Webinar: Integrating Predictive Lead Scoring in your Marketo
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Predictive lead scoring ("PLS") has grown into a hot topic in 2014. SiriusDecisions reports that there are nearly 14 times more B2B organizations using PLS today than in 2011. If you are thinking about PLS but are unsure where to start, join this webinar as we cover how can you implement PLS in your Marketo and see improvements in your conversion and win rates.
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Webinar: Integrating Predictive Lead Scoring in your Marketo
- 1. How to Integrate Predictive Lead Scoring in Marketo
- 2. #predictiveleadscoring 2 Housekeeping If you can see the slides and hear me please raise your hand in the GoToWebinar Dashboard
- 3. Your speakers Farkhanda Zhublawar Director of Customer Success Fliptop Jessica Cross Director of Marketing Fliptop #predictiveleadscoring 3
- 4. Today’s Webinar Agenda 1. Traditional vs. Predictive Lead Scoring 2. Marrying Predictive and Traditional Lead Scoring 3. The Fliptop Framework 4. Integrating in your Marketo 5. Q&A To submit questions during the webinar, please tweet them: #predictiveleadscoring @fliptop #predictiveleadscoring 4
- 5. Traditional lead scoring looks at just a few data points Industry Job title Location Budget Company size Marketing activity #predictiveleadscoring 5
- 6. Employees Total Gender Uses PHP Open Military-Specific Jobs Industry Code Open Legal Jobs Industry Age Open Job Postings Uses Perl JavaScript Menus Market Value Job title Cross-Browser Compatibility Funding Location Budget Company size Technology Stack Email address Marketing activity Hiring Open job postings Lead Scoring Open Job Postings Social Presence Youtube URL Social gender Website form Tech SEO Content Delivery Network Uses Apache LinkedIn URL Founded Year Email List Tech SEO Company Type Invested Capital Youtube views Traffic Rank Engine Optimizer Facebook Advertiser CRM Software Uses Python Age group Accepts Payments Tech Media Social Occupation Facebook Shares Company Age Stocks Facebook Likes Twitter URL Net Income Employees Total Twitter Match Score Social Profile Photo Paid Analytics Influence Score Social Presence Website form Social Profile Photo Open Job Postings Funding Business Twitter Followers iPhone App iPhone App Rating Android App Influence Score Android App Rating Has Website NAICS Code USSIC Code UKSIC Code Industry Ranking Cash Balance Market Capitalization Sales Growth Percentage Includes Videos Engine Optimizer Age Group Publicly listed Open Management Jobs Employee Count Uses DNS Has Disposable Email Invalid Phone Number Non-Business Email Uses .NET Uses Data Feeds Has Forms Uses PHP Uses JavaScript Uses Slideshows Javascript Menus Name server Parked Domain Includes Videos Employee Count Open Job Postings Influence Score Uses Java Name server Engine Optimizer Uses Data Feeds Content Delivery Network Uses Nginx Uses Tooltip Uses Wordpress Employee Count Open Production Jobs Android App Hiring Traffic Rank Social Occupation Net Income Uses Slideshows Parked Domain Open Job Postings Uses Ajax Uses .NET Publicly listed Non-Business Email Uses DNS Engine Optimizer Uses Ajax Uses Apache Includes Videos Open Job Postings Social Profile Photo Uses Tooltip Has Forms Uses DNS Uses Apache Open Other jobs Invested Capital #predictiveleadscoring 6
- 7. Traditional vs. Predictive Lead Scoring Traditional Lead Scoring Predictive Lead Scoring Limited Data LOTS of Data Guessing at manual scores Statistically generated scores No Direct Linkage to Sales Tied to Historical Sales Outcomes Requires manual review Updates automatically #predictiveleadscoring 7
- 8. Predictive Lead Scoring Can Improve: • Lead and opportunity conversion rates • Lead and opportunity velocity • Marketing and sales efficiencies • Sales forecasting #predictiveleadscoring 8
