Driving Predictable Revenue with Lead Scoring (39
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The document discusses how TrackMaven, a competitive intelligence company, used predictive lead scoring from Fliptop to improve their marketing and sales processes. TrackMaven was experiencing high lead volumes but low conversion rates. By building a predictive model with Fliptop, TrackMaven was able to score new leads and prioritize "Grade A" leads for sales, resulting in a 4x higher conversion rate for top leads. This improved TrackMaven's marketing qualified lead to sales qualified opportunity conversion and overall win rates.
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Driving Predictable Revenue with Lead Scoring (39
- 1. The Quest for the Holy Grail: Driving Predictable Revenue Ian Walsh, TrackMaven Jessica Cross, Fliptop Patrick Chen, Marketo © 2014 Marketo, Inc. Marketo Proprietary and Confidential
- 2. Ian Walsh CMO TrackMaven @ianwalsh © 2014 Marketo, Inc. Marketo Proprietary and Confidential Jessica Cross Director of Marketing Fliptop @jfayesf Patrick Chen Sr. Marketing Operations Manager Marketo @patkchen
- 3. Quick Housekeeping • Chat box is available if you have any questions • There will be time for a Q&A at the end • We will be recording the webinar for future viewing • All attendees will receive a copy of the slides and the recording of today’s webinar • Twitter hashtag: #LaunchPoint Page 3 © 2014 Marketo, Inc. Marketo Proprietary and Confidential
- 4. Some stats to get us started • 44% of respondents said they are currently using lead scoring • 16% of respondents said they are planning to implement a lead scoring solution within the next 12 months • 60% of respondents put predictive lead scoring on their wish list of capabilities going forward. Source: Decision Tree Labs Page 4 © 2014 Marketo, Inc. Marketo Proprietary and Confidential
- 5. Why predictive lead scoring • Helps marketing prioritize and invest in the right leads • Increase lead to MQL conversion • Improve sales & marketing alignment Page 5 © 2014 Marketo, Inc. Marketo Proprietary and Confidential
- 6. Who is • Fliptop’s predictive scoring platform lets you close more deals, faster • Headquartered in San Francisco - Founded in 2009 • Easily integrates with: Page 6 © 2014 Marketo, Inc. Marketo Proprietary and Confidential
- 7. TrackMaven • Competitive intelligence for digital marketers • Identify marketing opportunities • Optimize content distribution • Track real-time progress • Mission is to help marketers get more out of their content marketing efforts • Believes a little corgi goes a long way Page 7 © 2014 Marketo, Inc. Marketo Proprietary and Confidential
- 8. How TrackMaven Markets to Marketers • Inbound: Great Content • Blog, eBooks, Reports • Nurturing • Live Events • PR Page 8 © 2014 Marketo, Inc. Marketo Proprietary and Confidential
- 9. TrackMaven’s Marketing Plan • Greater than 300% growth rate target for 2014 • Balance of inbound and outbound approach • A lot of great content marketing coupled with prospecting, events and sponsored webinars • Goal – steady growth in MQL’s and SQO’s Page 9 © 2014 Marketo, Inc. Marketo Proprietary and Confidential
- 10. TrackMaven’s Demand Gen Goals All Names Prospect Lead MQL SQO Opportunity Customer Page 10 © 2014 Marketo, Inc. Marketo Proprietary and Confidential Goals • Improve MQL to SQO by 20% • Improve Opportunity close rate by 30%
- 11. TrackMaven’s Challenges • Large volume of inbound leads • Bandwidth constraint on Sales Development (SDR) team -- They are (almost) literally drowning in leads • Filtering the right leads to the SDRs Page 11 © 2014 Marketo, Inc. Marketo Proprietary and Confidential
- 12. How did they do it? • Build a predictive model using Fliptop • Pushed high probability “Grade A” leads to sales first • Strong SLAs on “Grade A” leads • Fliptop score compliments Marketo behavior score • Nurtured low probability “Grade D” leads Page 12 © 2014 Marketo, Inc. Marketo Proprietary and Confidential
- 13. Results so far! • Grade A leads converting at 4x rate vs. Grade C & D • Improved MQL to Opportunity Conversion rate • Overall improvement in win rate Page 13 © 2014 Marketo, Inc. Marketo Proprietary and Confidential
