Predictive Lead Scoring: Data Quantity Drives Prediction Quality

Two factors determine the quality of your predictive scoring: the predictive model and the underlying data. The number of data-points and their accuracy are crucial for driving great results. 

Predictive Lead Scoring Data

Modeling experts have an expression: “garbage in, garbage out.” What they mean is that if you take inaccurate data and apply it even in a state-of-the-art predictive model, your results will have dubious quality at best. Therefore, one of the crucial things that every marketer who is involved in predictive marketing should pay attention to is data quality and quantity.

CRM data: Turning lead into gold

The first source of potentially inaccurate data is the company CRM.  CRMs typically contain data from multiple sources, of varying quality, that were updated manually by multiple sales reps, business development reps and sales operations managers.  The result may be a total mess.

Furthermore, CRM data is typically not standardized.  Some examples that we found at Mintigo include using full, abbreviated and code to denote state—California, Calif. and CA. While humans understand that these all refer to the same state, models will take them as three different states.  This is even worse with job titles, where Director of Marketing, Marketing Director and Dir. Marketing are only a few examples of the plethora of permutations that can be found.

Therefore, to make CRM data usable for Predictive Marketing, it has to go through a cleansing and standardization process. The result of this process is that multiple variations of the same variable will be regarded as one.

However, while we can alleviate the problem of CRM data quality, what we cannot solve is quantity.  The average company CRM contains 10 data points on each lead.  These typically include: name, location and company demographics such as revenue, industry and number of employees. Our experience shows that it is nearly impossible to get any predictive power from CRM data.

Web data: Mining the gold nuggets

To increase the predictive power of the model, CRM data needs to be augmented. What makes more sense than sourcing additional data from the thousands of variables that can be obtained by mining the Web? But unfortunately, unlike CRM data, Web data is not organized in a big table.  There are two challenges in leveraging Web data to improve the predictive power of a model:

  1. Discovering Marketing Indicators

Up until now, the only way for finding companies that use Microsoft’s SQL Server was to have an army of telemarketers cold-calling companies and asking them (hoping to get someone who can provide this answer). Now, we present a robust data mining approach.

For example, to discover users of Microsoft’s SQL Server, Mintigo mines billions of webpages and looks for relevant clues such as Microsoft Partner indication on the website, job openings or current employees who specialize in SQL server. In addition, Mintigo looks at news feeds and press releases to detect any clues that will lead to the conclusion that the company uses SQL servers. But the secret sauce is the data-mining algorithms that combine all data and calculate the probability that a company is using Microsoft’s SQL Server.

  1. Matching

Finding the right data is the first challenge.  The second challenge is to match it with the existing CRM records to create the robust database needed for effective prediction.  How would you match IBM with International Business Machines Inc? The accuracy of this process is crucial for the overall accuracy of the data.

Matching is done through a multi-stage algorithm that tries to match based on various keys.  However, we think that our matching engine is so powerful, that we, unfortunately, cannot reveal it here, as it is a part of our competitive advantage.

Conclusion

Data quantity and accuracy leads to prediction quality.  To achieve that, it is important both to continuously clean CRM data, and to augment it with data from across the Web.  By using data mining to ensure accuracy, and increasing the number of data-points from a dozen to thousands, it’s possible to drastically improve the quality of predictive lead scoring models.

Opening the Black Box: What’s Predictive Marketing and How It Can Help You Drive Revenue

Predictive Marketing helps you focus 100% of your effort on the 20% of your marketing programs that actually work.  Here is how it’s done.

If you can’t explain it to a six-year-old, you don’t understand it yourself.” – Albert Einstein

Many marketers have different views on what Predictive Marketing actually means.  Some of the confusion is actually the fault of solution providers, who don’t reveal the magic behind the technology and only speak of “digital crystal balls,” or “artificial intelligence”.  However, at Mintigo, we decided to open the black box and let marketers understand what Predictive Marketing is, and how it is going to help in driving higher revenues and ROI.

Marketing as “Mission Impossible”

As every marketer knows, the Mad Men style three martini lunches are over.  In fact, marketing today is becoming “mission impossible” for two main reasons: the amount of data that marketers need to handle has increased exponentially and the mix of marketing channels is ever expanding.

Marketers need to make decisions in a data-rich environment, where piles of data are flowing from sources like social, the website, blogs, events and email marketing.  In fact, some of Mintigo’s customers are handling 10 to 20 million records in their marketing database.

The new marketing “big data” is hard to handle and maintain. The data continuously change—people move jobs within the company or to a different organization and companies have new needs for products and services.

The second piece is how to market to these individuals in your database.  Today, the mix of marketing channels is extensive—social, email, search, mobile and more.  Marketers need to choose the right marketing collateral for the right persona and engage him or her through the right channel, and all of this at a large scale.  No matter how good you are as a marketer, this is impossible to do without the use of technology.

Therefore, three waves of innovation in B2B marketing have emerged:  At the beginning, marketers used CRMs to manage their prospects’ data. However, with the rise of online marketing, it was clear that it was not enough, so Marketing Automation helped marketers to measure engagement throughout their demand generation process.  But with the increase in data and channels, a third wave is coming—Predictive Marketing.

