Decoding Predictive Marketing AN INTRODUCTORY GUIDE

Transcription

1 Decoding Predictive Marketing AN INTRODUCTORY GUIDE

2 ContentING PAGE 3 Introduction to Predictive Marketing PAGE 10 Hidden Inight in CRM and Marketing Automation PAGE 13 Undertanding Predictive Model PAGE 18 Lead Scoring PAGE 23 Account-Baed Marketing PAGE 25 Account Scoring PAGE 27 Cutomer Expanion PAGE 32 Concluion and About Lattice

3 Introduction to Predictive Marketing Given growing revenue target, many top modern marketer are realizing that they need deeper intelligence to keep up with hifting buyer behavior. Predictive marketing work by taking all the data in the world including account-level information about the buinee we ell to and the lead-level information about the people we actually ell to and applying modern data cience to olve top marketing challenge. Some common quetion and challenge include: Who i going to be my next cutomer? How can I find more of thee ideal cutomer? How do I convert them? 3

4 Here i a ample of the predictive attribute at the contact and account level that are hidden acro a wide variety of ource. INTERNAL EXTERNAL Source Marketing Automation CRM Sytem Product Uage Log Purchae Hitory Cutomer Support Hitory Public Webite Company Webite Social Webite Media Selected Attribute Contact name, title, company, open rate, unubcribe, web viit, page viited, lead core, video view, download Company, contact information, win/lo, deal value Feature ued, login, eion length, collaboration Product purchaed, price paid, dicount, contract term Complaint, reolution Job poting, grant, litigation, patent, contract, location, growth Language(), product, hopping cart, executive team profile Company and peronal profile, like, comment, update, friend/ connection/follower, uage New article and torie, product launche, announcement, pre releae, litigation Predictive analytic ha emerged a poibly the ingle-mot important technology and competitive differentiator for B2B marketer to adopt. Private Databae Credit rating, financial hitory, contruction permit/ tart, deployed technologie Matt Heinz, Heinz Marketing 4

5 By applying new technology to the wealth of data at their dipoal, marketer now have acce to predictive modeling without having to turn to a team of data cientit. By better undertanding buyer behavior and intent, marketer can core lead and prioritize account in addition to elling more to their exiting cutomer. Upell Cro-Sell It no wonder that predictive analytic i emerging a central to the modern marketing organization. By leveraging data cience to make ene of all the data in their midt, leading B2B marketer are marketing and elling more intelligently. Shahi Upadhyay, CEO, Lattice Engine Retain Picking Up Where Marketing Automation Leave Off Perhap you re thinking, I m already marketing efficiently by uing marketing automation. It true that marketing automation oftware can treamline the marketing proce from end to end. It can alo track the hitoric behavior of propect and core lead. 5

6 Taking Modern Marketing to the Next Level A marketing evolve from a cot center to a revenue driver, companie that have uccefully implemented CRM and marketing automation are now looking at what' next CRM ytem emerged a a mut-have for companie large and mall. Marketing automation ytem quickly became a taple for companie to digitally engage their databae. Progreive companie are turning to predictive app to drive converion. Marketer once had to gue where their weet pot wa. Now we can ue data cience to tell u. Marketing automation ha allowed marketer to collect more data on their propect than ever before. However, from a performance tandpoint, the bet that marketing automation can do i provide a view into what happened in the pat. The action marketer can take on marketing automation data are purely reactive. You learn omething about a lead then you ue that data to take action end them an , add them to a campaign, alert ale that they ve done omething or core them baed on thoe action. By contrat predictive marketing i proactive. It take all that data into account and blend it with data that i uneen by the naked eye, allowing marketer to guide their buyer' journey baed on all that i knowable. Meagen Eienberg, Vice Preident of Demand Generation, DocuSign 6

