Infer & Lattice Engines: Making a Case for Predictive Sales & Marketing
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Brought to you by industry leaders Infer, Lattice engines with the support of SiriusDecisions. Are you looking to deploy a predictive marketing solution, but need more details on how to get started, what different use cases predictive can support, and how to measure success? Watch this webinar to learn: -Differences in approaches to launching a predictive solution -Top use cases for getting started -How to measure success
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Infer & Lattice Engines: Making a Case for Predictive Sales & Marketing
- 1. Making the Case for Predictive
- 2. Your Speakers Today Kerry Cunningham Sr. Research Director @KerrySirius Sean Zinsmeister Sr. Director, Product Marketing @SZinsmeister Nipul Chokshi VP Marketing @nipulc
- 3. 3 © 2016 SiriusDecisions. All Rights Reserved@KerrySirius What is Predictive Analytics for B-to-B marketing? Internal: MAP | SFA | Web |CX External: Intent | Data Internal and External Statistics To find patterns **Statistics and advanced algorithms allow marketers to identify patterns that identify buyers that would be otherwise invisible; Machine Learning involves feedback loops embedded within statistical modeling processes to enable continuous model refinement Predictions Discovered patterns **Some vendors also add in additional services like data enrichment to enable more effective marketing and sales tactics **Predictive Providers provide external data, which creates a much more complete view of who the prospects are and whether they are in the market for solutions
- 4. SiriusPerspective: © 2016 SiriusDecisions. All Rights Reserved4@KerrySirius Grouping Waterfall Issues Addressed by Predictive Prospects in the TAM AQLs for tele to call effectively Inquiries hitting MAP TGLs from tele AQLs from TQLs C/W from pipeline Calls to conversations Problem Example Solution Too Many Too Few Conversion Prioritize Source Engage Demand Waterfall® Total Addressable Market Inquires AQLs TQLs Closed/Won Retained Grown Knowing where to start with predictive begins by considering waterfall performance and understanding where performance needs to improve.
- 5. SiriusPerspective: © 2016 SiriusDecisions. All Rights Reserved5@KerrySirius Grouping Waterfall Issues Addressed by Predictive Knowing where to start with predictive begins by considering waterfall performance and understanding where performance needs to improve. Problem Solution Too Many Too Few Conversion Prioritize Source Engage Demand Waterfall® Total Addressable Market Inquires AQLs TQLs Closed/Won Retained Grown Predictive Analytics Use Cases, Applications Predictive
- 6. 6 © 2016 SiriusDecisions. All Rights Reserved@KerrySirius There’s A Predictive Application For That Govern prospect outreach with findings from statistical analysis: best time to call by persona, region, etc. Contact Engagement Augment sales rep estimates for how likely a prospect is to buy, along with purchase timing and deal value Opportunity Scoring Determine existing customers’ propensity to buy additional products and identify attrition risk Customer Scoring Create more precise segmentation using an array of third-party data Segmentation Identify which buyers are more likely to prefer your brand and offerings using big data and analytics Predictive Personas Prioritize prospects for sales follow-up; Replaces or augments existing point-based scoring systems Prospect Prioritization Detect buying journeys, deliver the next best communication using machine learning Tactic Matching Acquire new prospects that share traits and behaviors with known buyers or target prospects: look-alikes Prospect Sourcing Monitor shopping behaviors. Source accounts that exhibit surges in shopping behaviors Intent Monitoring
