0 CMO Council: CMOs and CIOs Are Not Aligned

Summary: a CMO Council survey shows that CMOs and CIOs agree they need to cooperate, but disagree on how well they're doing and what their roles should be. Both sides need to work harder to close an increasingly-unacceptable gap. This affects marketing automation vendors too, since they sell to both sides.

The CMO Council and Accenture Interactive recently released a study Aligning the CMO and CIO to Achieve Agile Intelligent Marketing based on parallel surveys of about 300 members of each group. The free 32-page summary provides a detailed analysis and commentary. Key conclusions are:

"While customers have now broadly and deeply embraced new digital and social media channels of engagement, interaction and transaction, most senior marketers and IT executives admit their companies lack a clear understanding of how customers are using their channels and are not highly prepared to leverage those channels. Meanwhile, the relationship between marketing and IT too often remains dysfunctional, with marketers complaining about insufficient support from enterprise IT departments, and IT complaining about marketing departments that forge forward with technology implementations without IT involvement. While marketing believes customer intelligence is critical to competitive advantage, it is struggling to gain IT support and budget for better integration and mining of disparate customer data that is often isolated and under-utilized across organizational silos.

"Yet, there is also significant common ground on which to build a new era of cooperation and synchronization between marketing and IT. There is mutual agreement on the central role technology now plays in defining the customer experience, delivering strategic customer insight, and in reaching and engaging a digitally driven marketplace."

The CMO Council generously shared the detailed data with me. This provided hours of amusement as I recrunched the numbers from my own perspective. I’ve included some of results below with their permission (although of course the interpretations are my own). If you care about these issues, it’s well worth $199 to see the rest.

CMOs and CIOs agree they need to work together, and why. Both cited customer insight and analytics as top reasons to work together. Marketing measurement and ROI rank lower, although CMOs care about them more than CIOs seem to think.


They also agree they have much work to do:

- only 3% of CMOs and 1% of CIOs said their company was heavily committed and invested in interactive digital marketing strategies”

- just 8% of CMOs and 6% of CIOs said they had fully integrated online and offline analytics

But the relationship is far from perfect.

CMOs and CIOs often blame each other for failures.

- Many more CMOs than CIOs said they had “problems or challenges implementing marketing solutions or IT projects to further marketing effectiveness” (64% of CMOs vs. 48% of CIOs). That is, marketers are less happy that IT realizes.

- CMOs blamed failure on lack of IT priority, lack of IT expertise, IT keeping marketing “out of the loop” and IT resistance to solution sourcing.

- CIOs blamed failure on marketing bypassing IT and working directly with the vendor and marketing taking control and isolating IT.

- Both groups did agree on other causes including insufficient funding, lack of time and technical resources, solution complexity and lack of management support.


CMOs and CIOs select tools separately.

- CIOs were much more likely to base selections on consultation with technical groups (enterprise IT, web, contact center and back office) and somewhat more dependent on vendor interactions at conferences.

- CMOs rely more on consultations within marketing, including internal meetings, strategic planning and audits and assessments. But the CIO figures are still fairly high: CIOs are not ignoring marketing’s input, although marketing may be largely ignoring IT.

- Incidentally, both groups cited online research much more often than industry analysts, peer groups or formal needs assessments and RFPs. This reinforces the common notion that online information is increasingly more important for marketing system buyers.


CIOs are less in tune with digital marketing efforts than they think – and CMOs know it:

- 76% of CIOs felt their CIO understood marketing objectives and requirements, but just 54% of CMOs felt their CIO understood marketing needs.

- CMOs were nearly twice as likely as CIOs to feel their company is growing its digital marketing spend (35% of CMOs vs 20% of CIOs).

- CMOs were also more likely to think their firm was aggressively adopting new marketing technologies or was testing new solutions (combined 39% of CMOs vs. 28% of CIOs). CIOs were more likely to think the company was still at the evaluation stage or not a priority (combined 24% of CMOs vs. 34% of CIOs).

- CIOs were much more likely than CMOs to feel that IT was playing a major leadership role in digital marketing strategy. But the majority of both groups also listed the CMO as a digital marketing leader. Near-majorities also credited senior management.


