Optimize Your Marketing for the Mobile Web

Mobile has changed the way people engage with email, social and web browsing. B2B marketers will have to adjust as well.

According to the Pew Research Center, 58% of American adults have a smartphone and 42% have tablets. Of smartphone owners, 81% use their phone to send text messages, 60% access the Internet, 52% check their email and 50% download apps. No online marketer can ignore the shift to mobile. Mobile Internet and data is growing at 1.5x per year and projected hit 30% of Internet traffic by 2015.

Mobile web statistics

While potential buyers are shifting their time and attention to mobile, marketers aren’t responding. Only 45% of marketers have a mobile-optimized website, according to Adobe. Only 25% of marketers test their email for accurate mobile rendering, according to marketing automation platform Pardot.

One of the reasons that mobile is not becoming a part of every marketer’s tool set is that mobile is different. It has its own challenges, metrics and objectives, different from the web. Marketers must learn new tactics and acquire talent to succeed.

New skills required

To succeed in the mobile web, marketers need other ways to engage. For example, while PC users download and read whitepapers, long content and forms are hard to digest on a 5-inch screen.

Welcome to the App Economy. According to Flurry, apps account for 86% of the time people spend on the web compared to just 14% of time spent on mobile browsing. This shows the importance of apps to succeed in the mobile web.

Mobile web

Mobile apps don’t need to recreate all the functionality of your web experience, particularly at first. You may decide to have a portfolio of apps or a single one to concentrate your efforts. The app store is the new SEO.

Hubspot created a mobile app that allows users to access their platform from iPhone and Android phones, analyze data, connect with leads and track progress. With this mobile app, you can stay on top of your marketing campaigns on the go. Salesforce.com started with a portfolio of apps and consolidated them down to one in the Salesforce 1 platform.

Screen Shot 2014-11-19 at 5.37.49 PM

When creating a mobile app, metrics are different. You need to track downloads from the App store. But downloads aren’t enough. Retention is critical. Research from Loyalitics indicates that 20% of apps are only opened once and 60% of apps are opened fewer than 10 times.

Email is the workhorse of B2B marketing on the web. It remains a significant driver of B2B lead generation and engagement. Most marketing automation platforms use email as their main channel for leads.

However, emails viewed from mobile are very different from emails viewed from a PC. Emails need to be mobile-optimized and fit the small screen. Emails aiming to send people to a landing page with long forms will fail in conversion. You need to optimize the entire experience.

In addition, you need a content strategy suited for the mobile web. Native advertising puts your content alongside organic content. High quality, relevant marketing content, boosted with advertising, can generate more clicks than organic content. The shift to content marketing has been running for some time, but mobile has made your ability to create quality content even more urgent.

Finally, you’ll also need new sources of data. For example, you’ll need to track downloads and usage from vendors like App Annie. You’ll need to build dashboards to track your campaigns across multiple mobile networks. Some but not all of your web vendors have made the mobile shift.


As consumers shift to mobile, marketers need to respond in kind. Mobile marketing requires a new set of strategies and tactics. Email alone won’t work anymore. Welcome to the world of apps, native advertising, and optimization for the small screen.

Staying in place isn’t an option. Comments are welcomed!

About Brian Goffman

Brian GoffmanBrian is an Internet executive with general management, marketing, sales, and product management background in mid-to-large-size organizations. Currently he is a venture partner at Technology Crossover Ventures, a growth-stage venture capital firm. At TCV, he works with the firm’s partners to evaluate new investments in consumer Internet and software, and advises leadership teams at existing portfolio companies, with a bias to disruptive products that upend existing business models. Prior to TCV, he led the global enterprise marketing team at LinkedIn across Talent Solutions, Marketing Solutions, and Sales Solutions.

Off the Grid Data: How to Develop Highly Targeted Solutions and Messaging Based on Data Mined From the Web & Social

Some of your most valuable marketing data may actually reside outside of your database. Data mined from the web and social networks contains a wealth of information that can inspire a new way of understanding customer profiles and creating progressive and targeted solutions and messaging.  

Off the Grid Data

Data is one of the core assets of the modern marketer. When the CMO Survey asked top marketers how often they base their decisions on data, 61% said they use data for decision-making “some of the time” or more. 4% of marketers are basing every decision they make on data and analytics.

Data for decision-making

Therefore, marketing data is used for better decision-making, targeting, performance measurement and product development. Yet in recent years, marketing data is changing as well. While in the past, marketers used to work in an environment of data scarcity, today they work in an environment of data abundance, which gives them the opportunity to make more data-driven decisions.

However, there is a caveat related to data abundance. While data captured by the organization grows quickly, data that resides outside of the organizational database grows even faster. As organizations are implementing tools such as marketing automation and web analytics platforms, CRMs and other tools that help to capture and store data, the more interesting data is captured elsewhere—on the web and social networks.

