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Decoding Predictive Marketing AN INTRODUCTORY GUIDE ContentsING PAGE 3 Introduction to Predictive Marketing PAGE 10 Hidden Insights in CRM and Marketing Automation PAGE 13 Understanding Predictive Models PAGE 18 Lead Scoring PAGE 23 Account-Based Marketing PAGE 25 Account Scoring PAGE 27 Customer Expansion PAGE 32 Conclusion and About Lattice Introduction to Predictive Marketing Given growing revenue targets, many top modern marketers are realizing that they need deeper intelligence to keep up with shifting buyer behavior. Predictive marketing works by taking all the data in the world — including account-level information about the businesses we sell to and the lead-level information about the people we actually sell to — and applying modern data science to solve top marketing challenges. Some commons questions and challenges include: Who is going to be my next customer? How can I find more of these ideal customers? 3 s s s s How do I convert them? Here is a sample of the predictive attributes at the contact and account levels that are hidden across a wide variety of sources. EXTERNAL Selected Attributes Marketing Automation Contact name, title, company, open rates, unsubscribes, web visits, pages visited, lead score, video views, downloads CRM System Company, contact information, win/loss, deal value Product Usage Logs Features used, logins, session length, collaboration Purchase History Products purchased, prices paid, discounts, contract terms Customer Support History Complaints, resolutions Public Websites Job postings, grants, litigation, patents, contracts, locations, growth Company Websites Language(s), products, shopping cart, executive team profiles Social Websites Company and personal profiles, likes, comments, updates, friends/ connections/followers, usage Media News articles and stories, product launches, announcements, press releases, litigation Private Databases Credit ratings, financial history, construction permits/ starts, deployed technologies Predictive analytics has emerged as possibly the single-most important technology and competitive differentiator for B2B marketers to adopt. —Matt Heinz, Heinz Marketing 4 s s s s INTERNAL Source By applying new technology to the wealth of data at their disposal, marketers now have access to predictive modeling without having to turn to a team of data scientists. By better understanding buyer behavior and intent, marketers can score leads and prioritize accounts in addition to selling more to their existing customers. Upsell It’s no wonder that predictive analytics is emerging as central to the modern marketing organization. By leveraging data science to make sense of all the data in their midst, leading B2B marketers are marketing and selling more intelligently. —Shashi Upadhyay, CEO, Lattice Engines Cross-Sell Retain Picking Up Where Marketing Automation Leaves Off 5 s s s s Perhaps you’re thinking, “I’m already marketing efficiently by using marketing automation.” It’s true that marketing automation software can streamline the marketing process from end to end. It can also track the historic behavior of prospects and score leads. Taking Modern Marketing to the Next Level 1990-2000 CRM systems emerged as a must-have for companies large and small. 2000-2010 Marketing automation systems quickly became a staple for companies to digitally engage their databases. 2010-2020 Progressive companies are turning to predictive apps to drive conversions. Marketing automation has allowed marketers to collect more data on their prospects than ever before. However, from a performance standpoint, the best that marketing automation can do is provide a view into what happened in the past. The actions marketers can take on marketing automation data are purely reactive. You learn something about a lead then you use that data to take action — send them an email, add them to a campaign, alert sales that they’ve done something or score them based on those actions. By contrast predictive marketing is proactive. It takes all that data into account and blends it with data that is unseen by the naked eye, allowing marketers to guide their buyers' journeys based on all that is knowable. Marketers once had to guess where their sweet spot was. Now we can use data science to tell us. —Meagen Eisenberg, Vice President of Demand Generation, DocuSign 6 s s s s As marketing evolves from a cost center to a revenue driver, companies that have successfully implemented CRM and marketing automation are now looking at what's next. Predictive marketing can show how prospects engaged with various marketing channels, or which campaigns performed better than others. It can help answer the following: Which companies are the best targets? What marketing activity is most likely to yield the best results? How much new revenue could potentially be generated? Reporting IVE DESCRIPT 100 customers bought product X TIVE PRESCRIP Companies with increased hiring rates will buy product X here Sophistication of Technology Pairing product X with a Y at these accounts will generate $21 M in revenue here Predictive marketing combines predictive and prescriptive analytics to forecast what will happen and how to make it happen. 