7 Predictive marketing can how how propect engaged with variou marketing channel, or which campaign performed better than other. It can help anwer the following: Which companie are the bet target? What marketing activity i mot likely to yield the bet reult? How much new revenue could potentially be generated? Predictive Analytic Predictive Inight Buine Value Reporting PRESCRIPTIVE DESCRIPTIVE 100 cutomer bought product X Companie with increaed hiring rate will buy product X here Sophitication of Technology Pairing product X with a Y at thee account will generate $21 M in revenue here Predictive marketing combine predictive and precriptive analytic to forecat what will happen and how to make it happen. 7

8 The Time i Ripe for Predictive Marketing Why i predictive marketing a mut-have? 25 percent of all Fortune 500 companie and 76 percent of the larget SaaS provider are uing marketing automation. Overall adoption rate are above 50 percent for SMB and more than 70 percent in larger organization. Many of the marketer who embraced the promie of marketing automation have puhed the oftware to it limit. They ve refined their campaign and meaging baed on the information they ve collected via the ytem. They ve improved efficiencie but are now looking for way to optimize their performance. 25% A more B2B organization eek to win over entire buying committee involved in purchae deciion, they are moving from pure contact-level to trategic account-level marketing. Marketing automation wa developed around the concept of a contact databae. For that reaon, mot of thee ytem are le adept at addreing entire account veru individual. Yet marketer cannot afford to ignore the wealth of buying ignal or the account-level attribute that can provide key inight into the need of propect. Previouly only the mot ophiticated companie could make ue of predictive analytic. If marketer wanted to make marketing more predictive, they were forced to rely on a team of highly trained data cientit uing complex analytic platform to build predictive model from cratch. Since thee data team often erved a a hared reource acro the organization, marketer often waited week or month to have their requet fulfilled. Now, the power of predictive analytic i acceible to any company. A new generation of predictive marketing application i harneing the power of machine learning to democratize their ue by actual buine uer rather than by PhD. of all Fortune 500 companie ue marketing automation 76% of the larget SaaS provider are doing the ame 8

9 Take note from Internet giant like Amazon and Netflix. Both companie have become ucceful baed on developing recommendation from predictive modeling. In fact, Amazon note that 35 percent of it product ale reult from it recommendation engine. Both of thee companie combine profile and behavioral indicator from thouand of ignal from the Web, ocial media, new ource and beyond to power their predictive model. In eence, they re tapping into all the information that indicate when a cutomer i likely to need a pecific product. For example, you may not be looking for a hovel but Amazon know that your neighbor jut bought one, indicating you may need one too. By uing all the data in the world, every marketer including you can optimize hi/her revenue funnel to imultaneouly improve converion rate, increae revenue and improve lead velocity. Key Takeaway A marketing become a revenue driver, companie that have implemented CRM and marketing automation are looking for new inight to take their modern marketing effort to the next level. Predictive marketing work by taking all the data in the world about account and propect from both internal and external ource and applying modern data cience to optimize converion of all tage of the revenue funnel, and tackle other top-of-mind challenge. Modern-day data cience make it eay for companie of all ize to ue the ame technique Internet giant ue to develop recommendation. 35% of Amazon product ale come from recommendation engine 9

10 What Inight Are Hidden in Your Marketing Automation and CRM? A the adoption of CRM and marketing automation mature, it no urprie that companie with thee technologie are itting on a wealth of data. Out of the box, CRM and marketing automation are deigned to capture and tore a rich et of information on cutomer and propect. Many companie add additional cutomization and integration to turn thee ytem into robut marketing and ale data warehoue. Good ale rep are already pouring through CRM to review contact and account information prior to dialing a propect. They are earching for opportunitie created, looking at recently won deal and urveying lot opportunitie. Uing robut API, companie are connecting CRM to internal ytem that provide product trial and uage data and cutomer upport data. They re alo mining ocial network, looking for clue into buyer need and trying to find connection. While thi behavior i effective, it i time not pent cloing deal. The bet rep know how to take advantage of thi full et of information to determine whether or not a propect i ready to be engaged. They want to drive productivity by focuing their time on the highetvalue lead. 10

