Typing Relationship Management Models

Last week we wrote about the confusion that seems to rage over what exactly CRM is and is not.  So, we've decided to start a little series of posts here.

First, the bad news: there is no ordained definition of what "CRM" is, but there is a fairly strong consensus among marketing strategists what the phrase "customer relationship management" intends.  So let's start by stating, as can be found in many resources:

CRM is a business strategy.  It is not a software application, platform, or system.  This business strategy is directed to understanding, anticipating, and responding to the wants and needs of an enterprise's current customers in order to grow the life-time relationship value.

A couple of points right away:

  1. Current vs. Prospective Customers. Some will argue that CRM equally applies to potential or prospective customers as it does existing customers.  We disagree.  The business strategy called CRM is really about retention marketing, not acquisition marketing.  The former is unique in that the tools of CRM focus more on the middle to late stages of the customer life cycle (i.e., selection, satisfaction, loyalty and advocacy), not the early stages (i.e., awareness, knowledge, consideration and selection.)  We'll save the nuanced counter argument about addressing the same customer in future cross-sell or up-sell opportunities as an iteration through the entire life cycle.  One could argue that a new product is a return to raising awareness and increasing knowledge, etc.  Its different. Full stop.  Please ask us to explain or watch for the book :-)

  2. The Definition of a Customer.  This is the immediate threshold element in understanding why some CRM solution providers confuse matters.  A "customer" is one who purchases your product or service.  A customer can be an individual consumer; a client; a constituent; a retailer; a distributor; a purchasing cooperative; etc.  The key is to ask: "Is the individual, group, or entity a purchaser of my goods and services?"  Next it is imperative to know whether the "customer" as you have identified it is a consumer or a "proxy" or representative of a consumer.  The former amounts to a so-called "B2C" relationship whereas the latter amounts to a "B2B" relationship.  And this distinction will be essential to determining your CRM strategy, approach, requirements, and tools.  For example, where the customer is a consumer, client, or constituent, the individual is who you are establishing the relationship and therefore addressing their individual needs (which often tend toward to emotional values of your brand promise).  On the other hand, where the customer is a business, the proxy is generally a purchasing agent (in larger enterprises) or the owner (in small business) and in any event is some decision maker with whom you are estbalishing a relationship and therefore addressing their business needs (which often tend toward the rational values of your brand promise -- remember our position that CRM is brand management?).

The challenge for us today is that the Internet has driven many Manufacturer/Wholesalers to seize the tantalizing opportunity to connect directly to consumers of their brands, often unwittingly trampling on their established distribution and retail channels.  Of course, there are some interesting solutions to address how integrated retail eCommerce can work to the benefit of both the brand and its channels. 

Nevertheless, where the enterprise is the maker of the goods and services , which they subsequently sell through channels, CRM becomes a bit confusing because they are typically looking for a solution to address both B2B (their distributors and retailers) and B2C (their ultimate consumers.)  To add some more alphabet soup to this, this latter approach to moving their products is often called "direct to consumer" (or DTC).

The Major Relationship Model Distinction

So, once you understand which kind of CRM your business requires (i.e., B2B or B2C, or actually both) and what the strategy right-sized to your enterprise will require, then comes the task of figuring out what type of CRM solution provider you may want to turn to for tools.  Some CRM experts attempt to delineate several models for academic purposes.  We're kewl with that, but think its useful at the outset to focus on just two:

  • Horizontal CRM Solutions
  • Vertical CRM Solutions

...and have an appreciation for their major differences and cost implications.

Horizontal CRM Solutions.  Horizontal CRM makers provide a non-specialized base platform intended for application across all industries.  They tend to be less expensive (up front), least common denominator solutions.  For example, an autombile manufacturer would adopt the same platform as a publisher.  Generally, although horizontal CRM solutions have a lower initial up-front cost, they tend to be more expensive in the long run because they typically require customization or tailoring to the particular industry and business model.  This tends to be similar to the great promise and paradox of earlier ERP; that is, the so-called 80-20 solution where 80% of the functionality was promised to be "out of the box" with the remaining 20% being the required customization.  In reality it was often the other way around, and certainly the cost model was more like 20-80.  Major CRM vendors offer horizontal CRM solutions.  A perfect example is Salesforce.Com.  In order to tailor a horizontal CRM solution, these companies may use industry templates to overlay some generic best practices by industry on top of the horizontal CRM solution.  And often they rely on a network of authorized resellers and systems integrators who have industry and application specific domain expertise to carry their platform into vertical markets.

Vertical CRM Solutions.  As you might expect then, vertical CRM manufacturers offer specialized, industry application-specific CRM solutions.  Vertical CRM solutions are typically more expensive up front but lower cost over the lnger term because they already incorporate best practices, specialized capabilties, and templates and tools that are specific to an industry and business model.  A good example of this would be a CRM solution where the "C" stands for "client" and the application is in the legal profession.

