Customer Experience Matrix

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How Many Ads Per Day Do You See? Fewer Than It Seems (I Think)

Customer Experience Matrix

ended up using figures from sources including INFOACRS (which itself quotes eMarketer, although from 2008) and MonetizePros. My cliché detector starts chirping as soon as anyone says today’s marketers face more competition than ever before. So competition has been tough for as long as anyone now living can remember. Well, I certainly thought so when I wrote the paper. Really? Interesting.

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SalesPredict Offers Highly Automated, Highly Flexible Predictive Modeling

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Back in, say, 2008, a product like this would be big news. A couple of weeks ago, I wrote that “predictive everywhere” is one of major trends in data-driven marketing. meant both that predictive models guide decisions at every stage in many marketing programs, and that models are used throughout the organization by marketing, sales, and service. User interface is a second differentiator.

Sailthru Offers End-to-End Omnichannel Personalization for B2C Marketers

Customer Experience Matrix

Sailthru was founded in 2008. I know this is blasphemy, but I’m beginning to have doubts about solution selling – the idea that marketers should describe the customer problems they solve, not the features of their products. The issue, at least in marketing technology, is that all systems address pretty much the same general problem of sending the right messages to the right customers (in the right time, place, medium, device, language, tone, etc.). This means that solution statements sound pretty much alike, even when the actual products are different. Sailthru is a good example.

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New Marketing Automation Options for Small Business in the VEST Report

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While GreenRope is new to the VEST report, the company itself was founded in 2008. I’m revving up for the next edition of our B2B Marketing Automation Vendor Selection Tool (VEST) report, which will include six first-time entries. I’ve already written about two of those, Inbox25 and AutopilotHQ (formerly Bislr). Here are thumbnails of the others. Most clients have fewer than ten employees.

Optify Lets Agencies Provide Small Business with Marketing Automation, Distributed Marketing, and Sales Enablement

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Optify was founded in 2008 and launched its original product, a search engine optimization (SEO) tool, about a year later. By contrast, the fourth trend seems to be driven by recognition that small business presents a huge opportunity. only mention this because I’ve recently been looking at a lot of new (to me) vendors and haven’t been able to write about many of them.

Balihoo Offers "Local Marketing Automation" for Channel Partners

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Balihoo entered the “local marketing automation” business in 2008 and now has more than thirty enterprise clients serving more than 100,000 local businesses. One of first marketing automation systems I ever saw – this would be during the Reagan Presidency – was a custom-built dial-up network to distribute collateral to farm equipment dealers. This and similar seeds have since sprouted into a forest of products that help brand marketers work with dealers, distributors, franchisees, and agents. The best I can do is make an occasional visit to see what’s new. could easily stop here.

A Framework for Real Time Decision Management: How SAS RTDM Fits In

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RTDM has been around in some form since 2008, although the tight integration with SAS Marketing Automation is more recent. I’ve had a couple of consulting projects recently that involve real-time decision systems (a.k.a. real time interaction managers), which are used to select the best treatment during a Web visit, telephone call, or other interaction. Maybe each of these plays a role.

Insights from Eloqua's IPO Registration Statement

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Client counts are reported only for 2008 through mid 2011. Operating expenses grew sharply through 2008, nearly outpacing revenue. Summary: Eloqua's registration statement offers new and interesting details about its business. My analysis is below. Hopefully it's accurate -- I think that SEC rules prevent them from commenting if it's not. Eloqua’s was no exception. years (1.0 +.80

More on Marketo Financials: Despite Past Losses, Prospects Are Bright

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The table below throws in a reasonable guess for 2008 as well. My records suggest the company had about 25 average employees in 2008, for $5 million in expenses. The company started selling in late February 2008 and my records show it ended that year with 120 clients. Assume the equivalent of 50 annual clients at $15,000 and you get 2008 revenue of $750,000, for $18.75

Quantivo Offers High-Volume Customer Analytics at a Modest Price

Customer Experience Matrix

Quantivo launched as a cloud-based service in 2008 and has a small number of paying clients. These are exciting times in the world of analytical systems. The Web has created new demands to handle unprecedented data volumes and semi-structured data. Cloud-based deployment offers near-infinite hardware scalability and flexibility. Acquisitions by enterprise software giants have opened opportunities for smaller, more nimble alternatives. The result has been an explosion of companies using new techniques for managing and analyzing huge data volumes. Yes there are trade-offs.