- 7. 7 © 2016 SiriusDecisions. All Rights Reserved@KerrySirius Predictive users must map the results of bottom-up modeling processes to top-down derived beliefs Top Down v. Bottom-Up Approaches Top Down – We already know what constitutes a good prospect. What sales says qualifies a prospect: What the math says qualifies a prospect: Implicit Explicit BANT Intent data Firmo Buy Signal s Tech – Inferred Model variables MAP Scoring/ Sales Qualification Bottom Up – We let the data and statistical methods tell us how to identify the best prospects. Tech
- 11. Sales Prioritization Cost of Sales Team $1.2M 30% $360,000 Bad Leads $$$$ Number of Sales Reps 20 Sales Reps $60,000 $1.2M Average Monthly Cost per Rep $$$$
- 12. Sales Prioritization in Action Kevin Gaither Focused sales on highest value lead targets Decreased time spent on bad leads by almost 20% Established data-driven workflows with aggressive follow-up Drove top-line results through effort reallocation SVP of Sales Fully Loaded Cost of the Inside Sales Team Headcount 19% $$$ Sales Effort Saved by Not Working Bad Leads Monthly Cost Savings from Reduced Effort on Bad Leads Cost Savings 4x Conversion Rate By prioritizing leads based on data and following up more aggressively 3x Average Deal Size By spending more time on the right customers
- 13. Sales Prioritization in Action Kevin Gaither Focused sales on highest value lead targets Decreased time spent on bad leads by almost 20% Established data-driven workflows with aggressive follow-up Drove top-line results through effort reallocation SVP of Sales Total Revenue by Lead Score Before Infer (Day Zero) After Infer (Day 60) Infer A-Leads Infer B-Leads Infer C-Leads Infer D-Leads +53% +154% +180% +690%
- 15. Aligning Lead Effort with Impact Lauren Licata Reps call Infer A and B-Leads First Email A Leads within 5 min and call within 1 hour vs. 8 hours it takes Increased effort spent on A-Leads by 3x & decreased effort spent on D-Leads by 1.5x Result: Increased sales by 30% VP of Marketing
- 16. +30% increase in sales- qualified leads +30% lift in sales ~3x increase in the effort spent on A-Leads 1.5x decrease in the effort spent on D- Leads 2x lead to demo conversion
- 17. Using Fit & Behavior Buying Signals Together Isaac Wyatt Route best fit & engaged free-trials directly to sales Find hidden segments of leads Prioritize daily sales outreach Interpate buying behavior Director of Marketing Strategy & Operations
- 19. ALEXANDRE PAPILLAUD DIRECTOR, GLOBAL DEMAND CENTER, INTEL SECURITY Lattice helps us filter out low probability leads before they reach sales. I love the ability to dive deep into the predictors of what makes a good lead…and our sales team loves Lattice because they know they are focused on the best opportunities. Proprietary & Confidential “20%Lower cost per opportunity
- 20. Top 11% Convert at 6x Higher Rate
- 21. Data-driven Insights for Prioritizing Leads Share of Servers Virtualized at Company Public vs. Private Cloud at Company Network-Based Storage at Company Company is Undergoing Rapid Growth Company is Using Amazon Web Services 10,000+ Business and Tech Attributes on 100M+ Entities
- 22. 30%Greater velocity We wanted sales to work the most enterprise-ready accounts. Lattice was able to surface accounts with high likelihood of conversion and accelerating them in the pipeline.” “ Shantel Shave Director, Demand Gen Accelerating the Enterprise Business
- 23. TOO FEW LEADS
- 24. Security Software Provider: Creating Pipeline with Competitive Plays 26%Higher Win Rate
- 25. 20%Higher call to win rates on list-based outbound efforts VP/GM of Distribution We understand a good customer when it sees one, but with a small sales team, it would be impossible to visit millions of websites to find the ideal prospects. With Lattice, we can identify the right revenue opportunities.” “ Increasing Penetration into SMB $1B+ Financial Payments Processor: Optimize list buys 30%Lower acquisition costs
- 26. Lattice customer since 2010 3xHigher conversions Personalize Sales Interactions with 360- degree views of the customer