- 14. How Fliptop builds predictive models Page 14 © 2014 Marketo, Inc. Marketo Proprietary and Confidential 3,500+ Signals 40+ Data Sources Predictive Machine Model Historical Sales CRM
- 15. How Fliptop builds predictive models Page 15 © 2014 Marketo, Inc. Marketo Proprietary and Confidential Grades Scores Predictive Machine Model Marketo Inbound Leads
- 16. Marketo Webhook to Score All New Leads Page 16 © 2014 Marketo, Inc. Marketo Proprietary and Confidential
- 17. What Fliptop looks like in Marketo Page 17 © 2014 Marketo, Inc. Marketo Proprietary and Confidential
- 18. Fliptop Model Page 18 © 2014 Marketo, Inc. Marketo Proprietary and Confidential
- 19. Q&A Page 19 © 2014 Marketo, Inc. Marketo Proprietary and Confidential
- 20. Ian Walsh CMO TrackMaven Jessica Cross Director of Marketing Fliptop Page 20 © 2014 Marketo, Inc. Marketo Proprietary and Confidential Patrick Chen Sr. Marketing Operations Manager Marketo
- 21. Thank you! © 2014 Marketo, Inc. Marketo Proprietary and Confidential
Hinweis der Redaktion
- Add handles and hashtag
- Fliptop is a predictive scoring platform that lets you close more deals faster. We provide our customers with Predictive Lead Scoring Predictive Forecasting Campaign Optimization All in an easy to use package that quickly integrates with Marketo via API, along with Salesforce Fliptop was founding 2009, headquartered in San Francisco. ON today’s webinar Fliptop and Marketo are excited highlight the success story of our mutual customer TrackMaven. With that I’m going to turn things over to our joint customer Ian Walsh, CMO of TrackMaven
- With the classic lead scoring process, a lead visits your site, fills out a form and you gather information about them that you can score on and the lead is synched into your CRM for the sales rep to call on. With this process you are only able to score on what the lead provides to you, which is very limited. The Fliptop process starts with looking at your historical sales, historical outcomes are the best predictor future events. We start by looking in your CRM first so we can figure out what success looks like for your company. This means all closed won and closed lost opportunities to figure out your ideal customer. We then map all those opportunities back against 3,500+ data signals and 40+ proprietary data sources to find the commonalities. These are signals like, job postings, funding, company size, location, industry, technology stack, website tags, all sorts of things. Then we build a couple predictive model, and put them against each other in a machine learning model tournament to see which is most predictive to your business. From there we can apply grades based on how closely a lead matches the ideal customer, Grade A meaning the lead is high quality and very likely to convert to a customer. Grade D means the lead is a bad fit for your solution and has a low probability of converting to a customer.
- We can then grade all leads, contacts, accounts, and opportunities in both Marketo and Salesforce based on their probability of converting to a customer. And continue to score net new inbound leads Marketo marketing activity is incorporated into the model. Typically our customers connect both their Marketo and Salesforce with Fliptop, allowing all leads to be graded. The normal flow is a lead fills out a form on the website, gets written to Marketo and then to salesforce, Fliptop then applies a predictive grade.
- Additionally we have a webhook configuration to allow net new leads to be scored and graded within 60 seconds of being created.
- This is what the Fliptop grades look like inside Marketo. Leads can be sorted by their spendgrade, which can then be used in segmentation, lists, nurturing and prioritizing follow up for sales teams
- We also provide all our customers with full transparency of all the signals that go into the model. This information can be used to gain better understanding of your ideal persona and target buyer, their location, industry, role, level, technology stack. We expose how each signal fits into the model, how many wins it represents and it’s average conversion rate.