Data from the web and social

In 2014, nearly 500 million tweets are sent per day. There are over 2.3 million blog posts written per day and over 2.5 billion Google searches conducted every day. This data for a marketer is like a kid in a candy store—endless opportunities for interesting analysis and insights. However, this is off the grid data, as it exists outside of the company database.

Indicators for predictive lead scoring

CRMs were historically built in order to accommodate sales data. Therefore, they mostly contain names, contact information and some company demographics. In fact, Mintigo estimates that the average company CRM contains around 10 data points. By mining the web and social networks, companies can find thousands of data points on each prospect, out of which about 300 are relevant marketing data.

By combining data from the CRM with data from social and the web, marketers can get a robust data set for their analysis. However, this addition is not trivial—web and social data is unstructured and therefore poses some challenges.

  • Merging CRM data with web and social data is challenging. Matching the person on a twitter account or the company that authored a blog post to the record in your CRM is not trivial. You have to use robust algorithms to match this data accurately.
  • You need to make sense of social and web data. Social and web data may appear in various forms—status updates, job descriptions on job board or even code on the website. You need to translate these into data points that you can enter into your CRM. Let’s say that you would like to add a variable to your CRM whether a company uses Google Analytics; you will have to mine the web and make a decision about every company if it’s a yes or a no. Making these decisions the right way require some deep predictive marketing algorithms.

The advantages of getting robust data from web and social to your CRM are enormous. Robust data will give you deep understanding of customer profiles and let you segment your market better. It will also let you understand complementary technologies that your prospects are using and the DNA of your perfect prospective buyer.

Furthermore, robust data let’s you drive leads faster through the funnel by developing clear and targeted messaging and marketing collateral that resonates with them. It can also let you develop solutions that are better suited for them and may give you the competitive edge.


While marketers are relying more on data to make better decisions, the best data may reside outside the organization’s database. Combining this data with your CRM and making sense of it requires robust algorithms. However, the benefits are tremendous—you’ll get better understanding of your customer profiles to create more targeted solutions and messaging.


About Todd Forsythe

Todd Forsythe EMCTodd Forsythe is the VP of Global Marketing at EMC, a global leader in enabling businesses and service providers to transform their operations and deliver information technology as a service (ITaaS).

Data in the Clouds: Oracle’s Quest to Win the Marketing Cloud

Oracle may have been a latecomer to the sales cloud, but now it is making an aggressive bid to win the marketing cloud—according to John Furrier from Forbes.

John Furrier

John Furrier.

Oracle is set to take over the new and emerging marketing cloud, says John Furrier from Forbes. In a recent analysis of the marketing cloud he says: “This marketing cloud marketplace is the new lucrative market and battleground for the coveted and growing set of marketing and sales applications. Oracle is investing heavily to win the battle for the marketing cloud, and the dividends are starting to appear.” According to John, the strategy integrates technology and a business model similar to the Apple App Store or Amazon Web Services.

John gives Jive as an example of the difference between the new marketing cloud and the old model of enterprise software. He argues that Jive failed because companies don’t want to see sales people “proposing the moon” with huge upfront cost, before any value is realized.

Instead, companies prefer the Software-as-a-Service model, or SaaS, where software is cloud-based and does not carry a huge upfront cost. In addition, the cloud has leveled the playing field, allowing small startups to offer robust software solutions that once required on-premises software.

According to John, “Oracle has decided to draw a line in the sand and invest heavily in the marketing cloud with R&D. Through organic development, R&D, acquisitions, and partnerships, Oracle has assembled a formidable marketing cloud offering.”

The article also quotes R. “Ray” Wang, the founder and principal analyst of Constellation Research who said, “Oracle still has to find the right balance of partnerships, alliances, and acquisitions to serve the growing marketing buyer’s needs.”

Third-party ecosystem

Another strategy that Oracle is developing is a 3rd party ecosystem. “For example, the Oracle AppCloud is an API system and set of tools that allow tighter integrations between the functionality of Oracle and partner applications,” says John.

At Mintigo, we partnered with Oracle’s Marketing Cloud vision and developed an app on their AppCloud that integrates with Eloqua. This app allows marketers to enrich their leads with data and execute sophisticated campaigns directly from Eloqua, based on our robust indicators.

According to Forbes, the marketing cloud is becoming a crowded space with companies like Oracle, Salesforce, and LinkedIn now building marketing clouds. “If Oracle could pull off the ‘Apple for the enterprise software’ then it will be checkmate for their competitors. The key will be in building a credible 3rd party application marketplace,” John concluded.