Pareto is our Marketing Hero

With CRMs and Marketing Automaton, the goal was to get as many leads into the funnel as possible, with the hope that some lucky few will close. On the other hand, Predictive Marketing helps you focus on the leads that matter—using the 80/20 rule.  The 80/20 rule, also known as the Pareto Principle, is not a new. In fact, the Italian economist Vilfredo Pareto invented it over 100 years ago.

Pareto rule

The Pareto Principle says that 20% of the input (time, resources, effort) account for 80% of the output (results).  In marketing terms, it means that you have to put 100% of your effort on only 20% of the leads and campaigns.  Predictive Marketing helps you identify those 20% that work.

Predictive Marketing identifies the needle in the haystack—the leads and actions that increase conversion vs. the leads and campaigns that drain your marketing budget.  It helps you focus your efforts on the actions that deliver results.

At Mintigo, we define Predictive Analytics as “Leveraging data science to predict which marketing actions have high probability to succeed, and which have high probability to fail.”  More specifically, Predictive Analytics should help you identify leads with great fit versus leads that you should exclude and identify which leads have the highest probability to respond to specific marketing campaigns or marketing asset.  If you have a portfolio of products, Predictive Analytics will also help you identify which leads are better fit for product A and which for product B.  All of these insights exist in the data, and Predictive Marketing simply helps you discover it.

How does Predictive Marketing Work?

Predictive marketing process

Predictive Marketing identifies patterns in the data that can make predictions with a high level of certainty.  For example, let’s say that you would like to predict which leads have the highest likelihood to buy your product or service.  You can take your most profitable existing customers as positive examples and leads with bad fit as negative examples for the algorithm.  The algorithm then “learns” the data patterns that can predict for any lead, whether it is going to be a good or a bad fit.  Now based on this learning, you can predict for every lead in your database whether it’s a good or a bad fit.

What is different about Mintigo is that our process is completely transparent.  We don’t believe that algorithms should be a “black box” and therefore we can actually show you those “learnings” that come from the algorithm.  The result is that Mintigo actually becomes an integral part of your marketing ecosystem.

No black box predictive marketing

Conclusion

Predictive Marketing uses data science in order to help marketers focus 100% of their effort on the 20% of their leads and campaigns that are likely to generate revenue.  By finding hidden patterns in the data, predictive algorithms can predict who is likely to buy, based on your past customers.

Predictive Marketing uses complex math, but it is not complicated to understand.  Therefore, don’t be fooled by black boxes.  If someone cannot explain how their algorithms work, they may not completely understand it themselves.

Predictive Marketing: 20 Indicators that Increase the Likelihood to Buy

Predictive Marketing relies on thousands of indicators.  Here are some of the indicators that helped Mintigo’s customers increase conversions.

Amazon’s recommendation engine revolutionized ecommerce. Predictive algorithms identify people’s needs and desires—without any explicit intent.  These phenomenal algorithms allow Amazon to solicit the right products and services at the right time.  This technology drives 35% of Amazon’s revenues and is powered by a powerful combination of data and predictive algorithms.

What makes Predictive Marketing so fascinating is that you can’t really rationalize it.  There is no system of rules, which say that people who ordered a book about Yoga are going to buy Granola.  But when analyzing data from millions of transactions, predictive algorithms can detect, with a high degree of likelihood, what you’re going to buy next.

Luckily, the same works for B2B marketing.  While the average CRM contains about 10 indicators, the Web contains over 1,600 indicators. We picked 20 indicators that are under the radar for most marketers, but have proven to increase conversions and revenue to our clients.

 

20 Indicators that Increase the Likelihood to Buy

Do you know which leads are going to convert? Predictive marketing can take data that you’ve never considered and turn it into powerful predictors.

  # of companies with indicator in Mintigo’s database What is this indicator?
 Jira 32,343 Jira Project management tool
 Magento 13,139 Magento eCommerce platform
 BBB 8,078 Better Business Bureau business accreditation
 Webinar 35,528 Use of online conferencing
 oracle 14,226 Use of data center software and hardware
VMWare 12,668 Use of computing virtualization
 Infographic 3,372 Placing infographics on websites
 Facebook 653,563 Using Facebook share or like button
 Blog 444,904 Having a blog on website
cloudfront 31,497 Using Amazon Cloudfront content delivery network (CDN)
 SAP 6,646 Using SAP’s ERP Software
API 3,633 Supporting Application Programming Interface (API)
 truste 1,217 Using Truste online privacy management service
 Pinterest 95,113 Placing a Pinterest “pin it” button
 woocommerce_logo 8,626 Using WooCommerce’s online store
 Dropbox 5,699 Using Dropbox’s Cloud Storage
 SaaS 24,090 Company’s product is Software as a Service
b2b 1,910,716 Selling business to business
 Linkedin 108,855 Placing LinkedIn share button
 Cisco 4,527 Hiring Cisco experts

SmartBear’s CMO Built the Perfect Lead-Gen Machine. This is How He Did It

Building on his experience commanding a nuclear submarine, Bryan Semple built a high performance, high velocity lead-generation machine that delivers 50-100 leads per rep/week.