11 At the ame time, marketer are creating laer-targeted campaign within their marketing automation platform baed on lead demographic and behavior. They have cutomized nurture tream for buyer at variou tage of the funnel or by vertical, perona or product interet. Savvy demand-generation team are alo enriching their lead with third-party data. Their goal i to target the right propect with the right meage, at the right time and then pa them off to ale when they are mot likely to convert. Data-driven marketer are maniacal about meaurement. They are keen to undertand the attribute in marketing automation that indicate a lead i ready to buy and i, therefore, ready to be paed to ale. Let take a look at ome ample poitive and negative predictor of buying intent that can be found within the CRM and marketing automation: Sample Attribute of Buyer Intent from CRM Exiting cutomer Product uage data Trial product and trial date Lot opportunitie Cutomer upport cae 11

12 Sample Predictor of Buyer Intent from Marketing Automation repone and open Content engagement Event participation Webite viit Webinar attendance Thee are jut ome of the attribute that can carry predictive value. Companie continue to enhance the breadth and richne of data they tore in CRM and marketing automation. Predictive marketing can leverage thi rich data to help marketer pa on the mot lucrative lead to ale. Key Takeaway A wealth of data that i predictive of buyer intent i hidden in CRM and marketing automation. Predictive marketing can turbo-charge your CRM and marketing automation effort to highlight the mot ale-ready lead. 12

13 Undertanding Predictive Model Not All Predictive Model are Created Equal Two key ingredient are required for efficient, highly predictive model data and analytic. While the data i crucial, the algorithm and analytic behind the predictive model are the engine that do mot of the heavy lifting and differentiate good prediction from great prediction. FEATURE SELECTION DATA NORMALIZATION PREDICTIVE MODELING Determining the correct mix of attribute for incluion Enuring that each attribute maximize the contribution to the model Mining the data to fingerprint what make a good lead or propect 13

14 FEATURE SELECTION Statitical model perform bet when they incorporate the mot optimized et of attribute (or feature ). It i typical to have thouand of candidate attribute that could potentially be included in the pattern-matching algorithm. To tart, it i common to apply variou tatitical technique to determine which attribute hould be retained and which hould be dicarded. Many predictive model alo look at the creation of derived attribute, which tranform the raw data in a native attribute into a form that i more meaningful in a predictive model. For example, the founding date of a company i ued a the bai for a derived attribute called company age that i likely far more predictive than founding date. DATA NORMALIZATION While data repreent a key input into any predictive algorithm, it can take many hape and form. Some attribute like number of open or annual pend are relatively traightforward to mine, wherea attribute like job title or geography need pre-proceing before they can truly hine. MODEL EXECUTION The real value of machine learning come out when the model are finally elected and launched. While data repreent a key input into any predictive algorithm, it can take many hape and form. Matt Pollock, VP, Lattice Engine 14

15 Here i a ample of the technique that can be ued in predictive model. LOGISTIC REGRESSION i a type of regreion analyi ued for predicting the outcome of a categorical dependent variable. Logitic regreion i very reource-intenive, conuming a great deal of memory on a large data et; however, it i very table and work particularly well when you have continuou feature or attribute like revenue data. DECISION TREES are very powerful algorithm that help identify the bet predictor. Deciion tree are intuitive to analyze and uually produce great reult when applied to a mixture of categorical (i.e. SIC CODE, indutry vertical, location) and numerical attribute. RANDOM FOREST i one of the technique behind the recommendation engine in Netflix and alo a popular technique in the Hadoop framework. The main idea i to build a foret of many deciion tree over different variation of the ame data et and take weighted average of the reult. Thi technique i very powerful becaue it can effectively identify pattern acro a large noiy dataet. The technique i computationally expenive, but it can be eaily run in parallel. NEURAL NETWORKS are a compoition of neuron combined together to decribe a data et. While machine-intenive, it i very powerful when you try to decribe event that are non-linear (for intance, a ale campaign that pan acro multiple market egment). Neural network are typically ued to identify very complex pattern. K-MEANS CLASSIFICATION CLUSTERING can be very ueful for propecting. For example, take the numerou exiting cutomer and potential propect in your CRM oftware. Clutering allow you to find imilaritie between account and rank them according to the degree of imilarity. NAÏVE BAYES i a probabilitic claifier. It i very ueful in identifying pattern and behavior of an account for cro- and upell purpoe. For example, thi account bought product A and B, o the probability of buying C i very high. 15