7 s s s s Business Value Predictive Analytics Predictive Insights The Time is Ripe for Predictive Marketing Why is predictive marketing a must-have? 25 percent of all Fortune 500 companies and 76 percent of the largest SaaS providers are using marketing automation. Overall adoption rates are above 50 percent for SMBs and more than 70 percent in larger organizations. Many of the marketers who embraced the promise of marketing automation have pushed the software to its limits. They’ve refined their campaigns and messaging based on the information they’ve collected via the system. They’ve improved efficiencies but are now looking for ways to optimize their performance. 25% As more B2B organizations seek to win over entire buying committees involved in purchase decisions, they are moving from pure contact-level to strategic account-level marketing. Marketing automation was developed around the concept of a contact database. For that reason, most of these systems are less adept at addressing entire accounts versus individuals. Yet marketers cannot afford to ignore the wealth of buying signals or the account-level attributes that can provide key insight into the needs of prospects. of all Fortune 500 companies use marketing automation 76% of the largest SaaS providers are doing the same 8 s s s s Previously only the most sophisticated companies could make use of predictive analytics. If marketers wanted to make marketing more predictive, they were forced to rely on a team of highly trained data scientists using complex analytic platforms to build predictive models from scratch. Since these data teams often served as a shared resource across the organization, marketers often waited weeks or months to have their requests fulfilled. Now, the power of predictive analytics is accessible to any company. A new generation of predictive marketing applications is harnessing the power of machine learning to democratize their use by actual business users rather than by PhDs. Take note from Internet giants like Amazon and Netflix. Both companies have become successful based on developing recommendations from predictive modeling. In fact, Amazon notes that 35 percent of its product sales result from its recommendation engines. Both of these companies combine profile and behavioral indicators from thousands of signals from the Web, social media, news sources and beyond to power their predictive models. In essence, they’re tapping into all the information that indicate when a customer is likely to need a specific product. For example, you may not be looking for a shovel but Amazon knows that your neighbor just bought one, indicating you may need one too. By using all the data in the world, every marketer — including you — can optimize his/her revenue funnel to simultaneously improve conversion rates, increase revenue and improve lead velocity. Key Takeaways of Amazon’s product sales come from recommendation engines •As marketing becomes a revenue driver, companies that have implemented CRM and marketing automation are looking for new insights to take their modern marketing efforts to the next level. •Predictive marketing works by taking all the data in the world about accounts and prospects — from both internal and external sources — and applying modern data science to optimize conversions of all stages of the revenue funnel, and tackle other top-of-mind challenges. •Modern-day data science makes it easy for companies of all sizes to use the same techniques Internet giants use to develop recommendations. 9 s s s s 35% What Insights Are Hidden in Your Marketing Automation and CRM? As the adoption of CRM and marketing automation matures, it’s no surprise that companies with these technologies are sitting on a wealth of data. Out of the box, CRM and marketing automation are designed to capture and store a rich set of information on customers and prospects. Many companies add additional customizations and integrations to turn these systems into robust marketing and sales data warehouses. 10 s s s s Good sales reps are already pouring through CRM to review contact and account information prior to dialing a prospect. They are searching for opportunities created, looking at recently won deals and surveying lost opportunities. Using robust APIs, companies are connecting CRM to internal systems that provide product trial and usage data and customer support data. They’re also mining social networks, looking for clues into buyer needs and trying to find connections. While this behavior is effective, it is time not spent closing deals. The best reps know how to take advantage of this full set of information to determine whether or not a prospect is ready to be engaged. They want to drive productivity by focusing their time on the highestvalue leads. At the same time, marketers are creating laser-targeted campaigns within their marketing automation platforms based on lead demographics