PREDICTIVE LEAD SCORING. Guide For Modern Marketers

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2 Executive Summary According to a 2014 survey conducted by SiriusDecisions, ninety percent of B2B organizations see more value with predictive lead scoring than traditional lead scoring. Ninety eight of the respondents said that they would purchase predictive lead scoring again. 90% 98% 88% of users of respondents of respondents agree that predictive lead scoring provides more value than traditional (MAP and Point-Based Attribute and Activity Based) Lead Scoring Approaches. say they would purchase predictive lead scoring again. agree that they are receiving value from their predictive lead scoring platform. Source: The SiriusDecisions Predictive Lead Scoring Study Infographic The reason for this is that many marketers find the traditional lead scoring and nurturing that come out of the box of marketing automation platforms difficult to implement because they are unable to determine what combination of profile and engagement data indicates a true propensity to buy. This becomes even more difficult as prospect behavior isn t static and changes over time, and also because organizations lack the data to make these models work and the predictive power in order to validate them. Fortunately, predictive lead scoring changes all that. It mines data from the web and augments the organization s CRM. In addition, it takes into account thousands of variables in order to segment leads into powerful personas for nurturing and to identify those golden nuggets that should be passed to sales. But most importantly, predictive lead scoring changes the sales and marketing dynamic from lead to revenue and helps both teams focus on finding the next deal in the database haystack. Mintigo Inc. All Rights Reserved. 2

3 The Marketing Funnel & The Need To Prioritize Leads The Demand Waterfall. Otherwise known as The Funnel, this framework introduced by the research and advisory firm SiriusDecisions defines a shared view between marketing and sales of the lead management process. Though many organizations have variations of the funnel defined within each of their organizations, here s the official and most updated version of the SiriusDecisions Demand Waterfall. Source: Image used with permission from SiriusDecisions, The Demand Waterfall, Rearchitected Mintigo Inc. All Rights Reserved. 3

4 The context for commonly-used terms in B2B like marketing qualified leads (MQLs) and sales qualified leads (SQLs) is found within this framework. Performance of marketing and sales teams are typically measured by KPIs aligned along the funnel, with target/quota metrics that are calculated (or oftentimes assumed!) by backing in from the bottom of the funnel (i.e., closed won customers) all the way to the top (i.e., inquiries or prospects). Many demand generation marketers will then plan campaigns and programs that are meant to drive conversions throughout the funnel with the aim at meeting or exceeding these performance goals. However, as the saying goes the best laid plans of mice and men often go awry. 95% of leads coming from your website are not ready to speak with sales. Brian Carroll, author of Lead Generation for the Complex Sale 98% of MQLs never result in closed business. SiriusDecisions If this is the case, and if we have a certain number of wins we need to acquire at the bottom of the funnel as our target, then the easiest thing for a marketer to do is pour more into the top of the funnel. More leads! I want more leads! Said no sales person ever. Sales wants ready-to-close deals with a neatly tied bow on top. If they can t get that then the next best thing is more opportunities. With the growth of marketing technology, marketers have gotten better and better at creating larger and larger volumes of leads. The problem is of course that not all leads convert, and there are numerous reasons why. But what inadvertently happens is that in order for marketing to meet their MQL targets, they generate more and more leads and throw them over the wall to sales in the hopes that if conversion rates stay stable then more will flow through the middle and bottom of the funnel. Mintigo Inc. All Rights Reserved. 4

5 In reality this torrent of leads contain a high amount of low quality leads with some great-fit leads sprinkled in here and there without any reliable way to identify them, so it s not uncommon to hear sales tell marketing that the leads they re passing over are not qualified. As a result, sales stops following up on all of the leads and marketing begins blaming sales by saying that they don t call on the leads. It s a vicious cycle that breeds mistrust between the two teams, which leads to the stereotypical misalignment between marketing and sales. Junior sales or business development reps (SDRs and BDRs) are hired as a stopgap to crank out a high volume of prospecting calls with the goal of finding the good leads to pass on to their account executives. With the growth of marketing technology, marketers have gotten better and better at creating larger and larger volumes of leads. The problem is of course that not all leads convert Mintigo Inc. All Rights Reserved. 5