16 Top Conideration For Marketer Evaluating Predictive App There ha been an exploion of interet in uing machine learning to build model to predict cutomer behavior, and many companie offer product and olution in thi domain. A you go through the journey to predictive marketing, you ll want to keep the following conideration in mind. 1 Think about the problem you are trying to olve. If you don t have a particular deciion or marketing problem you are trying to olve in mind, then it may not be time to invetigate predictive marketing and analytic. Analytical model are all built with ome et of aumption about what they are trying to predict and undertanding the problem you are trying to olve will help enure that you get value from predictive modeling. 2 Enure you have the right data aet to olve your problem. Organizing data can be a daunting tak. It may eem like you can get an acceptable anwer with a mall ubet of the row or column, but you ll want to enure that you have enough data o that the reult are not mileading. 3 Undertand what ucce look like for your company. Identifying ucce for any type of predictive model typically conit of weighing all of the variou factor of the model into a ingle model quality factor. Here are ome example of how uch model quality could be defined: The lift in the top 10 percent of cored recommendation The difference in the lift in the top 10 percent of cored recommendation and the percentage of recommendation that fall in the lowet 30 percent The order value for the top 20 percent of recommendation 16

17 4 Think through what you will do with the output. Knowing what you want to do with the output i critical from the tart. You may uncover inight that ultimately change a well-etablihed proce within your organization. Change management i often an after-thought but hould be conidered at the beginning of any predictive marketing initiative. Conider planning out communication or training chedule if the need arie. Data cience ha an enormou potential to bring immediate and ignificant impact to the performance of a wide variety of buine problem. Vendor in thi pace bring expertie, oftware and data that can accelerate the impact of thi trend on your buine. It not imply enough to have a broad, high-quality et of buying ignal or have a ingle, great predictive algorithm. Key Takeaway Undertand the problem you are trying to olve firt. Thi ultimately help inform the model and enure a higher rate of ucce. The data and the model matter. Some pre-proceing work may be neceary in order for predictive marketing to work. Undertand what ucce look like before you get tarted. 17

18 Lead Scoring Numerou apect of marketing could be vatly improved with better predictive inight, but many marketer are finding predictive lead coring i the bet place to tart. Why? Many marketer have found their current lead coring initiative have failed to live up to their expectation. According to a report from Deciion Tree Lab, 44 percent of companie uing marketing automation have implemented lead coring. However, on average, urvey repondent graded their lead coring program five out of 10. Why? Mot commonly, it a imple lack of good inight into what contitute actual buying behavior. In fact, SiriuDeciion report that 94 percent of MQL will never cloe depite all of our effort. It no wonder marketer can increae their converion rate only o much by making do with the current crop of rule-baed lead coring engine. Traditional lead coring prioritize lead baed on variou fit and behavior criteria in hope of getting a picture of a good lead. However, thi approach tap into jut a mall percentage of data that could be gleaned from propect and require a heavy doe of gut intinct and intuition. 18

19 Marketer are forced to make critical deciion about paing to ale baed on a limited amount of information. In a ene, thi baic lead coring i little more than a gueing game. A a reult, many marketer truggle to demontrate tangible return on it invetment in marketing automation. A better option i to tap into the power of predictive lead coring. Thi advanced lead coring approach augment the demographic and behavioral attribute that are part of baic lead coring with thouand of additional data point. Example include whether the company in quetion recently received funding, moved to a new location or hired new deign engineer. In eence, predictive lead coring empower marketer to build a ophiticated model that actually predict which lead attribute matter mot. Thi approach allow them to: Combine contact- and account-level attribute to get a complete 360-degree view of all buying ignal not jut thoe captured in marketing automation. Uncover the true definition of a good lead through the ue of data cience rather than intuition or conenu. Determine the actual probability of each propect becoming a cutomer with unmatched preciion. 19