Marketers are used to tackle challenges every day at work.  According to Bryan Semple, CMO of SmartBear, when you operate a high velocity, high volume lead-generation machine, where you try to deliver 50-100 leads per rep every week, all of these challenges are multiplied by an order of magnitude.  In a presentation at the Marketo Marketing Nation Summit 2014, Bryan explained how to break the lead-generation process into its most basic parts and build it into a high performing marketing operating process.

In the past, Bryan used to do enterprise sales and marketing with very high touch, high quality interactions.  The challenge with high velocity inbound model is that you have so many channels to choose from:  “There were all of these things coming at me, the Twitter, the Facebook posts, the pay-per-click advertising, hundreds of thousands of dollars spent with paid advertising companies and all of these people coming to me asking why is the website orange rather than blue,” he said.  “I realized that I had to quickly come up with a way to put this into an operating process that I can figure out how to make the right resource allocation decisions and how to show the results that I was making.”

Bryan Sample - Smartbear

Bryan Semple. Source: SmartBear

To do that, Bryan turned to his 5-year experience in the Navy, operating nuclear submarines.  “I ended up relying on that experience, more so than on my Sales and Marketing Experience,” he explained. According to Bryan, there are a lot of great resources on B2B marketing out there, but the real question Bryan wanted answered when he started building the system is “What do I need to start doing tomorrow?”

According to Bryan, the solution was to break the problem into small pieces: “I know that if I did that, I can figure out a solution for each piece,” he explained.

This was Bryan’s plan:

  • Break the problem into small pieces.
  • Figure out how to measure each piece.
  • Organize the team around pieces and goal them appropriately.
  • Turn up the crank.

Illustration of a Nuclear Submarine Engine

Source: Marketo Marketing Nation Summit 2014

Bryan’s idea was that you should model out the marketing process in a way similar to how the engine of a nuclear submarine works.  He explained that if each of the parts of the nuclear submarine works, you know that the submarine is going to move, and you don’t necessarily have to know which part of the system made it happen.

“At no point I can say: if the ship’s screw turned a quarter turn, which neutron made it happen,” he said.  “That is much of what is incorrect with B2B marketing.  People come in and say: ‘how much revenue does this piece of collateral drive?’  My answer is: ‘I don’t know and nor should you care’.  If each of the components work, I know that the system works.”

SmartBear's Lead Generation Plan

Source: Marketo Marketing Nation Summit 2014

According to Bryan, the parts of the system are as follows: Drive traffic to the website and convert it (A, B & C).  Generate leads through pay-per-lead programs (PPL) and events (D and E), score the leads and send the ones that reach the threshold score to Sales; nurture the rest of the leads (F) until they reach the score and can be passed to Sales. Make sure that sales reps are well-trained.

The pieces of the marketing system

Traffic

The one thing that marketers should do very well is to figure-out Google Analytics and understand traffic and segmentation. One resource that Bryan recommends is Avinash Kaushik’s blog.  “I am a CMO but I still do traffic segmentation and it helps me in immeasurable ways,” he said.

The segmentation that Bryan looked at was paid, organic and referral traffic.  “The more I started to think through this, I said: ‘I can pay someone to do paid traffic and the same for referral and organic traffic.’”

  • Organic Traffic: According to Bryan, organic traffic requires getting into the details, since there are a lot of nuances.  There are all of these communities on Facebook, other social networks, IT blogs and each operates differently.  Therefore, he had to assign people to handle each of these.
  • Paid Traffic:  Within paid traffic, there is pay-per-click and pay-per-impression advertising, explains Bryan.  Then there is the black box of the pay-per-lead advertising: “These are the guys that say: ‘Give us your money and we will give you leads…’  This almost sounds too good to be true—I don’t have to do all of this content stuff, I don’t have to do any advertising, all I have to do is give you money and you give me leads.  We did that once and we realized that it does not make a lot of sense, or you have to be very selective to find the good ones.”

Lead nurturing and scoring

Bryan is a big Marketo scoring user.  With scoring, Bryan broke the leads into two streams—leads that can go directly to Sales and leads that require nurturing. The hard thing was to break it and give it to someone.  Bryan told his Nurturing team “Look, these leads didn’t make it into the sales guys. You own these leads, and I am going to measure you on how many of these leads go to the sales guys.”

Sales

The leads that reached a high score are sent to the sales team.  According to Bryan: “The sales team is interesting because this is the first time a human gets into your assembly line […] Now you’ve got a human that sits at the table all day and says ‘Junk, good, bad, call.’”

Measurement

The aim of measurement is not to measure which piece of collateral drove which traffic.  The aim is to measure the different pieces:

  • Traffic: “Each week I want you to say how much traffic you drove and what was the conversion rate on offers and elements across the site. By doing this they can drive quality traffic that converted. “ This led Bryan to surprising conclusions: “We invested a lot in LinkedIn communities, and our LinkedIn traffic skyrocketed—but never converted. This helped us to understand where we want to spend our time and effort.”
  • Tradeshows: the typical measure was swipes, but according to Bryan, some of these swipes did not convert—and did not convert after they did a demo.  Therefore, he figured out that giving out giveaways may not give him the results that he wanted and he needed to tighten their operating procedures.
  • Nurturing: According to Bryan, if you send quality emails, you get clicks and more traffic to the website and this is how you measure your nurturing.
  • Sales: One of the important things is to check that the sales team is going to sort leads in a correct way.  They measured how many reps went through new employee training, attendance to sales training and test scores to make sure that reps retained the materials.  They also made sure that all of the updated sales materials are available.
  • Conversion: Because it is hard to actually measure the path to conversion, Bryan measured the “short-term lead disposition”, basically whether a lead created an opportunity, was put into nurture, or had bad data.  “This is how we judge a lot of the success of our marketing efforts,” Bryan said.