Hard Data to Justify Your Marketing Automation Investment

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Summary: So you want some hard numbers to prove the value of marketing automation? Here's a bunch. client asked yesterday if I had some benchmark information to justify the cost of her marketing automation project. This set off an hour-long scavenger hunt through my hard drive, followed by sporadic afterthoughts later in the day. Since this is a question that comes up pretty often, I figured I’d share some of the more useful results. If anyone else cares to expand on this list, even better. 1. Neolane “ Making the Business Case for Enterprise Marketing Software ”. Statistics include: • 16.5%

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Demand Gen vs. CRM Paper Now Available

Customer Experience Matrix

Over the weekend I completed "Demand Generation vs. Customer Relationship Management", the third in my trio of papers explaining where demand generation systems fit into the larger world of customer management software. Like the other two, they are available at the Raab Guide to Demand Generation Systems Web site [link]. It's all good stuff, in my humble and unbiased opinion. Do take a look.

Demand Generation Overview

Customer Experience Matrix

As I promised (threatened?) in my last post, I've been furiously writing articles to explain demand generation for the new Guide Web site. just finished #2, the not-very-creatively titled "Introduction to Demand Generation Systems" and posted it there. won't recap the piece in detail, but am pleased that it does contain pictures. Anyway, you probably knew all that. But I do like the pictures.

One More Chart on QlikTech

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Appearances to the contrary, I do have work to do. But in reflecting on yesterday's post, I did think of one more way to present the impact of QlikTech (or any other software) on an existing environment. This version shows the net change in percentage of answers provided by each user role for each activity type. It definitely shows which roles gain capacity and which have their workload reduced.

Visualizing the Value of QlikTech (and Any Others)

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As anyone who knows me would have expected, I couldn't resist figuring out how to draw and post the chart I described last week to illustrate the benefits of QlikTech. The mechanics are no big deal, but getting it to look right took some doing. Per Friday's post, I listed four roles: business managers, business analysts, statisticians, and IT specialists. Sorry the tables are so small.

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ADVIZOR's In-Memory Database Supports Powerful Visualization

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Back when I was writing a great deal about QlikView , I proposed that its fundamental value came from empowering business analysts to do work for themselves that would otherwise require IT support. See, for example, this post , which has the virtue of pretty graphics.) This same notion of considering which users do which work has permiated my ideas of usability measurement for demand generation systems and usability in general. This still requires the IT staff to design new data cubes and loading processes. Both capabilities must be available for the analysts the work independently.

Simplifying Demand Generation Usability Assessment: No Obvious Answers

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My feelings are hurt, people. No one has commented on last week’s post about usability measurement. know it’s not the world’s most fascinating topic but I really wanted some feedback. And I do, after all, know how many people visit the site each day. Based on those numbers, there are a lot of you who have chosen not to help me. Oh well, no grudges here -- ‘tis the season and all that. I’m guessing the reason for the lack of comment is that the proposed methodology was too complex for people to take the time to assess and critique. Fair enough. But who would believe them? Forget it.

A Modest Proposal for Demand Generation Usability Measurement

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As Tuesday’s post suggested, my thoughts on usability measurement have now crystallized. To provide a meaningful and consistent comparison of usability across demand generation vendors, you could: 1. Define a set of business scenarios that must be supported by the system. Each scenario would describe a type of marketing campaign and the system tasks required to run it. These tasks would cover system set-up, materials creation, campaign design, execution and evaluation. Some tasks would be common to several scenarios, while others would be unique. small sample is below. sigh**. per year?

Two Interesting Blogs on Demand Generation

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I noticed a visitor to the Raab Guide to Demand Generation Systems site from a new source the other day, which turned out to be a mention on blog by Jason Stewart of Demandbase. The context was a discussion of system selection and the Raab Guide was paired with a reference to a very excellent series of posts on system selection on Maria Pergolino’s Inbound Marketer blog. Actually, only two of promised three parts are posted, but the first two are well worth a look. In particular, part 2 has a long list of evaluation criteria. Thanks a bunch, Maria.

Measuring Usability: A Task-Based Approach

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I think we all know that the simplest practical measure of intelligence is how often someone agrees with you. On that scale, University of Ottawa Professor Timothy Lethbridge must be some kind of genius, because his course notes on Software Usability express my opinions on the topic even better and in more detail than I’ve yet to do for myself. Happily, Lethbridge provides additional refinement of the concepts. language skills, physical disabilities, etc.). I’d be more specific and add skills such as analytical or technical knowledge. efficiency , defined as proficiency of an expert.

Pardot Offers Refined Demand Generation at a Small Business Price

Customer Experience Matrix

My little tour of demand generation vendors landed at Pardot just before Thanksgiving. As you’ll recall from my post on Web activity statistics , Pardot is one of the higher-ranked vendors not already in the Raab Guide to Demand Generation Systems. So I was quite curious to see what they had to offer. What I found was intriguing. While last week’s post found that Marketbright aims at more sophisticated clients, Pardot explicitly targets small and midsize businesses (or SMBs as we fondly acronymize them [yes, that’s a word, at least according to [link] ]). Back to Pardot.

