From Conceptual to Practical: Applying Predictive to Your Everyday Marketing Programs
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From Conceptual to Practical: Applying Predictive to Your Everyday Marketing Programs
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From Conceptual to Practical: Applying Predictive to Your Everyday Marketing Programs
- 2. © 2018, Mintigo. All Rights Reserved. Today’s Speakers Gail Morag VP of Partners & Business Development @ Mintigo Gaea Connary Consultant @ DemandGen
- 3. © 2018, Mintigo. All Rights Reserved. Agenda • Predictive and AI transformation • ICP discovery • AI for marketing results • Operational success through use-cases • Current assessment & benchmarking • Real-world application of predictive • Aligning sales & marketing • Preparing for predictive
- 4. © 2018, Mintigo. All Rights Reserved. Housekeeping • Slides and recording will be sent out via email after the webinar • Tweet us @mintigo and @demandgen • Questions? Please type them in the chat box and we will try our best to answer them in our Q&A
- 5. © 2018, Mintigo. All Rights Reserved. “Radically Improve Sales Productivity and drive Double-Digit Sales Growth with minimal additions in their sales teams and cost base “Revenue Growth (2.3x industry average), Profitability (3 to 5% additional return on sales)”. “Gartner clients have reported payback within one quarter of being in production and Revenue Increases up to 10 times in first two years”. “2.9x more likely to report Revenue Growth at Higher Rates 2.1x more likely to occupy Commanding Leadership position 1.8x more likely to Consistently Exceed Goals”. Predictive and Digital Transformation – Unlock the Potential
- 6. © 2018, Mintigo. All Rights Reserved. Bizible Drives Higher Conversion & ASP Use cases: • Automate Selection of Account and Prioritization • Optimization of SDR Outbound outreach - focus on the high value accounts • Account Data (Fit and Intent) Enrichment for Personalization, Segmentation and Precise targeting • Sales Enablement with Actionable Intelligence for better engagement and conversions
- 7. © 2018, Mintigo. All Rights Reserved. What is Predictive and How Does it Work ? Discovers Your High-Def ICP Your Data House ListPositive List Mintigo Global Database Over 600M Positive Data Points • Job titles • Hiring • Financials • Firmographic • Personal profiles • Technologies • Website content • Browsing behavior • Content engagement • Marketing activities Account or Lead Based Model Matching your data with our data Predictive models Ideal Customer Profile (ICP) Prioritization, Scoring & Insights ABM Segmentation & Discovery 25M+ Companies 200M Contacts 4,250+ Fit and Intent Indicators MAP Your Database CRM 3rd Party Bulk API Import / Export
- 8. © 2018, Mintigo. All Rights Reserved. A Full Customer Lifecycle AI Solution Predictive ICP Discovery Accounts and Leads Prioritization Insight-Driven Engagement Geographic-Specific Modeling Enterprise / Mid-Market / SMB Segmentation Customer Marketing Up-sell, Cross-sell Retention Program Sales Enablement
- 9. © 2018, Mintigo. All Rights Reserved. A practical approach to predictive analytics Illustrating operational success through use cases Gaea Connary, Consultant | DemandGen
- 10. © 2018, Mintigo. All Rights Reserved. Where are you today? Do you already have a marketing automation platform? Have you already implemented lead scoring? How well is it working for you?
- 11. © 2018, Mintigo. All Rights Reserved. Benchmarking your Models Question Metric to answer Are ratings being calculated and synced to the CRM? Do they show a healthy overall distribution of scores? Rating Distribution Is the volume of leads qualified by the model sufficient? Lead Volume Are the qualified leads being accepted by Sales at a healthy rate? Conversion Rate Are the qualified leads moving to acceptance, qualification and won more quickly? Velocity Benchmark as many of these metrics as you can prior to launch of a new model so you can demonstrate improvement. You should also snapshot these and analyze at least every 6 months.
- 12. © 2018, Mintigo. All Rights Reserved. Comparing Results A 356 A 329 B 19 B 98 C 136 C 42 D 18 D 62 E 5 E 44 - 44 - 3 Rules-Based Model Predictive Model 578 Total Opps Won $454,455 $8,900 $293,166 $4,751 $1,900 $8,564 $471,915 $168,780 $60,476 $44,381 $25,166 $1,018 A B C D E - Total Won Amount - Rules-Based Model Total Won Amount - Predictive Model $771,736 Total Opp Won Amount
- 13. © 2018, Mintigo. All Rights Reserved. The challenge: Which customers merit higher-value touches to drive the most business?
- 14. © 2018, Mintigo. All Rights Reserved. The solution: A sophisticated application of Mintigo predictive analytics Four Mintigo models to predict likelihood to: Grow Decline Churn Win Back Some models a hybrid of predictive and rules-based Segmented customer database and ran models on a weekly cadence High-scorers created as new leads in rep’s CRM queues
- 15. © 2018, Mintigo. All Rights Reserved. Good initial results, but then… • Huge lead backlog • Reps complaining about quality of leads • Multiple high-scores from different models for the same customer (e.g. both a Grower and a Decliner with a few weeks of each other) • Marketing had little visibility via feedback loopsIncreasing perception that Marketing was creating work, rather than enabling efficiencies
- 16. © 2018, Mintigo. All Rights Reserved. What went wrong? Models all running side-by-side against same initial dataset but with no linkages Differing segmentation of customer audience between marketing and sales Poorly-governed service level agreements between Sales and Marketing Low trust of the predictive model due to limited understanding Misaligned KPIs between Marketing and Sales HINT: It wasn’t the platforms!