and behavior. They have customized nurture streams for buyers at various stages of the funnel or by vertical, persona or product interest. Savvy demand-generation teams are also enriching their leads with third-party data. Their goal is to target the right prospect with the right message, at the right time and then pass them off to sales when they are most likely to convert. Data-driven marketers are maniacal about measurement. They are keen to understand the attributes in marketing automation that indicate a lead is ready to buy and is, therefore, ready to be passed to sales. Let’s take a look at some sample positive and negative predictors of buying intent that can be found within the CRM and marketing automation: Sample Attributes of Buyer Intent from CRM Existing customer Product usage data Trial products and trial dates Customer support cases 11 s s s s Lost opportunities Sample Predictors of Buyer Intent from Marketing Automation Email response and opens Content engagement Website visits Event participation Webinar attendance These are just some of the attributes that can carry predictive value. Companies continue to enhance the breadth and richness of data they store in CRM and marketing automation. Predictive marketing can leverage this rich data to help marketers pass on the most lucrative leads to sales. Key Takeaways •A wealth of data that is predictive of buyer intent is hidden in CRM and marketing automation. •Predictive marketing can turbo-charge your CRM and marketing automation efforts to highlight the most sales-ready leads. 12 s s s s Understanding Predictive Models Not All Predictive Models are Created Equal Two key ingredients are required for efficient, highly predictive models — data and analytics. While the data is crucial, the algorithms and analytics behind the predictive models are the engines that do most of the heavy lifting and differentiate good predictions from great predictions. Determining the correct mix of attributes for inclusion DATA NORMALIZATION Ensuring that each attribute maximizes the contribution to the model PREDICTIVE MODELING Mining the data to “fingerprint” what makes a good lead or prospect 13 s s s s FEATURE SELECTION FEATURE SELECTION Statistical models perform best when they incorporate DATA NORMALIZATION While data represents a key input into any predictive algorithm, it can take many shapes and forms. Some attributes like “number of email opens” or “annual spend” are relatively straightforward to mine, whereas attributes like “job title” or “geography” need pre-processing before they can truly shine. MODEL EXECUTION The real value of machine learning comes out when the models are finally selected and launched. While data represents a key input into any predictive algorithm, it can take many shapes and forms. —Matt Pollock, VP, Lattice Engines 14 s s s s the most optimized set of attributes (or “features”). It is typical to have thousands of candidate attributes that could potentially be included in the pattern-matching algorithms. To start, it is common to apply various statistical techniques to determine which attributes should be retained and which should be discarded. Many predictive models also look at the creation of derived attributes, which transform the raw data in a native attribute into a form that is more meaningful in a predictive model. For example, the founding date of a company is used as the basis for a derived attribute called “company age” that is likely far more predictive than founding date. Here is a sample of the techniques that can be used in predictive models. LOGISTIC REGRESSION is a type of regression analysis used for predicting the outcome of a categorical dependent variable. Logistic regression is very resource-intensive, consuming a great deal of memory on a large data set; however, it is very stable and works particularly well when you have continuous features or attributes like revenue data. DECISION TREES are very powerful algorithms that help identify the best predictors. Decision trees are intuitive to analyze and usually produce great results when applied to a mixture of categorical (i.e. SIC CODE, industry vertical, location) and numerical attributes. RANDOM FOREST is one of the techniques behind the recommendation engine in Netflix and also a popular technique in the Hadoop framework. The main idea is to build a forest of many decision trees over different variations of the same data set and take weighted averages of the results. This technique is very powerful because it can effectively identify patterns across a large noisy dataset. The technique is computationally expensive, but it can be easily run in parallel. NEURAL NETWORKS are a composition of neurons combined together to describe a data set. While machine-intensive, it is very powerful when you try to describe events that are non-linear (for instance, a sales campaign that spans across multiple market segments). Neural networks are typically used to identify very complex patterns. K-MEANS CLASSIFICATION CLUSTERING can be very useful for prospecting. For example, take