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Marketing Automation: Begging for Machine Learning
1.
MARKETING AUTOMATION: BEGGING FOR
MACHINE LEARNING Seth Myers Senior Data Scientist Demandbase smyers@demandbase.com Advanced Spark and TensorFlow Meetup
2.
© 2016 DEMANDBASE|SLIDE
2 What We Do We automate how companies market to, sell to, and advertise to other companies (B2B) Old methods of B2B Target as many companies as possible Example: Send out generic spam sales emails New method Customized/Personalized approached to each customer Focus on the right companies instead of all companies Scaling personalized approach is huge opportunity for machine learning
3.
© 2016 DEMANDBASE|SLIDE
3 Examples Business Knowledge Graph A monthly crawl of 2 to 5 Billion web pages to build a network of companies, people, and products Uses in-house-built NLP to infer relationships between entities from natural text (e.g. which companies buy from/compete with/acquire other companies Technologies: Elastic MapReduce Predicting company buying behavior Currently tracking 700 Billion web interactions a year. Predicts when a company is shopping around for a new product. Technologies: Spark, BigQuery Predicting our client’s next customer (Who to Target) Takes current customers as positive training examples, trains a classifier Scores 14 Million known companies inside of 20 minutes (other solutions take months to implement) Technologies: Spark/mllib Content Recommendations for our clients’ websites
4.
© 2016 DEMANDBASE|SLIDE
4 Generating Page Recommendations An important potential customer is on your website – how do we make sure they find the right pages that will sell them? Our clients are big companies that sell a lot of products Different visitors are looking for very different things Using IP addresses and cookies, we know the company where each website visitor works Based on visitor’s company, generate personalized recommendations
5.
© 2016 DEMANDBASE|SLIDE
5
6.
© 2016 DEMANDBASE|SLIDE
6 Learning to Recommend Build a supervised classifier that predicts which page a website visitor will go to next: Prob(current page next page) Recommend the most likely next page. Training data: our client’s historical website visitor logs Feature vectors are extracted from: Visitor’s company (revenue, industry, buying behaviors, corporate structure, etc) Current and next pages Previous visitor behaviors Visitor Page view sequence Visitor 1 Page 7, Page 4, Page 8 Visitor 2 Page 2, Page 9
7.
© 2016 DEMANDBASE|SLIDE
7 Planning Sequences of Recommendations Our clients first reaction: “meh.” They only really care about traffic to certain target pages Signing up for webinars Downloading whitepapers Requesting a demo Becomes a reinforcement learning problem: Generate a sequence of page recommendations r1, r2, r3,… that maximize
8.
© 2016 DEMANDBASE|SLIDE
8 Closed-Form Solution Assume Markov property – next page is independent of previous pages Define V(pi) = Prob(eventually landing on target page | current page = pi) If pi is a target page, then V(pi) = 1 The probability of going to a particular page pj, and then to the target page is V(pi) is the probability of going to any page times that page’s V()
9.
© 2016 DEMANDBASE|SLIDE
9 If we recommend page pj, we assume the user will land on target page with probability We recommend the best pj Closed-Form Solution So Bounce rate, measured directly Original ML Classifier
10.
© 2016 DEMANDBASE|SLIDE
10 Implementation For each client, train the Pr(pi pj ) classifier in Spark (mllib) from web logs, and serialize the classifier in Redis. For each new visitor to the website – calculate Pr(pi pj ) between all pages pi, pj on the site Solve for V(pi) for all pages. On each page view, generate next-page recommendations:
11.
© 2016 DEMANDBASE|SLIDE
11 But Does it work? A/B Test on Demandbase.com with recommendations Bounce Rate: -52% Page views per visitor: +107% Time on Site: + 231% Target Page # 1 – Sales Demo Requests: +311% Target Page # 2 – Downloading Whitepapers: +593% Similar results across all clients
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© 2016 DEMANDBASE|SLIDE
12 Shameless Plug We’re hiring very aggressively Pre-IPO Just raised $65M, mostly earmarked for expanding our data science team and infrastructure We build customer-facing machine learning solutions – no revenue modeling for the finance team here High impact – all of our recent hires are already building new products. Proprietary datasets - a lot of fun to work with Voted “Best 10 Places to Work” multiple times by Glassdoor and SF Business Times Interested? Seth – smyers@demandbase.com