The effort needed to create marketing automation rules are causing B2B organisations to lose time, money and new business.

It's time to use predictive machine learning instead.

Is marketing automation delivering?

As marketers, we live in a world where the number of choices that we have to make to deliver the right message to the right person at the right time is increasing exponentially.

Marketing has moved from mass advertising where you sent one message to everyone, to segments where messages are sent to a limited number of people, to now having to understand individual customer journeys.

Marketing automation has emerged as a supposed panacea to this problem, yet despite years of propaganda from vendors promising the world, many B2B enterprises that have bought marketing automation are finding that it is not quite the silver bullet they expected. 

The Annuitas 2015 B2B Enterprise survey of over 100 B2B enterprise marketers from organizations with annual revenues that exceed $250m revealed that only 2.8% of respondents believed demand generation campaigns achieve their goals.

Similarly, Econsultancy’s Email Marketing Industry census surfaced that only 7% of respondents deemed their in-house automated campaigns to be “very successful”. 

The truth is that even if you avoid marketing automation mistakes (such as these), you are still lumbered with the task of using marketing automation rules and decision logic to select and deliver campaign messages.

marketing automation success

Three big problems with marketing automation rules

At the heart of all marketing automation technology and outputs are the rules used to tell the marketing automation platform which content or message to select and send to which particular contacts in your database.

This structure necessarily leads to three big problems for B2B organisations:

1) Marketing automation rules cannot cope with complex buyer journeys

All marketing automation relies on preset logic (“If this X happens then do Y”, “if X does not happen, then do Z”) and traditional purchase-funnel theory to architect marketing campaigns and trigger communications.

The problem is that the B2B buyer journey is much more complex than marketing automation vendors would have you believe. 

2) Rules cannot adapt to changing contexts

The nature of marketing automation rules is that once they have been activated they remain active until you manually deactivate them.

This mean that they are not adaptive and they cannot learn from a campaign’s results, only repeat them.

Sure, you can create a rule that says: IF [Marketing Automation score] [increases] [+5] THEN [remove from] [LISTNAME] AND [add to] [NEW LISTNAME], but rules cannot cope with the reality that prospects are continually evolving in their interests and needs, not just their sales stage or marketing automation score. 

3) Marketing automation rules mean more - not less - staff 

As counterintuitive as it sounds, marketing automation often means having to bring on more – not less – staff.

As well as a marketing manager, a database manager, a demand gen exec, a content strategist, you will most likely need a marketing technologist who is able to help you get the most out of your new system.

All of these people have input into creating the rules that are used and the cost of hiring will ultimately prolong the time it takes to see positive ROI on your marketing automation purchase.

complexity of marketing automation

As soon as you begin to understand the three big problems with marketing automation rules, it all becomes clear why 60% of content in B2B organisations is wasted and why one of the biggest issues in demand generation is interest abandonment.

What are the solutions to the marketing automation rules problem? 

As the co-founder of a B2B technology company, and having spent the past few years refining our demand generation process, I know just how powerful a good marketing automation system and practice can be - but I am also cognisant of the above problems.

This has led us to try the following solutions:

Create more rules



It’s true - one way to address the problem of imperfect marketing automation rules is to create more marketing automation rules to try and meet every kind of conceivable customer journey, context or need. 

However, you can only create so many rules. It is perhaps feasible when an organisation has a limited product portfolio or few content assets, but when you are a high-volume publisher with a wide variety of products and customer types (such as a wealth and asset management firm) this is impossible.

The problem is that although the number of choices is increasing, the number of rules that we can make (to make the decisions to govern those choices that we can create) is very limited. 

Hire more people



We can only create so many rules whilst retaining the same number of marketers before the Law of Diminishing Returns kicks in.

The next option then is to increase the number of rules and increase the number of marketing staff to create and manage these rules.

The problem here is that number of available marketers is finite and the number of marketers that one can afford is even more finite, so CMOs that are on a hiring spree will still ultimately be faced with this fundamental gap between the number of choices they need to make and the number of marketing automation rules that their team can can create to make those choices. 



No More Rules - use predictive machine-learning



This leaves us with a third option - eschewing marketing automation rules altogether by turning to predictive, machine-learning technologies that use algorithms to make decisions, rather than rules.

Although some marketers may baulk at the idea of turning over marketing decisions to artificial intelligence, it is becoming an increasingly common and accepted practice.

The benefit of using predictive machine-learning is that it can learn from new information and quickly decide what the next best action is for an optimal outcome.

Machine learning is well-suited to environments where CMOs face complex buyer journeys, constantly evolving user profiles and myriad pieces of content that need to be categorised and structured before being served across multiple channels.

Better yet, these technologies can be integrated with your marketing automation platform. 

Rather than relying on restrictive rules-based logic, a ‘no more rules’ approach adapts to the unique signals and interactions of each buyer and automatically decides the best message, content or product to send to them.

It’s an approach that saves both the prohibitive operational costs of hiring more staff and time-intensive stress of having to create rules that can govern every scenario in the ever-complex B2B buyer journey.

Andrew Davies

Published 14 September, 2016 by Andrew Davies

Andrew Davies is co-founder and Director of idio and a contributor to Econsultancy. 

13 more posts from this author

Comments (2)

Parry Malm

Parry Malm, CEO at Phrasee Ltd.

YES! YES! YES!

People spend so many hours on automation programmes, pat themselves on the back, set-it-and-forget-it... and then have no benchmarks to determine whether it works or not. Plus, ROI is usually calculated on a "cost-only" basis, not on "total cost of ownership".

Further, convoluted automation programmes also assume that the future will be the same as the past. Or that your assumptions are correct, without any validation. If the world were perfectly static, it would be a great strategy. But the world isn't static. The world sure is annoying, right?

Just because you can automate, doesn't mean you should.

Great stuff Andrew.

about 1 year ago

Andrew Fowler

Andrew Fowler, andrew.fowler@apptus.com at Apptus Technologies

Great article and well written. This principle applies to other online challenges like selecting which products to show different visitors (aka merchandising).

In mathematics these problems are known as optimisation problems and algorithms/computing techniques are extremely well suited to solving such challenges.

To Parry's point above, the future isn't always the same as the past, but the past is the best indicator we have of the future and if that can be combined with human intuition and knowledge in a harmonious way, that's a powerful combination.

about 1 year ago

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