Organizing the team

Bryan organized the team to support the marketing model.  The strategy that he used is to build an “operational marketing organization” to match what needed to get done.  That puts the responsibility on the demand team, and everyone else on the team works in order to help them deliver demand.  According to Bryan’s model, when the traffic team gets the traffic and nurturing gets the email, the demand-team can deliver the lead.  According to Bryan: “There aren’t any leaders in this model; the demand team is leading this and the rest of the team are specialists that are responsible to get traffic.”

This also changes the structure of the team. Bryan did not have a content marketer: “Content does not do anything for me.  Traffic does things for me. We actually started with a content person, but we realized that they actually help you drive SEO traffic. So we made our content person be responsible for SEO.”

Conclusion

Bryan ended his presentation with some final thoughts:

  • Eliminate what doesn’t work.
  • Measurement: measure what you can, and understand that there is a cost for measuring.
  • Let Sales drive scoring
  • MQLs without conversion rate mean nothing, as they have no value.
  • Use Google Analytics for everything

Bryan’s conclusion is that marketing is an art, so you should realize that it’s not all about the numbers!

Here is Why Marketing Automation Got Scoring all Wrong, and How Predictive Analytics will Fix It!

New surveys show that marketers find only limited value in traditional lead scoring.  By replacing cumbersome rules-of-thumb with powerful predictive analytics, this is about to change.

It seems to make perfect sense.  You keep giving leads points for every action that they take, and when they reach a threshold score—voila! They are sales ready.  However, as many marketers realized, this simplistic model hardly provided a robust qualification for leads’ tendency to buy.  This has two main reasons: first, the methodology seems not robust enough, and second, these programs are hard to implement.

Lead scoring weighs two major factors in order to determine a lead’s score:

  • Demographic: Scoring on dimensions such as job title, industry, revenue and number of employees.
  • Behavioral: Engagement with content such as click on emails, eBook downloads, whitepaper downloads and website visits.  All of these should suggest interest.

Lead nurturing is used to engage leads with a series of touches and engagements aiming to educate them about the product and service, as well as keep brand awareness high.  There is also another role for lead nurturing—to keep increasing leads’ behavioral score by eliciting them to download more and more content.

However, according to this way of thinking, eBook lovers are the perfect prospects, moving up the ladder quickly, while busy executives, who may not be avid consumers of content could be overlooked. In reality, this should have been the other way around.

Scoring also gave marketers adverse incentive.  The main aim was to create crowd-pleasing content to attract wide audience and push leads’ score higher.  The challenge here is that while this more general content was successful in generating clicks and downloads, it did not necessarily teach people more about the product and service, and therefore have pushed scores up artificially.

However, the biggest challenge is that lead scoring did not yield the results that justify the effort in setting it up.  According to a research published in David Raab’s blog, lead scoring and nurturing are among the least effective marketing tactics and are clearly among the hardest to execute.

Marketing Automation Effectiveness

Source: David Raab

The difficulty of setting up lead nurturing and scoring as compared to the average returns is hurting Marketing Automation. Shockingly, the same study shows that 82% of companies that adopted marketing automation are making limited use of it, or are not using it at all.

In another post, David Raab explores the share of marketers that are using the full features of marketing automation.  Raab compared results from four different reports and normalized the results, so that the best answer equals to 100.

According to his account, email is the most commonly used feature in marketing automation.  Lead nurturing is used a lot less, while scoring is trailing way at the bottom. This study shows that in its current state, marketers use marketing automation platforms mostly as a fancy email marketing software.

Survey Summary

Source: David Raab

Why is that? There are two main reasons why so many of the lead nurturing and scoring programs fail:

  • Not enough data: The demographic data in most companies’ CRM typically does not include a lot more than contact information, job title and company.  These hardly make for robust profiling.
  • Not enough value:  Lead scoring and nurturing use a complex set of “rules of thumb” that are based on “common sense” rather than statistical validation.  These rules are hard to set up and maintain, specifically if they don’t drive superior results.

How does predictive lead scoring and nurturing work?

Predictive lead scoring and nurturing prioritizes leads and suggests content that pushes them down the funnel by using statistical probability rather than rules of thumb.  It uses learning algorithms to identify the leads with the highest probability to buy—as well as which solution they are most likely to need.  In addition, it segments the leads to buyer personas and suggests the type of content that is most likely to resonate with them.

Predictive lead scoring and nurturing resolves the challenges of traditional lead scoring and nurturing by both expanding the data and improving results.

