7 Business Challenges You Can Quickly Solve with Predictive Marketing

The combination of big data and predictive marketing lets companies drive growth more quickly and effectively.

Predictive Marketing

Entrepreneur Elon Musk once said: “If you go back a few hundred years, what we take for granted today would seem like magic—being able to talk to people over long distances, to transmit images, flying, accessing vast amounts of data like an oracle.” Marketers don’t have to go back that long to realize that what data can do for them today was considered impossible just a few years ago.

Predictive marketing propels a revolution in how companies engage with their prospects and sell their products. The combination of big data and predictive analytics lets companies find the right prospects and engage them with the most relevant offer. Now, when companies can predict who is a likely buyer, marketers can be more precise and effective.

Here are 7 business challenges that you can quickly solve using predictive marketing:

1.    Engage the right buyer

9 out of 10 companies now use content as a part of their marketing. While it’s relatively easy to measure content engagement using analytics tools such as Google Analytics, the question is whether this content engages the people who matter most, i.e., your potential buyers.

This is where predictive marketing can help you create better content that engages the right people. There are several approaches for better engagement: for one, predictive marketing can analyze the path to conversion to see how each content piece contributed to conversion. Another approach is to analyze those who engaged with the content (if they are identified leads) and see how many of them have the potential to become prospective buyers.

2.    Find new prospects

This is where every marketer stumbles. There are never enough new leads to curb sales’ appetite. With predictive marketing however, you can find new prospects that fit your buyer persona.

Using predictive marketing, you can build a quantitative model that describes your most likely buyer. This model will weigh indicators such as size, technologies and job titles that are prominent among your potential buyers. Then, it will search your house list for prospects with similar characteristics and high likelihood to buy.

3.    Grow new markets

When entering a new market, you can use all of the knowledge that you accumulated in one market in order to quickly increase sales in the new market. For example, you can use predictive marketing to analyze the “DNA” of your US customers, and then using a predictive model, find similar potential buyers in Germany. Predictive marketing finds the common characteristics among your current buyers to help you find people similar to them in your new market. If you want to learn more about growing new markets with predictive marketing, download this ebook.

4.    Cross-sell and upsell

When launching a new product or feature or when you have a very large house list spread across different products and services, you can use predictive marketing to match prospects to the products that he or she are most likely to buy. The secret is finding your ideal buyer persona for each product. And then, by enriching each lead in your database with thousands of data points (sourced from the Web and other databases), to find those who most resemble this ideal persona. Predictive marketing can also help you identify products and services that users are most likely to buy. This ebook is a great source for learning about cross-selling and up-selling with predictive marketing.

5.    Predictive lead scoring

Predictive lead scoring predicts a lead’s likelihood to buy by weighing demographic and behavioral attributes. Each lead in the database then receives a score from 0 to 100, based on how likely he or she is to becoming a customer. Marketers can use predictive lead scoring in order to decide which leads should be sent to sales reps.

6.    Segmentation

Imagine Adobe, for example. Adobe has multiple products that cater to different audiences: analysts, designers, illustrators, web developers and more. Now, when a new lead arrives, which product should Adobe offer first?

This is where predictive segmentation can help. Predictive segmentation can take this lead, enrich it with additional data sourced from the Web and add it to one of Adobe’s segments. The result is more relevant marketing and sales efforts.

7.    Recommendations

Predictive recommendations

Another great use of predictive marketing is recommendations. You probably already know recommendation from Amazon, where you can find offers that are related to what you’ve looked for initially. Birst, for example, wanted to understand which campaigns would resonate with its audience. They discovered that when they targeted companies who used competitor products, they were able to increase open rate to 35% and click through rate to 10%.


