In some cases, the information also educates the marketplace in order to prepare future customers for adoption.) Most importantly, this substantive content is offered for free, either open source (for code) or creative commons for documentation and other content. In return, it is fair to request — and we do — attribution when this material is used.
We have previously termed this complete panoply of substantive content a total open solution [7]. Some might find the provision of such robust information crazy: How can we give away the store of our proprietary knowledge and systems?
But we find this kind of thinking old school. In an open source world where so much information is now available online, with a bit of effort customers can find this information anyway. Rather, our mindset is that customers do not want to pay again for what has already been done, but are willing to pay for what can be done with that knowledge for their own specific problems. Offering the complete storehouse of our knowledge in fact signals our interest in only charging the customer for new answers, new value or new formulations. The customers we like to work with feel they are getting an honest, square deal.
Consider your substantive content to be your flag, a unique banner for conveying and packaging your specific brand. It is thus important to find appropriate flagpoles — in the virtual territories that your customers visit — for raising this content high for them to see. Since the role of these flagpoles is to create awareness in potential prospects — who you do not likely know individually or even by group in advance — it makes sense to raise your offerings up on many flagpoles and on the highest flagpoles. Visibility is the object of the approach.
This approach is distinctly not leafletting or cramming links or emails into as many spaces as possible. The idea of substantive marketing is to fly valuable content high enough that desirous potential customers can discover and then inspect the information on their own, and only if they so choose. In this regard, substantive marketing resembles permission marketing [6].
Being visible helps ensure that the needful, questing prospect that you would never have been able to target on your own is able to see and be aware of your offerings. And, since they are seeking information and answers, your collateral needs to be of a similar nature. Solutions and substance are what they are seeking; what you have run up the flagpole should respond to that.
The mindset here is to respect your prospective customers and to allow them to chose to receive and inspect your offerings, but only if they so choose. If flown in the right venues with the right visibility, customers will see your flags and inspect them if they meet their requirements.
Some of the venues at which you can raise your flags include:
The observant reader will have already concluded that each of these venues develops slowly, and therefore raising visibility is generally a slow-and-steady game that requires patience. Start-up vendors backed by venture firms or those looking for quick visibility and cashout will not find this approach suitable. On the other hand, customer prospects looking for answers and self-sustaining solutions are not much interested in flash in the pan vendors, either.
The real drivers for this changing paradigm come from customer prospects. Sophisticated buyers of enterprise IT and instrumental change agents within organizations share most if not all of these characteristics:
More often than not we find our customers to have already installed and used our existing substantive materials for some time before they approach us about further work. They appreciate the tutorial information and have taught themselves much in advance. By the time we engage, both parties are able to cost-effectively focus on what is truly missing and needed and to deliver those answers in a quick way. Re-engagements tend to occur when a next set of gaps or challenges arise.
Though it may sound trite or even unbelievable to those who have not yet experienced such a relationship, the square deal value offered by substantive marketing can really lead to true partnerships and trust between vendor and customer. We experience it daily with our customers, and vice versa. We also think this is the adaptive approach that our new environment demands.
Once prospects learn of our substantive offerings, many may decide independently that what we have is not suitable. Others may simply download and use the information on their own, for which we often never know let alone receive revenue. We are completely fine with this, as shown for three different cases.
First, some of these prospects need no more than what we already have. This increases our user base, increases our visibility and often results in contributions to our forums and documentation.
Then, some of these prospects come to learn they need or want more than what our current offerings provide, leading to two possible forks. In one fork, the second case, they may have sufficient skills internally or with other suppliers to extend the system on their own. Some of this flows back to an improved code base or improved installation or documentation bases.
In the other fork, the third case, they may decide to engage us in tailoring a solution for them. That case is the only one of the three that leads to a direct revenue path.
In all three cases we win, and the customer wins. Maybe enterprise software vendors of decades past rue this reality of lower margins and shared benefits; we agree that the absolute profit potential of substantive marketing is much less. But we gladly accept the more enjoyable work and steady revenue relationships resulting from these changes. We are not engaged in some pollyann-ish altruism here, but in a steely-eyed honest brokering that best serves our own self-interest (and fairly that of the customer, as well).
Great IT product does not come from idle musings or dreamed up functionality. It comes solely and directly from solving customer problems. Only via customers can software be refined and made more broadly usable.
A slipstream of those who have previously become aware and tested our offerings will choose to engage our services. This generally takes the form of an inbound call, where the prospect not only qualifies itself, but also establishes the terms and conditions for the sale. They have chosen to select us; they are fish that have jumped into the boat.
To again quote Peter Drucker, “. . . the aim of marketing is to make selling superfluous. The aim of marketing is to know and understand the customer so well that the product or service fits him and sells itself. Ideally, marketing should result in a customer who is ready to buy. All that should be needed then is to make the product or service available . . .” [8]. This is precisely what I meant earlier about the shift in emphasis from sales to marketing.
Even at this point there may be mismatches in needs and our skills and availabilities. If such is the case, we do not hesitate to say so, and attempt to point the prospect in another direction (from which we also gain invaluable market knowledge). If there is indeed a match, we then proceed to try to find common ground on schedule and budget.