Here is an important rule of thumb from our experience: it is 10-12X more expensive to build a vertical solution from a horizontal CRM platform  than it is to choose a vertical solution already tailored to your business model at least (e.g., B2C vs. B2B) and at best, tailored to your industry if it is vertical or specalized in what you make and how you sell and support the good or service.

This latter point is a hurdle for providing sound CRM strategy and advice.  The reason is the horizontal makers want you to adopt and purchase their CRM solution, but one type does not serve all.  For instance, we have a pair of clients right now who both are historically a product maker in a B2B business model who now desire to expand into a B2C busines model.  And they're both being vigorously told that SalesForce.Com will serve all of their needs -- they simply define customer records and set a flag for the customer type.  Nonsense. 

Clearly, Salesforce.Com is a powerful platform primarily intended to serve the B2B and customer-service models.  However, the data, information, outreach and marketing tools for serving a customer who is another business is considerably different from what is required to implement a CRM strategy for serving an individual consumer (in a B2C/DTC business model).  The former's audience amounts to Procurement Managers on behalf of their businesses.  The latter's audience are individual consumers who are buying directly from the enterprise.

Next up, we'll share some more about some of the academic model distinctions, and the challenges of implementing the best customer intelligence strategies.


Its About the Data Stupid

The White House Office of Science and Technology Policy (OSTP), together with the National Science Foundation (NSF) and several other Federal 3-lettered Agencies will hold an event in Washington, D.C. this coming Thursday to address the challenges and opportunities related to “Big Data.” The event will be webcast live from 2:00pm to 3:30pm EDT.

According to their media advisory:

Researchers in a growing number of fields are generating extremely large and complicated data sets commonly referred to as “Big Data.” A wealth of information may be found within these sets with enormous potential to shed light on some of the toughest and most pressing challenges facing the nation. To capitalize on this unprecedented opportunity to extract insights and make new connections across disciplines we need better tools and programs to access, store, search, visualize and analyze these data.  To maximize this historic opportunity — and in support of recommendations from the President’s Council of Advisors on Science and Technology — the Obama Administration is launching a Big Data Research and Development Initiative, coordinated by the White House Office of Science and Technology Policy and supported by several federal departments and agencies.

In addition, they're going to trot out a panel of thought leaders from academia and industry,  moderated by the New York Times' technology writer Steve Lohr.

And you, our trusting reader asks, "OK, and I should care because?"  Fair enough.

Its not so much that C[IQ] is about data, lots of data, data for customer intelligence... as it is about the implications in play here that sorta link back to our prior blather about the Semantic Web and the larger points about, well, the data. 

We'll leave aside the amusing query whether the Administration's announcement has any link to their likely play on the importance of innovation, the Tech Sector, and the Internet in their re-election campaign.  After all, once before, the tag line was "Its about the economy, stupid."  And it is again, albeit perhaps said differently.  So, here's the short of it (as short as we can make it):

The amount of data in our world has been mushrooming (um, that is a word, right?), and analyzing large data sets, the so-called big data will become a key basis of competition, underpinning new waves of productivity growth, innovation, and above all consumer insight.  And here are four big ideas we think you should consider:

  1. Data has flooded into every industry and business function and is now a critical factor of production, next to labor and capital. Researchers at McKinsey & Co. report that by the end of 2009, nearly all sectors in the U.S. economy had at least an average of 200 terabytes of stored data per company with more than 1,000 employees.
  2. There are at least five ways to leverage big data: [1] Big data unlocks value by making information transparent and usable at much higher frequency. [2], Compiling more transactional data, they create more accurate and detailed longitudinal performance information on everything, thereby unlocking competitive advantage if applied. [3] sophisticated analytics can substantially improve decision-making. [4] Big data allows finer segmentation of customers and far more precisely tailored products or services. [5] Big data can improve development of the next generation of products and services.
  3. The use of big data will become a key basis of competition and growth.  See #2 for details.

  4. Several issues need to be addressed to leverage the potential of big data. Policies related to privacy, security, intellectual property, and even liability will need to be addressed. Companies need to find and leverage the right talent and technology.  They also need to (re)configure workflows and incentives to optimize the use of big data.  Access to data is critical, and companies will increasingly need to integrate information from multiple data sources, often from 3rd parties.

OK, so the last point may have been a shameless self plug for a Firm whose brand echos the point -- customer intelligence for smarter marketing.  But it really is about the data.  And the government appears to get it; let's hope to the point of making innovation easier and not necessarily over-burdening us immediately with more regulatory schemes.  That said, we take the privacy and integrity issues seriously.  And you should too.

It starts with checking out their webcast this Thursday.  We'll give you our take afterwards.



