marketing automation 2.0: how to add the ‘automation’ · marketing automation 2.0: how to add...

MARKETING AUTOMATION 2.0:

HOW TO ADD THE ‘AUTOMATION’

Learn how artificial intelligence will help

marketing automation finally meet its promise

in delivering Account-Based Marketing.

By Venkat Nagaswamy

1MARKETING AUTOMATION 2.0: HOW TO ADD THE ‘AUTOMATION’

TABLE OF CONTENTS

Introduction: Where’s the ‘Automation’? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

An Uncertain Revolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

The Challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Challenge#1: The Persona Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Challenge#2: Lead Generation Is Lackluster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Challenge#3: The Infrastructure Issue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Challenge#4: Execution Lag and The “Opportunity Gap” . . . . . . . . . . . . . 9

Challenge#5: The Big Data Ceiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 0

Just Add A.I. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1

Solving The Issues of 1.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 3

Tomorrow Is Being Automated...Today . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 5

How You’ll Make It Happen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 7


INTRODUCTION:

WHERE’S THE ‘AUTOMATION’?Remember this dusty old quote? “Half the money I spend on advertising is wasted; the trouble is, I don’t know which half,” retailer John Wanamaker supposedly lamented.

Today, we’re better at measurement and targeting than we’ve ever been. Our channels and content options are more diverse and dynamic than ever. And the crowning achievement of this digital marketing epoch? The apotheosis of engagement that will achieve that holiest of grails, the personalization of every encounter between customer and marketer? Marketing automation.

But there’s a problem. Marketing automation is still stalled at Version 1.0.

In fact, if we were going to be a stickler for labeling, it’s barely even 1.0.

You see, while Marketo, Salesforce, Eloqua, Pardot, Hubspot and the rest have given

companies powerful marketing platforms, no one has effectively delivered on the

promise of automation.

That’s where we’re now beginning to make headway against the key issues holding back

marketing automation from delivering as, well, advertised.

Until that happens, we’ll have to just paraphrase Wanamaker:

A lot of the money we spend on marketing automation is wasted; the trouble is, we know why – but we can’t do anything about it.


AN UNCERTAIN REVOLUTION A shakeout is due in the marketing automation space, as you can guess from the fragmentation in the chart below. But while many players have piled into the market, none of them has found a formula for total customer satisfaction.

28.2%

19.5%

13%

9.6%

6.6%

3%

Hubspot

Marketo

Pardot

Eloqua

Act-On

Gleam

SALEmanago

Unica

UberFlip

ClickDimensions

Drip

LeadFormix

SharpSpring

SalesFUSION

Net-Results

Selligent

CoreMotives

ProspectEye

Optify

Hatchbuck

Marketing Automation Market Share, 2015 (Source: Datanyze)

The fact is, many companies that have adopted marketing automation aren’t over the moon about the

investment. One recent survey by Raab Associates brought that to light, with nearly 70% of respondents

saying they were unhappy or only “marginally happy” with the results.

49.7%

14.0%

1 2 3 4

n=157

5(well)(poorly)

Satisfaction Score

On a scale of 1 to 5, how did the software live up toyour expectations and improve marketing results?

22.9%9.6%

3.8%

Source: Raab Associates, 2014


Even the platform providers admit this dissatisfaction exists; Marketo and Ascend2 recently asked

buyers to rate the success of their marketing automation, and 61% were only seeing “somewhat

successful” results.

While marketers in tech sectors have rushed to embrace it, marketing automation has only been adopted

by 22% of companies overall, according to a Salesforce study.

There are a host of reasons behind that, from lack of corporate will to bad experiences with previous

enterprise software adoptions. Or a company may not have data capture and collation capabilities, or isn’t

willing to be patient with how long it takes for marketing automation to demonstrate appreciable ROI. It

doesn’t help when only 7% of adopters claim to be seeing good ROI from their investment.

Marketing Automation Strategy Survey, N-317, Ascend2 and Marketo, Published April 2015

25%

Verysuccessful

13%

1%

Somewhatunsuccessful

Veryunsuccessful

61%

Somewhatsuccessful

How do you RATE THE SUCCESS of marketing automationto achieve important objectives?


THE CHALLENGES As we see it, at the heart of these struggles are some intrinsic issues bedeviling present-day marketing automation. These are the key challenges it has to evolve past to reach the full promise of Marketing Automation 2.0.