the numerous existing customers and potential prospects in your CRM software. Clustering allows you to find similarities between accounts and rank them according to the degree of similarity. NAÏVE BAYES is a probabilistic classifier. It is very useful in identifying patterns and behaviors of an 15 s s s s account for cross- and upsell purposes. For example, this account bought products A and B, so the probability of buying C is very high. Top Considerations For Marketers Evaluating Predictive Apps There has been an explosion of interest in using machine learning to build models to predict customer behavior, and many companies offer products and solutions in this domain. As you go through the journey to predictive marketing, you’ll want to keep the following considerations in mind. 2 3 Ensure you have the right data assets to solve your problem. Organizing data can be a daunting task. It may seem like you can get an acceptable answer with a small subset of the rows or columns, but you’ll want to ensure that you have enough data so that the results are not misleading. Understand what success looks like for your company. Identifying success for any type of predictive model typically consists of weighing all of the various factors of the model into a single “model quality factor.” Here are some examples of how such model quality could be defined: • The lift in the top 10 percent of scored recommendations •The difference in the lift in the top 10 percent of scored recommendations and the percentage of recommendations that fall in the lowest 30 percent •The order value for the top 20 percent of recommendations 16 s s s s 1 Think about the problem you are trying to solve. If you don’t have a particular decision or marketing problem you are trying to solve in mind, then it may not be time to investigate predictive marketing and analytics. Analytical models are all built with some set of assumptions about what they are trying to predict and understanding the problem you are trying to solve will help ensure that you get value from predictive modeling. 4 Think through what you will do with the output. Knowing what you want to do with the output is critical from the start. You may uncover insights that ultimately change a well-established process within your organization. Change management is often an after-thought but should be considered at the beginning of any predictive marketing initiative. Consider planning out communication or training schedules if the need arises. Data science has an enormous potential to bring immediate and significant impact to the performance of a wide variety of business problems. Vendors in this space bring expertise, software and data that can accelerate the impact of this trend on your business. It’s not simply enough to have a broad, high-quality set of buying signals or have a single, great predictive algorithm. Key Takeaways •Understand the problem you are trying to solve first. This ultimately helps inform the model and ensures a higher rate of success. •The data and the model matter. Some pre-processing work may be necessary in order for predictive marketing to work. •Understand what success looks like before you get started. 17 s s s s Lead Scoring Numerous aspects of marketing could be vastly improved with better predictive insights, but many marketers are finding predictive lead scoring is the best place to start. Why? Many marketers have found their current lead scoring initiatives have failed to live up to their expectations. According to a report from Decision Tree Labs, 44 percent of companies using marketing automation have implemented lead scoring. However, on average, survey respondents graded their lead scoring programs five out of 10. Why? Most commonly, it’s a simple lack of good insight into what constitutes actual buying behavior. In fact, SiriusDecision reports that 94 percent of MQLs will never close despite all of our efforts. 18 s s s s It’s no wonder marketers can increase their conversion rates only so much by making do with the current crop of rules-based lead scoring engines. Traditional lead scoring prioritizes leads based on various fit and behavior criteria in hopes of getting a picture of a good lead. However, this approach taps into just a small percentage of data that could be gleaned from prospects and requires a heavy dose of gut instinct and intuition. Marketers are forced to make critical decisions about passing to sales based on a limited amount of information. In a sense, this basic lead scoring is little more than a guessing game. As a result, many marketers struggle to demonstrate tangible return on its investment in marketing automation. A better option is to tap into the power of predictive lead scoring. This advanced lead scoring approach augments the demographic and behavioral attributes that are part of basic lead scoring with thousands of additional data points. Examples include whether the company in question recently received funding, moved to a new location or hired new design engineers. In essence, predictive lead scoring empowers marketers to build a sophisticated model that actually predicts which lead attributes matter most. This approach allows them to: Combine contact- and account-level attributes to get a complete 360-degree view of all buying signals — not just those captured in marketing automation. Uncover the true definition of a good lead through the use of data science rather than intuition or consensus. 