Predictive marketing revolutionizes business by replacing guesswork by data science. All of these 7 business solutions were considered magic just a few years ago. However, now, with the power of big data and predictive marketing, CMOs can be more effective, efficient and increase revenues faster.
About Ariana Beil

Ariana BeilAriana runs Mintigo’s Customer Success organization. She brings more than 20 years of experience in customer-facing leadership roles within software and SaaS companies. Prior to joining Mintigo, Ariana held senior marketing positions for several Silicon Valley software companies including Extole, Dasient (acquired by Twitter), and Fortify Software (acquired by HP). Earlier in her career, Ariana worked in enterprise software sales for IBM, Rational Software, and Pure Software. Ariana earned a Bachelor of Business Administration from The European University in Switzerland.

Predictive Marketing: May the Best Scientist Win

Better science and robust data provide better results. Therefore, to evaluate predictive marketing, you don’t need to be a scientist, just to measure who delivers the best results.

Predictive Lead Scoring Funnel

Retail magnate, John Wanamaker, once said: “Half of my advertising is wasted. The trouble is I don’t know which half.” We have great news for Mr. Wanamaker—marketing has taken such a leap forward, that today you can tell not only which half is wasted, but actually predict what will be wasted even before a marketing campaign is even launched.

Unlike in Wanamaker’s time, marketers today would never think about launching a campaign without careful tracking of results and ROI. Marketers still apply the same creativity and imagination as before, but they have more tools to know whether their initial hypothesis about the campaign actually resonates with their target audience.

Predictive marketing takes tracking a step further. Now marketers can use robust data in order to predict what works and who is going to respond to campaigns. The ability to preempt rather than react is a game-changer in a discipline that is consistently struggling to get better performance and ROI. But how do you know which predictive models work best? Like any other marketing initiative—you have to track the results.

Evaluating predictive marketing

It seems that when evaluating predictive marketing, marketers get intimidated by industry terms such as significance, random forest classifiers and neural networks. Let me make a bold claim—marketers should only worry about business results and let the scientists handle the science. The reason is that consistent superior business outcomes typically stem from better science and, therefore, one leads to another.

Predictive analytics should be evaluated like Web design. In the past, marketers had to listen to lengthy explanations why blue implies trust and confidence while green means balance and growth. Today, marketers can simply A/B test two designs and see what works.

Predictive marketing delivers tangible results that can be measured. Therefore, marketers don’t have to evaluate the quality of the model by counting the number of PhDs on the wall. They can test the quality of prediction by simply looking at two models and testing which one provides a more accurate prediction.

Demystifying the “mystery file”

One great exercise that Mintigo does with clients in order to show the power of predictive marketing is the “mystery file”. The client picks a list of leads, without telling us which ones ended up as closed deals. Our job is to discover those deals out of the “blind” list.

For example, as a test for one client, Mintigo had to predict which leads that were generated in 2013 were going to convert in Q1 2014.  Results were phenomenal—Mintigo identified 80% of leads that converted.  With another large client, Mintigo identified 82% of the leads that converted out of a random list of leads, and also found that only about 15% of the leads are likely to convert in the future.

In short, predictive analytics is like weather forecasting. It doesn’t really matter if you use sophisticated models for your forecast if at the end you get soaked without an umbrella.

The 2 secrets of powerful predictions

These results that Mintigo achieved are not coincidental. In fact, Mintigo has achieved similar results across clients. In addition, when compared with competitors in a head-to-head evaluation, Mintigo has achieved better results across the board, including with clients like SolarWinds and Neustar. There are two secrets for getting powerful and accurate predictions—robust data and smart modeling.

Referring to bad data that leads to bad results, modeling experts say: “garbage in, garbage out”. To provide stellar results, Mintigo scrutinizes its data to create the industry’s most robust and up-to-date database. Mintigo’s database is mined from the Web and provides very high coverage of companies and decision-makers. Mintigo scans billions of Web pages, news sites, databases as well as social networks to collect and process the data. This robust and continuously updated data is then fed into our models.

Modeling is our second competitive advantage. Mintigo has some of the brightest minds in the field of machine learning and data science. These individuals had to overcome major challenges in modeling big data, such as handling missing values or merging Web data with CRM data. Better models combined with better data lead to great predictive results.

