The Marketing Cloud: A Deep Dive

In a new webinar, John Stetic, Group VP of Products at Oracle, takes an in-depth look into the strategy and vision behind the Oracle Marketing Cloud.

The marketing cloud offers a huge promise to marketers to enable the grouping of powerful individual technologies and provide an end-to-end solution in accelerating deal velocity and increasing revenue. In a new webinar, John Stetic, Group VP of Products at Oracle, takes a deep dive into the Oracle Marketing Cloud, a solution that aims to change how marketers work. You can watch the full webinar by clicking here.

John Stetic

John Stetic, Group VP of Products at Oracle. Source.

Data is becoming one of the foundations of marketing. How can doing more with data enable you to understand more about your customers? According to John, the Oracle Marketing Cloud has 3 key areas of focus:

  1. Delivering marketing simplicity to the marketer–Allowing marketers to leverage technology in order to make very effective campaigns that drive leads and revenue to their company.
  2. Delivering real customer centricity–Allowing marketers to understand their consumers enabling the creation of rich and engaging experiences for their customers and leads.
  3. Enterprise scale–Enabling both fast-growth and large-scale enterprise organizations to leverage these capabilities.

According to John, the marketing cloud helps marketers identify the “who, what, how and why” of marketing.

  • Who. Who is the customer and to whom are we talking? It’s important to have a broad understanding of that who, no matter what stage they might be in the life cycle, whether they’re an existing and well-nurtured and very loyal customer, or just someone with a brief interest in or some buying tendencies of your product.
  • What. What types of products or services are offerings that we want to be able to deliver to a customer in order to help the customer solve their problems?
  • How. How do you talk to that customer? What type of channels and what types of content do you actually deliver through those channels in order to be able to effectively engage with those contacts?
  • Why. Being able to tie that what, who and how together helps you get some really powerful insights into how to best engage with your customers. How you’re effectively allocating revenue back to your marketing activities helps you optimize your marketing.

John explains that data helps you learn more about your customers, score your leads, segment your contacts, and target and personalize content. In addition, there are ways in which your data can even get more useful–and this is where the marketing cloud is going. These new directions are about being able to combine a good deal of the data sources with the CRM system marketing automation platform.

The marketing cloud insider’s view

What does this really look like for the marketing cloud customer? John says that, for example, if a marketer would like to define an audience that they’re interested in targeting, they can select a profile from over 700 million profiles, that they can drill and then tune these audiences, and create highly targeted communications and advertising to enable effective prospecting of new customers. The prospects then engage with those ads, and you can start leading them down the engagement funnel that we’re all used to within the marketing automation world.

An open platform

One of the critical enablers, John explains, is this very large app ecosystem that brings together the most comprehensive marketing tech and ad tech ecosystem for marketers. Oracle marketing cloud provides the most integrated open and global platform for fast growth and enterprise marketers, being able to deliver a whole suite of best-in-breed apps, data and media partners, allowing you to extend your reach as marketers, allowing you to engage more effectively with customers, and better understand them.

According to John, Oracle is able to do this because it’s an open platform that leverages all of the great innovations across the entire market space. “So of course, our partners, Mintigo here, have done a fantastic job of leveraging our open platform being able to build an app that can plug directly into your marketing platform. This allows pushing audiences into different campaigns or triggering external actions, or making decisions based on data that exists outside of the marketing clouds.”

Conclusion

In conclusion, John says that the marketing cloud then becomes a comprehensive suite of capabilities for marketers, “It involves not just the products and capabilities that we offer from Oracle, but an entire ecosystem as well. That’s why Oracle is so excited to be working with partners like Mintigo, who have really innovated in this space to deliver a very interesting solution.”

Predictive Marketing University: Learn How to Leverage Data Science to Improve Your Marketing

Mintigo launches an online course to help teach marketers the fundamentals of predictive marketing.

PMU-banner

Predictive Marketing fundamentally changes marketing by introducing data science to traditional marketing. As an up-and-coming topic, there weren’t any resources available online to develop deep understanding of this exciting field. This is why we launched Predictive Marketing University.

Predictive Marketing University is a video course that lets marketers learn and understand the key principles of predictive marketing. Each module is comprised of multiple videos and each short video reviews a key topic in Predictive Marketing.

PMU is free! To access PMU, click here.

Salesforce: 42% of Marketers See Predictive Marketing as Critical for Creating a Cohesive Marketing Journey

A survey of over 5,000 marketers finds that the majority of marketers see technology as critical to craft their customer journey.

New research from Salesforce finds that the majority of marketers are eager to leverage technology in order to shape their customers’ journey to purchase. The survey, which included over 5,000 respondents, asked marketers what are the most effective technologies that they use in order to control and shape the touch points that a prospect has with the brand and product prior, and even after purchase.

Predictive Marketing and Effective Customer Journey

According to Salesforce, 86% of senior level marketers say that it is critical or very important to create a cohesive customer journey. The survey shows that technology is widely used in order to design, shape, and measure the customer journey to purchase.

The idea behind most of these technologies is to create a one-to-one brand experience and to cater the marketing and sales effort specifically and uniquely to each prospect. This would enable marketers to achieve higher engagement and higher relevancy to their audience.

Mobile is the most important technology for most marketers to shape the customer journey—with 57% of marketers consider it critical or very important. Mobile is helpful specifically in the B2C environment.

The power of predictive marketing

Next, we see a group of related technologies—Marketing analytics, CRMs, content management, marketing automation, and predictive intelligence, or the way we call it at Mintigo, predictive marketing. These tools are considered critical or very important by 42-54% of marketers.

These are very important and related tools. CRMs and marketing automation platforms provide the content and the execution to many campaigns. Content is the fuel that drives customer engagement. However, predictive marketing, which is becoming ever more important, has the power to propel campaigns forward.

Predictive marketing takes vast amounts of data from the web, combines it with data from your CRM and marketing automation and, using sophisticated algorithms and models, predicts favorable marketing outcomes. Predictive marketing can help you identify what content will drive engagement from which lead in your CRM and when. In fact, predictive marketing should be used to drive optimal decisions in planning and designing your customer journey.

The vast majority of top marketers acknowledge the power designing a customer journey and most use technology to support it. Predictive marketing, a nascent technology, has the power to bind all other technologies and help marketers execute better campaigns and design optimal customer journeys.

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.

Conclusion

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.

Conclusion

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.

Conclusion

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%.

Conclusion

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.