CHALLENGE #1:

THE PERSONA PROBLEMMarketing automation platforms rely on personas, of course. Though as of just a couple years ago, according to ITSMA, only 44% of B2B marketers were using personas.

Why not? Because they were leery of the hard work involved, or didn’t think personas were apt for their

segment, or management didn’t get it yet...even though Marketing Sherpa found that deploying buyer

personas drove a 900% increase in length of visit, a 171% increase in marketing-generated revenue,

a 111% increase in email opens and a 100% increase in the number of pages visited.

So how could there be any kind of an issue with personas if marketing automation adopters are getting

results like those? Let’s go down the list:

PRESENT-DAY PERSONAS AREN’T PERSONALIZATION, they’re models built to approximate

the demography, psychography and whatever other insights a marketer can assemble about

their target “archetype.” Real personalization -- whether it’s an email customized to the

recipient or a landing page displaying behaviors-based content -- isn’t as prevalent or evolved

as it should be, so marketing automation platforms can’t reach their full potential.

THEY’RE ONLY AS GOOD AS THE PEOPLE AND PROCESSES CRAFTING THEM, and are

at the mercy of their expertise, biases, diligence and other factors. One survey found only

15% of respondents had used in-depth qualitative buyer interviews to create their personas.

Was it coincidence -- or corollary -- that only 15% of those surveyed felt their personas were

“very to significantly effective”?


THEY’RE BUILT AROUND ROLES, NOT REALITY. Most personas are title-based profiles,

because a marketer wants to engage “Susie, the Homemaker” or “John, the IT Director,”

to name just a couple. Any complexity or nuance devolves into whatever can fit into the

archetype, especially since it’s not cost-effective to do hypersegmentation of an audience

by (human) hand.

THEY’RE GUESSWORK. Even with extensive research and planning, personas are only

approximations until published content reveals the actual behaviors and preferences of

the audience. At that point personas, need to be adjusted, then adjusted yet again, in an

ad infinitum cycle, because...

PERSONAS ARE STATIC, BUT PEOPLE EVOLVE. Human-built personas will invariably lag

behind the behaviors, wants and needs of their target and the contexts they’re acting within.

They’ll lag considerably if there’s not continual work done to keep them updated.

UPDATING PERSONAS IS COSTLY, in time and dollars alike, but it’s essential if they’re going

to remain viable for feeding the funnel. But let’s take it up a notch: imagine you’re a global

marketer who needs to address multiple segments in different countries. The complexity

of administering personas ascends to a whole new plateau.

PERSONAS CAN’T KEEP UP WITH CONSUMER EXPECTATIONS. Consumers, starting with

millennials, want intensely personalized digital experiences across every digital touchpoint

and they’re more discriminating every day about them. A Janrain study found 74% of present-

day consumers become frustrated when a website’s content appears to have little or nothing

to do with their real interests. So precise yet immediate personalization will be more and

more crucial as touchpoints multiply thanks to the mobile web and IoT ubiquity.


CHALLENGE #2:

LEAD GENERATION IS LACKLUSTER

Whether they’re obtaining leads via marketing automation systems or through other sources, marketers aren’t happy with the quality of the leads they’re handed. According to a recent B2B marketing report, 80% of marketers claimed their lead generation programs were only slightly or somewhat effective, and 59% didn’t think their current solutions were identifying the best quality leads, even though 68% of them ranked “generating high-quality leads” as their top priority.

Marketers often use the data collected by their MA systems to cull leads for their sales teams, of course.

But since the system depends on those tricky personas, the “leads” it identifies may be originating from

flawed or incomplete profiles.

Another issue? What’s the quality of the data being captured from user behaviors, or from warehoused

customer/user data? How good is any third-party data being imported?

As anyone involved in Big Data implementation knows, there can be plenty of issues to contend with

on when it comes to herding together the data that allows good targeting and predictive analytics: it may

be rigorously siloed, not shared, inside the enterprise, or it’s of sketchy quality even when it’s available.

Then all that data has to be analyzed via hard-coded workflows, hand-built by expensive, fallible and slow

human beings (I’m one of them, so I can say that). More about that below.


CHALLENGE #3:

THE INFRASTRUCTURE ISSUEWhen SaaS-based enterprise platforms arrived, it was a glorious day indeed. They’d delivered agility,

scalability at predictable cost, ease of adoption...and they’d obviated the need for all that expensive,

all-consuming infrastructure. Right?