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Marketbright Targets Sophisticated Demand Generation Users

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I had a preliminary conversation last week with Mike Pilcher of Marketbright , one of the vendors I’ll probably end up adding to the Raab Guide to Demand Generation Systems. We didn’t look at the software itself, so I can’t comment on it in any detail. The slides did list a few unusual features, including “prospect portals” that help buyers and sellers to share information related to a project; a sales proposal builder; and features to work with sales partners. These seem pretty minor, although they do insert Marketbright more deeply into the sales process than most demand generation products.

Ranking the Demand Generation Vendors by Popularity (Yes, Life Really Is Just Like High School)

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As you might imagine, I’ve been trying to decide how to expand the set of products covered in the Raab Guide to Demand Generation Systems. My original plan had been to add several marketing automation vendors with significant presence in this market. The tentative list is Unica , Aprimo , Alterian , and Neolane. But I’ve also been approached by some of the other demand generation specialists. My original set of products was based on a general knowledge of which companies are most established, plus some consultation with vendors to learn who they felt were their main competitors. How to choose?

Usability Is Just One Piece of the Puzzle

Customer Experience Matrix

A funny thing happened as I was writing one of my usual rants on incorporating usability into the selection process. The resulting paper is on the [link] site, creatively titled "Building Usability into Your System Selection".) Step 3 in a conventional process would identify features needed rather than tasks, while steps 5 and 6 would be replaced with research into system features. What I realized as I was writing this was that the real focus is not on usability, but on defining processes and tasks. Usability measures are something of a by-product. You won't be surprised to find I think it is.

LucidEra and Birst Blaze New Trails for On-Demand BI

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I spent a few minutes last week on the Web sites of about eight or nine on-demand business intelligence vendors, and within a few days received emails from two of them ostensibly asking about much earlier visits where I must have registered with my email address. Given my current obsession with demand generation systems, I’m pretty sure this was no coincidence: they had deposited a cookie linked to my email address during the earlier visit, and used this address to react when I returned. suppose they felt that was better than the creepy feeling I might get if they said they knew I had visited.

Worksheet to Calculate Demand Generation Business Value Now Available

Customer Experience Matrix

The fourth and final item I had planned to add to the Demand Generation Guide Web site was posted yesterday. This is a spreadsheet on calculating the business value of a demand generation system. Basically it defines a formula for calculating profit based on factors that are affected by a demand generation system: number of leads, lead-to-customer conversion rate, net margin per customer, acquisition cost, lead handling cost and sales cost. These are set out on a spreadsheet so users can enter the current values and then make changes to reflect gains expected from the demand generation system.

Marketing Automation vs. Demand Generation: What's the Difference?

Customer Experience Matrix

One of the first people I told about the new Guide to Demand Generation Systems -- an experienced database marketing consultant, no less -- was receptive to the Guide but asked whether there was any real difference between "demand generation" and "marketing automation" in general. This set off all kinds of alarms, since this was someone who clearly should have been familiar with the distinction. As with most things, my immediate reaction was to write up an answer. All this is on top of the piece I was in the middle of writing, on how to cost-justify a demand generation system.

Department of the Obvious: Anti-Terrorist Data Mining Doesn't Work

Customer Experience Matrix

I've emerged from the cave where Osama bin Laden and I were working on the new Guide to Demand Generation Systems (oops -- the Osama part was supposed to be secret) and am now catching up with the rest of the world. One news item that caught my attention described a recent National Research Council report that concluded data mining to find terrorists "is neither feasible as an objective nor desirable as a goal of technology development efforts." See " Government report: data mining doesn't work well " from CNET.

Sample Guide Entries Now Available on the New Site

Customer Experience Matrix

The new Guide Web site is now fully functional at www.raabguide.com. Please visit and comment. If you want to make a purchase, even better. Per yesterday's post regarding the comparison matrix and vendor tables, extracts of both are available on the site (under 'Look Inside' on the 'Guide' page). These will give a concrete view of the difference between the two formats. I'm sure I'll be adding more to the site over time. For the moment, we have to turn our attention to marketing: press release should have gone out today but I haven't heard from the person working on it. Tomorrow, perhaps.

Free Usability Assessment Worksheet!

