To view the full webinar, please visit:
http://www.mintigo.com/webinar-data-predictive-analytics-marketing-clouds-platform-modern-marketer/
Description:
In today’s B2B marketing world, there’s an increase in marketing complexity and an explosion in the quantity and types of data. Left unmanaged, complexity and data overload could prevent marketers from finding and developing their ideal customers.
Enterprise software companies including Oracle are offering an expanding marketing platform to the CMO under the category of marketing clouds. It’s no secret that many are expecting CMOs to outspend CIOs on technology purchases within the next several years, and marketing clouds and data tools will be front and center for marketers.
Check out this webinar to learn how predictive analytics combined with marketing clouds will bring silos of data and customer experiences together by enabling marketers to understand who their most likely buyers are and centrally manage and orchestrate campaigns.
The topics covered will include:
- The rise of the marketing cloud as the centerpiece in customer-centric marketing
- How data and predictive analytics uncovers the insights to power the marketing cloud
- Where marketing clouds are headed and what marketers need to understand to succeed in their organization
Hello everyone! Thank you all for joining us and welcome to our webinar titled “Data, Predictive Analytics & Marketing Clouds: The Platform For The Modern Marketer”! My name is Tony Yang from Mintigo, and I’ll be your host for today’s session. I hope you’re all as excited as I am because we have an absolutely stellar group of presenters lined up. We have the privilege of having Jay Famico from SiriusDecisions, John Stetic from Oracle Marketing Cloud, and our own John Bara from Mintigo.
Now before we get started, we’ve got a few housekeeping items to address. The first item is more for the benefit of those logged in but can’t hear audio…so if you can hear me then you’re all set. Second, this session is being recorded, so be on the lookout for an email from Mintigo within a day or two if you’re interested in getting access to the slides and the on-demand recording. Third, we’ll be reserving some time at the end of today’s session for some Q&A, so please type in any questions you may have into the chat window at any time during the course of the presentation.
And lastly, because we have a bunch of people joining this session who might not be familiar with Mintigo, let me just give a brief intro.
Mintigo is a leading enterprise predictive marketing platform. Our mission is to master data science to revolutionize the way you market and sell. By combining the power of predictive analytics, big data and your customer data, you can quickly prioritize your prospects to focus on the ones most likely to buy, and with the additional data and insights that we provide to you, you can offer the most relevant messages and content to the right people at the right time. If you would like to learn more, please visit us at www.mintigo.com.
Now our first speaker for today is Jay Famico, the Technology Practice Director at SiriusDecisions. Jay is a thought leader focused on helping companies gain maximum value from their investments in sales and marketing technology. As service director for SiriusDecisions’ Technology practice, Jay helps clients select and optimize marketing and sales technology, and understand the challenges and opportunities that technology and process standardization present and how individual applications should link to form a unified ecosystem. He is also responsible for SiriusDecisions marketing and sales technology coverage and online research tools and frameworks.
So without further ado, let me turn it over to Jay. Jay, take it away!
The increasingly crowded marketing technology landscape has given rise to the marketing cloud – marketing solutions that integrate with each other for the purposing of enhancing overall function.
Loosely described we view the marketing cloud as being comprised of the following six solution areas:
Data enhancement services. Third-party data solution can fill in the gaps in the full data profile and allow the organization to standardize where necessary.
Web conversion optimization. Real-time personalization, tailored content delivery and dynamic forms result in higher Web conversion.
Marketing Automation. Engagement of contacts through lead nurture.
Social listening, publishing and analysis. Advanced competitive monitoring, semantic analysis, reporting and triggering based off of social interactions
Analytics. Using statistical techniques and external data sources to predict outcomes can improve the accuracy and benefit of marketing tactics and lead prioritization.
Content marketing and management. Solutions enhance content activation, curation and organization by enabling collaboration, calendaring, workflows and resource assignment, publishing and storage of content.
So, with the understanding of how marketing clouds can benefit marketing organizations, the question then becomes how well does the average marketing organization preform?
In short, in looking at current conversion rates suggests that there’s a lot of room for improvement.
First, many of you will already be familiar with the demand waterfall…
…the Demand Waterfall as the standard for defining, measuring and optimizing shared demand creation efforts. It reveals key alignment points between functions, and core set of metrics to measure, diagnose and improve demand creation performance.