6 Limitations Of Traditional Lead Scoring For Prioritizing Leads The fact of the matter is that there are still a lot more wasted calls on poor leads than on the good ones. So as a way to prioritize leads for follow-up, the concept of lead scoring was born. According to the Oracle Marketing Cloud Lead Scoring Guide For Modern Marketers, lead scoring is an objective ranking of one sales lead against another. It assigns a ranking to each sales prospect based on understanding of their interests and buying intentions. Lead scoring typically weighs two major factors in order to determine a lead s score: Demographic: Scoring on dimensions such as job title, industry, revenue and number of employees. Behavioral: Engagement with content such as click on s, ebook downloads, whitepaper downloads and website visits. All of these should suggest interest. With greater adoption of marketing automation, the built-in functionality for lead scoring in these tools are used to identify which leads have a higher propensity to buy or likeliness to convert to the next buying stage but often with inaccurate results. The idea of lead scoring isn t broken, and implementing the mechanics of a very basic scoring system in your marketing automation tool is easy enough. In fact, many marketing automation systems do a great job at tracking a prospect s demographic, firmographic and behavioral data that can be used to build a scoring model upon. The challenge that many marketers face, however, is knowing exactly what data points to base the scoring upon, how much weight to assign to these data points, what those scores actually mean, and how to utilize those scores effectively. Mintigo Inc. All Rights Reserved. 6

7 Thus, while lead scoring that occurs in marketing automation platforms today has provided tremendous benefits, it also has some serious limitations. As B2B marketers, we all know that having a lead scoring system to identify sales-ready leads or potential buyers is critical to running successful demand generation programs and to maintaining marketing-and-sales alignment. But implementing a lead scoring system that actually works is easier said than done - setting up a lead scoring system that accurately identifies the best leads takes a lot of time and effort. Why is lead scoring so hard to do and to get it right? Here are a few different reasons. 1 Not Enough Data Is Available For Accurate Scoring The challenge that many marketers face... is knowing exactly what data points to base the scoring upon, how much weight to assign to these data points, what those scores actually mean, and how to utilize those scores effectively... The first reason is that most marketers don t have enough data in order to build a precise scoring model off of. Much of the data are simply captured upon form fills on a website or landing page, and the types of data are generally basic contact and demographic info about the lead or company such as company size, industry, and job titles. Best practices tell us that the shorter the form, the better the on-page conversion. However, the less data you collect, the less you know about your prospect. In addition, this is assuming that the prospect is not entering any incorrect info, either purposefully (e.g., asdf@abc.com ) or unwittingly ( fat-fingering it while typing on a mobile device). Mintigo Inc. All Rights Reserved. 7

8 2 Lead And Company Data Is Often Not Accurate, Standardized Or Up-To-Date Company and lead data often found in CRM and marketing automation systems get stale and are usually filled with errors or contain variations of the same information. Typically, after a lead or a contact is created in the CRM, sales reps rarely update them with new information. However, people change jobs or get promoted, companies grow or launch new products, needs change over time, etc.. Customers and prospects keeps changing but the data in internal systems that is used for targeting don t get updated. In addition, most CRM data is not clean or normalized. When multiple people enter data over a long period of time, it becomes less and less accurate...especially without any data validation or management rules in place. For example, a data field for country can include US, U.S.A., United States...which means the same thing to a person but from a data perspective these are all different. 3 Traditional Scoring Models Are Often Based On Guesswork We don t know if the data that we re using to score are actually the right ones to use. We follow general rules of thumb, such as scoring visits to our pricing page or looking at long on-page dwell times as a qualifier. Thus, we say to ourselves, let s assign 50 points to any leads that engaged in these activities. This is essentially basing our scoring on gut feeling (aka guessing). The problem is that we don t know if these actions are truly applicable or how much importance they should carry. Mintigo Inc. All Rights Reserved. 8

9 4 Irrelevant Data Points Used In Scoring Will Give Inaccurate Results Lastly, (and this is related to the second reason above), our scoring may be based on false correlations because we performed eyeballing-analysis. What this refers to is basically the fallacy of placing emphasis on specific data points because we see them as common data points across our customers, which may not be great indicators. For example, let s say we noticed in our data that four out of the last five customers who purchased our CRM software had red hair. So, we decide to score any new prospects with red hair really high. We even decide to get this critical info by asking what hair color they have on our landing page forms. This is obviously a very silly example, but many lead scoring models are based on this type of shoot-from-the-hip analysis. In general, traditional lead scoring methods are based off of data that only gives a small glimpse of the makeup, and more important buyer-fit potential, of the company. They are also typically using a set of rules of thumb that are based on common sense rather than statistical validation off of data. These rules are hard to set up and maintain, specifically if they don t drive superior results. So how do we identify the right prospects who are ready to buy without having to rely on guesswork or after-the-fact behavioral data that may take a long while to gather, which extends the sales cycle and perhaps puts you at risk of losing a hot lead because you re still gathering the data to score that very lead? Mintigo Inc. All Rights Reserved. 9