- 27. Top LinkedIn Post on Tuesday, 10/27/15 50% Lower cost per opportunity Direct Mail for High-Value, High Intent Targets
- 28. Direct Mail for High-Value, High Intent Targets Identified high value targets Determined buyer stage Created custom data visualization Added to direct mail campaign Offer to meet and explain Tangible package Automated follow-up Email & Phone call from rep • How we created this? • Insights gained about your network
- 29. Improving Marketing Efficiency Kevin Bobowski Route highest best leads to sales for immediate follow-up Develop regular full-funnel pipeline forecasts Continuously score marketing channels to test & invest Optimized content syndication and list-buy programs CMO
- 30. +50% marketing efficiency +50% increase in monthly pipeline creation 2.2x higher converted A-Leads than average
- 32. Campaign 1 Campaign 2 Leads Cost $ /Lead 140 110 $5,000 $5,000 $35.71 $45.45 Campaign Campaign 1 appears best under CPL metrics
- 33. A B C Leads Opportunities Lead to Opp 3,000 5,000 7,000 500 325 125 16.7% 6.5% 1.8% Type of Lead A-Leads worth almost 3x B-Leads
- 34. A B C 10 30 100 140 $5,000 Type of Lead Campaign 1 Leads Cost $ / Lead Fcast Opps Forecast & / Opps 1.7 2.0 1.8 5.4$35.71 $926 A B C 35 30 45 110 $5,000 Type of Lead Campaign 2 Leads Cost $ / Lead Fcast Opps Forecast & / Opps 5.8 2.0 0.8 8.6$45.45 $582 Campaign 2 wins on quality weighted cost
- 35. Adam von Reyn Instant campaign feedback Reduced cost-per-lead Tests new marketing copy against D-Leads Developed MQA for ABM strategy Decreased 40% of total lead flow VP of Growth Marketing
- 36. Scour your Marketing Systems
- 37. Real Results 5000 Marketing-qualified leads were unconverted in its database, leading to a dramatic run-rate increase 3x The number of leads converted to closed deals tripled +150% Conversion rates increased by 150%, from 0.8% to 2% +76% Closed deals for new solutions were boosted by 76%
- 38. POOR CONVERSION
- 39. • Ran a series of roadshows to drive pipeline for their Enterprise business • Scored their database to identify high fit accounts who would receive targeted ads promoting the roadshows • Identified high fit late stage buyers who would be invited directly by sales (in addition to receiving an ad) • Enriched Marketo with account data so they could deliver hyper- personalized messages Hyper-segmentation for ABM at Scale 40% 70%Greater ROI on ad spend Increase in pipeline 35% Higher engagement within target accounts Ads Email SDR Calls
- 40. Every account gets scored and the next steps for engagement are initiated. Lift curve changed to protect customer confidentiality Score and Prioritize Targets
- 41. Lattice identifies the attributes that make a lead and account a good target for you
- 42. Orchestrate Multi-channel Outreach to Maximize Conversion “A” Targets Targeted Ads Personalized Email Invites SDR Calls only for those in market “B” and ”C” Targets Generic Email Invites
- 43. Orchestrate Multi-channel Outreach to Maximize Conversion “A” Targets Targeted Ads Personalized Email Invites SDR Calls only for those in market “B” and ”C” Targets Generic Email Invites Personalize based on key attributes: • Complementary tech • Amazon AWS • Google Cloud • Microsoft Azure • Industry • Financial Services • High Tech • Telecom
- 44. Standard Ad Ad for companies using Amazon AWS Ad for companies using Google Cloud Example: Customer personalized their ads based on developer platforms they were using (e.g. Amazon AWS, Google Cloud, etc). Hyper-personalize Content and Messaging
- 45. Standard Ad Ad for companies using Amazon AWS Ad for companies using Google Cloud Example: Customer personalized their ads based on developer platforms they were using (e.g. Amazon AWS, Google Cloud, etc). Hyper-personalize Content and Messaging
- 46. Social Tables Challenge Steady flow of 1,400 trial leads every month like clockwork Took on a paid content marketing strategy Leads skyrocketed to 6,000 total Net New Leads per month #humblebrag