Predictive Marketing: Brand is Not Dead; But Data is Your New Friend

Brand is still important. However, modern marketers need to be comfortable using data in order to help their organization engage high likely buyers.

Predictive Analytics and Marketing

Four of the top five most valuable brands are technology companies, according to Forbes. Apple tops Forbes’ list, and is followed by Microsoft, Google, Coca-Cola and IBM. Ultimately, a great brand drives sales, high margin and profitability (as the lines outside Apple Stores show over and over again).

Where did all of these branding dollars go?

However, getting a strong brand awareness that pulls buyers in is a long and hard journey. Becoming top of mind for potential buyers takes a lot of upfront investment and a long time to show results. However, unlike B2C companies that address a large audience, B2B companies target a relatively small and well-defined niche.

In B2B organizations, sales-marketing alignment is critical for the organization to be able to meet its revenue target. Branding activities are typically viewed unfavorably by the sales organization that questions their contribution to revenue. This is further exacerbated by the fact that measuring branding results is not straightforward—organic traffic, branded keyword searches or brand surveys may have value, but they don’t show contribution to business and revenue objectives.

Therefore, B2B marketers that are too focused on branding find themselves facing questions from management and specifically Sales about the value that they bring to the organization. This puts marketers in a defensive position of being a “cost center” rather than having a seat at the revenue table.

Organizational Goals for B2B Content Marketing

Still, marketers are focused on branding, as branding is what they are trained to do. According to a survey by the Content Marketing Institute and MarketingProfs, the most important objective for content marketing is brand awareness. Lead generation only comes second.

In an age where marketers are tasked with doing more with less, generating brand awareness without real accountability for revenue hurts marketers’ credibility. In fact, most organizations would find it hard to add more resources to branding without any hard evidence that it contributes to revenue objectives.

Engage the people who matter

Enter marketing data. Data lets marketers optimize all of their marketing efforts towards revenue objectives and get back the credibility that they deserve. Using marketing data that is collected inside and outside the organization, marketers can now prove that they are bringing in relevant sales leads (and ultimately customers) and that these leads have multiple touches with the brand before they are passed to Sales and turn into revenue.

In B2B, where target audience is typically very focused and well defined, Data lets marketers prove that they are engaging relevant potential buyers. In addition, it lets them prove the value of branding campaigns and their ability to push prospects down the funnel towards purchase.

In fact, in B2B, general brand awareness is meaningless without the data. A student who is very interested in working for a company and has downloaded a whitepaper is a very different lead than a qualified buyer. Our ads and brand message may resonate with a wide audience, but is it really convincing potential buyers? These are the questions that can be answered with data.

One great application of data-driven marketing for B2B is predictive lead scoring, which is a part of predictive marketing. Predictive lead scoring allows companies to identify people with high likelihood to buy and invest more money and effort in engaging them.

Predictive lead scoring takes advantage of data from the company database, which is enriched with data from the web and social networks. The data is then crunched using sophisticated machine learning algorithms. The result is a score that signify the prospect’s likelihood to buy.

For example, ReadyTalk, which makes high quality web conferencing technology, was looking for a way to generate campaigns that resonate with potential buyers. The marketing team decided to apply predictive marketing. Using Mintigo, ReadyTalk identified potential buyers in their database and targeted them with a top of the funnel email campaign.

Results were impressive. Email open rate increased by 33%, from 15% to 20%, as compared to targeting their full database. Email click-through rate increased by 118%, from 1.65% to 3.60%. Unlike brand awareness alone, engaging qualified buyers has a direct contribution to revenue. These prospects that clicked were pre-identified as likely buyers. Now, they can be sent to Sales, with higher probability to convert into paying customers.


Branding is powerful and ultimately contributes to sales and your bottom line. However, marketers need to gain credibility by showing that they are engaging the right people and that these people have high likelihood to end up as customers. Predictive marketing helps to ensure that top of the funnel campaigns are not only engaging, but are also bringing your next buyer.

About Jeanne Hopkins

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.

Marketing Science: The Math Men Behind Mad Men

In a world where abundance of data and predictive algorithms can boost campaign results, being a Mad Man style prodigy is no longer enough. 

Technology is a glittering lure. But there is a rare occasion when the public can be engaged on a level beyond flash – if they have a sentimental bond with the product.”

– Don Draper, Mad Men

The Math Men Behind Mad Men

Don Draper, Mad Men. Image Credit: AMC


Our job as marketers is to communicate the benefits of our products and make our message resonate with as many people as possible. To reach our audience and encourage a sentimental bond with our products, we need to send the right message to the right people. But the explosion of online and social media in recent years has made traditional marketing efforts significantly harder to execute successfully.