Data

Powerful databases and online data mining are the basis of predictive lead scoring and nurturing.  CRM and behavioral data is augmented with thousands of additional data points, allowing scoring and nurturing to be based on a robust set of data.  Data sources include:

  • Social networks activity
  • Business contact databases
  • Mining companies’ websites
  • Technologies and SaaS products used
  • Government databases
  • Financial reports
  • Investors
  • News and press releases
  • Job boards
  • Marketing activity such as PPC and retargeting
  • Sales and marketing tactics such as whitepapers, demos

And much more…

Improving results

This is where the “predictive” part comes in.  Unlike traditional lead nurturing and scoring that uses rules of thumb and best practices to score leads, predictive nurturing and scoring uses learning algorithms that constantly evolve to measure which leads are most likely to buy at any time.

The secret sauce is to continuously analyze the attributes of the highest value customers and find prospects like them in the lead database.  The similarity between your highest value customers and the lead in addition to the level of engagement is the actual score.

Predictive nurturing can leverage data in order to identify needs, and find the solution with the best fit and the content that is most likely to be engaging.  Unlike metrics such as CTR or form fill rates, predictive lead nurturing focuses on revenue.  Therefore, even if content gets high engagement but fails to engage the high value leads (such as interesting content with low relevancy) it will not be considered a success.

In conclusion, traditional lead scoring was based on rules of thumb, was hard to set up and most importantly, did not deliver the value that it promised.  Predictive lead scoring, on the other hand, is based on robust data from the web and statistical validation.  Predictive scoring will give marketers the power to prioritize a prospect who likes your product, from a prospect that simply likes your content.

How to Increase the Velocity of your Marketing and Sales Funnel

Jeanne Hopkins from Continuum gives invaluable advice on getting leads on the fast track towards closing a deal.

Manage your sales funnel strategically, and it will flow more smoothly. Faster. And faster conversion is what enables your company to take on new markets, engage more new leads and sell more products. It’s the cycle of success.

B2B Sales Cycles

chartofweek-01-24-12-lp

Source: MarketingSherpa

Here are some strategies guaranteed to boost the velocity of your marketing and sales funnel:

Assemble deeper market intelligence for better targeting.

Finding the proper contacts within each market means your messages get to the right people. You need data to do that. Use every means at your disposal — the internet, social media, third-party databases, etc. – to discover additional contacts for your pool of prospects. Learn as much as you can about them, by studying their interests and their social and buying behaviors.

A more holistic understanding of each marketing persona and, for that matter, each individual prospect, leads to more accurate targeting. And that means attracting top quality leads. Creating customer profiles and target personas also helps you craft messages that are timely and relevant for those targets. They’re more likely to pay attention and respond.

Mine your marketing data to improve lead nurturing.

What’s your average time-in-funnel now? Where are the sticking points? Where are you losing prospects? Finding ways for marketing to address these issues is like opening the valve wider – leads will flow faster through your pipeline.

Study trends and patterns in your analytics, to gain insight and continuously refine your marketing. Better tracking builds stronger engagement, and that supports faster conversion, too.

Build momentum with content and offers.

Engagement simply plays on the time-honored sales technique of getting prospects to say “yes.” But to get to that positive response, you have to produce the right content, and present it in the right way – format, channel, timing – to the right target. The more worthwhile you make it, the faster they’ll flow through your funnel.

But you have to build relationships before you can close sales. Those in the top and middle of your funnel aren’t sales-ready, and not everyone enters your funnel at the top. Customizing content for each persona and each buying stage influences how prospects respond, and how fast. That’s why segmentation is so important.

Get automated.

Automating your marketing streamlines processes and helps pinpoint the strongest leads, to make the most of your sales team’s time.

Predictive lead scoring identifies the most likely buyers and those most likely to buy soonest. Focusing on the highest quality leads improves conversion rates as well as speed-through-funnel. Automated segmentation enables you to better match content and products to each target, ensuring relevance. Combined, lead scoring and segmentation allow you to precisely target broad campaigns and also to target each persona with specifically-tailored campaigns.

B2B funnels can be notoriously slow-paced, but automation helps you break through to the next level, effectively increasing that velocity. You can focus human effort on the most valuable, sales-ready leads. You can close more deals, sooner. Your marketing will be more productive, and more cost-effective, and you’ll experience faster growth.

Automation also helps build strong, trusting collaboration between marketing and sales teams, who all too often seem to be working at cross-purposes.

Follow up quickly.

How fast you respond to requests, etc., can cement a budding relationship or kill it. It’s an indication of your company’s commitment to customer service at every step, including after the sale.

Growth is every company’s goal. For that, you need effective marketing. But profitability depends on cost-effective marketing, too. The faster you can move prospects through your sales funnel – to a successful conclusion – the faster your company can grow.

 

Jeanne HopkinsAbout Jeanne Hopkins. Jeanne Hopkins is the Senior VP of Marketing and CMO at Continuum Managed Services. She was Vice President of Marketing at HubSpot, where her marketing leadership helped the company become the second fastest-growing software company in the Inc. 500, by generating over 50,000 net new leads each month. She was also CMO of SmartBear and MECLabs, owner of MarketingSherpa, MarketingExperiments and InTouch, as well as Senior Director, Marketing Programs and Communications for Symmetricom.

Follow Jeanne on Twitter @jeannehopkins.

It’s all about the Data

Former Eloqua CEO, Joe Payne, explains how data will soon deliver the vision of a fully automated marketing machine.