Here’s a personal anecdote...or maybe it’s more of a bad flashback? In a previous life, managing the

marketing automation operations of a Very Large Enterprise, I found I was spending about 30% of my

budget to constantly update our platform of choice, Eloqua.

That’s no knock on Eloqua: it was just the cost of doing business, because there’s an enormous need

for human intervention with current marketing automation models. Just look at the single workflow

diagram below:

Since these rules are hard-coded, they’re inflexible, built around what someone has archetyped as a

“quality lead.” Configuration becomes a process bottleneck. Moreover, these workflows will very likely break

if a website is redesigned, hamstringing a marketer’s agility on multiple fronts.

Remember that grand notion about ridding ourselves of all that knotty infrastructure?

We’ve swapped out physical hardware for a new albatross that’s also costly, complex and in need

of constant attention, versus being thinner, simpler, quicker and more responsive.

A workflow like the one above is only a single layer of the onion, part of a system so complex that one of

the key benefits a marketer hopes to get from their automation investment ends up being compromised:

speed and responsiveness.


CHALLENGE #4:

EXECUTION LAG AND THE “OPPORTUNITY GAP”

All those workflows can result in squandered chances for engagement and sales. Depending on workflow complexity, it can commonly take hours (or, yes, worse than that) before a marketing automation platform spits out something actionable -- like passing a lead along to the sales team or CRM system, generating an email to lure in a prospect.

Opportunity has a half-life, and any delay between insight

and execution, or a hit on a trigger and the ensuing

response, can see it decay and disappear. Why?

If there’s one scarcity among today’s audiences, it’s patience. Why wait on your response

when there’s a competitor a quick search away?

A prospect’s context can shift in an eyeblink, whether it’s his/her need state (“I need a

coupon!” “I want a sell sheet!”), location (“I’m at your trade show booth right now!”), life event

or other situation.

As we move toward omnichannel marketing, personalization will have to occur seamlessly,

at scale and in real time across multiple channels.


CHALLENGE #5:

THE BIG DATA CEILINGMarketing automation systems typically treat all your customer data in the same manner: audiences get segmented into broad categories, which is why marketers see in too many undifferentiated “leads” being shoveled into the top of the funnel.

That does a disservice to good data acquisition and collation efforts, because no matter how granular the

customer data, it winds up in buckets as it enters the system. That’s an expedient driven by the cost and

complexity involved in inputing and managing data while tracking a wide range of touchpoints.

Marketing automation systems dictate a ceiling on how much insight and value you can extract from your data.

The promise of Big Data can only be fulfilled through deeper, smarter analysis and quicker execution

to exploit each opportunity. Personalization has to be delivered in the moment, not after a target’s gaze

has meandered to other things. That’s why in social media, especially, real-time execution is vital --

and decisive.

In robotics, there’s a perfect term for how this should go down: Forward chaining, in which a machine

learning platform uses an inference engine to evaluate events or data and adapt its behaviors. Marketing

automation platforms integrated with Big Data ought to be instantaneously sensing, analyzing and acting

upon every valid opportunity.

AUTOMATION CAN LIVE UP TO ITS NAME

Is there a way out of this oxymoronic muddle? Absolutely.

We’re already seeing it begin to happen, thanks to the genius and hard work of third party developers and

startups who spied the opportunity these problems presented, and seized it.

Let’s dig into how artificial intelligence and machine learning are already proving their mettle for digital

marketers, pushing us all toward the promise of Marketing Automation 2.0.


FOR MARKETING AUTOMATION 2.0,

JUST ADD A.I.We’re taught to be skeptical about anything that seems to offer a panacea. But there’s an elegant solution to

the problems besetting Marketing Automation 1.0. It’s not some pundit’s hypothesis, either, but an answer that’s

being implemented in the here-and-now.

Artificial intelligence is the cure, making one-to-one personalization and account-based marketing (ABM)

into actionable realities.

How does that work in practice?

First, an AI/deep learning platform gathers customer and audience data at a very large scale.

Mariana’s own neural network imports customer purchase data, then codifies individuals by

drawing on up to 50,000 smart data points from internal sources (like a client enterprise’s existing

marketing automation platform), external public data and our own proprietary data sources.

Then hierarchical and pyramidal systems categorize, sort, and prioritize all these data sets

simultaneously, yielding accurate, brain-like associations.

As these disparate data sets are aggregated and cleansed, the AI is able to generate rich profiles

of each individual user.