Gregory Miller, CTO

Greg has been in the tech sector as a software architect and engineer, product manager, marketing and biz dev exec., and even IP and privacy lawyer for 3 decades. He is currently on the Board of a non-profit tech foundation reinventing America's election technology, is a venture adviser in the Silicon Valley, and serves as the CTO for C[IQ] Strategies, Inc.

Semantically Speaking, This Changes Everything

Week before last you may have heard that Google is readying the next major refresh of its search engine.  We spent some copius spare time reflecting on that, mostly because a couple of our clients inquired.  So here is what we thought you should know.  Refinements have been nearly continuous for Google, but this next step reported over the course of the week of March 12th begins a shift that will impact the very foundations of how you start and sustain conversations with your customers.

In the ensuing months, using Google search will net you more than just links to their best shots based on ranking.  Gradually, results will also include more pertinent data and direct answers to queries that can be formed more like natural language questions.  Industry experts agree, this will mark the greatest change (and improvement) in Google history and could affect tens of millions of web sites that depend on page ranking.

To put a fine point on this, Google isn’t replacing its keyword search process, which determines the importance of a site based on its textual content, the frequency other sites link to it, and several other metrics.  Actually, Google is refining its engine to deliver more relevant results by integrating “semantic search” technology.  This means Google is adding the capability of its search engine to comprehend the actual meaning of words.  “Semantic search” uses semantics; that is, the science of meaning in language.  This will produce even more relevant search results.  In most cases, the goal is to deliver the information queried by a user rather than have a user sort through a list of loosely related keyword results. 

For digital marketers, semantically speaking (sorry, we couldn’t resist), this changes everything.  And that’s because Google search will begin (and do better over time) to look more like how we humans understand the world.

Some observe that this is the first real movement toward “Web 3.0” or from a content or knowledge stand point the Semantic Web.  Think of it this way:

  • Web 1.0 was the linking of pages
  • Web 2.0 is the linking of people; and
  • Web 3.0 will be the linking of data

This will empower an even more personalized experience for the individual user.   But a personalized experience is only part of this.  From our perspective, this model presents the Web through the lens of knowledge.  There is also the lens of utility.  That is to mean, if the first generation of the web was content-centric, and the second generation is social-centric, then the third generation will be services or utility-centric.  Utilities (i.e., applications or “apps”) are made possible by APIs and web services that enable machines to interact with machines (and not just people) to deliver information management tools.  Both models are creating a more delightful, meaningful, useful digital resource.

Just to frolic and detour a little further, the utility potential is enormous.  Consider apps today like online reservations (e.g., United.com, which will have more to say about at some point, post Continental merger); logistics services (e.g., FedEx.com); bookkeeping (e.g., Intuit.com Quicken online); or emerging utilities like Yakima Racks online “rack configurator” (e.g., Yakima.com).  And that is definitely only the tip of the proverbial iceberg.  To this end, the ‘Net is completely re-inventing the applications software industry (really: when is the last time you went to a store and purchased software in shrink-wrapped box?)  We’ve digressed enough.

Back to the higher quality of knowledge consumption, the personalization of experience in the process of search is already happening.  Consider that Google actually personalizes search results.  We mentioned earlier that Google utilizes several variables to determine the importance of sites in ranking.  Similarly, Google personalizes search results based on some 57 different factors or signals that is collects users, including the browser that they are using, where they’re located, keyword searches and browser history.  Two people can each enter the same search term today, but will be presented with entirely different results.  Some suggest that over time, this may not be that good of an idea (to see just how bad, you should definitely view this link when you have a second).  Regardless, tailored search combined with recommendation engine technology and emerging tools such as attention analytics are certain to change the personal experience of the Web, and more importantly here (re-grouping with the point of our post) will forever reshape digital marketing—whether for acquisition or retention.

In short, the Semantic Web (3.0) will be able to compile information for a specific user in relation to specific requests, interests and needs based on a vast data set that could span multiple sites, domains and service providers.  In effect, Web 3.0 will offer true utility to consumers by “knowing” almost precisely what you want.  And it is forecast Web 3.0 will understand the meaning of content and data and relate that to the user’s specific inquiries.  This third generation of the Web presents three issues for digital marketing:

  • More data and more information
  • More consumer-controllable filtering; and
  • Less reliance on brand sites

This means Brands will need to determine their enterprise data, brand data, and other information to be shared in the semantic web.  And it will be necessary to figure out how this data and information can help build, establish, and sustain customer relationships.  The trick here is to recognize how this data can be useful to customers in different contexts. 

In other words, in the 3rd generation of the Web context will be king.  At the risk of falling into another rabbit hole, we defer that discussion for another post.  Just margin note this for now that understanding context will be essential effective brand management, which is saying “relationship marketing.”

