Master the Prospect-to-Resident Journey with Predictive Analytics

Transcription

1 June 24-27, 2015 Mandalay Bay Resort & Convention Center Las Vegas Master the Prospect-to-Resident Journey with Predictive Analytics NAA Education Conference 2015

2 Agenda Introduction Predictive analytics in Multifamily Housing Customer lifecycle analysis Choosing your tools Metrics for data-driven business decisions Takeaways Q&A

3 Chris Eckert, G5 INTRODUCTION TO PREDICTIVE ANALYTICS

4 What are Predictive Analytics?

5 What are Predictive Analytics? The use of data to answer a question based on the probability of its occurrence

6 Why Do Predictive Analytics Matter in Modern Marketing? Replace hunches with data Credit scores Insurance premiums

7 Analytics that used to reduce risk, are used by marketers to decrease the length of the sales cycle

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10 $1.1 Billion

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13 Lisa Trapp, Sequoia Equities PREDICTIVE ANALYTICS IN MULTIFAMILY HOUSING

14 Are All Leads Created Equally? How are your leads scored today? Is there enough time in the day to effectively follow up on all leads? Are they modeled to advise your associates on how to communicate with prospects?

15 Set Expectations and Exceed Them

16 Create a Realistic Vision and Act Guide on-site team s time management Create a scoring system for leads

17 Most Companies Still Struggle 33% use predictive analytics to make business decisions 66% have appointed Chief Data Offers Source: Accenture

18 Audience Poll Are you using either scoring or predictive analytics today?

19 ROI is the Goal 57% are quite satisfied or very satisfied with predictive analytics ability to improve ROI Source: Accenture

20 Biggest Challenges in Predictive Analytics Erroneous data Capturing data on differing platforms Unreliable tools Understanding the customer and the customer journey

21 Biggest Opportunities in Predictive Analytics Prioritize the best leads Know their lead source What will their conversion rate be Predict renewal with satisfaction scores

22 Notify Your Leasing Agents of a Good Lead and a Better Lead

23 Sequoia Equities Case Study: Leads per Post on Craigslist

24 Sequoia Equities Case Study: Social Media Referrals Convert

25 Predict Behaviors Based on Personal History

26 Customer Journey Mapping Five stages 1. Persona development 2. Map key touch points 3. Validate the hypothesis 4. Ideation 5. Measurement and optimization

27 Lisa Trapp, Sequoia Equities CUSTOMER LIFECYCLE ANALYSIS

28 Are the Days of Incentives Over?

29 Today s Resident Values Relevance and Service

30 The Path to Revenue Optimization Step 1: Know your customer persona Who are they? What makes them take action? What keeps them engaged?

31 Jessica 32 years-old Lives alone No children Has a dog College degree Income is $122k

32 The Path to Revenue Optimization Step 2: Understand the customer journey Screen or Rent Online

33 The Path to Revenue Optimization Step 3: Benchmark your touch points and track engagement Sites Search Social Advocacy (reviews)

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35 The Path to Revenue Optimization Step 4: Layer measurement on top of trends, on top of analytics Frequency What behaviors precede that decision? Engagement How do they engage with us? Using which channels? Duration How long does that take between interactions? How long does each interaction take?

36 Audience Poll Do You Know Your Path to Revenue Optimization?

37 Chris Eckert, G5 TOOLS FOR DEVELOPING PREDICTIVE ANALYTICS

38 Cheap, Fast, or Free But Not All Three

39 Completeness of Solution Tools for Developing Predictive Analytics Sites Search Social Google Universal Analytics Call Tracking Packages Reputation.com Moz Pro Reach Local Moz Local Crazy Egg Hootsuite Facebook Insights analytics.pintrest.com Predictive Value

40 Lisa Trapp, Sequoia Equities METRICS FOR DATA-DRIVEN BUSINESS DECISIONS

41 Capture Metrics Your CEO Wants to Understand Length of sales cycle Volume of leads by source Cost-per-lead Cost-per-lease Social and anecdotal data

42 Inform a Variety of Business Decisions Maintenance Leasing office Human resources Hiring

43 PREDICTIVE ANALYTICS CHECKLIST

44 Predictive Analytics Checklist Define key digital touch points Know these touch points Measure and engage Monitor the customer lifecycle, for all properties Collect data, measure data, then infer conclusions Check your hypotheses and take corrective action as needed

45 Chris Eckert: Lisa Trapp: THANK YOU