<<Advance slide>>
First, some acronyms –
We talk a lot about qualifying and scoring leads in marketing automation. The leads that come out of marketing automation, we call automation qualified leads, or AQLs
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Leads that make it all the way through the telequalification function are called teleprospecting qualified leads, or TQLs
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And for the leads that are deemed worthy of going into pipeline with a dollar value and timeframe, the term we use is sales qualified lead, or SQL
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So, what our data show is that only about 10-30% of leads that qualify in marketing automation also qualify when teleprospecting talks to the prospect and does its qualification. So, that eliminates a lot of the leads our marketing automation thought were qualified.
<<Advance slide>>
But it gets worse…
When sales gets leads, only 10-30% of them get as far as sales qualified -- pipeline
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Taken together, that means that for every 100 leads that are automation qualified, only a few at best make it to the Sales qualified stage. We are losing 97% or more of our automation qualified leads through the teleprospecting and sales qualification processes.
This suggests that an awful lot of qualification is going on AFTER a lead passes from our marketing automation systems
<<Advance build>>
Well, consider what teleprospectors and sales people are doing.
It’s generally nothing more than calling that prospect and having a conversation. And in the course of that conversation, they are asking questions.
Those questions often include buying cycle info – BANT – but they often include a lot more than that. They go into detail about the prospect’s real needs, the ability of their own solution to address those prospect needs and the prospect’s attitudes and ideas about the possible solutions
<<Advance slide>>
And this process is very effective at separating wheat from chaff. By the time sales qualifies a lead, they have a very high propensity target.
However…
it’s is very expensive
It takes a long time to reach and speak with all these prospects
And it doesn’t help you find new prospects that have the right characteristics
<<Advance slide>>
So, the question then is: how can we make teleprospecting and sales more effective by moving more of that qualification from expensive tele and sales resources back into automation?
And how do we apply the same logic to the universe of unknown prospects to find net new ones?
And that is the role of modern data science: Find clues that exist out in the world, which reliably point to qualifying criteria you would ask the decision-maker if you could get him/ her on the phone?
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We analysts like to plot things on grids. So, this is my 4-box grid, considering depth and speed or cost of lead qualification
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And we’ll look at how it works out under 3 scenarios. For historical purposes, we’ll look first at the purchased list scenario, then lead qualification under marketing automation, and then finally with analytics
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So, in the historical, list purchase scenario, buying lists is fast, so it’s to the far right of the horizontal axis, but It is a very light qualification, generally just based on a couple of filters at the company level.
Now, if you know which companies you want to pursue, and those companies are all established and well known, that may be all you need to start
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But, in general, under the list purchase condition, both teleprospecting and sales qualification are slow and laborious – which makes them expensive. So, cheap list buy, but very expensive tele and sales operations.
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With the advent of marketing automation and lead nurturing, leads are now much more qualified in general than before, prior to the time a teleprospector or sales rep picks up the phone. And for organizations that do a good job of nurturing prospects, you can dramatically cut down on the amount of time sales spends developing its own leads.
<<Advance slide>>
But now, with the advent of predictive lead scoring, the prospect is that we qualify leads much more deeply prior to every talking live, and then tele is much more effective, and sales gets even more efficient.
So, that’s what we are going for.
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So, with that in mind, I want to tackle the 67% myth, and we’ll see why understanding it is so important to understanding why our current view of prospects may not be adequate…
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So, pop quiz….
Fill in the blank…..
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Read
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That is not correct. So, let’s try another…
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Read
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That also is incorrect
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Read
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Correct, but there’s more to it…
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Read….
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read, then….
So, it’s really important to understand that the buyer’s journey isn’t just about interacting with our assets, and the digital part of it doesn’t end when a prospects first talks with a sales rep. Assume that a prospect is self-educating all throughout the buyer’s journey
<<Advance slide>>
So, why does that matter? It matters because it means the buyer’s journey doesn’t look like the way it is commonly depicted.
<<Advance slide>>
So let’s have a look at that.
We tend to think that the buyer’s journey looks like this….
We have our buyer on the right, and the long and windy road to qualification and sales-readiness on the left
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And there’s our buyer in the distance…
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And closer
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Now she’s really getting close
<<Advance slide>>
Bam! She has arrived…
<<Advance slide>>
And according to that scenario, the next conversation might sound something like this…
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Read….
But, of course, we all know it isn’t really like that, at least not very often.
<<Advance slide>>
In fact, there are quite a few things wrong with that previous scenario. So, let’s take a look at another visualiziation of the buyer’s journey.