CMOs and CIOs disagree on the CIO's job.

- CMOs take a limited view of the CIO's role. Mostly, they want the CIOto handle the mechanics of data and integration.


- CIOs agreed that better data was their first priority, but otherwise felt they should focus on new types of systems and interactions. They show particular interest in social media.


CMOs and CIOs have different views of spending, too.

- CMOs and CIOs agreed that they spend the most on operational activities including email, Web analytics and CRM. But...

- CMOs reported much more spending on campaign management, marketing platforms and marketing analytics than CIOs. Either the CIOs aren’t paying attention to marketing systems or the CMOs are spending money outside of IT. Or both.

- CMOs also reported more spending on email and search marketing than CIOs. That makes a bit more sense: much of that money doesn’t flow through IT.

- CIOs reported more infrastructure spending such as content management, data warehouse, customer interactions and call centers than CMOs. That one also makes more sense since many of those items fall outside of marketing.


Two more observations on spending:

- Reported spending on multi-channel campaign management and integrated marketing platforms (27% and 23% for CMOs, respectively) was much higher than spending on enterprise marketing management or marketing resource management (both at 10%). I personally think they are close to the same thing, so I’m guessing this is mostly about labels. But perhaps enterprise marketing management is truly a broader and more advanced concept – suggesting there is indeed a large untapped market.

- Spending on marketing performance analytics (22% of CMOs) was more common than spending on ROI modeling and performance measurement (11%) . Could be another labeling issue – or maybe ROI is more specific, more demanding and less common. You decide.

0 Alsa Marketing Adds Multi-Language Capabilities to Low-Cost Marketing Automation

Summary: Alsa Marketing is a late entry to small business marketing automation. They support multiple languages, which should gain them some business. Otherwise, though, it will be tough for them to compete with better-established players.

It’s harder every day for a new company to enter the business-to-business marketing automation industry. Of the three classic competitive strategies – low price, great service and innovative products – there are plenty of low price options and leading vendors work aggressively to help their clients succeed. This leaves unique features as the only viable strategy for a new firm.*

Alsa Marketing, a Montreal-based firm that launched its product in June, has one significant differentiator: it supports multiple languages (French, Spanish and English) in its user interface and in lead profiles. So far as I know, this is unique in the lower end of the market (Alsamarketing starts at $750 per month for up to 10,000 leads and 25,000 emails). Not surprisingly, the company’s 35 or so clients are mostly Canadian and European.

Alsa has some other unusual features. These include support for multivariate tests on landing pages (but not emails); automated posting of Jigsaw and social media data into lead profiles; fractional revenue attribution; and SugarCRM synchronization. These can all be hard to find, although they’re certainly not unique. (As I discussed last week, I’m no fan at all of fractional attribution – but Alsa tells me their clients like having it as an option. **sigh**)

The system also provides a solid set of standard capabilities. Users can import lists, compose and send emails, build landing pages, execute multi-step event-triggered campaigns, monitor Web behaviors, score leads, exchange data with SugarCRM or (soon) Salesforce.com, and run reports. Also can capture results of Google Adwords campaigns and has a URL-shortener to track traffic from social media. The user interface and functionality are perfectly nice but not exceptional.

Alsa also provides prebuilt templates for standard campaign workflows. The vendor argues that this removes a critical roadblock for many marketers, who have trouble building such workflows on their own. I’m not sure it’s really a big issue and, in any case, other vendors provide similar help. In general, Alsa says it has concentrated on helping its initial clients to use the system successfully: again, while this is clearly important (and might save some consulting fees), similar help is available from its competitors.

At best, superior support could built Alsa a small but loyal customer base. Multi-language might open a larger market, at least until competitors match it. I wish Alsa nothing but the best and will be interested to see how things work out.

0 Fractional Response Attribution is Worse Than Nothing

Summary: Should companies apply fractional revenue attribution when more sophisticated methods are impractical? I think not: it gives inaccurate results that could result in bad decisions. Better to avoid financial measures at all if you can't do them properly.