Making predictions

Wanamaker didn’t have the technology to evaluate what works. However, with predictive marketing, it’s easy to measure the best predictions. Science matters, but should be left for scientists. Marketers should care about one thing—performance! Great performance is the result of great science.

5 Signals That It’s Time for Predictive Marketing

Predictive marketing helps you find buyers faster.  Here are 5 signals that indicate that you should move beyond guesswork.

Yogi Berra

Yogi Berra. Source: Academy of Achievement.

Baseball legend Yogi Berra once said: “Its tough to make predictions, especially about the future.” That was over 40 years ago. When SolarWinds, Neustar, SmartBear and others needed help identifying buyers among their stream of incoming leads, Mintigo used predictive marketing to take on the challenge and help them predict who is going to convert.

SmartBear called it “The Mintigo Challenge”.  Mintigo had to predict which leads that were generated in 2013 were going to convert in Q1 2014.  Results were phenomenal—Mintigo identified 80% of leads that were converted.  Overall, Mintigo found that focus helps—80% of the revenue came from 27% of the leads, and 95% of the revenue came from 40% of the leads.

Predictive marketing can give any company a competitive advantage, but how would you know that it’s time for your organization to get the most value out of Predictive Marketing?

Here are 5 signs that will help you.

1. Your marketing efforts do not convert to revenue

You are attending industry trade shows and your content and promotion are generating a lot of leads. However, very few of these leads actually get converted to sales. You’ve tried nurturing but still, nothing moves the needle.

Predictive marketing can help you optimize your marketing efforts and identify which campaigns and tradeshows are bringing in buyers, and which ones just create noise in the system. Predictive marketing will also help you prevent spending time and money on nurturing leads that will never bloom into a deal.

2. Your CRM and Marketing Automation data are weak

One of the problems that most companies are facing is lack of relevant data. If you are selling an online marketing management tool, don’t target companies that never use PPC. PPC spending data, however, is hardly available.

By mining data from the web, Predictive Analytics identifies thousands of variables that let you enrich your database with the information that is most valuable for your product. Whether you need data on companies that use PPC, Salesforce or cloud storage, it all exists on the web. Using sophisticated data mining algorithms it can be extracted and put to use.

3. Your CRM and Marketing Automation data needs cleansing

You may have spent years generating leads and collecting a sizable database. However, marketing databases typically decay at a rate of 30% per year. People change jobs, or change organizations; companies go bust, while others grow.

Cleaning up your database may, by itself, increase conversion rate significantly. It will stop clogging the system with leads that are no longer valid and will allow Sales and Marketing to focus on real prospects.

 4. You have a hard time matching prospects with products

Matching the right prospect with the right product in your portfolio—both in nurturing and in the sales process, is key for closing deals. However, unless leads specifically indicate what they are interested in, marketers find it challenging doing it for them.

Predictive marketing uses machine learning to segment leads and find the product with the best fit for the prospect. This can help avoid nurturing leads with irrelevant content, or funneling leads to wrong sales teams.

5. Your lead scoring does not accurately identify buyers

Lead scoring can be a powerful tool to identify sales-ready leads. However, two main challenges prevent many scoring models from successfully identifying buyers.  First, most companies do not have robust and clean data in their marketing database. Second, traditional scoring models are based on rules-of-thumb and assessments rather than sound statistical modeling.

Predictive Marketing solves these two challenges. First, it enriches and cleans your marketing database and second, it uses predictive analytics to build your scoring model. The result is better scoring, which accurately predicts leads’ propensity to buy.


Predictive lead scoring is a powerful tool that can help you identify buyers. If you’ve seen one of these signs in your organizations, perhaps it’s time to move beyond guesswork and consider boosting your marketing with the power of predictive analytics.

7 Reasons Why Your Data May Not Be a Good Fit for Lead Scoring

High quality data leads to powerful lead scoring models. However, the data in your CRM and marketing automation platform may not be robust enough.