Marketing Automation has been the first revolution in using data to create better and more effective marketing campaigns through scoring, nurturing and revenue attribution. However, a new revolution is fermenting around the idea of Customer Intelligence. With Customer Intelligence, smart algorithms will be able to execute fully automated marketing campaigns by independently learning from users’ responses about interests and needs.

This revolution is similar to automation innovations that are happening in other areas of our economy and are likely to improve our lives. The self-driving car, for example, may free up commuters’ time to work or speak with their friends. Already we have small robots like Roomba that can clean our floors. It’s exciting to see a deep level of automation come to the realm of B2B marketing.

Let’s take a look at the three key technologies that underlay this exciting automation revolution and are changing the marketing landscape:

1. Data-mining

The quantity of data on the Web is enormous and growing by the second. Big-data from the Web includes valuable attributes such as technologies, key hiring, financials, press releases and announcements, as well as people’s bios and social feeds. IBM, for example, is already saying that social data is becoming more important than data stored in organizations’ CRMs. The challenge is that this data, while valuable, is scattered across websites and social networks, is unorganized and constantly changes.

Marketing Data DNA

Data-mining techniques can process big-data from the Web and separate the signal (that data points that matter) from the noise (all other data). The ability to separate the signal from the noise is key in identifying and taking advantage of the plethora of data and information about companies and individuals on the Web.

2. Predictive Analytics

Predictive Analytics allows companies to analyze their historical data and apply the results to a set of Web data on prospects and make a set of predictions about them. With a high degree of confidence we can then determine what type of content they are likely to click on, how likely they are to convert, and what their expected lifetime value to the organization could be. These are powerful insights that can help marketers make critical allocation decisions that will drive more revenue to their organization.

3. Recommendation Engines

Netflix, Amazon and many other innovative companies are already using recommendation engines to find and present products and movies that consumers are likely to buy. These recommendation engines are constantly learning from people’s actions and finding hidden links between products based on data from millions of people.

Netflix Recommendation engine

For B2B marketers, recommendation engines can be used to analyze topics that are relevant to their audience and recommend new topics for marketing assets such as eBooks and blog posts.

The Marketing Waze

The combination of data mining, predictive analytics and recommendation engines will create something like Waze for marketers. Waze gets you to your destination as efficiently as possible by automatically taking into account disparate data including traffic count, driving speed, user reports, and distance. All you do is set the objective and the app does the rest.

As a marketer, you will soon be able to do the same. You will choose the objective and the technology will choose the most efficient ways to convert the prospects—all with the power of data. As with Waze, the more data the system ingests, the more accurate and effective the campaigns are going to be.

Improving Performance with Customer Intelligence

The ability to use robust data to drive marketing decisions has produced outstanding results. For example at Birst, a fast-growing business intelligence company, matching content to prospects has improved CTR by 567% over a period of three months. The company used Mintigo to mine data on 80,000 prospects from the Web and used predictive analytics to predict who would respond to their marketing assets.

Customer Intelligence Campaign Results

SmartBear is using Customer Intelligence to match products with prospects. The company has multiple product lines and each one caters to a different audience. By segmenting their marketing database to personas and sending the right eBook for each persona, SmartBear improved CTR by 577% on the first eBook and 176% on the second eBook.

I’m excited about the use of Customer Intelligence technologies—data mining, predictive analytics and recommendation engines—to drive better marketing. By knowing more about their customers and prospects, marketers can better tailor offers that are relevant. This improves the experience for buyers and sellers alike. B2B CMOs that choose to embrace this new technology and data-driven approach will undoubtedly thrive in the years ahead.

Joe PayneAbout Joe Payne:  Joe Payne is an Executive and Board Member with more than 20 years of leadership experience and a proven track record as CEO of high growth software companies. He currently serves on the Board of Directors of public companies Cornerstone OnDemand (NASDAQ: CSOD) and DealerTrack (NASDAQ: TRAK). He also serves on the boards of private companies TrackMaven and Plex Systems, as well as the advisory board of Mintigo. Joe’s most recent full time executive role was as the Chairman and Chief Executive Officer of Eloqua. He joined Eloqua in 2007 when it was an $11M revenue company. He assembled and led a world-class management team that grew Eloqua into a $125M revenue SaaS business in six years. Joe led Eloqua to a successful IPO in 2012 and a sale to Oracle in 2013. Recognizing Eloqua’s leading market position and its robust customer base, Oracle paid the highest multiple of revenue in its history for a public company. Prior to Eloqua, Joe held executive positions at iDefense, eSecurity, eGrail, MicroStrategy, and InteliData. Joe began his career in brand management where he worked on the Coca-Cola brand and the Mr. Clean brand. Joe received his M.B.A. from the Fuqua School of Business at Duke University where he was a Fuqua Scholar. He is a Magna Cum Laude graduate of Duke University. You can find Joe on Twitter @paynejoe.

Marketing Innovation in the Marketing Cloud Era

DocuSign’s Meagen Eisenberg explains how the company is growing its business by leveraging technology for marketing automation, targeting, social and content.