- 9. How Do Predictive and Traditional Lead Scores Work Together? #predictiveleadscoring 9
- 10. The Fliptop Predictive Lead Scoring Framework #predictiveleadscoring 10
- 11. The Fliptop Predictive Lead Scoring Framework #predictiveleadscoring 11
- 12. Money in the Bank • High Predictive Lead Score – SpendGrade A • High Activity Score – Lead is interested in your solution • e.g. Target buyer that requests a demo • Action – Send directly to sales #predictiveleadscoring 12
- 13. • High Predictive Lead Score – SpendGrade A + B • Little to no activity score • e.g. list upload of target buyers • Action – Sales should prospect these leads – Marketing should create content/events to activate these leads #predictiveleadscoring 13 Diamonds in the Rough
- 14. • Low Predictive Lead Score – SpendGrade C + D • High Activity Score – Could include students, researchers, competitors – These leads will rarely buy • Action – Keep in nurture queue #predictiveleadscoring 14 Heavy Clickers
- 15. • Low Predictive Lead Score – SpendGrade C + D • Low activity score • Action – Keep in nurture queue – They can turn into Heavy Clickers #predictiveleadscoring 15 Zombies
- 16. The Predictive Lead Scoring Framework Focus your sales reps here #predictiveleadscoring 16
- 17. The Predictive Lead Scoring Framework Focus your sales reps in this order #predictiveleadscoring 17
- 18. Putting It Into Action In Marketo #predictiveleadscoring 18
- 19. Money in the Bank – Smart List #predictiveleadscoring 19
- 20. #predictiveleadscoring 20 Money in the Bank – Flow
- 21. Diamonds in the Rough – Smart List #predictiveleadscoring 21
- 22. Diamonds in the Rough – Flow #predictiveleadscoring 22
- 23. #predictiveleadscoring 23 Heavy Clickers – Smart List
- 24. #predictiveleadscoring 24 Heavy Clickers – Flow
- 25. Predictive Lead Scoring Results Customer Success Story #predictiveleadscoring 25
- 26. “Using Fliptop we were able to improve our lead to MQL conversion rate from 8% to 17%. That’s a 2x improvement.” Joe Lucas Director of Demand Gen & Marketing Operations Case Study #predictiveleadscoring 26
- 27. Q&A – 10 Minutes Thank you. Contact us for your own predictive lead scoring model info@fliptop.com 27 Farkhanda Zhublawar Director of Customer Success Jessica Cross Director of Marketing #predictiveleadscoring
- 28. Conventional lead scoring looks at just a few data points Industry Job title Location Budget Company size Marketing activity 28
- 29. Employees Total Gender Uses PHP Open Military-Specific Jobs Industry Code Open Legal Jobs Industry Age Open Job Postings Uses Perl JavaScript Menus Market Value Job title Cross-Browser Compatibility Funding Location Budget Company size Technology Stack Email address Marketing activity Hiring Open job postings Lead Scoring Open Job Postings Social Presence Youtube URL Social gender Website form Tech SEO Content Delivery Network Uses Apache LinkedIn URL Founded Year Email List Tech SEO Company Type Invested Capital Youtube views Traffic Rank Engine Optimizer Facebook Advertiser CRM Software Uses Python Age group Accepts Payments Tech Media Social Occupation Facebook Shares Company Age Stocks Facebook Likes Twitter URL Net Income Employees Total Twitter Match Score Social Profile Photo Paid Analytics Influence Score Social Presence Website form Social Profile Photo Open Job Postings Funding Business Twitter Followers iPhone App iPhone App Rating Android App Influence Score Android App Rating Has Website NAICS Code USSIC Code UKSIC Code Industry Ranking Cash Balance Market Capitalization Sales Growth Percentage Includes Videos Engine Optimizer Age Group Publicly listed Open Management Jobs Employee Count Uses DNS Has Disposable Email Invalid Phone Number Non-Business Email Uses .NET Uses Data Feeds Has Forms Uses PHP Uses