20 Weaving Predictive Lead Scoring into the Buyer Journey It important that any predictive tool fit into marketing exiting workflow and tool et. Regardle of how an organization view it revenue funnel, the key i to apply predictive lead coring at each crucial converion point epecially the critical hand-off between marketing and ale. The firt converion occur when marketing pae a qualified lead to inide ale to further qualify and accept it a a ale-ready lead (MQL -> SQL). With predictive lead coring, marketer can be aured they are only paing ale the contact who are mot likely to buy. Traditional Lead Scoring veru Predictive Lead Scoring A, B, C 10%, 30%, 40% Let compare the probability to convert. The traditional lead core doen t explain the difference in quality between lead, where a the percentage i very clear and provide far better prioritization. A a reult, ale will no longer wate time trying to track down and qualify contact who would be better erved by a nurture program until they are actually ready to purchae. 20

21 THE DEMAND WATERFALL The econd converion point happen when the ale team i taked with qualifying a huge volume of lead (SAL -> SQL). Without a olid mechanim for deciding where to focu, ale either randomly follow up with lead or cherry-pick lead baed on a very uncientific proce. Becaue of the time required to contact o many lead, often a large percentage of good lead fall through the crack while ale i pinning it wheel with the bad one. If marketing can tell the team how likely a given lead i to convert, the ale rep can prioritize their effort uing cience rather than chance. INBOUND AUTOMATION QUALIFIED LEADS TELEPROSPECTING ACCEPTED LEADS TELEPROSPECTING QUALIFIED LEADS INQUIRY MARKETING QUALIFICATION SALES GENERATED LEADS SALES QUALIFICATION TELEPROSPECTING GENERATED LEADS SALES ACCEPTED LEADS SALES QUALIFIED LEADS OUTBOUND The Demand Waterfall From SiriuDeciion CLOSE WON BUSINESS Source: SiriuDeciion 21

22 Here i a quick look at how marketer can make predictive marketing actionable. 40% Different contact trategy by egment Probability to Convert 35% 30% 25% 20% 15% 10% end to ale/bdr end to nurture Predicted Average 5% 0% Account/Lead Key takeaway: Key Takeaway According to Demandgen report, mot marketer are unhappy with their current According approach to to Deciion lead coring. Tree Lab, mot marketer are unhappy with their current approach rule-baed to lead lead coring. coring technique typically only account for one to five percent Rule-baed of data available lead coring on a propect. technique typically only account for one to five percent of data Predictive available on lead a coring propect. empower marketer to build a ophiticated model that Predictive actually lead predict coring which empower lead attribute marketer matter to mot, build baed a ophiticated on uncovering model true that actually buying predict ignal which from internal lead attribute and external matter data. mot, baed on uncovering true buying ignal from internal and external data

23 Account-Baed Marketing: The Mied Opportunity? For year, B2B marketing tactic have focued largely on individual, a the majority of channel uch a , phone and even event target at the individual level. However, a technologie have evolved, the idea of account-baed marketing (ABM) ha really drawn a great deal of appeal. After all, mot B2B buying deciion happen becaue of a company need and typically involve an entire team of participant in the buying proce. Unfortunately, one of the greatet aet in the demand generation technology tack ha emerged a a barrier to ABM. Marketing automation tool were fundamentally built around the concept of a contact, rather than an account. Even with the account object and account-level attribute that can be tored within marketing automation, marketer are fairly limited in term of the data collection, coring tool and egmentation option they can rely on for creating account-level campaign. Recent improvement have come about to roll lead coring up to the account level. Some marketer are uing third-party provider to help with data appending to add better firmographic inight to help with egmentation. However, true account-level coring and more complex filtering and egmentation capabilitie are till lacking. Miing the ability to tore and track richer, more dynamic account-level data mean marketing automation imply in t the ideal olution to advance the ABM caue. 23