We have the same buyer, but this time, we have a much longer, much windier road, and a road that clearly isn’t all in the desired direction, nor is it all visible to us
<<Advance slide>>
So our buyers starts down there in the lower left hand corner…
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Whoops. We seem to have lost her. Where’d she go?
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Wait, is that her. She seems even farther away now
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Ah, she’s popped up again, and she seems much closer now
<<Advance slide>>
Ok, she’s making progress…
<<Advance slide>>
Darn it… still heading the other way….
<<Advance slide>>
And when finally you do get in touch with her, it often sounds more like this…
<<Advance slide>>
read
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read
And then the sales rep is left thinking….
<<Advance slide>>
<<Advance slide>>
<<Advance slide>>
The truth is, buyer’s journeys often don’t look like the models we construct in our nurture paths, and buyer’s often duck in and out of them. And, they often engage with us during only parts of it.
<<Advance slide>>
So, let’s take a look at what we know about buyer’s journeys so far….
<<Advance slide>>
They are like the deer in this image….
You may see one in this picture. But, there are two…
Same with buyers. Sometimes, we don’t get clear enough signals from their interaction with our assets to allow us to see them.
<<Advance slide>>
And just because a prospect stops responding to our nurturing does not mean they are no longer on a buyer’s journey. There’s a lot going on in a buyer’s world, and most of it is unrelated to their interest in us.
<<Advance slide>>
And that suggests that ….read
<<Advance slide>>
And this is critical: Buyer's demand relevant content or they will tune you out. But, you don't get a pass on being relevant just because a buyer has taken a pause in interacting with your digital assets. They aren't keeping track of where they are in the buyer's journey for each provider. If you don't help them decide, they will simply dismiss you.
So, let’s shift gears. We’ve talked extensively conversion rates and the buyers journey. Now lets look at B-to-B organizations that are using predictive technology.
A study we conducted at the start of the year showed that about 68% of companies using marketing automation were scoring leads.
>>> advance slide <<
On contrast, another small study we did reported that …. [read from slide]
One caveat, unlike the earlier survey, this was a survey of current users of predictive lead scoring so there’s some bias built into this scoring… However, given the satisfaction levels reported in the prior study, we think this is important
>>> advance slide <<
Read from slide
>>> advance slide <<
And all that may explain why we are seeing such a sharp move to predictive.
Another caveat, however, as the numbers on the vertical axis indicate, total adoption is still very low..
>>> advance slide <<
Which leads me to my final section. Lessons from the Irish Development Authority.
In the 1980s, the IDA conducted an ad campaign with this theme…. And their point was that…
This… is not a pre-requesite…
For this.
The story involves a lot of socio-economic factors that ultimately resulted in there being in Ireland a well educated population of young people who were not mired in dead-end factory jobs.
Likewise….
Some of you might just be exploring lead nurturing, scoring and marketing analytics right now.
Might I suggest that failure and sub-optimization is not a pre-request. Rather than struggling to implement processes and procedures that will get you half way there - skip the industrial revolution and focus on the technology, skills and processes necessary to fully achieve your organization objectives – not partially.
Thank you Jay. Our second presenter for today is John Stetic.
John leads the product group for the Oracle Marketing Cloud where his role encompasses product strategy and development. Each day, John drives the Oracle Marketing Cloud Product team forward to solve hard problems and transform the way marketers work. Prior to Oracle, John has held a number of senior product leadership roles across a wide range of high-tech organizations from large-scale multinational organizations to fast-growth start-ups. He holds an Engineering Degree from Queen’s University and executive business education from Harvard Business School.
John, it’s a pleasure to have you with us today. Now, let me turn it over to you.
As Kevin noted last night, our vision for the Oracle Marketing Cloud centers around three areas: Marketing Simplicity, Customer Centricity, while being enterprise ready.
With this integration, 1st party data can travel in the other direction as well.
Key segments and data in Oracle Cross-Channel Marketing – such as engagement with e-mail or online engagement – can surface as audience categories inside of the Oracle Data Management solution.
The data will then be available for activation in the DMP….
Activating this third party data can help us reach a variety of different channels to acquire new customers, such as search, display and social.
Activating this third party data also allows us to personalize our marketing efforts to our entire available market. Something that was unachievable previously.
In addition, we can leverage email and mobile messaging inside of the Cross-Channel solution to retain these customers over time.