What an amazing time to be a marketer.  We have more technology and data than ever at our disposal – and the investment is only growing.  The arms race to build out the best platform and solution is hot – we have Oracle, Salesforce, and other major players building out their marketing cloud and VCs investing more than ever in marketing and sales technologies to fill the acquisition pipeline.  Not only are we leveraging our own technology, DocuSign, to streamline our marketing and sales organizations, but we have more than a dozen other marketing technologies we use to optimize our efforts.

Marketing Technology Landscape 2014Source: Chiefmartec.com

If I look back just a year and a half ago at what marketing technology investments were in the budget, it was our marketing automation platform, Eloqua, and our targeting and web optimization software, Demandbase.  While still our number one and number two marketing technology investments respectively, we now have so many other important technologies and a full marketing systems budget so we can invest in technologies that leverage big-data, social, customer advocacy and content.  And these topics are hot in B2B marketing.

Diving into our investments in big-data technologies, we are doing some very cool things with companies like Mintigo.  Mintigo is helping us optimize our campaigns and scoring by understanding our ideal targets in new and existing markets by analyzing our existing customer DNA.  And they are delivering new leads that convert at a faster pace because they leverage our customer DNA top attributes to locate new targets out in the massive internet (the future CRM).  Big-data in this example includes external databases such as financials, social and company websites to access attributes not obtainable on the short forms that we collect leads with today on our website.  The ultimate goal is to improve sales efficiency so sales reps can focus on the top converting leads.  At 90,000 leads a quarter, DocuSign needs to focus on the deals that will most likely close within a quarter.

Our desire to leverage social to fill the B2B funnel has introduced us to companies like Social123, InsightPool, LittleBird, Inadco and Nudge.  The initial challenge with social programs for B2B was the lack of business context and contact information.  Sales needs the business title, phone number and email address.  With companies like Social123, you can now get the business data appended to social leads.  If you want to apply the nurture techniques to social leads that you typically see in email marketing, InsightPool is the way to go.  You can acquire and build a relationship with your connections.  And if you are looking for the right business influencers, LittleBird is a great technology to start with.  I see it as the product marketer’s social influencer research site.  DocuSign has done a lot of campaigns within LinkedIn and Twitter, with the ability to really segment and target.  And Inadco has allowed us to capture more than just social data from the lead cards with the ability to capture fields within the social ads, such as title, business phone and company. With the excitement and value around social selling, Nudge has just entered the scene from our colleagues that used to be core to Eloqua.

As marketers, we are tasked to acquire, keep and grow customers.  We are seeing amazing results with our customer advocacy software – Influitive.  To engage with our customers through gaming best practices and to extend our reach and effectiveness through their networks is priceless.

And content marketing has been a hot topic for a while now.  The growing list of technologies includes Kapost, Compendium, LookBookHQ, Expressions by MarcomCentral, Vidyard, and even Bizo delivering targeted content in the ad space.  DocuSign has seen increased conversions on our emails that leverage the Expressions technology to personalize our images with name and company.

Hard to believe our DocuSign marketing systems budget incorporates more than a dozen technologies and several more in the queue to review.  Gartner wasn’t kidding when they said CMO spend and budget will outpace IT by 2017.  And Consumption Economics is definitely on to something when they talk about the SaaS movement shifting IT budget and ownership to LOB owners. I recommend the read to any SaaS marketer.

While I think the proliferation of marketing technologies definitely supports the new CDO (Chief Digital Officer), I would rather see a rise of the MIO (Marketing Innovation or Information Officer).  I think it is more than just implementing and managing the technology, but figuring out how to innovate and grow the pipeline effectively with the increased access to big-data, social, our customers and content in the marketing clouds of today and the future.

 

Meagen EisenbergAbout Meagen Eisenberg.  Meagen is the VP of Demand Generation at DocuSign. She has spent over 19 years in high-tech. In 2012 she received the SuperNova Award in Matrix Commerce and in 2011 the Marketing Visionary Markie award within the marketing automation field. She has an MBA from Yale, and a Bachelor of Science degree in MIS and minor in CSC from Cal Poly, San Luis Obispo.

 

5 Extremely Effective Sales Cycle Acceleration Strategies

Don’t let long sales cycles slow you down. Targeted and engaging quality content, addressing buyers’ needs and carefully planned execution move deals faster and smoother.

Slow sales cycles can be a source of significant frustration for B2B marketers. Fortunately, there are many steps you can take to grease the skids, so to speak, and speed up the sales process. The key to turning your sales funnel into a “slipstream” is to use a broad range of marketing techniques and tactics in order to accurately identify the right targets and supply them with the right information.

One of the most critical parts of lead acceleration is lead qualification. By qualifying leads correctly, you can focus your marketing and sales efforts on those leads that are most likely to buy. In addition, top quality content is critical. High quality content helps you to capture and retain your audience’s attention as you lead them down the funnel. By qualifying the right leads and engaging them with the right content, you can accelerate (and increase) conversions.

Listen to your audience

Where and how is your audience searching for information online to make buying decisions? What are their pain points, and how can you help soothe them? What are they talking about within their social networks? This information should drive your marketing decisions and help you define content, delivery formats and choose the right platforms to attract and engage prospects. It also enables you to segment your audience for tighter targeting.

