Marketer s Guide to Prioritizing Leads. Focus your time, money and effort on bringing new business by identifying and engaging the most likely buyers

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1 Marketer s Guide to Prioritizing Leads Focus your time, money and effort on bringing new business by identifying and engaging the most likely buyers

2 The deal in the haystack To be honest, the leads are crap, we hardly look at them. As a marketer, this was not the feedback that Nick expected to get from Joe, the VP of Marketing. Until that moment, Nick, the lead generation director for a large Business Intelligence solution provider, thought that everything was going remarkably well. Nick had a lot to show for his efforts and programs. His PPC, and content syndication campaigns were generating over ten thousand leads per month. The marketing database contained over two hundred thousand names. Marketing campaigns were consistently getting off-the-charts results. So what went wrong? The challenge was that while Nick was producing torrents of leads, many of these leads were not ready to be passed to Sales. Furthermore, to increase the conversion rate, Nick used a very short form that contained little information about prospects, making it even harder to prioritize leads. Nick was also doing lead nurturing and it seems that many of the leads are proving to be avid consumers of content and BI advice. However, while very successful with top of the funnel content, the nurturing program was not making any progress in pushing leads down the funnel. As expected, business development and sales reps loved all of those contact sales and product demo requests. These were leads that were already in the consideration phase, and if the rep didn t do anything terribly wrong, were likely to close. Naturally, these were limited in number and if the company was going to meet its sales target, reps will inevitably need to be able to handle leads that were higher in the funnel. By definition, Nick was a great marketer, or so he believed. He managed to generate and distribute the content that resonated with his audience, busted click-through and lead generation benchmarks left and right and got the leads in. So, why weren t his marketing programs successful in the mind of his executives? Why weren t these leads converting into more sales? Leads without data are meaningless The short answer is lead prioritization. You probably know the 80:20 rule, where 20 percent of your customers are responsible for 80 percent of your revenue. With Sales and Marketing, it s actually worse. Less than 1% of your website visitors are responsible for nearly all of your revenue, according to Totango. The truth of the matter is that leads without data and scoring are meaningless, as it is nearly impossible for Sales to know who is a highly qualified decision maker doing the research before purchase and who just wants to add another ebook to his collection. Furthermore, passing unqualified leads to Sales damages the trust between Marketing and Sales. Low quality leads result in an erosion of respect by sales. Reps feel that the leads are a waste of time. Sales reps may feel that in order to meet their target, they are better off spending their time prospecting independently rather than leaving more voic s and hearing more rejections. On the other hand, data may change all of that. Simply put, data and predictive scoring and nurturing enable you to prioritize leads for revenue rather than on weak and irrelevant metrics such as leads, clicks, 2

3 or form conversion rate. Leads with robust data can be segmented into buyer personas and prioritized for processing in an optimal manner. Robust data-driven prioritization can stop the cookie-cutter approach to leads and yield significant benefits to both Sales and Marketing: Marketing Sales Optimize campaigns to produce high value leads and maximum revenue. Improve lead nurturing to accelerate leads journey down the funnel. Eliminate spending on unqualified leads, while boosting spending on high value leads (with in-person events, direct mail and invitations). Give white-glove treatment for high priority leads based on the likelihood to buy. Stop harassing people who are never going to buy; instead spend time closing business. Have relevant and consultative conversations with prospects by identifying needs and challenges and offering optimal solutions. According to Brian Carroll, author of Lead Generation for the Complex Sale, 95% of leads coming from your website are not ready to speak with Sales. This ebook explains how to identify these precious 5% and how to grow them by using predictive lead scoring and nurturing. What is wrong with scoring and nurturing today? While in the early days, marketing automation vendors pitched lead nurturing and scoring as the B2B marketing panacea, recent data shows that many of the promises have not come to fruition. This has two main reasons: first, the methodology lacks the rigor of predictive intelligence based on broad data, and second, these programs are hard to implement. Lead scoring 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. Lead nurturing is used to engage leads with a series of touches and engagements aiming to educate them about the product and service, as well as keep brand awareness high. There is also another role for lead nurturing to keep increasing leads behavioral score by eliciting them to download more and more content. While this sounds great in theory, in practice these tactics do not yield the results that justify the effort in setting them up. According to a research published on David Raab s blog, lead scoring and nurturing are among the least effective marketing tactics and are clearly among the hardest to execute. 3

4 Source: David Raab The difficulty of setting up lead nurturing and scoring as compared to the average returns is hurting Marketing Automation. Shockingly, the same study shows that 82% of companies that adopted marketing automation are making limited use of it, or are not using it at all. In another post, David Raab explores the share of marketers that are using the full features of marketing automation. Raab compared results from four different reports and normalized the results, so that the best answer equals to 100. According to his account, is the most commonly used feature in marketing automation. Lead nurturing is used a lot less, while scoring is trailing way at the bottom. This study shows that in its current state, marketers use marketing automation platforms mostly as a fancy marketing software. Source: David Raab Why is that? There are two main reasons why so many of the lead nurturing and scoring programs fail: Not enough data: The demographic data in most companies CRM typically does not include a lot more than contact information, job title and company. These hardly make for robust profiling. Not enough value: Lead scoring and nurturing use a complex set of rules of thumb that are based on common sense rather than statistical validation. These rules are hard to set up and maintain, specifically if they don t drive superior results. 4

