Decoding Predictive MarketingAN INTRODUCTORY GUIDE

ContentsINGPAGE 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

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Who is going to be my next customer?

How can I find more of these ideal customers?

How do I convert them?

Introduction to Predictive MarketingGiven growing revenue targets, many top modern marketers are realizing that they need deeper intel-ligence 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:

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Contact name, title, company, open rates, unsubscribes, web visits,pages visited, lead score, video views, downloadsCompany, contact information, win/loss, deal valueFeatures used, logins, session length, collaborationProducts purchased, prices paid, discounts, contract termsComplaints, resolutionsJob postings, grants, litigation, patents, contracts, locations, growthLanguage(s), products, shopping cart, executive team profilesCompany and personal profiles, likes, comments, updates, friends/connections/followers, usage

News articles and stories, product launches, announcements,press releases, litigation

Credit ratings, financial history, construction permits/starts, deployed technologies

Marketing Automation

CRM SystemProduct Usage LogsPurchase HistoryCustomer Support HistoryPublic WebsitesCompany Websites

Social Websites

Media

Private Databases

Source Selected Attributes

INTERN

AL EXTERN

AL

Here is a sample of the predictive attributes at the contact and account levels that are hidden across a wide variety of sources.

Predictive analytics has emerged as possibly the single-most important

technology and competitive

differentiator for B2B

marketers to adopt.

—Matt Heinz, Heinz Marketing

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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. Cross-SellUpsell

Retain

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. 

Picking Up Where Marketing Automation Leaves OffPerhaps 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.

—Shashi Upadhyay, CEO, Lattice Engines

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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.

Taking Modern Marketing to the Next Level 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.

1990-2000 2000-2010 2010-2020

CRM systems emerged as a must-have for companies large and small.

Marketing automation systems quickly became a staple for companies to digitally engage their databases.

Progressive companies are turning to predictive apps to drive conversions.

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

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Predictive InsightsPredictive

AnalyticsReporting

Pairing product X with a Y at these accounts will generate $21 M in revenue here

PRESCRIPTIVE

DESCRIPTIVE

100 customers bought product X

Companies with increased hiring rates will buy product X here

Business Value

Sophistication of Technology

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?

Predictive marketing can show how prospects engaged with various marketing channels, or which campaigns performed better than others. It can help answer the following:

Predictive marketing combines predictive and prescriptive analytics to forecast what will happen and how to make it happen.

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of all Fortune 500 companies use marketing automation

of the largest SaaS providers are doing the same

25%

76%

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.

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.

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 sci-entists 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 democra-tize their use by actual business users rather than by PhDs.

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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 indi-cate 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 • 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.

of Amazon’sproduct salescome fromrecommendationengines

35%

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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 integra-tions to turn these systems into robust marketing and sales data warehouses.

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 net-works, 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 highest-value leads.

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Existing customer

Lost opportunities

Product usage data

Trial products and trial dates

Customer support cases

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 buy-ers 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

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Email response and opens

Website visits

Content engagement

Webinar attendance

Event participation

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.

Sample Predictors of Buyer Intent from Marketing Automation

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FEATURE SELECTION DATA NORMALIZATION PREDICTIVE MODELING

Determining the correct mix of attributes for inclusion

Ensuring that each attribute maximizes the contribution to the model

Mining the data to “fingerprint” what makes a good lead or prospect

Understanding Predictive ModelsNot All Predictive Models are Created EqualTwo 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.

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FEATURE SELECTION Statistical models perform best when they incorporate 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.

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

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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 account for cross- and upsell purposes. For example, this account bought products A and B, so the probability of buying C is very high.

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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.

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 under-standing the problem you are trying to solve will help ensure that you get value from predictive modeling.

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

Top Considerations For Marketers Evaluating Predictive Apps

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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 sim-ply enough to have a broad, high-quality set of buying signals or have a single, great predic-tive 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.

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Lead ScoringNumerous 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 buy-ing behavior. In fact, SiriusDecision reports that 94 percent of MQLs will never close despite all of our efforts.

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 per-centage of data that could be gleaned from prospects and requires a heavy dose of gut instinct and intuition.

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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.

Determine the actual probability of each prospect becoming a customer with unmatched precision.

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Weaving Predictive Lead Scoring into the Buyer JourneyIt’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.

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.

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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

THE BUYER’S GUIDE TO PREDICTIVE LEAD SCORING

Source: SiriusDecisions

11

INBOUND OUTBOUND

AUTOMATION QUALIFIED LEADS

TELEPROSPECTING ACCEPTED LEADS

SALES QUALIFIED LEADS

WON BUSINESS

TELEPROSPECTINGGENERATED LEADS

SALES QUALIFICATION

INQUIRY

MARKETING QUALIFICATION

CLOSE

→ THE DEMAND WATERFALL

TELEPROSPECTING QUALIFIED LEADS

SALES GENERATEDLEADS

SALES ACCEPTEDLEADS

From SiriusDecisions

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Key Takeaways • According to Decision Tree Labs, most marketers are unhappy with their current

approach to lead scoring. • Rules-based lead scoring techniques typically only account for one to five percent of

data available on a prospect. • Predictive lead scoring empowers marketers to build a sophisticated model that

actually predicts which lead attributes matter most, based on uncovering true buying signals from internal and external data.

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Key takeaways: • According to Demandgen report, most marketers are unhappy with their current

approach to lead scoring. • rules-based lead scoring techniques typically only account for one to five percent

of data available on a prospect. • Predictive lead scoring empowers marketers to build a sophisticated model that

actually predicts which lead attributes matter most, based on uncovering true buying signals from internal and external data.

0%

5%

10%

15%

20%

25%

30%

35%

40%

Accounts

send to sales/BDrs

send to nurture

Different contact strategy by segment

Predicted

Average

Here is a quick look at how marketers can make predictive marketing actionable.

Prob

abilit

y to C

onve

rt

Accounts/Leads

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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 partici-pants 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 funda-mentally 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.

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.

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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

customer accounts.

of marketersnoted thatABM providedsignificantbenefits toretaining orexpandingexistingcustomers

—Marketo

84%

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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.

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 com-mon 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.

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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.

The Power of Blended ScoringMost 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

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Customer ExpansionImprove Marketing Performance by Targeting Existing CustomersCompanies 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.

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.

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A Great Opportunity to Improve Marketing Performance Hiding in Plain SightFor 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?

• 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.

Reve

nue C

ontri

butio

n

Customer Marketing Opportunity Traditional Demand Gen Opportunity

Existing Customers

Churn/Attrition

New CustomerAcquisition

Unsell/Cross-sell

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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 SightA 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.

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.

of marketersidentifycapturing andintegratingcustomerdata is a keyconsideration

49%

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Predictive Analytics Illuminate the Path to Improved Marketing PerformanceBy 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 matri-ces, arming sales with the right targets, the right products and right messaging can finally unlock the full potential of that 50 percent customer opportunity.

BUYING SIGNALS FOR EXISTING CUSTOMERS ARE FRAGMENTED

Marketing Automation

PurchaseHistory

External DataCRM

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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.

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.

By 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

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ConclusionThere 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 EnginesLattice 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 predic-tive 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.