Listen to your team

Your sales team, customer service, and even accounting or finance specialists within your organization may all have different types of interactions with prospects and customers. Their varying perspectives can augment what you learn about your audience from other sources, in ways that are entirely pertinent to your company and targets. These can help you craft the most timely, irresistible offers for prospects, regardless of their status within your funnel.

Find the right formats and channels

B2B Content Marketing Strategies

Target your content topics carefully, to appeal to different audience segments and buying stages. Deliver your content over a broad range of formats and channels, to reach more people in ways they want to receive information. Do that by studying what resonates most quickly and memorably with your hottest prospects:

  • Specific website pages
  • Blog posts
  • Social media
  • Case studies (storytelling from a B2B perspective)
  • Email campaigns
  • Podcasts
  • Webinars
  • eBooks or whitepapers
  • Tip sheets
  • How-to demonstrations
  • Visuals, such as infographics, photos and videos

It can be especially valuable to tackle potential negatives up front – addressing objections before they are raised. This shows prospects that you are “transparent” and, more importantly, replaces concerns with confidence to buy from your company.

Do your research

Better data begets better-informed decisions – in this case more accurate targeting. Who are the people you need to reach? Where are they? Who are their influencers? Drawing from a wide spectrum of online and offline resources enables you to learn as much detail you can.

Adopting the right software can both facilitate and strengthen this process, by helping you with predictive lead scoring and segmentation. Smartly integrating automation allows your sales people to focus on the personal side of closing sales. That’s still as important as ever, because ultimately people buy from people.

Track your results

Subject your content to continuous improvement, based on your analytics. You want your funnel to flow rapidly, but you also want a continuous flow. Closely tracking and evaluating your results will show where you can improve every aspect of lead generation and nurture.

Conclusion

Well-planned strategies enable you to bring all these elements together to shorten your sales cycle. When you do that, you’ll see an improvement in sales and marketing morale. You’ll see an increase in conversion rates as well as conversion speed. And that means you can dramatically improve cash flow, strengthen your company’s position vis-à-vis competitors, and increase your company’s value to investors or other stakeholders.

Jeanne HopkinsJeanne Hopkins is the Senior VP of Marketing and CMO at Continuum Managed Services. She was Vice President of Marketing at HubSpot, where her marketing leadership helped the company become the second-fastest growing software company in the Inc. 500, by generating over 50,000 net new leads each month. She was also CMO of SmartBear and MECLabs, owner of MarketingSherpa, MarketingExperiments and InTouch, as well as Senior Director, Marketing Programs and Communications for Symmetricom.

Follow Jeanne on Twitter @jeannehopkins.

1 in 8 Tech Companies Uses Marketing Automation; Will It Thrive Everywhere Else?

New study from Mintigo shows that while 11.8% of the Software and Internet industry has already adopted Marketing Automation, overall adoption is mere 4.9%. Can it cross the chasm?

If there is one clear trend from the new research by Mintigo, it is that technology marketers are early adopters of technology. According to the research, 11.8% of technology companies have already adopted Marketing Automation Platforms as compared to 4.9% overall.

Adoption of Marketing Automation Platforms

To conduct the research, Mintigo analyzed more than 180,000 US companies in the business-to-business arena. Results show that Real Estate and Construction, Financial Services and Manufacturing hardly use Marketing Automation at all. Telecommunications, Computer & Electronics and Health & Pharma have average adoption of between 4% and 6%.

While Marketing Automation has gained ground with early adopters, it is hardly a new concept. According to Google Trends, searches for the names of the largest Marketing Automation vendors have been increasing steadily since 2005, almost nine years ago—hardly showing a hockey stick curve.

Marketing Automation Search Trends

Why is adoption so hard? The truth is that Marketing Automation strategy is hard to implement. According to David Raab, when looking at the most effective marketing tactics as compared to the easiest to execute, lead nurturing and scoring and use of marketing automation fall short behind all other tactics. No surprise that Raab found that only 26% of marketers actually make extensive use of Marketing Automation.

marketing automation ease of use

In another blog post, Raab analyzed which Marketing Automation tools are actually being used. These results show that while marketers make extensive use of the email feature, nurturing, landing pages and scoring are used far less often.

Use of Marketing Automation Features

marketing automation features

Furthermore, Marketing Automation requires marketers to produce massive amounts of content to be able to nurture leads over time. Yet, the majority of marketers lack both the time to produce content and are struggling to produce enough content, according to the Content Marketing Institute and Marketing Profs.

challenges to B2B marketing

Fortunately, there is a silver lining. One of the challenges in setting up scoring models and nurturing programs is the guesswork and lack of a structured approach that delivers results. Customer Intelligence, however, will change all that.

For scoring, Customer Intelligence will identify prospects that are likely to buy even before they have downloaded large amounts of content. Furthermore, it can find hidden gems in a large database of prospects.

For nurturing, Customer Intelligence will match the right content for the prospect based on the product that he is likely to buy and past responses of similar peers. In addition, it will predict the channel that the prospect is most likely to respond to.

Finally, the largest promise of Customer Intelligence is that all of this is going to be turnkey—no setup required! Big-data, which was once the domain of mad scientists and number crunchers may actually be the bridge that will help marketing automation to cross the chasm from early adopters to every marketer’s toolset.