- 47. Month 1 Month 2 Month 3
- 48. At Social Tables all leads get assigned… all 6,000…to 4 BDRs
- 50. Hyper-Segmentation with Profiling Ray Miller Launched high-value outreach with personalized nurture Identified 900+ high-potential prospects for sales Hyper-segmented current and past trialers into ICPs Prioritized A & B-Leads for accelerated sales follow-up Senior Marketing Operations Manager
- 51. AT A GLANCE +7% boosted overall revenue +10% increased average deal size +$500k/mo grew opportunity pipeline +35% increased trial signups +25% Expanded MQL volume
- 53. Sales & Marketing Alignment Demand Gen compensation plan built on Infer Uses Infer to negotiate w/ partners and Lead providers Leverages Infer to overcome the Sales and Marketing divide to define MQL Nick Ezzo VP Demand Gen
- 54. Sales & Marketing Alignment Demand Gen compensation plan built on Infer Uses Infer to negotiate w/ partners and Lead providers Leverages Infer to overcome the Sales and Marketing divide to define MQL Nick Ezzo VP Demand Gen
- 55. +23% Increase in Average Deal Size -53% Decline in poor quality leads Sales & Marketing Alignment +200% Increase in incremental revenue +31% Lead to MQL improvement Every company should use predictive analytics to gain clear customer parameters that the whole organization can agree on – now that we have predictive scores, I’ll never go back. Our Infer model makes all the difference when it comes to sales and marketing alignment. Nick Ezzo, VP Demand Gen
- 56. DATA QUALITY MATTERS A Predictive Model is only as the good as the data that goes into it. YOUR BUSINESS IS NOT ONE-SIZE-FITS-ALL And a One-Size-Fits-All modeling approach leads to bad results across your business. OPERATIONALIZATION IS CRITICAL Marketing and Sales cannot execute without Full Transparency and Actionable Insights. LACK OF ENTERPRISE SECURITY IS A NON-STARTER You are giving the vendor access to your CRM and MAP and your data needs to be protected. Key Operational Considerations
- 57. THANK YOU! Q&A • Write your questions in the tab above • Check out the attachments tab • Please leave feedback Kerry Cunningham Sr. Research Director SiriusDecisions @KerrySirius Sean Zinsmeister Sr. Director, Product Marketing Infer, Inc. @szinsmeister Nipul Chokshi VP Marketing, Lattice Engines @nipulc SPEAKER INFO:
Editor's Notes
- How do predictive applications apply to marketing and sales effectiveness? Predictive marketing applications solve three primary problems, each of which may occur at points before and throughout the Demand Waterfall and customer lifecycle. Too many inquiries – prioritize. The rise of inbound marketing has allowed many organizations to attract more prospective buyers than they can service. While this may seem like a good problem to have, organizations spending money on inbound marketing programs that cannot be serviced effectively waste valuable marketing funds and may harm their brands among audiences who do not receive followup. Thus, having accurate estimates in advance of the likely response to marketing programs helps organizations deploy marketing and sales budgets efficiently. Too few inquiries – source. Despite advances in inbound marketing, most b-to-b marketers still face a paucity of prospects entering the Demand Waterfall. As a result, most organizations must execute outbound marketing to attempt to engage prospects. Outbound tactics can be prohibitively expensive, however, if they are not planned and executed effectively. Conversion problems – optimize. Whether attracting too few or too many inquiries, organizations that fail to optimize the conversion of legitimate buyers will struggle. Failure to convert may result from poorly targeted messaging; poor sales and sales development followup; competitive disadvantages in features, function, pricing and reputation; and poorly executed or timed marketing campaigns and tactics.