JavaScript Uses Slideshows Javascript Menus Name server Parked Domain Includes Videos Employee Count Open Job Postings Influence Score Uses Java Name server Engine Optimizer Uses Data Feeds Content Delivery Network Uses Nginx Uses Tooltip Uses Wordpress Employee Count Open Production Jobs Android App Hiring Traffic Rank Social Occupation Net Income Uses Slideshows Parked Domain Open Job Postings Uses Ajax Uses .NET Publicly listed Non-Business Email Uses DNS Engine Optimizer Uses Ajax Uses Apache Includes Videos Open Job Postings Social Profile Photo Uses Tooltip Has Forms Uses DNS Uses Apache Open Other jobs Invested Capital #gotleads 29
- 30. How we build your model Historical Sales Machine Learning Model Tournament 3,500+ Signals / 40+ Data Sources
- 31. How we build your model Model is ready 3,500+ Signals / 40+ Data Sources Scored Lead
- 32. Works with the technology you already use
- 33. Traditional Lead Scoring 33 All Names Engaged Prospect & Recycled Source: www.marketo.com Lead Sales Lead Opportunity Customer Behaviors • Early stage content: +3 • Attend webinar: +5 • Visit any webpage/blog: +1 • Visit careers pages: -10 Demographics • Pricing pages: • +10 regular, +15 detailed • Watch demos: • +5 overview, +10 detailed • Mid-stage content: +8 • Late-stage content: +12 • Searches for “Marketo”: +8 #predictiveleadscoring
- 34. Target Persona With Traditional Score VP of Sales 34 Lead Score • Job Title: +20 • Attend webinar: +5 • Visit any webpage/blog: +1 • Visit careers pages: -10 • Possible Score = 16 #predictiveleadscoring
- 35. Target Persona With Traditional Score Social Media Manager 35 Lead Score • Early stage content: +3 • Attend webinar: +5 • Visit any webpage/blog: +1 • Watch demos: +5 • Mid-stage content: +8 • Late-stage content: +12 • Possible Score = 34 #predictiveleadscoring
- 36. Predictive = Fit First approach Social Media Manager VP of Sales #predictiveleadscoring 36
Hinweis der Redaktion
- Welcome. Good morning good afternoon and good evening. Thank you for joining us today for our webinar. We are eager to share with you the science behind predictive lead scoring.
- Before we go any further, I have a couple housekeeping items. Want to make sure everyone can hear me and see the slides. Please raise your hand in the go to webinar dashboard if you can hear us. Please raise your hand in the go to webinar dashboard if you can see us. Great. Let’s get started
- Your speakers today are Farkhanda Zhublawar, director of customer success for fliptop. Previously she worked at Demandbase, Epocrates, and Equilar. Farkhanda’s focus on building customer relationships forever. And ever. My name is Jessica Cross and I’m the director of marketing here at Fliptop. Previously, I worked at DNN Software, Bazaarvoice, and PowerReviews. I’m also a Marketo Champion and have built at least 4 lead scoring models in my time.
- On today’s agenda I’m going to cover what most of us on the call know as lead scoring as advocated by marketing automation solutions. We are interested to hear your thoughts on the matter so please tweet along with us using the hashtag #predictiveleadscoring. You can also post your questions directly in the GoToWebinar module. We will try to get to as many of them at the end We will be sending out slides and recording to all registrants in the days after this webinar.
- The conventional lead scoring that I have been talking about up till now typically takes into consideration 20-30 demographic indicators into what builds a score. By mining the data inside your CRM and data available on the public web, predictive lead scoring can factor in 500+ different data points to build a scoring model.
- By mining the data inside your CRM and data available on the public web, predictive lead scoring can factor in 3500+ different data points to build a scoring model.
