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How Microsoft is Using Artificial Intelligence in Marketing: Today and Tomorrow

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Charles Eichenbaum, Microsoft's Director of Marketing Technology & Applied Artificial Intelligence shares how Microsoft's Marketing organization is using conversational AI solutions to engage and convert more leads into opportunities for Sales.

Watch the full webinar here: https://youtu.be/tc_qkALedQQ

Publicada em: Tecnologia
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How Microsoft is Using Artificial Intelligence in Marketing: Today and Tomorrow

  1. 1. How Microsoft is using Artificial Intelligence in Marketing: Today & Tomorrow Carl Landers Chief Marketing Officer Conversica Charles Eichenbaum Director of Marketing Technology & Applied AI Microsoft
  2. 2. AI really is everywhere Spam filters Email categorization Airline autopilot Uber pricing Fedex routing Mobile check deposits Ecommerce fraud prevention Snapchat facial filters Online retail recommendation Voice to text typing Bing search Plagiarism checks Touch ID Windows Hello Netflix recommendation Video games Customer support bots Pandora music Ring doorbell motion detection High frequency trading Credit decisions iPhone animoji Vehicle crash detection & mitigation Social sentiment analysis Driverless cars (coming soon)
  3. 3. TRENDS Why AI in Marketing now
  4. 4. Demand Creation Funnel Acquisition Automation-based Qualification Human-based Qualification Opportunity Creation XX% XX% XX%
  5. 5. DRIVERS OF IMPROVED LEADS Three tactics to improved leads
  6. 6. Prioritization of Leads Processed Lead’sRisk-AdjustedLifetimeProfit Process First Process Last Valid Phone Valid Email Valid Phone Invalid Email Name: John Doe Email: JDoe@contoso Company: Contoso Phone: 555-867-5309 Lead • Reduce costs by identifying leads with bad data quality. Improving leads through AI
  7. 7. Prioritization of Leads Processed Lead’sRisk-AdjustedLifetimeProfit Process First Process Last Improving leads through AI
  8. 8. Prioritization of Leads Processed Lead’sRisk-AdjustedLifetimeProfit Process First Process Last Improving leads through AI
  9. 9. Lead’sRisk-AdjustedLifetimeProfit Process First Process Last Are you interested? Yes please! • Avoid processing leads that are unprofitable. • Prioritize most valuable leads, based on chance to convert. Improving leads through AI Process First Process Last CapacityLimit
  10. 10. Lead’sRisk-AdjustedLifetimeProfit Process First Process Last CapacityLimit Are you interested? Not right now… Are you interested? Yes please! • Confirm high-value lead is likely to purchase. Improving leads through AI
  11. 11. Lead’sRisk-AdjustedLifetimeProfit Process First Process Last Improving leads through AI CapacityLimit Are you interested? Definitely!! • Avoid working leads that won’t buy, despite prediction.
  12. 12. Lead’sRisk-AdjustedLifetimeProfit Process First Process Last Are you interested? (No Response) Are you interested? Not right now… Are you interested? Yes, please! Are you interested? (No Response) Are you interested? Not right now… • Work leads that will buy, despite initial prediction. Improving leads through AI CapacityLimit Are you interested? Not right now… Are you interested? (No Response) Are you interested? Not right now… Are you interested? Not right now… Are you interested? Yes, please! Are you interested? (No Response) Are you interested? (No Response) • Process more leads at a lower cost than with humans.
  13. 13. Lead’sRisk-AdjustedLifetimeProfit Process First Process Last CapacityLimit OriginalCapacity Improving leads through AI
  14. 14. DRIVERS OF IMPROVED LEADS Three tactics to improved leads
  15. 15. Our lead scoring evolution Rule-Based Approach (2015) Using experience and judgement to determine what makes a good quality lead “A” + “B” = “C” Data Driven Approach (2016) Model based on historical data to determine which behaviors and characteristics are associated with higher conversion rates AI Lead Scoring(2017-2018) ML driven scoring using real-time ‘outcomes’ to tweak the model so it is always optimized on most recent data
  16. 16. STACK RANKED leads means increase threshold always results in higher MQL quality RE-SCORE leads in real-time after every new activity PLATFORM and model also used by Marketing and Sales to prioritize leads MODELS are self-learning and optimized by product by area MULTIPLE models defined by geographies & products USE AI TO OPTIMIZE LEAD SCORING TODAY the probability to convert to Sales Qualified Opportunity (SQO)
  17. 17. DRIVERS OF IMPROVED LEADS Three tactics to improved leads
  18. 18. Customer is acquired through VSO and nurtured Trial Nurture eBook Jun. 8th 2017 Jan. 26th 2018 Azure Trial sign-up Customer becomes a MAQL; classified as “low-propensity” MAQL expires Feb. 9th 2018 Bot emails customer Feb. 12th 2018 Customer replies via email to Bot Hi Kristin. Actually, I’d like to set-up a Dynamics 365 demo environment, as I have some pretty complex requirements. Can you help? Bot ID’s lead as hot; sends to MSX Feb. 13th 2018 Feb. 22nd 2018 Inside Sales qualifies lead and discusses with customer OPPORTUNITY: Dynamics 365 for Sales 350+ seats | $250K A recent opportunity found by bots
  19. 19. The result Conversion rate to sales qualified (SQO) 30-60% © 2018 Microsoft. All rights reserved.
  20. 20. Learnings 1. Think about the business outcome you’re trying to drive. 2. Identify a scenario along that outcome that AI is well suited for. 3. A ton of the value comes not from “inventing” something new with AI, but improving something that already exists. 4. Ensure the customer experience will be great. 5. What about pre-requisites – data sets? data scientists? UI endpoints? Process? 6. AI doesn’t have all the context. Beware of un-intended consequences. 7. Even a narrow minimally viable product can make a big difference.

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