Inherent in reaching these customers through these channels is the ability to tap into the marketer’s ecosystem.
From Facebook to Google to Twitter, we can help you activate this data to reach your ideal customers across these different channels.
Create an ad on Facebook, or bid for an AdWord on Google.
www.bluekai.com/partner
If we can do this, we can deliver a more seamless cross-channel experience.
In this example, we can develop a profile of our customer.
From there, we can deliver them an Ad on Google.
As they browse the web, we can serve up a display ad for the same product, driving them all the way to the website and into an active buying cycle.
From there, we can assign a lead score and leverage our Email Marketing to nurture the prospect through the entire buyers journey.
The first key use case is around customer acquisition.
By leveraging 1st party data about your known users – and using that to inform what customers you target across the DMP’s 700 million anonymous customer profiles – you can connect with more ideal customers that drive revenue now, but also stay longer over time.
The second is around personalized retargeting.
With re-targeting, we can leverage behavior data to stay top-of-mind with the right customers.
Maybe they don’t want to engage with e-mail, for example, but a display ad brings in revenue by reminding the customer about a relevant product.
The third use case is for retention marketers:
We can use this data to individualize the customer experience and ensure we message to them at the right place, on the right channel, at the right time.
For example, we could alert customers that a new solution is launching, and then serve them up a push notification with a special promotion to tune in to the launch event.
The business outcomes from this approach can be a longer relationship that drives revenue over time.
Lastly, over time, as we break down these marketing silos, we can have one platform to unify all our marketing data and customer interactions.
By doing this, we can eliminate redundant costs and ensure a consistent cross-channel experience
[We’re architecting a platform that is open for use with other applications. 3 types of partners: ads, data, and applications]
That’s why the Oracle Marketing Cloud is making major investments in partnering with best-in-class marketing and advertising companies.
With an open platform, we have built the industry’s most comprehensive marketing technology ecosystem to help marketers extend the power of the platform and reach their target audience wherever they may be.
I’m excited to announce that The Oracle Marketing AppCloud now features more than 260 best-of-breed apps for marketers as well as the 200 dedicated partners of its BlueKai Data Partner Program.
This makes us the most integrated, open, global platform that scales for the enterprise.
Thank you John.
Our last presenter today is John Bara, the President & CMO of Mintigo. John specializes in fast growth marketing with 25 years as an executive in Silicon Valley. Prior to Mintigo, John held CMO and SVP of Marketing roles at companies including XenSource (which acquired by Citrix for $500M), Interwoven, Thismoment, and Genesys Telecommunications Labs (which went public and then was later acquired by Alcatel for $2B). John also held a variety of marketing and finance management positions at Intel. John holds an MBA degree from Harvard Business School, and a BA from Oberlin College.
John, you have the bridge.
Mintigo is a leading enterprise predictive marketing platform.
We combine the power of predictive analytics, big data and your customer data so that you can quickly prioritize your prospects to focus on the ones most likely to buy from you. You can use Mintigo as a standalone SaaS platform, or integrate it’s predictive lead scoring and data enrichment capabilities directly within your marketing automation systems such as Eloqua or CRM system including Salesforce
Mintigo’s vision is that by the end of the decade all enterprise marketers will enable their marketing clouds with predictive marketing capabilities.
Why is this? Here is why…the marketing black hole. Where your budget goes in, and nothing comes out.
Let’s drill into why this occurs
A major reason the marketing black hole exists has been previously described by Jay Famico of Sirius Decisions. At Mintigo we call this the marketing cliff. Sirius Decisions research shows that fewer than 3 out of 1,000 inquiries become customers. That’s 0.3%. From MQL to customer the conversion rate isbetter but still only 1.4%. That means 98.6% don’t become customers. That is poor operational performance with very little leverage for your P&L.
A big part of the problem is data.
It’s everywhere. I
It is incomplete
(NEXT SLIDE) It’s a black box to most marketers.
So what do most marketers do when faced with the problem of poor demand gen conversion and limited data?
(NEXT SLIDE) Load up the wheelbarrow…
And pour more cash into the system.
(Next Slide)
The result? It becomes a self fullfilling phophecy.
The definition of insanity is repeating the same behavior and expecting a different result.
(Next Slide.)
Mintigo thinks there is a better way, a different way.
We believe the modern marketer embraces data. Mintigo’s true SaaS platform helps make your data understandable, approachable and easy to use for a marketer.