Paradoxically, this square deal and honesty about the readiness and weaknesses of our offerings often leads to forgiveness from our customers. For example, for some time we have lacked automated installation scripts that would make it easier for prospects to install our open semantic framework. But, because of compensating value in other areas, such gaps can be overlooked and tackled later on (indeed, as a current customer is now funding). By not pretending to be everything to everyone, we can offer what we do have without embarrassment and get on with the job of solving problems.
For larger potential engagements, we typically suggest a fixed price initial effort to develop an implementation plan. The interviews and research to support this typical 4- to 6-weeks effort (generally in the $5 K to $10 K range, depending) then result in a detailed fulfillment proposal, with firm tasks, budget and schedule, specific to that customer’s requirements. Just as we respect our prospects’ time and budget, we expect the same and do not conduct these detailed plans without compensation. With respect to fulfillment contracts, we cap contract amount and limit milestone payments to pre-set percentages or time expended, whichever is lower.
This approach ensures we understand the customer’s needs and have budgeted and tasked accordingly. Capped contracts also put the onus on us the contractor to understand our own effort and tasking structures and realities, which leads to better future estimating. For the customer, this approach caps risk and potential exposure, and ensures milestones are being met no matter the time expenditures by us, the contractor. This approach extends our square-deal basis to also embrace risks and payments.
Thus, when customers engage us, they spend almost solely on new functionality specifically tailored to their needs. In doing so, we suggest they agree to release the new developments they fund as open source. We argue — and customers predominantly agree — that they are already benefitting from lower overall costs because other customers have funded sharable, open source before them. We point out that the new customers that follow them will also be independently creating new functionality, to which they will also later benefit.
(This argument does not apply to specific customer data or ontologies, which are naturally proprietary to the customer. Also, if the customer wants to retain intellectual ownership of extensions, we charge higher development fees.)
Once these new developments are completed, they are fed back into a new baseline of valuable content and code. From this new baseline the cycle of substantive marketing can be augmented anew and perpetuated.
All of these points can really be boiled down to three guidelines for how to make substantive marketing effective:
What we are finding — as we continue to refine our understanding of this new paradigm — is that through substantive marketing the fish are finding us and they sometimes jump into the boat. We like our enterprise customers to pre-qualify themselves and already be “sold” once they knock on the door. One never knows when that phone might ring or the email might come in. But when it does, it often results in a collaborative customer as a partner who is a joy to work with to solve exciting new problems.
Visualization + Analysis Pushes Aside CytoscapeThough I never intended it, some posts of mine from a few years back dealing with 26 tools for large-scale graph visualization have been some of the most popular on this site. Indeed, my recommendation for Cytoscape for viewing large-scale graphs ranks within the top 5 posts all time on this site.
When that analysis was done in January 2008 my company was in the midst of needing to process the large UMBEL vocabulary, which now consists of 28,000 concepts. Like anything else, need drives research and demand, and after reviewing many graphing programs, we chose Cytoscape, then provided some ongoing guidelines in its use for semantic Web purposes. We have continued to use it productively in the intervening years.
Like for any tool, one reviews and picks the best at the time of need. Most recently, however, with growing customer usage of large ontologies and the development of our own structOntology editing and managing framework, we have begun to butt up against the limitations of large-scale graph and network analysis. With this post, we announce our new favorite tool for semantic Web network and graph analysis — Gephi — and explain its use and showcase a current example.
Three and one-half years ago when I first wrote about Cytoscape, it was at version 2.5. Today, it is at version 2.8, and many aspects have seen improvement (including its Web site). However, in other respects, development has slowed. For example, version 3.x was first discussed more than three years ago; it is still not available today.
Though the system is open source, Cytoscape has also largely been developed with external grant funds. Like other similarly funded projects, once and when grant funds slow, development slows as well. While there has clearly been an active community behind Cytoscape, it is beginning to feel tired and a bit long in the tooth. From a semantic Web standpoint, some of the limitations of the current Cytoscape include:
Undoubtedly, were we doing semantic technologies in the biomedical space, we might well develop our own plug-ins and contribute to the Cytoscape project to help overcome some of these limitations. But, because I am a tools geek (see my Sweet Tools listing with nearly 1000 semantic Web and -related tools), I decided to check out the current state of large-scale visualization tools and see if any had made progress on some of our outstanding objectives.
There are three classes of graph tools in the semantic technology space:
One could argue that the first two categories have received the most current development attention. But, I would also argue that the third class is one of the most critical: to understand where one is in a large knowledge space, much better larger-scale visualization and navigation tools are needed. Unfortunately, this third category is also the one that appears to be receiving the least development attention. (To be sure, large-scale graphs pose computational and performance challenges.)