- Not just about data here are the other problems we can solve
- Other uses cases for pls and ways our clients are leveraging PLS
- You guys are like eHarmony for sales for marketing Some of our customers start with a very simple approach to incorporating predictive lead scores within their organization. It can be as basic as routing rules. Grade as go to AEs, Grade Bs goes to SDRs. And Cs and Ds get put into a slow track nurturing queue. For some of our sophisticate customers like to take this a step further and layer activity scoring on top of the fliptop predictive grades in order to better prioritize their leads. Lets share with you the our framework for how we recommend you layer to the two scores together.
- ON the y axis we have the Fliptop SpendGrade from D to A, D being the least likely to convert into an opportunity and A being the most likely. ON the x axis we have your Marketo activity score, ranging from low to high activity. We purposefully did not put numbers in here because every one of our clients assigns different scores to the wide range of activities that a lead can take. Again, that’s why we recommend using predictive score and activity score in conjunction.
- Added to that X and Y axis, on this graph we have four icons. You’re probably wondering what the icons mean, well they are the four groupings of leads, money in the bank, diamonds in the rough, heavy clickers, and zombies. Next I will go through our explanation of each of these groupings. Now we will jump into what each one of those groupings means and how to treat each group of leads.
- These are leads with a high predictive score for us that means Spendgrade A + B, and little to no activity score. The example of this is say you upload a large list of leads. With activity lead scoring these leads would have no score. With Fliptop, you would be able to assign a predictive score based on their likely hood of converting to an opportunity and doing business with you. These leads should be assigned to sales reps who can prospect, say SDR
- How you handle these leads can very from organization to organization and depend on the type of product you sell. Based on their predictive score they have a very low probability of turning into a customer for you However, There could be opportunities for you to serve this market with a different product or service.
- If you have an influx or an abundance of leads we recommend you focus your sales reps time on just the diamonds in the rough and money in the bank. According to Fliptop Grades these are the leads that have the highest propensity of converting into a customer for you. Zombies and heavy clickers may never turn into business so it does not benefit the sales reps time to call those leads.
- For the clients that don’t have the challenge of too many leads to handle, we recommend following this frame work to prioritize leads in the order of, money in the bank first, then diamonds in the rough, after that sales reps can call through the heavy clickers and if you’re really having a slow day zombies.
- We used that score a threshold, again the number can vary from instance to another.
- We used that score a threshold, again the number can vary from instance to another.
- Let’s talk about how our customers are implementing this framework InsideView built strict 5 minute SLAs on what we consider to be “Money in the Bank” category of leads, this means SpendGrade A with high activity score. Then they gave the Outbound sales team the “Diamonds in the rough” to prospect in to. Using this methodology they were able to improve their Lead to MQL conversion rate from 8 to 17% in about a quarter’s time. They also decreased the time it took for a lead to convert to an MQL by 3x. All of this resulted in more pipeline for the sales. Read more about InsideView and Fliptop partnership on our website. We published a story and video.
- If you are interested in replicating the success that Insideview has seen, contact us for your own predictive lead scoring model. We will
- The conventional lead scoring that I have been talking about up till now typically takes into consideration 20-30 demographic indicators into what builds a score. By mining the data inside your CRM and data available on the public web, predictive lead scoring can factor in 500+ different data points to build a scoring model.
- By mining the data inside your CRM and data available on the public web, predictive lead scoring can factor in 3500+ different data points to build a scoring model.
- Most of us on the call are familiar with this image. This is Marketo’s depiction of how lead scoring works to tell us how leads are moving through the funnel.
- Make icon for social media manager, make it CLAIRE
- With traditional lead scoring you can have a lead with hundreds of points. If they visit the site every day and download all your content they could rack up a score of 100, say even 1,000. But what does that actually mean in terms of its sales readiness and propensity to buy? Unless you have built a more sophisticated lead scoring, I’m sure many of you on this call suffer from this problem.