24 So Why the Sudden Interet in Account-Baed Marketing? For one, many of the newer, more innovative marketing channel rely on ABM to make them effective. ABM can target many or all employee within a given account with peronalization, diplay ad, product-level campaign and field marketing event. For example, with tool like Demandbae you can target your content dynamically, baed on the inbound company IP addre. Thi create powerful meaging that i far more relevant if you know which account you are going after. You can alo dramatically reduce your diplay ad pending by ignoring account that aren t relevant to your buine. For example, if you know a propect account ue Saleforce.com, you can diplay an ad that i relevant to that audience, wherea a viitor from an account uing Siebel or Microoft Dynamic CRM would not be targeted. Thi increae effectivene while alo reducing cot a perfect torm of marketing effectivene. ABM provide an opportunity to fuel growth from marketing. Key Takeaway A B2B marketer, we need to remember that we ell to both people and companie. Many exiting marketing technologie are focued on contact, rather than account. Account-baed marketing i critical when thinking about retaining or growing exiting cutomer account. 84% of marketer noted that ABM provided ignificant benefit to retaining or expanding exiting cutomer Marketo 24

25 Account Scoring In a B2B environment, people generally don t buy product or ervice their company doen t need. In reality, it a combination of event and action that park a purchae deciion. For mot conidered to be in B2B buying cycle, the proce begin with ome kind of trigger event within the company, followed by a reaction from a peron or team to look for a olution. The company need dictate a human-lead buying proce. For example, a new round of funding for a buine might lead to an office expanion neceitating a lew of different buying cycle for anything from office furniture to networking equipment. So What Doe All thi Have to Do with Lead Scoring? Quite imply, the tandard practice of coring purely at the contact level mie the much bigger picture. Yet ignoring the behavior of individual within that organization alo limit your perpective. Only by blending the two will you get a complete 360-degree view of your propect buying ignal. When mot companie tart invetigating account coring, they are often building it by uing ome limited firmographic data along with contact-level attribute. The mot common approach i to aggregate or average the core of individual aociated with the account, but thi i really jut cumulative contact coring not true account coring. 25

26 Jut like individual, companie alo exhibit digital body language. For intance, firmographic data may tell you a company fit the right indutry profile or ize. What mot marketer mi are the account-level buying ignal uch a growth trend, hiring pattern, government grant, patent filing or technology uage, jut to name a few. Thee account-level activitie are often the earliet buying ignal, poibly preceding contact-level activitie by week, month or even year. The Power of Blended Scoring Mot marketer look at account and contact eparately, and at bet can create an account core that i an aggregate of contact core. By taking thi blended approach, marketing can much more accurately predict which lead to pa to ale, get ignal much earlier in the buying proce and ultimately create much better alignment between marketing and ale. Key Takeaway A contact level-only approach doen t account for the full picture. Smart marketer are taking a blended approach to lead coring, which combine contact with account-level attribute. Growth trend, hiring, funding and technology uage are all ample account-level attribute that may be predictive of buyer intent. Marketer need to remember to look beyond the contact. There are a ton of great inight you can learn at the account-level that can be ued to target campaign and drive more ale. Jon Miller, VP Marketing and Co-Founder, Marketo 26

27 Cutomer Expanion Improve Marketing Performance by Targeting Exiting Cutomer Companie have improved their marketing performance through the adoption of B2B marketing automation to reach and attract new propect. Depite thee tool and technologie, very few marketing team have applied them with the ame type of rigor for actually retaining and expanding cutomer relationhip. According to a recent urvey by the DemandGen Report: The act of managing multiple, diparate ytem wa cited a the top obtacle to achieving cutomer marketing goal followed by inufficient data. In mot cae, the ale or ervice group till have excluive ownerhip of expanion opportunitie. With uch roadblock, it no wonder marketing i o laer focued on new cutomer acquiition. 27