Mintigo integrates natively with your marketing automation system incluidng Oracle’s Eloqua and your CRM
It all starts with the data.
(Next Slide)
Mintigo is built from the ground up as a B2B data company. Let me share an example of how Mintigo Data plus your data can create a better picture of who your customers really are and how you as a marketer can use the results to improve you marketing campaigns and processes and find your buyers faster.
Typically, you have data on your customers an prospects, but it is very limited, and often incomplete or out of date. Similarly, you probably do some basic lead scoring based on behavioral data which is important but still very limited. Having your BDR or inside sales rep start a conversation with ‘”I see you downloaded a whitepaper” is a dead end discussion without more intelligence about the customers business and buying intent.
(Next slide)
You may even do some base level of lead scoring. But sending leads to your sales teams without information about why the customer is scored highly is “dumb scoring”.
I can predict the result of most dumb scoring exercises. Score without data or predictive intelligence end up going you know where?
(Next slide)
More marketing budget down the rat hole with limited follow up or result.
What if there was a way to change the mindset by actually finding your best buyers using the signals in the data and empowering sales with valuable information about the prospect’s business and buying intent?
That is the Mintigo way…
(Next slide)
At Mintigo we take your data from your systems, we put it through the following steps:
-Cleanse
We combine it with our Mintigo data, which is vast. Mintigo data includes 100M unique invividuals, across 7M companies. For these 100M individuals, we have up to 2100 unique marketing indicators we call MI’s.
-Enrich
–Append
-Analyze
-Score
-Predict
We don’t believe in black boxes. We make data and our Mintigo model accessible and transparent for marketers.
So instead of a blurry or incomplete picture of your customers you get a much more focused view of your TRUE CUSTOMER DNA, that looks like the next slide (NEXT SLIDE)
Remember the blurry picture? Here is is a sample of the 2100 Marketing indicators, and Mintigo tells you which are the most important. This is extremly enlightening for all marketers and guides segmentation, micro campaigns, nurturing, cross sell up sell and many many uses cases.
(Next Slide)
This shows Mintigo’s product. Notice that it is a product, not a consulting project. This is a product which is based on the ture SaaS model that we expect marketers to begin to use everyday in their lead generation efforts.
On the left you see Mintigo predictive lead scoring, which sorts and scores your lead based on the customer DNA which is generated by the Mintigo model. It is graphical, dynamic, and tunable. And we share it.
On the right you see the graphical impact of the marketing indicators with analysis of weightings and impact. Give it a try, you will like it. We call it the ultimate marketing machine.
If you enjoyed today’s presentation, here are 3 ways to learn more. As John Bara mentioned at the end of his portion of the presentation, come check out Oracle’s Modern Marketing Experience Conference in Las Vegas from March 31st through April 2nd. If you haven’t registered yet you can do so at modernmarketingexperience.com.
In addition, we have some great content around predictive marketing on the Mintigo website, including a recently launched online course which we call Predictive Marketing University that’s taught by our co-founder and CEO Jacob Shama. In addition to this, there are also other useful content in the form of ebooks and webinar recordings on the Mintigo website.
Lastly, Jay and his colleagues over at SiriusDecisions are some of the premier experts and thought leaders covering the B2B marketing and technology space. They work with a lot of great companies to help them grow and figure out how to turn their marketing and sales teams into world-class organizations. If you’re not already engaged with SiriusDecisions, we highly recommend that you reach out to them at their website show here.
Thanks John.
Okay, we have a few minutes left for some Q&A, so please go ahead and type your questions in the chat box. We’ve got a few already so here’s the first question for Jay.
For Jay: What recommendations do you have for someone to move beyond using marketing automation as a email batch & blast tool to a digital marketing hub?
For John Stetic: What do you think are the most critical components of a marketing cloud implementation? Marketing automation platforms seem to be the core, but what else do B2B marketers need to add or consider?
For John Bara: Can we still utilize predictive technologies if we are just getting started with using marketing automation and perhaps have dirty data in our systems?
Well that concludes our webinar today. Many thanks again to Jay Famico, John Stetic and John Bara for taking the time to join us from their busy schedules. And I want to thank all of you who participated in today’s webinar. As a reminder, we’ll make the recording of this session along with the slides available shortly, so please be on the lookout from an email from the Mintigo team within a day or so. Thanks again and have a great rest of your day everyone.