Customer Experience Matrix

I won’t claim a direct cause-and-effect relationship, but is it really just a coincidence that the stock market finally had a good day exactly when my new Guide to Demand Generation Systems is about to be released? Think about it. That said, the new Guide Web site is in the final testing and should be launched tomorrow. It might even be working by the time you read this: try [link]. The Guide itself has been circulating in draft among the vendors for about two weeks. The extra time was helpful since it allowed a final round of corrections triggered by the yes/no/maybe comparison matrix.

New Guide is Ready

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I've been distracted this week by an unrelated client deadline, but the new Raab Guide to Demand Generation Systems is indeed complete.

More Thoughts on Comparing Demand Generation Systems

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I have mostly been focused this week on formats for the new Demand Generation Guide. Since this is of interest to at least some regular readers of this blog, I suppose it’s okay to give you all an update. The issue I’m wresting with is still how to present vendor summaries. As of last week’s post , I had decided to build a list of applications plus some common issues such as vendor background, technology and pricing. Ease of use was still a nagging issue because ease of use for simple tasks can conflict with ease of use for complex ones. This is precise enough for the purpose at hand.

Comparing Demand Generation Systems

Customer Experience Matrix

Now that I have that long post about analytical databases out of the way, I can get back to thinking about demand generation systems. Research on the new Guide is proceeding nicely (thanks for asking), and should be wrapped up by the end of next week. This means I have to nail down how I’ll present the results. In my last post on the topic, I was thinking in terms of defining user types. But, as I think I wrote in a comment since then, I now believe the best approach is to define several applications and score the vendors in terms of their suitability for each.

Looking for Differences in MPP Analytical Databases

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“You gotta get a gimmick if you wanna get ahead” sing the strippers in the classic musical Gypsy. The same rule seems to apply to analytical databases: each vendor has its own little twist that makes it unique, if not necessarily better than the competition. This applies even, or maybe especially, to the non-columnar systems that use a massively parallel (“shared-nothing”) architecture to handle very large volumes. You’ll note I didn’t refer to these systems as “appliances”. review of my notes shows that no two are quite alike. Let’s start with Dataupia. Dataupia is a true appliance.)

Trust Me: Buyers Worry Too Much About Software Costs

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February 2008 study from Aberdeen Group , Managing the TCO of Business Intelligence (payment required), addresses the question of whether buyers do in fact focus primarily on software costs. I ranted a bit the other week about buyers who focus too much on software license fees and not enough on differences in productivity. The key to that argument is that software costs are a relatively small portion of companies’ total investment in a business intelligence system. This is self-evident to me, based on personal experience, and seems fairly widely accepted by others in the field.

More on QlikView - Curt Monash Blog

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I somehow ended up posting some comments on QlikView technology on Curt Monash's DBMS2 blog. This is actually a more detailed description than I've ever posted here about how I think QlikView works. If you're interested in that sort of thing, do take a look.

More on Vertica

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I finally had a conversation with columnar database developer Vertica last week. They have done such an excellent job explaining their system in white papers and other published materials that most of my questions had already been answered. But it’s always good to hear things straight from the source. The briefing pretty much confirmed what I already knew and have written here and elsewhere. Specifically, the two big differentiators of Vertica are its use of sorted data and of shared-nothing (MPP) hardware. Vertica supports both approaches. Each has its cost.

Still More on Assessing Demand Generation Systems

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I had a very productive conversation on Friday with Fred Yee, president of ActiveConversion , a demand generation system aimed primarily at small business. As you might have guessed from my recent posts, I was especially interested in his perceptions of the purchase process. In fact, this was so interesting that I didn’t look very closely at the ActiveConversion system. This is no reflection on the product, which seems to be well designed, is very reasonably priced, and has a particularly interesting integration with the Jigsaw online business directory to enhance lead information.

How to Report on Ease of Use?

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Yesterday’s post on classifying demand generation systems prompted some strong reactions. The basic issue is how to treat ease of use when describing vendors. It’s hard to even define the issue without prejudicing the discussion. Are we talking about vendor rankings, vendor comparisons, or vendor analyses? Ranking implies a single score for each product. The approach is popular but it leads people to avoid evaluating systems against their own requirements. So I reject it. Vendor comparisons give each several scores to each vendor, for multiple categories. The final ranking is just a byproduct.

How Do You Classify Demand Generation Systems?

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I’ve been pondering recently how to classify demand generation systems. Since my ultimate goal is to help potential buyers decide which product to purchase, the obvious approach is to first classify the buyers themselves and then determine which systems best fit which group. Note that while this seems obvious, it’s quite different from how analyst firms like Gartner and Forrester set up their classifications. Their ratings are based on market positions, with categories such as “leaders”, “visionaries”, and “contenders”. This approach has always bothered me. Did you detect a note of hostility?

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