Leads in Database

Lead scoring lets you discover these precious golden nuggets in your marketing database.  Scoring is also a matter of trust—a sales rep needs to believe that he or she should prioritize a lead with a high score as it has a higher likelihood to convert.  Therefore, the ability of scoring to reflect the likelihood to buy is a key for success.

One obstacle on the road to successful scoring is data.  If data are the basis of scoring, bad data will inevitably lead to bad scoring. Marketing databases typically contain data collected from the CRM and marketing automation platforms.  However, this data, if taken at face value is hardly good enough.  Here are seven reasons why:

1.    It’s not clean.

CRM and marketing automation data come from multiple sources.  These include web-forms, lists, conference-scans as well as manual data entry by sales reps. Data from multiple sources can lead to duplicates and bad records. The Data Warehousing Institute (TDWI) estimates that dirty data costs U.S. businesses more than $600 billion each year.

2.    It’s not accurate.

Does the “Industry” field tend to be more heavily weighted towards “Agriculture” and Accounting” than it should? How many times have you downloaded a whitepaper and chose the first option in the drop down just to get on with it?  This is what many of your leads are doing as well.

Not all inaccurate information is the result of laziness.  Some people simply fill out wrong data intentionally, as they do not want to give their full details. Lead lists may also have low quality data, which may be inserted into the database.

3.    It’s not complete.

Probably all of your leads have email addresses and most have first name and last name.  The rest depends on where they came from.  If you are asking specific questions on your web-forms (such as which marketing automation platform do you use), you are not likely to get this information for leads coming from lists and conferences. Missing values can jeopardize your entire lead scoring model.

4.    It’s not updated.

People change jobs or get promoted, but your leads stay the same.  Therefore, if your list is not updated on a regular basis, a large chunk of it may be wrong.  Our experience at Mintigo shows that about 30% of the marketing database is degraded in a single year. If most of your leads are older than two years, most of your database is stale.

5.    It’s not standardized.

Does CA and California look the same to you?  Is there a difference between a Director of Marketing and a Marketing Director?  For you, these look the same; for your scoring model these are completely different. Therefore, non-standardized data is a poor fit for lead scoring as leads who are virtually the same can be scored differently.

6.    It’s not the right kind of data.

Mintigo found that the average CRM database contains about 10 data-points for every lead.  These typically include name, email address, location, industry, revenue and employee-count.  However, these are not necessarily the data-points that matter.  Most products and services need more specific data-points: For example, CRM apps need to know what CRM platform a prospect is using, while revenue may not be as powerful in predicting likelihood to buy.

7.    It’s not robust.

While the average CRM data contains only 10 data-points for each leads, your can find thousands of data-points online.  At Mintigo, we use over 1,600 data-points for each lead in our scoring model and typically, 200-400 are statistically significant.  In scoring, data quantity leads to prediction quality.


Your CRM and marketing automation data may be a poor fit for lead scoring. However, the magic happens when you combine this data with the thousands of data-points that you can find online. Accurate and robust data is the first step to powerful scoring.

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, which makes it very powerful and accurate.


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


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.

  What is this indicator?
 Jira Jira Project management tool
 Magento Magento eCommerce platform
 BBB Better Business Bureau business accreditation
 Webinar Use of online conferencing
 oracle Use of data center software and hardware
VMWare Use of computing virtualization
 Infographic Placing infographics on websites
 Facebook Using Facebook share or like button
 Blog Having a blog on website
cloudfront Using Amazon Cloudfront content delivery network (CDN)
 SAP Using SAP’s ERP Software
API Supporting Application Programming Interface (API)
 truste Using Truste online privacy management service
 Pinterest Placing a Pinterest “pin it” button
 woocommerce_logo Using WooCommerce’s online store
 Dropbox Using Dropbox’s Cloud Storage
 SaaS Company’s product is Software as a Service
b2b Selling business to business
 Linkedin Placing LinkedIn share button
 Cisco Hiring Cisco experts

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.


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


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.