I spent most of the past week in San Francisco at overlapping conferences for the Direct Marketing Association and Marketo. My Marketo presentation was based on the marketing measurement white paper I recently wrote for them, which argues that measurement should be based on tracking buyers through stages in the purchase process. One corollary to this is not attributing fractions of revenue among different marketing touches. The analogy I’m currently using is baking a cake – it doesn’t make sense to assign partial credit for the final flavor to different ingredients: the recipe as a whole either works or doesn’t. Only testing can determine the impact of making changes.

Given this mindset, I was more than a little surprised to attend a DMA panel discussion where two of the more sophisticated marketing measurement vendors described their systems as providing fractional attribution. Both vendors also offer more advanced methods and both made clear that they used such methods in appropriate situations. But they seemed to feel that when adequate data is not available, fractional attribution is better than nothing.

I certainly understand their attitude. Many of the business-to-business marketers at the Marketo conference have exactly this problem: their data volumes are too small to accurately measure the incremental impact of most marketing programs. The best suggestion I can make is that they run whatever tests their volumes make practical. I’d further suggest that testing may actually be more practical than they realize if they actively and creatively look for opportunities to do it.

But, again, the vendors on my panel knew this. The examples they gave were situations where companies had previously attributed all marketing revenue to the “last touch” before an actual purchase or other conversion event. They used fractional attribution to help people (marketers and those who fund them) see that other contacts also contribute to those final results. The practical goal was to justify funding for early-stage programs that such as search engine optimization and display advertising that precede that “last touch” itself.

I’m all in favor of recognizing that early-stage contacts have value. But I still feel that assigning a fundamentally arbitrary financial value to those contacts is a mistake. The main danger is that people who don’t know any better may use these numbers to allocate marketing funds to the more “productive” uses. Such figures are not accurate enough to support such decisions.

I’d rather use non-monetary measures such as correlations between different kinds of touches and ultimate results. These can highlight the connections between early and later touches without providing financial values that are easily misapplied. Maybe this is just wishful thinking, but perhaps refusing to provide unreliable financial metrics will even highlight the need for tests that can provide truly meaningful ones—thus helping marketers to make the necessarily investments.

So what do you think: is fractional revenue attribution of reasonable compromise or a harmful distraction? Let me know your thoughts.

0 Doughnuts and Pizza Slices: Analyzing Consolidation and Competition Among Software Vendors

Summary: One way to understand consolidation and competitive trends affecting marketing software is to look at systems across several dimensions: how closely they relate to customers; whether they are operational or analytical; and whether they support online or offline activities. Combining these provides interesting insights into who competes with whom and what they're likely to do next.

On Wednesday, IBM announced formal completion of its acquisition of Unica. On Thursday, the New York Times reported speculation that Microsoft could buy Adobe.

Coincidence? Yeah, probably. And probably nonsense to boot.

But from my admittedly marketing-centric view of the world, Microsoft / Adobe makes more sense than one might think, as did IBM / Unica. Here’s how I think of things:

If you consider software applications from the view of customer relationships, they form a set of nested circles -- or perhaps a pile of doughnuts seen from above. At the center is the universe of potential buyers. The applications that reach these anonymous masses are applications that manage advertising.

The next circle holds marketing applications, which deal with both identified and unidentified prospects. The circle surrounding that holds CRM systems, which deal with identified customers as well as prospects. Surrounding that are ERM systems, which encompass both customer-facing and back-office applications. You can further extend the model by adding a circle for software platforms (operating systems and databases) and another for hardware.


Each larger circle can include the functions within the smaller circles. Thus, a marketing automation system like Unica or Aprimo includes advertising management; CRM systems like Salesforce.com provide marketing automation; and ERM suites like SAP and Oracle include CRM. Operating systems, databases and hardware support them all. In practice, the specialists in each field tend to be better than components of the larger surrounding suites: this is the essence of a “best of breed” strategy.

So far so good. But a system's relation to customers is just one dimension. Systems can also be classified functionally as operational or analytical, and as dealing with online or offline activities. If you combine those in a two by two matrix, this results in four classes of systems, which correspond nicely to real-world products: online operations (Web site management and ecommerce), online analysis (Web analytics), offline operations (traditional ERM systems) and offline analysis (business intelligence).