In the nearly four years since my last major survey of 26 tools in this category, the new entrants appear quite limited. I’ve surely overlooked some, but the most notable are Gruff, NAViGaTOR, NetworkX and Gephi [1]. Gruff actually appears to belong most in Category #2; I could find no examples of graphs on the scale of thousands of nodes. NAViGaTOR is biomedical only. NetworkX has no direct semantic graph importing and — while apparently some RDF libraries can be used for manipulating imports — alternative workflows were too complex for me to tackle for initial evaluation. This leaves Gephi as the only potential new candidate.
From a clean Web site to well-designed intro tutorials, first impressions of Gephi are strongly positive. The real proof, of course, was getting it to perform against my real use case tests. For that, I used a “big” ontology for a current client that captures about 3000 different concepts and their relationships and more than 100 properties. What I recount here — from first installing the program and plug-ins and then setting up, analyzing, defining display parameters, and then publishing the results — took me less than a day from a totally cold start. The Gephi program and environment is surprisingly easy to learn, aided by some great tutorials and online info (see concluding section).
The critical enabler for being able to use Gephi for this source and for my purposes is the SemanticWebImport plug-in, recently developed by Fabien Gandon and his team at Inria as part of the Edelweiss project [2]. Once the plug-in is installed, you need only open up the SemanticWebImport tab, give it the URL of your source ontology, and pick the Start button (middle panel):
Note the SemanticWebImport tool also has the ability (middle panel) to issue queries to a SPARQL endpoint, the results of which return a results graph (partial) from the source ontology. (This feature is not further discussed herein.) This ontology load and display capability worked without error for the five or six OWL 2 ontologies I initially tested against the system.
Once loaded, an ontology (graph) can be manipulated with a conventional IDE-like interface of tabs and views. In the right-hand panels above we are selecting various network analysis routines to run, in this case Average Degrees. Once one or more of these analysis options is run, we can use the results to then cluster or visualize the graph; the upper left panel shows highlighting the Modularity Class, which is how I did the community (clustering) analysis of our big test ontology. (When run you can also assign different colors to the cluster families.) I also did some filtering of extraneous nodes and properties at this stage and also instructed the system via the ranking analysis to show nodes with more link connections as larger than those nodes with fewer links.
At this juncture, you can also set the scale for varying such display options as linear or some power function. You can also select different graph layout options (lower left panel). There are many layout plug-in options for Gephi. The layout plugin called OpenOrd, for instance, is reported to be able to scale to millions of nodes.
At this point I played extensively with the combination of filters, analysis, clusters, partitions and rankings (as may be separately applied to nodes and edges) to: 1) begin to understand the gross structure and characteristics of the big graph; and 2) refine the ultimate look I wanted my published graph to have.
In our example, I ultimately chose the standard Yifan Hu layout in order to get the communities (clusters) to aggregate close to one another on the graph. I then applied the Parallel Force Atlas layout to organize the nodes and make the spacings more uniform. The parallel aspect of this force-based layout allows these intense calculations to run faster. The result of these two layouts in sequence is then what was used for the results displays.
Upon completion of this analysis, I was ready to publish the graph. One of the best aspects of Gephi is its flexibility and control over outputs. Via the main Preview tab, I was able to do my final configurations for the published graph:
The graph results from the earlier-worked out filters and clusters and colors are shown in the right-hand Preview pane. On the left-hand side, many aspects of the final display are set, such as labels on or off, font sizes, colors, etc. It is worth looking at the figure above in full size to see some of the options available.
Standard output options include either SVG (vector image) or PDFs, as shown at the lower left, with output size scaling via slider bar. Also, it is possible to do standard saves under a variety of file formats or to do targeted exports.
One really excellent publication option is to create a dynamically zoomable display using the Seadragon technology via a separate Seadragon Web Export plug-in. (However, because of cross-site scripting limitations due to security concerns, I only use that option for specific sites. See next section for the Zoom It option — based on Seadragon — to workaround that limitation.)
I am very pleased with the advances in display and analysis provided by Gephi. Using the Zoom It alternative [3] to embedded Seadragon, we can see our big ontology example with:
Note: at standard resolution, if this graph were to be rendered in actual size, it would be larger than 7 feet by 7 feet square at full zoom !!!
To compare output options, you may also;
It is notable that Gephi still only versions itself as an “alpha”. There is already a robust user community with promise for much more technology to come.
As an alpha, Gephi is remarkably stable and well-developed. Though clearly useful as is, I measure the state of Gephi against my complete list of desired functionality, with these items still missing:
Ultimately, of course, as I explained in an earlier presentation on a Normative Landscape for Ontology Tools, we would like to see a full-blown graphical program tie in directly with the OWL API. Some initial attempts toward that have been made with the non-Gephi GLOW visualization approach, but it is still in very early phases with ongoing commitments unknown. Optimally, it would be great to see a Gephi plug-in that ties directly to the OWL API.
In any event, while perhaps Cytoscape development has stalled a bit for semantic technology purposes, Gephi and its SemanticWebImport plug-in have come roaring into the lead. This is a fine toolset that promises usefulness for many years to come.
To learn more about Gephi, also see the:
Also, for future developments across the graph visualization spectrum, check out the Wikipedia general visualization tools listing on a periodic basis.