19 s s s s Determine the actual probability of each prospect becoming a customer with unmatched precision. Weaving Predictive Lead Scoring into the Buyer Journey It’s important that any predictive tool fits into marketing’s existing workflow and tool set. Regardless of how an organization views its revenue funnel, the key is to apply predictive lead scoring at each crucial conversion point — especially the critical hand-off between marketing and sales. The first conversion occurs when marketing passes a qualified lead to inside sales to further qualify and accept it as a sales-ready lead (MQL -> SQL). With predictive lead scoring, marketers can be assured they are only passing sales the contacts who are most likely to buy. Traditional Lead Scoring versus Predictive Lead Scoring A, B, C 10%, 30%, 40% Let’s compare the probability to convert. The traditional lead score doesn’t explain the difference in quality between leads, where as the percentage is very clear and provides far better prioritization. 20 s s s s As a result, sales will no longer waste time trying to track down and qualify contacts who would be better served by a nurture program until they are actually ready to purchase. → THE DEMAND WATERFALL The second conversion point happens when the sales team is tasked with qualifying a huge volume of leads (SAL -> SQL). Without a solid mechanism for deciding where to focus, sales either randomly follows up with leads or cherry-picks leads based on a very unscientific process. Because of the time required to contact so many leads, often a large percentage of good leads fall through the cracks while sales is spinning its wheels with the bad ones. If marketing can tell the team how likely a given lead is to convert, the sales reps can prioritize their efforts using science rather than chance. The Demand Waterfall From SiriusDecisions INQUIRY INBOUND OUTBOUND MARKETING QUALIFICATION AUTOMATION QUALIFIED LEADS TELEPROSPECTING ACCEPTED LEADS TELEPROSPECTING QUALIFIED LEADS TELEPROSPECTING GENERATED LEADS SALES QUALIFICATION SALES GENERATED LEADS SALES ACCEPTED LEADS SALES QUALIFIED LEADS CLOSE WON BUSINESS 21 s s s s Source: SiriusDecisions Here is a quick look at how marketers can make predictive marketing actionable. Different contact strategy by segment 40% Probability to Convert 35% 30% send to sales/BDrs 25% Predicted 20% 15% send to nurture 10% Average 5% 0% Accounts/Leads Accounts takeaways: KeyKey Takeaways According to Demandgen report, most marketers are unhappy with their current approach to leadlead scoring. • rules-based scoring techniques typically only account for one to five percent of data available on a prospect. • Rules-based lead scoring techniques typically only account for one to five percent of data available on prospect. • Predictive leadascoring empowers marketers to build a sophisticated model that actuallylead predicts which lead attributes matterto most, on uncovering true that • Predictive scoring empowers marketers buildbased a sophisticated model buying signalswhich from internal and external data.most, based on uncovering true buying actually predicts lead attributes matter signals from internal and external data. s s 18 22 s s s s • • According to to Decision Tree Labs, most marketers are unhappy with their current approach lead scoring. s s Account-Based Marketing: The Missed Opportunity? For years, B2B marketing tactics have focused largely on individuals, as the majority of channels such as email, phone and even events target at the individual level. However, as technologies have evolved, the idea of account-based marketing (ABM) has really drawn a great deal of appeal. After all, most B2B buying decisions happen because of a company need and typically involve an entire team of participants in the buying process. Unfortunately, one of the greatest assets in the demand generation technology stack has emerged as a barrier to ABM. Marketing automation tools were fundamentally built around the concept of a contact, rather than an account. Even with the account object and account-level attributes that can be stored within marketing automation, marketers are fairly limited in terms of the data collection, scoring tools and segmentation options they can rely on for creating account-level campaigns. 