Things get really interesting (to me, at least) when you combine these two models. A complete solution for any application includes all four of the matrix quadrants: operations, analytics, online and offline components. So the application's "doughnut" extends through each quadrant.


The other way to look at this is to treat all systems within each quadrant as a unit. Keeping with our junk food theme, let's call those pizza slices.


I find the combination of doughnuts and pizza slices a useful way to think about the relationships among industry vendors. Functional systems tend to start out as pizza slices: general solutions that cut across all applications within their quadrant. Applications typically start out as doughnut quarters: that is, specialists within a single quadrant, usually operations. For example, CRM systems started out mostly as offline operational solutions for call centers and sales automation; marketing automation began largely as campaign management for offline contacts by direct mail and telemarketing.

Functional vendors expand first by thickening features within their original quadrant and then by spreading into adjacent quadrants. Thus, offline operational systems can grow either by adding offline analytics or by adding online operations; online analytical systems can grow by adding online operations or offline analytics; etc.

Applications vendors expand by first completing their own doughnut and then moving into adjacent doughnuts, typically inward: ERM vendors beef up their CRM capabilities (e.g., Oracle buys Siebel Systems); CRM vendors add marketing capabilities (Pegasystems purchases Portrait Software); and marketing automation vendors add advertising support such as planning and marketing resource management (Unica buys MarketingCentral). It’s harder to move in the other direction, since the vendors in the surrounding circles are typically larger.

What makes IBM / Unica so interesting is that IBM has been buying pizza slices (CoreMetrics, Sterling Commerce, Cognos, SPSS) while Unica is a doughnut. The combination is tough to digest. Do you slice up Unica’s various components and reassign them to the Web operations, Web analytics and business intelligence units? Or do you keep the doughnut together and enrich it with parts of the other products? What happens to that little offline operations slice (direct mail, call center, etc.)? And do you fill in the center (advertising support) with something more substantial?

Adobe started as a specialized application for offline operations (printing), which would make it a thin pizza slice. It has grown by thickening its offline features and, more recently, by expanding into adjacent slices: online operations and, with the Omniture acquisition, online analytics. It has also narrowed its focus towards the center of the pizza, on the marketing and advertising doughnuts.

This narrowed focus is what makes Adobe an interesting partner for Microsoft. Microsoft doesn’t have much of the Web analytics slice and would like to strengthen its position in the marketing and advertising doughnuts. The blogger who called the Microsoft rumor “nonsense” suggested that Google is a more likely partner, basically as an anti-Apple move. That makes some sense, but Google already lives in the online marketing and advertising slice, so Adobe would just thicken their presence. Google would like to complete the offline portions of its marketing and advertising doughnuts, but Adobe doesn’t help much there. And I don’t think Google has much interest in moving into Adobe’s other slices.

The "junk food chart" offers quite a few other insights. One is the opportunity for many marketing automation vendors to grow by adding more advertising support -- a major weakness in most products. Another is the likelihood that Web site vendors and Web analytics vendors will themselves push more actively into marketing automation. A third would be the challenge that big online vendors will face in growing if they don't more into offline operations like CRM. You can also plot individual companies on the chart to see how they stack up against competitors both within and outside their existing markets. It's interesting stuff and a great way to work up an appetite.

0 Four Must-Have Metrics for Marketing Measurement

Summary: Four critical metrics tell you most of what you need to show the value of your marketing efforts and to optimize your results. And, here's a funny picture.

There’s still time to sign up for my October 7 Webinar on stage-based marketing measurement (sponsored by Marketo and hosted by the American Marketing Association). During my extensive, um, research, I was very pleased to find the following picture to illustrate the concept of stages:


I like this picture both because it's amusing (a major priority) and also because it illustrates that stage definitions are constructed, not discovered. (I suppose the proper science is that evolutionary stages are objective facts, in which case our monkey friend in the photo simply has it wrong. But the deeper point still stands: whether it’s evolutionary stages or purchasing stages, someone imposes conceptual order on the jumble of reality.)*

If the picture isn't enough reason to attend, the Webinar will also present four essential metrics of stage-based marketing measurement. (Quick review: stage-based measurement tracks the ability of marketing programs to move leads through stages in the purchase process. This is more meaningful than attributing some fraction of the final revenue directly to each program. I’ll cover this in the Webinar and also discuss it in a recent whitepaper Winning the Marketing Measurement Marathon).