10 Predictive Lead Scoring: The Promise Of Finding The Right Buyers Faster Now what if your lead scoring is based on the true profile of your ideal customers? What if it can help you target prospects who are most likely to become buyers for each of your products? And what if the lead scores can actually help us determine the best nurture path for each lead, instead of the other way around where we re using nurturing to simply gather behavioral data for scoring? The answer to these questions is predictive lead scoring. Lead scoring as a concept is actually meant to be predictive in nature because it is a methodology that ranks leads in order to determine their sales readiness. As mentioned before, the reality is that traditional lead scoring models aren t very accurate predictors of sales readiness because of the challenges that lie in the surface level analysis of the small data set that we use to build our lead scoring upon. So instead of relying on the aforementioned methods of rules of thumb, gut-feel, and guesswork to build a lead scoring model, a better approach is to utilize proven scientific and statistical methods to predict who is most likely to buy. The idea is to use data science (i.e., statistical analysis, look-alike modeling, machine learning, etc.) to identify both the data within your customer database as well as external data signals gathered from all over the web that have a high correlation to converting prospects into customers. This process is called predictive modeling, and the output of this process is a well-defined ideal prospect profile which provides a more comprehensive picture of leads that have the highest propensity to buy. This profile should be the target that you base your lead scoring against. Mintigo Inc. All Rights Reserved. 10

11 If we were to define predictive lead scoring, it would be this - a methodology for ranking leads to determine sales readiness by using predictive modeling and other data science techniques to discover the most accurate and relevant data points for which to score. What s implicit to this definition is the requirement of having access to data that isn t normally captured within typical CRM or marketing automation systems. While the data that resides in a CRM or marketing automation tool is important to use predictive models, it only gives you a limited view of the profile of your true buyers as previously described. Predictive scoring is a methodology for ranking leads or accounts to determine sales readiness by using predictive modeling and other data science techniques to discover the most accurate and relevant data points for which to score. Mintigo Inc. All Rights Reserved. 11

12 It Begins With Having Access To All The Right Data In order for us to use lead scoring to target the right audience, we need to understand which data fields to base the scoring model on to truly make it effective. These should include data points that are captured in your marketing automation system, CRM or other customer database, but even more important are the exogenous data that is available on the web that can tell you a lot more about your customers. And, what s just as important is to have these data points available at your fingertips within your marketing automation and your CRM so that you can build trust with your sales team and provide them with context around the score. More on this last point later. Mintigo Inc. All Rights Reserved. 12

13 Cleanse and Standardize Data From CRM And Marketing Automation You probably know some things about your leads: which campaigns they ve seen, where they clicked and what they filled on your form. This valuable data is the starting point to building a predictive model, but before you can do so, you need to make sure your data is ready by addressing the following: Data cleansing - updating or deleting dated and incorrect CRM records. Identify bad data such as duplicates, bad s, bounced s, bad names, bad titles, bad company names, etc. Data normalization/standardization - classifying and consolidating CRM data into standard formats. Company name, addresses and other data elements need to be standardized. The result of this process is a highly accurate and standardized database of your records, which then becomes a solid foundation for predictive lead scoring modeling. Some of the predictive technology vendors include data cleansing, normalization and standardization as part of their offering. Mintigo Inc. All Rights Reserved. 13

14 Big Data From The Web Fills In The Gaps About Your Ideal Customer Profile The amount of data that s generated across the web continues to grow every second of every day. For B2B, there exist clues in this data that can reliably point to which leads are more likely to close. Most of this data comes in the form of unstructured data, which is scattered around in millions of websites, blogs, social networks, job boards, news sites and online databases. Predictive technology that employs data crawling and data mining techniques gathers data from the web and looks for patterns and clusters in these large data sets, thereby bringing structure to unstructured data and helps to turn them into meaningful insights. For example, companies websites and social networks have a large list of profiles, each containing a person s job title. It would be hard to gain meaningful insights by looking at one profile after the other. Data mining and predictive marketing algorithms helps extract this data and structure it in a manner that standardizes and uses them to make meaningful prediction models about the future. Storing and processing this data on a regular basis require a set of technologies and processes that are different from those used with traditional data processing applications. Since predictive technologies typically use vast quantities of data mined from the web and this data is continuously updated, they require big-data processing capabilities. So why is all of this exogenous data useful? Predictive technologies that monitor the online behavior of millions of B2B companies and decision-makers can identify a digital footprint that can predict their fit and tendency to buy products and services. This information may include public information on financials, staff, hiring, technologies used, marketing and sales tactics, website source code as well as semantic analysis of on-page text, and even purchase intent signals. Mintigo Inc. All Rights Reserved. 14