28 A Great Opportunity to Improve Marketing Performance Hiding in Plain Sight For mot organization, exiting cutomer drive 50 percent or more of revenue. Given the limited capacity of individual ale rep, they mut make intinctive bet about which account and product to focu on. Compounding the problem, rep alo tend to gravitate toward the product and meage they are mot comfortable with, meaning that many newer product, ervice or meaging get limited attention. So what could thee mied opportunitie be coting? Effective cro elling and upelling drive ignificant acceleration in both revenue growth and renewal rate. Cutomer revenue potential i often three to five time larger than current ale opportunitie. Cloing a new cutomer can cot between three to five time a much a retaining or expanding an exiting cutomer. Revenue Contribution Exiting Cutomer Churn/Attrition Unell/Cro-ell New Cutomer Acquiition Cutomer Marketing Opportunity Traditional Demand Gen Opportunity 28

29 Why i Cutomer Marketing Often Ignored? Marketing automation platform are fundamentally deigned around cutomer acquiition. It a imple a that. Feature like propect profiling, egmentation, event management and web analytic are often tuned to gradually collect more information about lead or propect, but lack capabilitie for mining exiting cutomer information hidden in plain ight. Cutomer Buying Signal are Hidden in Plain Sight A urvey conducted by DemandGen Report and Retail TouchPoint revealed that capturing and integrating cutomer data i a key conideration for marketer, with more than 49 percent identifying it a a top priority. However, ome of the mot valuable data doen t come from marketing automation or CRM at all, but rather from tranactional ytem uch a order management, call center or upport log a rich data ource unique to exiting cutomer. 49% of marketer identify capturing and integrating cutomer data i a key conideration Beyond jut activity hitory, marketer alo need to look externally at account-level indicator uch a hiring trend, office opening, funding event or even ocial activity for hidden buying ignal that could repreent good trigger for cro-ell or upell. 29

30 Predictive Analytic Illuminate the Path to Improved Marketing Performance By combining contact- and account-level attribute, marketer can get a full view of the cutomer and apply predictive analytic to identify not jut the bet opportunitie for upell or cro-ell, but alo which product or ervice repreent the bet fit. For organization with complex product and cutomer matrice, arming ale with the right target, the right product and right meaging can finally unlock the full potential of that 50 percent cutomer opportunity. BUYING SIGNALS FOR EXISTING CUSTOMERS ARE FRAGMENTED Marketing Automation Purchae Hitory CRM External Data 30

31 Marketing no longer ha the luxury of focuing excluively on cutomer acquiition. With the right combination of data and predictive analytic, marketer can offer ale rep higher productivity and product leader the proper attention on the full breadth of product and ervice offering. By tapping into cutomer expanion, marketing performance can increae and help ource a much larger hare of total company revenue the other 50 percent. Brian Kardon, CMO, Lattice Engine Key Takeaway In mot companie, exiting cutomer drive 50 percent or more of the revenue. Marketing automation i inherently deigned to go after new cutomer. Sale rep typically gravitate toward the product and meage they are mot comfortable with, meaning that many newer product, ervice or meaging get limited attention. A predictive marketing approach can help marketer identify opportunitie for cutomer expanion through account-level targeting. 31

32 Concluion There ha been an exploion of interet in predictive marketing and uing machine learning to build model to predict cutomer behavior. Data cience ha an enormou potential to bring immediate and ignificant impact to the performance of a wide-variety of B2B marketing problem, helping marketer go beyond modern marketing. Still curiou about predictive marketing? Contact u today. About Lattice Engine Lattice i pioneering the predictive application market for marketing and ale. Lattice help companie grow revenue acro the entire cutomer lifecycle with data-driven marketing and ale application that make complex data cience eay to ue. By combining thouand of relevant buying ignal with advanced predictive analytic in a uite of ecure cloud application, Lattice help companie of all ize to top gueing and tart relying on predictive inight to increae converion rate and deal ize by more than three time. Lattice i backed by NEA and Sequoia Capital with headquarter in San Mateo, CA. Learn more at and 32