There are several reasons why these approaches have become more challenging:

  1. The Internet has removed barriers and contributed to the proliferation of media. While in the past, several publications accounted for most of the eyeballs in every space, today the same space may be covered by a myriad of websites, independent bloggers, corporate blogs and more. Fragmentation makes it harder to reach your audience effectively.
  2. Managers everywhere understand that marketing is key to success. With more marketing spend everywhere, it is becoming harder to rise above the clutter and reach people effectively. Therefore you actually need to spend more to become top of mind for your target audience.
  3. Social media has taken control of the conversation away from marketers. As people increasingly trust their friends more than marketers, customers and brand advocates may have more sway over our brand and product perception than marketers.

As a result of these factors, marketers are getting diminishing returns on their efforts. And so they look to increase ads, content, and website traffic to generate sales leads. But this is just applying an old solution to a new problem.

The reality is that marketers now need to work smarter, not harder. They can no longer afford to “spray and pray,” but rather need to enact a more targeted approach to marketing — one that allows them to spend more time and money on the people who matter. In every engagement, marketers must tailor their messaging to their target audience.

And this is where math men complement Mad Men

Predictive Marketing enables marketers to leverage vast quantities of data, combined with predictive analytics, to calculate which among their actions have a high probability to succeed and which have a high probability to fail. Predictive Marketing combines data from the CRM and marketing automation platforms with data from the web, to identify the people that are most likely to buy your product or service.

There are several use cases for Predictive Marketing. It can help score leads accurately based on their demographic and behavioral actions. Predictive Marketing can also help segment leads by attributes like revenue potential or product needs.

In addition, Predictive Marketing can help marketers find the right buyers in their CRM or find new prospects that are likely to buy outside of their database. It can also help to grow new markets or cross-sell, by finding likely targets in a new market or for a new product.

At Neustar, we develop and sell services to professionals in three industries: marketing, IT/security, and communication data. Because those audiences—and their needs—are so different, our ability to segment leads and nurture them with targeted content is critical to our success. Predictive Marketing is allowing us to do exactly that by enriching our data with indicators sourced from the web.

Furthermore, predictive lead scoring helps Neustar to reflect a lead’s propensity to buy in our database. With that ability, we can help sales reps be more effective by identifying the leads they should be calling on and reducing the amount of time dedicated to reaching out to leads that will likely never close.

Mintigo, our Predictive Marketing partner, has developed the technology that does all of the number crunching behind the scenes. Mintigo’s team of math men complements our creative marketing team to ensure that we reach our business objectives.

As Don Draper said: “change is neither good or bad, it simply is.” With the deluge of online data about customers and companies, increasing computing power and better algorithms that crunch it, marketing is changing as well. In today’s realm of marketing, Mad Men simply cannot succeed without the help of math men.


Lisa Joy Rosner, CMO at Neustar.

Lisa Joy Rosner Neustar

Lisa Joy is responsible for leading corporate and brand marketing across Neustar’s entire product and services portfolio. She has more than two decades of experience in building and transforming enterprise software brands, creating rapid revenue growth and initiating high-value partnerships in the Data, Analytics, E-Commerce, Personalization, Social Business and Cloud markets.

Prior to joining Neustar, Ms. Rosner led the brand transformation for display, email and web personalization provider MyBuys, where earlier in her career she had served as its Vice President of Marketing – launching the company, defining the category “personalized product recommendations,” and growing the organization to be the market share leader. She recently served as CMO at social intelligence company NetBase, where she re-positioned and re-launched the brand and brought new products to market that were commissioned by five of the top 10 CPG companies, including Coca-Cola and Kraft. In addition, Ms. Rosner mobilized global partnerships with SAP and J. D. Power & Associates and grew bookings 300 percent year over year. During her tenure as Vice President of Worldwide Marketing at BroadVision Inc., she oversaw transformation of the global brand and messaging, led a team to launch four new product lines and as a result was a catalyst in growing the stock 1200 percent. Ms. Rosner also held marketing positions at Brio Technology, DecisionPoint, SGI and Oracle.

An award-winning and patent-pending CMO, Ms. Rosner was named a 2013 “Silicon Valley Woman of Influence” and “B2B Marketer of the Year”. She has won OMMA and Silver Anvil awards for integrated marketing campaigns in 2012 and was named a 2011“Great Mind” by the Advertising Research Foundation. Lisa Joy has been a guest lecturer at the Hass School of Business, Stanford and Tuck School of Business.

Ms. Rosner currently sits on the marketing advisory boards of The Big Flip and PLAE Shoes. Previously, she served on the marketing advisory board of the Silicon Valley Red Cross, the content committees of Shop.org, the AMA and Benchmark. Lisa Joy graduated summa cum laude and phi beta kappa with a BA in English Literature from the University of Maryland.

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.