That results in far better lead generation for sales teams and outbound marketing.

A.I. allows you to flexibly deploy ABM at scale across all accounts, by removing the cost and

manpower limitations of manually-built workflows or other staff-dependent processes. There’s

no need to deny any account the benefits of ABM; with deep learning, every account and every

deal is viable for 1-to-1 engagement.

It also drives personalized digital experiences for prospects and customers, as AI provides real-

time insights and recommendations marketers can use to serve up truly customized content.


HOW IT PAYS OFF IN PRACTICE

Here’s an example: One Mariana client gave both us and a traditional lead-gen service the same list of

5,000 companies, then asked each of us to identify the individuals at those firms in charge of SEM:

The other service returned 55K names, but only 15% of them, (about 8K in all), aligned with

the client’s criteria.

Our deep learning engine recommended 20K names, but its list was 81% accurate, equalling

16K quality leads.

In other words, AI was able to extract twice as many qualified leads from the same data.

The power to make intelligent associations from a huge number of data points is how AI discerns patterns

and behaviors that identify targets, rather than relying on titles and roles.

In fact, old-school lead-gen can be stymied by the fact that many of the people a marketer wants to reach

don’t go by the title being used in its persona-building.

For example, we found that only 20% of people who are fulfilling the functions of a data architect actually

used the title “data architect.” The rest -- 80%! --went by titles like software engineer, senior software

engineer, development engineer or others.


SOLVING THE ISSUES OF 1.0 Deep learning can solve all five of the challenges we’ve outlined, where the last generation of marketing automation solutions come up short:

PERSONAS, as marketers have known them, are replaced by real-time personas instantly

generated by the AI as it tracks and analyzes internal and external data and the top-of-funnel

(or even above-the-funnel) behaviors of prospects and customers. Since they’re built on real,

observable data, AI-created personas are far more actionable: AI quickly identifies the best

leads, then recommends the right touchpoints and messaging for each target, not based on

generalized archetyping or supposition but on actual data about their proven preferences

and actions. Plus, the more engagement an AI has with each target, the more precise its

personalization becomes.

INFRASTRUCTURE is streamlined, because those hard-coded workflows and all that expensive

elbow-grease to maintain them vanish. A deep learning AI has the ability to learn and make

ongoing associations without supervision, learning and improving as you feed it oceanic

amounts of data, making the entire platform thinner and smarter. It’ll play nice with third-party

applications, too, keeping the entire marketing automation stack remarkably lightweight.

LEAD GENERATION becomes far more targeted and effective, thanks to the greatly-improved

quality of leads delivered at lower cost (and with a minimum of headaches) by AI.

SPEED-TO-RESPONSE is shortened almost exponentially, because an AI-enhanced marketing

automation system will initiate personalized engagement in real time, closing any “opportunity

gap” as it adjusts to each person’s context and needs. Better still, it will do so across the entirety

of an omnichannel marketer’s digital continuum (email, social, web, mobile messaging, apps

and more) with seamless consistency.

BIG DATA (or less-than-big data) sees its most optimal use within an AI platform, which can

collect and connect data from all sources, so adding it from even non-traditional and third-

party datapoints is elementary. Because AI is able to work with this wider variety of data, it can

deliver a richer, more nuanced profile of your target. This ability to assimilate a wide range of

data and learn from encounters with customers actually decouples marketing automation

from any need for an existing Big Data architecture. So, reaping the rewards of AI-enhanced

marketing automation won’t necessarily entail Big Data implementation headaches.


OTHER A.I. ADVANTAGES?

COST BENEFITS happen across the board. Not only has pricey marketing automation programming

and administration been removed, but the overall cost per lead declines, too.

ROI IMPROVES, not just because of cost containment but because quality leads drive more

(and richer) conversions. Plus, ROI happens faster because those leads are being supplied

from Day One.

EASE OF USE is a big bonus; as one Mariana client put it, using AI is a case of “set it and forget it”

while it does its job, freeing up marketers’ time and energy.

CONTENT OPTIMIZATION results from having a better understanding of your target, automatically

identifying high-performance keywords they’ll respond to, knowing what content gets shared the

most, keeping automated inventory of all content and intelligently recycling older content rather

than creating new material.

TOMORROW IS BEING AUTOMATED...TODAY Companies of all types are already implementing machine learning in areas beyond marketing. “Adopting A.I.” is a hot meme in martech, the same way “adopting marketing automation” was over the last few years.

