I’m sure you consider yourself data-driven.
You make decisions based on data. Problem is, most companies aren’t using their data to the capacity they could be. It’s almost a universal problem, and it means you’re leaving money on the table.
You see, the current structure of the modern marketing stack leads to a large amount of data fragmentation. As we collect more and more data, it’s becoming increasingly hard to piece together and manage that data, and more importantly, to use that data in real-time to build better campaigns.
A Brief History of Marketing Technology Software
Though it wasn’t intended, the last two articles I wrote on the PlainFlow blog have formed a series:
- The Modern SaaS Stack and the Unexploited Amount of Data is a walkthrough that shows how companies use Modern SaaS Stack to cover their Marketing/Support/Sales activities from day-0. How their product leaders and CMOs embrace the change adjusting product/marketing strategies based on new technologies.
- In AI implications on Marketing and Analytics, I placed my considerations explaining how and why Artificial Intelligence (AI) will shape the next generation of Analytics and Marketing SaaS products.
In retrospect, there is a clear thread between the two posts. That thread is what leads me to think that something is changing and today’s’ Marketing SaaS landscape might look like different very soon.
As very often happens, to understand the present, you have to know the past.
Contact Management: The Beginning
Back in 1986 when digital marketing was just taking off, a company called ACT! launched a contact management software. That was designed to allow information storage and manage customer contact information. All manual operations.
7 years later, in 1993, Tom Siebel thought that Oracle (the company where he was employed) could have sold the internal sales application as a standalone product. When Larry Ellison rejected his idea, he left Oracle and created his own company. It didn’t take too long for the Siebel Systems to become the leading provider on the market.
Siebel took off the most important features from the Database Marketing Systems and combined them with contact management solutions software. Et voilà: the first CRM.
Enter Cloud Computing and Marketing Automation at Scale
The industry had to wait for about 4 years to have a new genius disrupting the industry of CRM. His name was Mark Benioff (another former Oracle Executive) and in 1999 he had the pleasure to introduce the business world the first CRM in Cloud. The Salesforce era was only at the beginning.
After that, the adoption of the cloud as a more scalable and cost-effective approach allowed small/medium business to build CRMs around very specific market needs and establish dominance in new vertical segments.
In early 2000, the diffusion of Personal Computers transformed the way users made decisions and the paradigm buyer/seller changed again.
Mark Organ, saw a tiny space in the already very crowded CRM industry and founded Eloqua. It was 2003. Marketing automation (as we know it today) was just born. Eloqua was first the product built by marketers, for marketers. Organizing multi-channel campaigns, segmenting audiences, and distributing personalized content, suddenly appeared to be easy, like never before.
The Marketing Automation industry immediately became such a good opportunity for new businesses. Eloqua was the proof of that. It took less than a couple of years to see the beginning of the dances with the next generation of Marketing Automation Platforms: Marketo, Pardot, ExactTarget, and many others.
In only a few years from its launch, Marketing Automation was already the biggest subset of the whole CRM industry.
Limits and Problems of Marketing Automation
There are, of course, limitations with marketing automation as we know it today. These, in my mind, break down in three ways:
- Data accessibility
- Marketing automation fatigue
- The rise of PQL over MQL model
1. Data Accessibility
Back to those (early) days when Marketing Automation was just born, the world was web-centric. The situation is now completely different from that.
Now users interact with digital products in much more complex and different ways than a decade ago. As I previously explained in this article, the complexity of user interactions and the increasing of medium devices had led to an unusual proliferation of SaaS products vertical on specific markets with specific needs.
The perfect combinations of products with specific features not only is cost saving but can assure a better quality compared to the traditional all-in-one solutions.
SaaS stacks give companies the agility they need to move fast, but often they are the cause of a huge data fragmentation. Valuable customer data is buried in these disconnected tools.
Data continues to be the bedrock of success for a lot of departments in every company, no exception for marketing.
Your marketing is always as good as your data. The more complex your stack is, the more customer data you are spreading across many different tools, and the more time you (or your engineering team) will need to reassemble the puzzle and get a full reasonable picture.
This is a representation of how the data fragmentation will exponentially growth with the complexity of your stack.
In yellow, the “personalization-curve” tells you how much of your data you’re actually exploiting. While the SaaS stack complexity and the data fragmentation increase, you still have the same level of personalization. The huge amount of customer data you’re generating, it’s silo’d in these tools.
The blue intersection points out the “data dead-loss” – data that you have but you can not use.
2. Marketing Automation Fatigue
Recently, I’ve been reading what Highrise CEO Nathan Konty wrote on Signal v. Noise about how they do drip campaigns differently.
What Nathan pointed out is a very common issue with tactic “fatigue” that exists in many fields, like Human Aesthetic, Spoken Languages, or even Cinema. There is no exception for marketing. Andrew Chen explained this as the Law of Shitty Clickthroughs. Basically, a tactic’s effectiveness fades with time as an audience is exposed to it more often. This happens in advertising all the time.
This effect is even more clear when it comes down to marketing automation. When every marketing/product team at every company, in every industry adopt the same “best-practices” than those standards progressively lose their efficiency over time.
This “fatigue” has been explained by two psychologists with the Wundt-Berlyne curve.
When a stimulus is unlike anything encountered before, we are dealing with absolute novelty, and we experience pleasure. The hedonic value of a stimulus is regarded as a function, rising to a peak (X1, Optimal level of hedonic value) and then falling progressively to a Disillusion phase (X2).
The arousal is considered to be directly related to the novelty of the stimulus.
Marketing (just like many other industries) constantly needs new triggers to enable innovations and keep the arousal and the perceived hedonic value as much high as possible.