5 How does predictive lead scoring and nurturing work? Predictive lead scoring and nurturing prioritizes leads and suggests content that pushes them down the funnel by using statistical probability rather than rules of thumb. It uses learning algorithms to identify the leads with the highest probability to buy as well as which solution they are most likely to need. In addition, it segments the leads to buyer personas and suggests the type of content that is most likely to resonate with them. Predictive lead scoring and nurturing resolves the challenges of traditional lead scoring and nurturing by both expanding the data and improving results. Data Powerful databases and online data mining are the basis of predictive lead scoring and nurturing. CRM and behavioral data is augmented with thousands of additional data points, allowing scoring and nurturing to be based on a robust set of data. Data sources include: Social networks activity Business contact databases Mining companies websites Technologies and SaaS products used Government databases Financial reports nvestors News and press releases Job boards Marketing activity such as PPC and retargeting Sales and marketing tactics such as whitepapers, demos And much more Improving results This is where the predictive part comes in. Unlike traditional lead nurturing and scoring that uses rules of thumb and best practices to score leads, predictive nurturing and scoring uses learning algorithms that constantly evolve to measure which leads are most likely to buy at any time. The secret sauce is to continuously analyze the attributes of the highest value customers and find prospects like them in the lead database. The similarity between your highest value customers and the lead in addition to the level of engagement is the actual score. Predictive nurturing can leverage data in order to identify needs, and find the solution with the best fit and the content that is most likely to be engaging. Unlike metrics such as CTR or form fill rates, predictive lead nurturing focuses on revenue. Therefore, even if content gets high engagement but fails to engage the high value leads (such as interesting content with low relevancy) it will not be considered a success. 5

6 Create your own predictive lead scoring or nurturing While not as robust as using a predictive platform, there are a lot of things that you can do in order to prioritize your leads. These include enhancing your data, building your scoring model and iterating in order to perfect it. Here are a few steps that you can take to prioritize your lead database: 1. Augment your CRM data Scoring and nurturing all depend on robust data. Data allows you to give the right scores for demographic variables and build robust buyer-personas that predict who is going to buy. However, most organizations have limited data in their CRM and marketing automation platforms. To improve your scoring models and nurturing tracks, here are five leading data sources that you can use to boost your lead data: Hoovers Company information is crucial in order to segment the right buyer. Hoovers contains detailed companylevel information that can help you study the demography of a company. Use Hoovers in order to augment your data with variables such as industry, SIC-codes, location, revenue and the number of employees. By using these data points, you can reduce the score for leads coming from companies outside your desired market definition and focus on leads coming from high value companies. 6

7 BuiltWith Company demographic information is important, but don t stop there. If you are targeting companies that use a certain technology, like Salesforce.com or Microsoft Dynamics, or work only with some database technologies, like in our example, BuiltWith can provide you with this data. BuiltWith is mostly limited to Web technologies, but the result is a robust database that can help you discover which target companies fit in terms of technology. Monster.com 7

8 Hiring new people for a position that is relevant for your target market may suggest that your solution is high priority for that company. For example, hiring Sales Operations professionals suggests that Sales Effectiveness may be high on the priority list. How do you know who is hiring? That s easy By looking at job boards such as Monster.com or Indeed. com, you can get all of this data. Just note that this information is constantly updated, so make sure to check it regularly. Data.com Data.com allows you to source additional information about your contact that may be of interest, such as job titles as well as detailed and updated contact information. Databases similar to Data.com are NetProspex and ZoomInfo. LinkedIn LinkedIn contains a plethora of B2B information that is invaluable for anyone who is looking for granular information on leads. For regular members, you can only view people in your network, but with premium accounts, you can view the profile of every member. With LinkedIn, you can explore organizations and view up-to-date bios of your prospects to figure out if they are the right fit. 2. Identify the most impactful data points Find the data that is most meaningful to your business. Some points are the minimum requirements in order to qualify a lead (like using a certain CRM system) while other points are not critical but may increase the likely value of the lead (like company revenue). You should review the following: Highest revenue customers Web analytics and path to conversion CRM activities and the path to closing a deal 8

9 3. Align Sales and Marketing on scoring model Your Sales team must review and agree to the definition of buyer personas and of the scoring model. Specifically, Sales should agree on the score in which a hand-off is being made and what a Sales-ready lead means. 4. Implement your scoring model After you ve decided and agreed on the scoring model design, you should implement it in your marketing automation system. For specific guidelines on implementing scoring in your marketing automation platform, check out these online resources: Marketo: The definitive guide to lead scoring Eloqua: The grand guide to lead scoring 5. Review results and iterate Lead scoring is an on-going process, so as you go, you will need to tinker and change your model in order to improve it. You will find that as you go and build new content, improve your nurturing and expand your website and your blog, you will be able to increase the robustness of your model. 6. Nurture your leads to increase engagement and scoring Nurture your leads by persona and create persona-specific nurturing tracks to push your prospects down the funnel. This will both engage your potential customers and increase score as leads download more content and visit the website. Conclusion Predictive lead scoring and nurturing 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. Many marketers find the traditional lead scoring and nurturing that come out of the box of marketing automation platforms both hard to implement and of mediocre value. The reason for that is that organizations lack the data to make these models work and the predictive power in order to validate them. Fortunately, predictive lead scoring and nurturing changes all that. It mines data from the Web in order to augment the organizational 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 and nurturing changes the sales-marketing dynamic from lead to revenue and help both teams focus on finding the next deal in the database haystack. 9

10 About Mintigo MAKETER'S GUIDE TO PRIORITIZING LEADS Never run a bad campaign again with Mintigo s Customer Intelligence Platform. Using predictive analytics and big data from all over the web, we help you discover, target and engage the right prospects with the right campaigns at the right time. Mintigo works with marketers at companies like DocuSign, Autodesk and SmartBear to prioritize leads, identify cross-selling opportunities in their customer base, and target new markets to expand into. To learn more, visit 10

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