Machine Learning for Sales & Marketing
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Workshop slides from the Machine Learning for B2B Sales & Marketing event held at Georgia Tech in summer 2017
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Machine Learning for Sales & Marketing
- 1. Machine Learning for Humans in Sales & Marketing A workshop by SalesTing
- 2. HELLO! Piyush Saggi, Nitesh Chhajwani hi@salesTing.com Professional – b2b sales & marketing Academic – MS in CS, MBA in Marketing
- 3. Agenda »Lay of the land »Data to Decisions Workflow »Machine Learning Theory & Applications »What to do next?
- 4. “Without data you’re just another person with an opinion”
- 5. 1. Lay of the Land Let’s get a quick grasp of the world
- 7. Exploratory Analytics Example (Microsoft) »Dataset includes customer purchase history ⋄History of 5 fiscal years ⋄Different editions of the products purchased ⋄Purchased with/without maintenance contract ⋄Account demographics (geo, sales district, etc.) ⋄Model = RFM Analysis ⋄Algorithm = Bit-by-Bit Analysis ⋄Tool = Excel (ping me if you want template)
- 8. Concepts, Models, Algorithms, Tools » AI and Machine Learning – dream to be fit » Linear Regression – goal is to loose 15 lbs » Root Mean Squared – go to gym » Excel, SAS, Python, Azure ML, TensorFlow – run on treadmill for 40 mins » Target (Dependent) & Descriptive (Independent) variables
- 9. AI ML NN DL
- 11. 2. Data to Decisions The workflow to implement ML
- 12. Can we change? Success metrics Can we get enough? Can we get it timely? Can we get the data? Is data available? Business Problem Understand & Identify Categorical and Continuous variables
- 20. The Data Supply Chain inside the company Every system, machine & human is producing data. DUI has a new meaning. Data Utilization Index.
- 22. Intuition Learn some facts about the world and tell me something that I don’t know or I can’t compute.
- 24. Information-based Learning Will they “x”, given all these other things about them? Intuition – think of the game 20 Questions. Algorithm – Decision Tree (Random Forest variation)
- 25. Support Calls Support Call Change % Price Inc % Personnel Change No. of seats Churn Churn Churn Churn >17 <17 >70% <70% Yes No >26% <26% >900 No
- 26. Error-based Learning Life is a set of equations. Intuition – build me a magical equation that gives the answer. Building this requires minimizing errors. Algorithm – Linear Regression
- 27. Example CustomerID Age Eco-band Shopping freq Value Nitesh 63 b 1.6 $109 Ryan 37 c 4.9 $11 Lee 29 a 1.2 $161 Likelihood of buying = 0.2 (Age) + 0.9 (band b) + 0.7 (band c) + 0.8 (freq) + 0.5 (value)
- 33. Your Data
- 37. Probability-based Learning Given they have done x, will they do y? Intuition – there’s an 80% chance that if attend the Sirius Decision Summit, you will buy a new analytics tool this year. Algorithm – Naïve Bayes Model
- 38. Example CustomerID Loan History Co-applicant Accommodation Fraud LadyGaga Current Yes own Yes Justin Paid No rent No Harold Paid No rent ? Calculate probability of Fraud based on every other scenario
- 39. Similarity-based Learning Find me people/accounts similar to these others that have reacted positively. Intuition - spatially, plot these and give me the nearest neighbors. Algorithm – k-nearest neighbors
- 40. Example CustomerID Created Profile? Read FAQs? HelpForum? Newsletter? Liked on FB? Converted? LadyGaga Yes Yes Yes No Yes Yes Justin Yes No No No No No Spencer did a,b,c – will he convert?
- 41. Key terms Labeled data Training data
- 44. 4. What to do next? Take-aways from today
- 45. Upsell Prediction New locations Billing Info Product Usage Support Upsell Indicator Amounts Change Freq Product mix Time spent No. of calls
- 47. You’ll likely face one of these decisions Business Dilemma Someone will say - “We have a problem. Let’s do xyz.” Purchase Scenario Almost every product vendor is pitching AI. ML-worthy Problem “I have always wondered what patterns are hidden in wins/losses.” Hiring Scenario Evaluating candidates based on true skills.
- 48. THANKS! For questions or to learn how to use SalesTing’s ML technology, www.salesTing.com
- 49. Credits »Tom Tunguz (Redpoint Ventures) »Fundamentals of Machine Learning for Predictive Data Analytics »Spurious Correlations »Artificial Intelligence for Humans, Volume 3 »The Visual Display of Quantitative Information – Tufte »Data Mining with Decision Trees »Business Analytics Center at Georgia Tech