15 Identifying Your Ideal Customer Profile With Data Science Once you have the data from your marketing automation platform or CRM ready to go, you combine it with all the potential buying signals from the exogenous data and use predictive analytic machines to do all the number crunching and statistical analyses for you. This predictive modeling process is where the magic happens. By using data science methods such as machine learning, the predictive model will analyze all of the data and uncover the data points that actually matter and have the highest correlation across your customers. In other words, the model will identify the set of data indicators that make your customers unique compared to all of the other leads or prospects in your database. The output of this analysis is a picture or profile of your ideal customer, your target, who you want to get more of. We call this your CustomerDNA - a more complete set of data indicators that can be used to predict the likelihood for a lead to convert. Mintigo Inc. All Rights Reserved. 15

16 Predictively Scoring Leads Against Your Ideal Customer Profile The CustomerDNA, or ideal customer profile, as previously described becomes the barometer for predictively scoring prospects. By evaluating every prospect in your database as well as those coming inbound, any data indicators share by the prospect with the ideal customer is identified by the predictive model. This results in a predictive score to show how similar the prospect is to your customers - the more data indicators shared the higher the predictive score. The main point is the predictive scoring will show you how closely matched each lead is to this ideal customer profile, your target audience. This gives you a 360-degree profile of each lead in your database and identifies the leads with the highest propensity to buy. The secret sauce is to continuously, and in real-time, analyze the attributes of the highest value customers and discover prospects like them in your lead database. We have a better understanding of who we re selling to today and which companies are more likely to buy from us. Cameron Conway Director of Customer Insights at Red Hat Mintigo Inc. All Rights Reserved. 16

17 Benefits Of Predictive Scoring For B2B Demand Generation You can accomplish several goals with predictive scoring, including: Uncovering Hidden Gems In Your House List - Once you identify your ideal customer profile, you can predictively score your entire prospect database to find any low-hanging fruit - these are the untouched or recycled leads that look similar to your customers but didn t make it all the way through the sales funnel. Better Funnel Efficiency & Lead Velocity - By helping your sales reps more accurately identify the higher quality inbound leads, they ll be able to prioritize lead follow up more efficiently and start with working on the leads most likely to respond. With predictive scoring, marketing will be able to provide sales with a better idea of who are the best bets. Where should they be spending their time? Where should they be going the extra mile, because they have a higher chance of converting the lead? Data-Driven Persona Development & Fine - Tuned Segmentation - With knowledge of your ideal customer profile and the data elements that are part of its DNA, product and content marketers will be able to develop richer personas for better messaging, and marketing operations will be able to more effectively segment lists for targeted campaigns, relevant nurture tracks, and a more personalized customer experience. Sales Enablement - By providing the context behind the predictive score and making the exogenous data for each lead transparent and available directly within the CRM, sales will be armed with greater intelligence for each lead and understand why that lead will most likely buy. Find Cross-sell or Upsell Opportunities - If you re able to create multiple predictive models (one for each of your product lines, for example), then you re able to analyze a lead against each of these models to figure out which products the lead will most likely buy. Mintigo Inc. All Rights Reserved. 17

18 Conclusion Predictive lead scoring and nurturing resolves the challenges of traditional lead scoring by both expanding the data and improving results. It uses the power of algorithms in order to prioritize leads and focus the time, money and effort of marketing and sales on driving more revenue. So rather than building your scoring model on potentially arbitrary data points, predictive lead scoring will tell you what your ideal prospects should look like according to characteristics of your customers. It will show you how to build your lead scoring on the data points that actually matter, so you no longer have to guess. Mintigo Inc. All Rights Reserved. 18

19 About Oracle Marketing Cloud Modern Marketers choose Oracle Marketing Cloud solutions to create ideal customers and increase revenue. They use integrated information from cross-channel, content, and social marketing with data management to deliver personalized customer experiences across all marketing channels. Visit oracle.com/marketingcloud. Copyright 2014, Oracle and/or its affiliates. All rights reserved. About Mintigo Mintigo is the leader in enterprise predictive marketing. Mintigo s awardwinning predictive marketing platform helps enterprise marketing and sales teams find buyers faster. Enterprise customers including Oracle, Getty Images, TeleTech, Red Hat, Workfront, Equinix, SolarWinds, Time Warner Cable and Neustar work with Mintigo to transform their marketing automation and sales processes. To learn more, please visit mintigo.com. Copyright 2016 Mintigo Inc. All rights reserved.