Several years ago, doing a fine job of sensing the future, Scott Brinker of Ion Interactive, another one of us marketing technologist types, wrote a fabulous article on the future of semantic marketing.  We offer that assessment, because despite having posted 4 years ago, with the news of Google's infusing semantic search capabilities, we were able to successfully dust off the article and find it 100% spot-on.  For those lacking time right now to surf our reference links, we’ll try to do some justice to his work in summary here.

First, as we suggested at the outset, The World Wide Web Consortium (WC3)—a governing body for standards in building the Web, describe the “semantic web” as being about common data formats that simplify combining data from diverse sources.  (At C[IQ] we’re all about that!)  In other words, this amounts to mapping ideas expressed in human language such that it facilitates automatic processing, where software can automatically comprehend how different pieces of data are related.  And we agree with Scott’s immediate assertion that such sounds a bit too geeky for marketing folks to handle.  And yet, query just who is going to make this happen?  To explain, Brinker poses a bit of a conundrum (quoting from his post):

  1. If the semantic web is successful, it will unleash an enormous wave of information exchange between organizations and individuals, empowering a new level of discovery and compelling knowledge sharing;
  2. In order for the semantic web to be successful, enterprises, institutions and organizations must assign responsibility for their participation in it to someone who will drive it — otherwise the semantic web will settle into semantic soup;
  3. For someone to assume that role, there's must be incentive; in the commercial world, incentive translates directly into acquiring and retaining customers — which begins as a function of some form of discovery and compelling knowledge sharing.

Who is best suited to take on that roll?  Consider that “discovery” as the term is used above is about awareness raising tools, including advertisements, articles, blog postings, interviews, press releases, search engine optimization, etc.  And “knowledge sharing” as used above refers to case studies, white papers, feature comparisons, pricing, product reviews and critiques, etc.  And now you know who, from the enterprise perspective, is going to have that responsibility.  Marketing.

Yet, before everyone dives into semantic marketing, we need to understand that marketing in the semantic web will be very unlike marketing in the Web today, principally based on visual brand engagement.  Of course, marketing (e.g., branding, advertising and promotion, etc.) will continue in a similar manner as does today, but it will be accelerated and powered by semantic technology in areas such as search engine marketing.  

So, what will semantic marketing, or marketing in the semantic layer of the web amount to?  Scott suggests seven areas to be addressed.  We agree, and are already working with clients on these tasks today as a nearly inherent extension of customer IQ work.  Let’s summarize, and again encourage you to go see his article when you have time.

  1. Becoming the champion of data; not just regular old-school product and services data, but deep enterprise data that products and services are built on (without giving away IP assets of course)
  2. Managing data curating; the process of information architecture, structuring, sequencing, and organizing data including its (meta) tagging to maximize its combination, mashing, and discovery.  This is akin to having a “Chief Ontologist.”
  3. Setting distribution strategy; something akin to an SEO+ or perhaps SWO (semantic web optimization)
  4. Structuring incentives; necessary to convert semantic web interactions into real business objectives. This could be the greatest challenge in semantic web marketing, because there is a natural tension between openness and incentives. Achieving the right balance is part of the Enterprise’s marketing strategy. We think (as does Scott) that this is analogous to the dynamics of open source software.
  5. Tracking and attributing distributed data; this includes measuring the impact of the different content elements that contribute to customer and partner relationships. This is likely to be the toughest technical challenge.
  6. Leveraging external data; specifically applying data in your own data mash-ups. For consumer-facing apps, this is where your organization's data inputs and outputs surface into digital brand experiences. This next generation of apps and services will directly serve a highly engaged human audience.  And they will also serve market research, customer intelligence.  
  7. Data governance.  An entirely new level of data governance will be required to protect the integrity of your semantic presence in the Web.  This will require managing (if not policing) all aspects of data use from broken links to mishandled intellectual property.  On the one hand it might be argued that such governance needs to be handled by an audit-class element to ensure checks and balances.  On the other hand, marketing may well be in the best position to manage this because semantic quality control runs to online reputation management—clearly the responsibility of brand management.  And there is no more certain way to slow a process that—because of the speed of the Internet—absolutely must avoid bureaucratic overhead.  And Scott’s article suggests some other reasons.

Of course, at this point, we fear we may have overwhelmed you with a considerable amount of complexity in an emerging CRM and customer IQ issue.  However, the Google news about their search engine evolution triggered our internal revisiting this issue (the semantic web) on behalf of our clients.  And with a little research, we were compelled to bring this to our readers here. 

So consider this:  when Scott’s article first appeared four years ago this month, a Google search of “semantic marketing” produced 665 results.  Performing that same keyword search 4 years later, today produced 11,400,000 results.

In a shameless plug, if any of this has you thinking, perhaps in a semantic sort of way, we humbly suggest you might want to get in touch with us.