In case you can’t attend the Webinar, I thought I’d share the four metrics here.

1. Marketing ROI.
Purpose: to show the company’s return on its marketing investment.
Inputs: marketing costs and marketing-related revenue.
Metric: return on investment (= revenue / cost)
Comment: As with any ROI calculation, the trick here is to determine which costs are associated with which revenues. It’s always hard for marketers to know which revenues they helped to generate, but I’ll assume a database or digital environment that identifies the treatments applied to individuals and their actual purchases. In this situation, marketing ROI is calculated by summing all marketing costs for a cohort of customers sharing some common feature such as original source, acquisition date range or first purchase date. Note that a meaningful calculation must also include spending on people who never purchase, so a cohort based on purchase dates must somehow include non-buyers.

2. Program ROI
Purpose: measure the relative performance of individual marketing programs.
Inputs: incremental marketing cost, incremental revenue
Metric: incremental ROI
Comment: Obviously the key word here is “incremental”. Marketing programs exist in the context of other activities that influence buyer behavior. The only thing you can really measure is the incremental change that occurs when a particular program is added or removed from the mix. Combined with incremental costs, this gives an incremental ROI for the program. Spending more on high ROI programs and less on low ROI programs is how marketers optimize their results. Remember, though, that ROI is just one part of the equation. In practice, marketers must balance it against considerations such as revenue goals and marketing budgets.

Incremental measurement requires formal tests that compare performance of two similar groups which differ only in whether they received a particular program. These tests can cover any type of program, including nurture programs that don’t acquire new names. Proper measurement must track through the end of the buying cycle, since a program’s impact on early stages might vanish or even be reversed at later stages. One common example: a free introductory offer that yields higher initial response but doesn't add to the final number of paying customers.

3. Stage Results
Purpose: understand movement of leads through the buying stages
Inputs: marketing costs per stage, conversions (= number of leads that move to the next stage), conversion time (= time in stage before conversion to next stage; a.k.a. velocity), lead inventory (=number of leads in each stage)
Metrics: conversion rate, cost per conversion, average conversion time
Comment: These statistics describe how leads are moving from one stage to the next. The information is used to project future behaviors, to identify problem stages, to track changes in stage performance, and to compare the effects of marketing programs. Where leads in different cohorts (based on original source, acquisition date, marketing treatments, etc.) behave differently, statistics should be gathered separately for each cohort.

One statistic you can't calculate is the ROI for stage investments. This is counter-intuitive: stage ROI should be possible because you're making investments at each stage and the investments produce leads with higher values. But in fact the aggregate value of a cohort of leads remains the same as they move through the stages; all that happens is that unproductive (i.e., valueless) leads drop out. That is, even though the value per lead increases, there is no increase in the value of all leads combined. Without a value change, you can’t calculate a return on investment.

(Actually, there is a bit of value change as leads move through the stages because leads in later stages will need less additional investment to reach the final sale. But the expected revenue for the cohort stays constant. Of course, to the extent that a particular marketing program creates an incremental change in total value, this can be measured like any other program ROI.)

4. Revenue Forecast
Purpose: estimate future period revenues (by week, month, quarter, etc.) from the current lead inventory.
Inputs: lead inventory per stage, conversion rate per stage, conversion time per stage
Metric: revenue forecast by period
Comment: Revenue projections are among the most critical of corporate statistics. The stage-based approach allows more accurate projections of revenue over time, starting with the current lead inventory and known stage statistics. If the projections can distinguish marketing-generated leads from other leads, they can also give a concrete measure of the value that marketing has provided to the organization. If leads from different cohorts behave differently, the projections need to use separate assumptions for each group.

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* Platonists and creationists, with their respective theories of absolute Forms and divinely-created immutable species, might argue that species actually do have an independent existence. They're wrong.