Why MarTech Fails: A Data-Driven Answer

Do you suffer from Martech Fatigue Syndrome (MFS)?  Symptoms include obsessive concern with the number of martech vendors, anxiety at the prospect of evaluating new systems, and fear of missing out on an important new capability.  Severe cases have reported hallucinations of vendor logos covering vast surfaces and nightmares of being buried under a collapsed martech stack.  MFS is rarely life-threatening but can disrupt the quality of your every day marketing.  If you or someone you know shows symptoms of MFS, please call Scott Brinker immediately.

So far as I know, Martech Fatigue Syndrome is not yet a real thing.  But I've definitely sensed a certain weariness in recent discussions of marketing technology.  The initial excitement about new opportunities has become exhaustion as marketers realize they need to keep making investments even though they're not using their existing systems to the fullest.  See, for example, this Kitewheel study, which found 72% of agencies use less than 40% of their tools every week.

So what’s the problem? Have marketers simply purchased the wrong technologies – after all, they’re new at the system buying business and martech is filled with bright and shiny distractions. Or are they buying the right systems only to find that other roadblocks get in the way of success?

Many people have asked similar questions. So many, in fact, that I've found a half-dozen surveys in the past two months touched on the topic.  You can see the questions and their answers at the bottom of this post.

But each survey asks different questions and gets slightly different answers.  To look for over-all patterns, I’ve combined the answers on the following table, putting similar items on the same row and keeping related ones nearby.  I’ve grouped the answers into general topic areas: organization, management support, marketing strategy, data management, delivery systems, and external factors. High-rated issues within each survey are shaded orange and low-rated issues are shaded green.  In other words, orange cells are the biggest problems, green cells are the smallest, and white cells are in between.

The reason for all that careful arrangement is to see any clusters in the answers. Sure enough, some do emerge: the biggest problems are concentrated in organizational issues (lots of orange).  The one exception that people think their own skills are perfectly adequate. Of course.

The management support area is mostly neutral except for a slash of orange for Return on Investment.  That make sense: measuring ROI is always a challenge for marketers.  To be clear, the answers are referring to the ROI of marketing programs in general, not martech investments in particular.  If anything, the surprise is that related items like management support and budget are less of a roadblock.

Marketing strategy isn’t a major problem in most cases, with just one survey as an exception. As with skills, this basically means that marketers are confident they know how to do their jobs.

The next two items, data management and delivery systems, are where technology comes in. There’s more green here than orange, confirming our hunch that access to adequate technology isn’t marketers’ main problem.

The final group, external factors, is no problem at all.

As a final bit of analysis, I've normalized all the answers to create a combined ranking for each category, splitting out ROI and internal skills since they are so different from everything else.  Apologies in advance to any real statistician who is horrified at the procedural flaws in this approach.   The rankings do seem to come out about right, and putting it all into one graph meets the goldfish attention span test.

Bottom line: measuring ROI and organizational roadblocks are the biggest reasons marketers fall to get value from technology. Finding good technology and knowing what to do with it are less of a concern.  These answers aren't unexpected but now you have a data-driven answer next time anybody asks.



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Survey Details:

Closing the Gap Between People’s Expectations and Retail Realities Retail Touchpoints for Magnetic
Barriers to cross-channel experience:
55%	not enough data for full profile
53%	internal organizational silos
50%	don’t know what types of messages resonate best
50%	struggle to get right message to the right person
49%	delivery systems are not integrated
49%	struggle to integrate first and third party data
46%	don’t know how to use our data to create a better experience
The Outlook for Data 2017: A Snapshot Into the Evolving Role of Audience Insight Winterberry Group for IAB Data Center of Excellence
Obstacles to value from data-driven marketing:
	year ago
45%	26%	difficulty proving ROI
45%	35%	lack of internal experience
39%	45%	insufficient technology
36%	25%	siloed organization
25%	33%	inadequate first party data
23%	12%	tests have failed
20%	22%	lack of leadership support
19%	15%	competitive pressures
16%	8%	lack of guidance from agency/service partners
12%	17%	inadequate third party data
9%	7%	little demand from customers
Forbes Insights Research Report on the Future of Marketing Forbes Insights for Aprimo
Agile marketing challenges
47%	bureaucracy
37%	proving ROI
32%	employees not empowered
27%	can’t connect agile marketing to business outcomes
27%	hierarchical organization
26%	budget
23%	lack of management vision
17%	lack right technology
14%	lack IT talent
14%	lack marketing talent
12%	difficulty choosing right third party
Marketer’s Confidence Index January 2017 Kantar Vermeer for American Marketing Association
Not confident the organization’s marketing team
	year ago
41%	33%	has right operating model (people/structure/processes/tools)
38%	27%	understands ROI of efforts
32%	28%	has clear strategy
30%	17%	is investing in right customers
29%	24%	is doing the right things
28%	24%	has clear brand position
25%	19%	has right capabilities
2017 Personalization Development Study Monetate
Obstacles to 1-to1 personalization
91%	organizational constraints block personal accountability
79%	automating decisions at scale
68%	assemble real time customer view with full context
65%	understand buyer behavior in context
62%	creating compelling offers and content
55%	integrating third party data
54%	data quality
48%	understanding who to personalize when in which channel
38%	sustainable data architecture
Adobe Digital Intelligence Briefing, 2017 Digital Trends Econsultancy for Adobe
Most difficult customer experience components to master
40%	strategy
39%	journey design
38%	process
37%	data
35%	technology
34%	culture
32%	collaboration
31%	skills