- How do predictive applications apply to marketing and sales effectiveness? Predictive marketing applications solve three primary problems, each of which may occur at points before and throughout the Demand Waterfall and customer lifecycle. Too many inquiries – prioritize. The rise of inbound marketing has allowed many organizations to attract more prospective buyers than they can service. While this may seem like a good problem to have, organizations spending money on inbound marketing programs that cannot be serviced effectively waste valuable marketing funds and may harm their brands among audiences who do not receive followup. Thus, having accurate estimates in advance of the likely response to marketing programs helps organizations deploy marketing and sales budgets efficiently. Too few inquiries – source. Despite advances in inbound marketing, most b-to-b marketers still face a paucity of prospects entering the Demand Waterfall. As a result, most organizations must execute outbound marketing to attempt to engage prospects. Outbound tactics can be prohibitively expensive, however, if they are not planned and executed effectively. Conversion problems – optimize. Whether attracting too few or too many inquiries, organizations that fail to optimize the conversion of legitimate buyers will struggle. Failure to convert may result from poorly targeted messaging; poor sales and sales development followup; competitive disadvantages in features, function, pricing and reputation; and poorly executed or timed marketing campaigns and tactics.
- What core elements are required by each application? Each application has unique requirements, but all applications share the following requirement categories: Data. Predictive models require data that comes in two categories: predictor and outcome variables. Predictor variables are any data points that characterize the attributes and behaviors of prospects in relation to their likelihood of converting. Outcome variables are the events the organization is trying to predict. Each conversion step in the Demand Waterfall is a potential outcome variable, as are pipeline value, closed-won revenue and profit, among others. Until recently, b-to-b marketers were limited in their ability to utilize predictive analytics due to the lack of predictor and outcome data. The accelerating accumulation of data in b-to-b, however, has already enabled early adopters to gain competitive advantages. This is especially true in aspects of b-to-b marketing that create high volumes of data (e.g. scoring inquiries as they are nurtured). Much of the success of predictive analytics hinges on the availability of accurate historical sales and marketing data. Therefore, organizations that have maintained rigorous data collection and hygiene (and compliance practices), and those that have enforced lead development and sales activity dispositions in their SFAs have a great advantage in their ability to use predictive analytics. Data science. The best predictive models draw data from a huge variety of internal (e.g. MAP, SFA, Web site) and external (Web searches, social media conversations, syndicated content usage) sources. The task of reliably associating data from these disparate sources to individual companies and contacts is perhaps the hardest problem facing data scientists in b-to-b. Different techniques require different volumes of data, but most require 500 to 1,000 complete historical records that can be populated with each of the predictor and outcome variables. A poor match rate of predictor variables to prospects diminishes the utility of a predictive model. Mathematical/statistical models. The third component of predictive analytics is the math itself. While the mathematical modeling is sophisticated and requires years of training to master, the techniques themselves are generally flexible and may be applied to a wide variety of predictor and outcome variables. In fact, the same techniques that gauge the response of a patient population to a new pharmaceutical can predict the response of buyers to an offer. When used with other techniques (e.g. relative targeting), predictive modeling provides a more precise view of the addressable market and a better way to judge opportunities than was previously available.
- If a predictive vendor can’t explain how another client’s model worked – why it got the result it got – they probably won’t be able to do so for you, either.
- Prioritization & filtering
- With that, feel free to tweet me, email me and ask any questions during Q&A as I love to talk about marketing and I thank you for your time today and coming to my session!
- "We want to have the SDRs work on the most 'enterprise-ready' leads and that meant fast-tracking those that had a high score at the account-level," said Shantel Shave, director of demand generation strategy at Hootsuite. "Our return on investment has been very quick, especially as a result of surfacing accounts with a high likelihood of conversion through predictive lead scoring, and accelerating them in the pipeline.”
- Nurturing Competitive plays Optimize list buys Identify customer expansion opportunities for sales Identify net new based on fit+intent
- Scored their database to identify high fit accounts Enabled the to focus ad spend on accounts most likely to convert Identified late stage buyers not yet in funnel Enriched MAP with scores and data to drive richer segmentation Enabled them to deliver hyper-personalized campaigns 35% greater pipeline 40% higher engagement on ads 70% greater ROI on ad spend
- We doubled our volume just by running a test, in month three we threw almost our whole ad budget to see what results we could get - that resulted in a huge “victory” that I like to call champagne problems.