23 s s s s Recent improvements have come about to roll lead scoring up to the account level. Some marketers are using third-party providers to help with data appending to add better firmographic insights to help with segmentation. However, true account-level scoring and more complex filtering and segmentation capabilities are still lacking. Missing the ability to store and track richer, more dynamic account-level data means marketing automation simply isn’t the ideal solution to advance the ABM cause. So Why the Sudden Interest in Account-Based Marketing? For one, many of the newer, more innovative marketing channels rely on ABM to make them effective. ABM can target many or all employees within a given account with personalization, display ads, product-level campaigns and field marketing events. For example, with tools like Demandbase you can target your content dynamically, based on the inbound company’s IP address. This creates powerful messaging that is far more relevant if you know which account you are going after. You can also dramatically reduce your display ad spending by ignoring accounts that aren’t relevant to your business. For example, if you know a prospect account uses Salesforce.com, you can display an ad that is relevant to that audience, whereas a visitor from an account using Siebel or Microsoft Dynamics CRM would not be targeted. This increases effectiveness while also reducing costs — a perfect storm of marketing effectiveness. ABM provides an opportunity to fuel growth from marketing. Key Takeaways • As B2B marketers, we need to remember that we sell to both people and companies. • Many existing marketing technologies are focused on contacts, rather than accounts. • Account-based marketing is critical when thinking about retaining or growing existing 84% of marketers noted that ABM provided significant benefits to retaining or expanding existing customers —Marketo 24 s s s s customer accounts. Account Scoring In a B2B environment, people generally don’t buy products or services their company doesn’t need. In reality, it’s a combination of events and actions that sparks a purchase decision. For most considered to be in B2B buying cycles, the process begins with some kind of trigger event within the company, followed by a reaction from a person or team to look for a solution. The company need dictates a human-lead buying process. For example, a new round of funding for a business might lead to an office expansion necessitating a slew of different buying cycles for anything from office furniture to networking equipment. So What Does All this Have to Do with Lead Scoring? Quite simply, the standard practice of scoring purely at the contact level misses the much bigger picture. Yet ignoring the behaviors of individuals within that organization also limits your perspective. Only by blending the two will you get a complete 360-degree view of your prospect’s buying signals. 25 s s s s When most companies start investigating account scoring, they are often building it by using some limited firmographic data along with contact-level attributes. The most common approach is to aggregate or average the scores of individuals associated with the account, but this is really just cumulative contact scoring — not true account scoring. The Power of Blended Scoring Most marketers look at accounts and contacts separately, and at best can create an account score that is an aggregate of contact scores. By taking this blended approach, marketing can much more accurately predict which leads to pass to sales, get signals much earlier in the buying process and ultimately create much better alignment between marketing and sales. Key Takeaways • A contact level-only approach doesn’t account for the full picture. • Smart marketers are taking a blended approach to lead scoring, which combines • contact with account-level attributes. Growth trends, hiring, funding and technology usage are all sample account-level attributes that may be predictive of buyer intent. Marketers need to remember to look beyond the contact. There are a ton of great insights you can learn at the account-level that can be used to target campaigns and drive more sales. —Jon Miller, VP Marketing and Co-Founder, Marketo 26 s s s s Just like individuals, companies also exhibit digital body language. For instance, firmographic data may tell you a company fits the right industry profile or size. What most marketers miss are the account-level buying signals such as growth trends, hiring patterns, government grants, patent filings or technology usage, just to name a few. These account-level activities are often the earliest buying signals, possibly preceding contact-level activities by weeks, months or even years. Customer Expansion Improve Marketing Performance by Targeting Existing Customers Companies have improved their marketing performance through the adoption of B2B marketing automation to reach and attract new prospects. Despite these tools and technologies, very few marketing teams have applied them with the same type of rigor for actually retaining and expanding customer relationships. According to a recent survey by the DemandGen Report: The act of managing multiple, disparate systems was cited as the top obstacle to achieving customer marketing goals followed by insufficient data. 27 s s s s In most cases, the sales or service groups still have exclusive ownership of expansion opportunities. With such roadblocks, it’s no wonder marketing is so laser