- 17. © 2018, Mintigo. All Rights Reserved. A practical approach: Rebuilding trust between sales and marketing Architected workstreams, committees and communication plans Bi-weekly cadence of Sales & Marketing steering committee to inform and gain approval Identification and implementation of “quick wins” to get Sales’ buy-in Launched Mintigo “roadshow” for Sales to grow understanding of predictive Created feedback loop between Sales and Marketing by codifying lead management process and systematizing in CRM
- 18. © 2018, Mintigo. All Rights Reserved. The Mintigo solution Program Segment Status Product “Assigned” model score 6 months refresh, score all CRM accounts Logic-based (Industry, Size) Data augmentation Account status scores Multiple models, real time integrated Product scores Multiple models, real time integrated Segmentation ecosystem Mintigo application
- 19. © 2018, Mintigo. All Rights Reserved. Status model decision tree Data Warehouse Accounts Data (includes purchase and usage history) Churn Prediction Key Account Prediction High(er) Value Prediction Populate Key Account CRM indication Recapture Prediction Filter: Churn post-mortem detection SQL NoYes Save Play No Yes Opportunity Play Recapture Play Business Rules “Assigned” Model (applied against CRM data) Renewals (parallel workflow) Yes No Nurture Play
- 20. © 2018, Mintigo. All Rights Reserved. Preparing for predictive – what you can do today Capture granular use cases for predictive and action/resolution plans Firm up your lead management process Segment and accurately track target events in your MAP and CRM Prioritize data sources and work to gain highest completion Launch a predictive education roadshow Benchmark KPIs
- 21. © 2018, Mintigo. All Rights Reserved. Thank you! Questions? Thank You! Questions? @mintig o @demandgen
Editor's Notes
- Background script: DemandGen is a great partner, Gaea – here to unlock the potential of business value out of AI. It’s incredible to see the value that digital transformation unlocks. The benefits don’t happen with words alone. Companies need to be committed to changing how they run end-to-end including culture, competencies and metrics to realize this value. There is no doubt that organizations investing in AI competence today will be the dominant players for decades to come. I see it every day - at scale in real time. AI transformation is the new digital transformation - Why Artificial intelligence will be critical to your long-term success. Businesses realize that for truly unlocking business value, they need to not only weave AI into the fabric of their enterprise, but also operationalize it – with the right personnel and change management initiatives. Given that AI can bring both cost efficiencies to business as well as potentially new revenue streams, businesses today are exploring an ‘AI Transformation’ An AI Transformation is doubtless the most strategic subject to be tackled by organizations today. Successful transformations will ensure enterprises go beyond mere automation and cost-cutting strategies and unveil previously unseen business and revenue opportunities. Mindset of successful implementation – assess where you are, build a team , remove the siloes – especially between sales and marketing.
- Use Cases: Automate Selection of Account and Prioritization Optimization of SDR Outbound outreach - focus on the high value accounts Account Data (Fit and Intent) Enrichment for Personalization, Segmentation and Precise targeting Sales Enablement with Actionable Intelligence for better engagement and conversions
- With Mintigo, it’s a true platform in which you can build your own predictive models quickly…. We have customers with 100s of models like CA Technologies, a joint client Q: What are some of the predictive analytics concepts that you find are most difficult to grasp for marketers?
- Q: Which of these do you recommend as an easy starting place for organizations wanting to test out the power of predictive?
- Start building a culture of analysis – no more “set and forget” Benchmarking now also starts to expose some of the gaps in the data that you already collect, giving you early opportunity to improve tracking and capture.
- Distribution is more balanced in predictive model, and predictive As and Bs outperform for total won Opp amount. Q: Do you always see these kind of results when analyzing a clients’ models? A: Almost always, yes. We’ve compared rules-based versus predictive and hybrid versus predictive-only, and in almost all cases for all KPIs, predictive outperforms rules-based-only.
- B2B/B2C subscription sales model with thousands of customers of all sizes and industries Established, well-known brand, considered a thought leader in their space
- Client brought DemandGen in as an unbiased third-party to help diagnose issues resulting in decreasing performance.
- DemandGen’s approach: Discovery interviews with marketing and sales Review of documentation and reporting definitions Systems audit What we found: Siloes Models had no knowledge of each other’s outcomes Sales and Marketing looked at the customer differently, impacting routing and SLAs Sales didn’t understand the models and therefore didn’t believe the output scoring Marketing measured on lead volume (quantity), Sales measured on pipeline volume (quality)
- Work breakdown structure Communication plans and RACI charts – clarity on responsibility and accountability during transformation project Get Sales on board with quick wins – “what’s in it for me?” Get Sales educated on what the score means Crystal clarity on ownership, next actions and required timeframe to take those actions Q: Which of these tactics was most key toward “stepping on the gas” for this transformation? Or, which tactic could you not afford to skip or de-prioritize? A: Quick wins for Sales. You must convince the organization that you are working for the collective benefit, and not in a vacuum.
- Agreed-upon segmentation of customers Applied Mintigo capabilities at all points in the journey
- Decision tree executed in MAP to avoid duplication across models Defined Plays outlining action plan for customer owner Q: This seems awfully complicated. Does every organization need to take this approach? A: This is ideal when there is a likelihood that a customer (or prospect) could fall into multiple segments. It also drives the highest likelihood that you deliver the best solution for their needs.
- Q: What’s the most valuable step here? Or, which step can you not afford to skip or de-prioritize? A: Diligently capturing the data that you can.
- Which customers are most likely to buy from you? Which products? Q: Most organizations that we talk to freely admit that their data isn’t of the quality that they prefer. Can they even pursue predictive?