One quote that underscores the impact of AI throughout the enterprise comes from Gartner: “By 2018, 50% of

the fastest-growing companies will have fewer employees than (they do) instances of smart machines.”

When Fuze sought to augment its marketing automation with deep learning, it saw immediate

results by integrating a full-funnel SaaS AI solution that drive a 10X bump in conversion rates, with

an overall 200-300% lift in personalized email Click Through Rate (CTR). Other marketers have seen

their cost-per-click in social, search and display advertising cut in half, and a 10-50X improvement in

cold email campaigns. (Full disclosure? Fuze is a Mariana customer).

Facebook recently debuted DeepText, a deep learning-based tool capable of reading and

comprehending a thousand posts per second in over 20 languages, enabling it to do things like

scan Messenger for words or phrases that can trigger responses: if you type that you need a taxi,

DeepText can help send you prompts or links to call one. DeepText will soon monitor comments on

Facebook to surface high-quality ones while removing objectionable ones, as well as pointing people

toward relevant content. Another next step? Building new deep learning architectures capable of

understanding text and visual content together.

Coca-Cola is driving hard into the realm of AI, according to the company’s Digital Marketing

Strategist, Yuri Pereira: “New social platforms are being created every year and, as a result, brands

have to adapt to those platforms and allocate new resources to optimize content...What would be

very interesting is if, in the future, we are able to create systems that not only determine the right

message given the target group but also determine what kind of clusters of people are effectively

replying or engaging with the content. This would allow for rapidly adjusting our messaging/targeting

to generate more efficiency. In a way, machine learning allows marketers to not only create real-time

content but adjust in real-time as well.”



Netflix has embraced AI and predictive algorithms for years; as far back as 2012, it was

estimating that 75% of its subscribers’ viewing selections were based on algorithm-generated

recommendations. It also knows not to over-personalize the choices it puts in front of viewers,

so it occasionally stirs in a variant title.

Visit Clickotron.com and you might think some of the clickbait-y headlines there read

awkwardly, but they’re impressive when you consider the fact they were all written by an

AI. Norwegian developer Lars Eidnes designed a clickbait generator using a neural network

that’s able to create eyeball-grabbing headlines formulated to seize on our undying thirst for

sensationalism. Our favorite Clickotron headline so far? “Residents Can’t Remember If They

Lost Their Wine At The Same Time.”

Articoolo is an Israeli startup that’s taking a more straight-laced approach to machine-written

content: they sell actual content that’s been authored by their platform, which is based on

combining AI with natural language processing to emulate our way of thinking as we’re writing

text. Gartner predicts that by 2018, 20% of business content will be authored by machines.


HOW YOU’LL MAKE IT HAPPEN The best deep learning systems don’t require a marketer toss out their present marketing automation platform. They’ll partner happily with it, sitting atop a marketing stack and integrating fluently with existing inbound/outbound tools and third-party plugins alike.

Since these are SaaS systems, companies benefit from ease of integration and scalability, resulting

in quick ramp-up so they’re hard at work ASAP.

Follow the six steps you’ll see outlined next to add A.I. to your marketing automation suite, so you’ll

be perfectly positioned to reap the game-changing benefits of artificial intelligence.

MARKETING AUTOMATION 2.0: HOW TO ADD THE ‘AUTOMATION’

ABOUT

MARIANAMariana started out as a conversation between three friends at a Manhattan bistro in 2012, discussing how omnichannel marketing automation could reach “markets of one” and deliver true 1-to-1 engagement.

Since those three friends were among the best minds in deep learning and marketing data, it’s

no surprise that brainstorm put down stakes as a company in Palo Alto, California, in 2013. Its

mission? To drive a sea-change in marketing by reversing the way marketing is done.

Mariana precisely identifies and analyzes a marketer’s true targets before any campaign or

content gets rolled out. Its artificial neural network surfaces insights about each target by

analyzing social, web and proprietary data, regardless of source or vertical, in nonstop real time.

The longer it works, the brainier and more predictive Mariana becomes about each individual, so

a marketer can personalize every single point of engagement.

Able to seamlessly integrate with an existing marketing stack, Mariana plumbs an ocean of data

to connect marketers with the right person, using the right content, at the right time. Turning the

dream of Account-Based Marketing into bottom-line reality.

www.marianaIQ.com

marketing automation 2.0: how to add the ‘automation’ · marketing automation 2.0: how to add...

Documents