focused on new customer acquisition. A Great Opportunity to Improve Marketing Performance Hiding in Plain Sight For most organizations, existing customers drive 50 percent or more of revenue. Given the limited capacity of individual sales reps, they must make instinctive bets about which accounts and products to focus on. Compounding the problem, reps also tend to gravitate toward the products and messages they are most comfortable with, meaning that many newer products, services or messaging get limited attention. So what could these missed opportunities be costing? New Customer Acquisition Existing Customers Churn/Attrition Customer Marketing Opportunity Unsell/Cross-sell Traditional Demand Gen Opportunity 28 s s s s • Effective cross selling and upselling drive significant acceleration in both revenue growth and renewal rates. • Customer revenue potential is often three to five times larger than current sales opportunities. • Closing a new customer can cost between three to five times as much as retaining or expanding an existing customer. Revenue Contribution Why is Customer Marketing Often Ignored? Marketing automation platforms are fundamentally designed around customer acquisition. It’s as simple as that. Features like prospect profiling, segmentation, event management and web analytics are often tuned to gradually collect more information about leads or prospects, but lack capabilities for mining existing customer information hidden in plain sight. Customer Buying Signals are Hidden in Plain Sight A survey conducted by DemandGen Report and Retail TouchPoints revealed that capturing and integrating customer data is a key consideration for marketers, with more than 49 percent identifying it as a top priority. However, some of the most valuable data doesn’t come from marketing automation or CRM at all, but rather from transactional systems such as order management, call center or support log — a rich data source unique to existing customers. 49% of marketers identify capturing and integrating customer data is a key consideration 29 s s s s Beyond just activity history, marketers also need to look externally at account-level indicators such as hiring trends, office openings, funding events or even social activity for hidden buying signals that could represent good triggers for cross-sell or upsell. Predictive Analytics Illuminate the Path to Improved Marketing Performance By combining contact- and account-level attributes, marketers can get a full view of the customer and apply predictive analytics to identify not just the best opportunities for upsell or cross-sell, but also which products or services represent the best fit. For organizations with complex product and customer matrices, arming sales with the right targets, the right products and right messaging can finally unlock the full potential of that 50 percent customer opportunity. Marketing Automation Purchase History CRM External Data 30 s s s s BUYING SIGNALS FOR EXISTING CUSTOMERS ARE FRAGMENTED Marketing no longer has the luxury of focusing exclusively on customer acquisition. With the right combination of data and predictive analytics, marketers can offer sales reps higher productivity and product leaders the proper attention on the full breadth of product and service offerings. B y tapping into customer expansion, marketing performance can increase and help source a much larger share of total company revenue — the other 50 percent. —Brian Kardon, CMO, Lattice Engines Key Takeaways • In most companies, existing customers drive 50 percent or more of the revenue. • Marketing automation is inherently designed to go after new customers. • Sales reps typically gravitate toward the products and messages they are most comfortable with, meaning that many newer products, services or messaging get limited attention. • A predictive marketing approach can help marketers identify opportunities for customer expansion through account-level targeting. 31 s s s s Conclusion There has been an explosion of interest in predictive marketing and using machine learning to build models to predict customer behavior. Data science has an enormous potential to bring immediate and significant impact to the performance of a wide-variety of B2B marketing problems, helping marketers go beyond modern marketing. Still curious about predictive marketing? Contact us today. About Lattice Engines 32 s s s s Lattice is pioneering the predictive applications market for marketing and sales. Lattice helps companies grow revenue across the entire customer lifecycle with data-driven marketing and sales applications that make complex data science easy to use. By combining thousands of relevant buying signals with advanced predictive analytics in a suite of secure cloud applications, Lattice helps companies of all sizes to stop guessing and start relying on predictive insights to increase conversion rates and deal sizes by more than three times. Lattice is backed by NEA and Sequoia Capital with headquarters in San Mateo, CA. Learn more at www.lattice-engines.com and follow @Lattice_Engines.