Lead Tracking and Conversion - Todd Katler

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Todd Katler's AIM conference 2008 presentation on cost-effective buying of ads, leads and leases.

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Lead Tracking and Conversion - Todd Katler

  1. 1. Present Day Realities of Lead Tracking and Conversion
  2. 2. Topics: <ul><li>Lead Accuracy in Property Management Software </li></ul><ul><li>Lead:Lease Conversion Metrics </li></ul>
  3. 3. Lead Accuracy in Management Software Questions: <ul><li>How accurate is the lead source data contained in my property management software? </li></ul><ul><li>If the data is not truly accurate, where are my leases coming from? </li></ul><ul><li>Since 1 out of every 3 times people visit the property they get lost trying to find it, how can ‘Drive By’ be responsible for 30% of my leases? </li></ul>
  4. 4. Lead Accuracy in Management Software Solving The Equation: <ul><li>Call centers and other software packages digitally record the originating ad source for each rental inquiry (no human bias/error). </li></ul><ul><li>We conducted a study where the leases transacted were compared with the original guest cards using a 3 rd party software application to determine matches. </li></ul><ul><li>The results show if the ad source data in the property management software matches the original records as well as other interesting discussion points. </li></ul>
  5. 5. Lead Accuracy in Management Software The Results: For the population of leases where a match with a Level One guest cards exists: 32.6% of all leases were sourced correctly (67.4% had a source other than what was recorded at lead origination)
  6. 6. Lead Accuracy in Management Software Other Interesting Observations: <ul><li>Of the 13 paid ad sources included in the analysis, 11 created more leases than they were credited. </li></ul><ul><ul><li>One of the sources was only given credit for 26.3% of the leases it generated. </li></ul></ul><ul><li>Craig’s List was the most over-reported source – it received credit for 40.6% more leases than it generated. </li></ul>
  7. 7. Lead Accuracy in Management Software Other Interesting Observations, part 2: On April 6 th , 2008, www.pizza.com sold for $2.6M. Internet-Other received credit for 13 times more leases than it generated! As the most successful ad source in the industry, www.internet-other.com is now owned by Todd Katler. The eBay auction will start next week with a $1M reserve…
  8. 8. Lead Accuracy in Management Software Other Interesting Observations, part 3: <ul><li>22.3% of leases were incorrectly attributed to ‘Drive By’ or ‘None’. </li></ul><ul><li>Of the renters that set an appointment to tour and subsequently leased, 64.5% were recorded incorrectly despite the source being transmitted with the visit notification. </li></ul><ul><li>Of renters that applied sight-unseen, 60% were incorrectly recorded despite the source being transmitted with the rental application. </li></ul>
  9. 9. Lead Accuracy in Management Software Other Interesting Observations, part 4: <ul><li>18.2% of all leases came from leads that originated when the property was closed. </li></ul><ul><li>And since you are all going to ask anyway…. </li></ul><ul><li>Both pay-for-performance ILS’s created more leases than they were given credit for in the management software. </li></ul>
  10. 10. Lead:Lease Conversion Metrics Questions: <ul><li>How can I rank my ad source effectiveness using Lead:Lease ratio? </li></ul><ul><li>What are the limitations of using this metric? </li></ul><ul><li>Everybody talks about Cost-Per-Lease, but how can I really track that accurately? </li></ul><ul><li>How do I manage the trade-off between ad source effectiveness, lease volume and cost per lease? </li></ul>
  11. 11. Visit Set Ratio (VSR) Demonstrates That Not All Leads Are Created Equal This graph shows variance from the median VSR across four companies. Be mindful that you will have wildly different experiences with each ad source that will often differ by property or sub-market.
  12. 12. Lead:Lease Conversion Metrics Solving The Equation: <ul><li>Isolate the “real leads” from the “fake leads” (family, friends and vendors). </li></ul><ul><ul><li>You can also assume “fake leads” are a constant. </li></ul></ul><ul><li>The data must allow at least 3 months for the leads to convert into leases. </li></ul><ul><ul><li>It will take 5 months to achieve 80% accuracy and 8 months to achieve 90%+ accuracy. </li></ul></ul><ul><li>To establish conversion, you have to compare the renter names from the leads to lease names in your management software. </li></ul><ul><li>Do not use the source tracking in your software and simply divide those leases into the total leads- this will produce a highly inaccurate ratio. </li></ul>
  13. 13. Lead:Lease Conversion Metrics Solving The Equation, part 2: <ul><li>Use matching software to determine which of your leads became leases. Computer algorithms are far more consistent and are not prone to fatigue. </li></ul><ul><li>Use a significant sample over several months (if not longer). </li></ul><ul><li>Do a smell test! Across you portfolio, lead:lease conversion should be between 3% - 7%. If it is not in that range, there may be an issue with the data. </li></ul><ul><li>Depending on the accuracy of your data, Cost-Per-Lease becomes a trivial calculation at this point. </li></ul>
  14. 14. Lead:Lease Conversion Metrics Lead:Lease Conversion Limitations: <ul><li>Not all leases from an ad source start as phone calls or emails. 20% - 50% of the leases generated by your ad sources will not be considered in this exercise (even higher for sources like Craig’s List). </li></ul><ul><li>As the old saying goes: </li></ul><ul><ul><li>#@!* in, #@!* out </li></ul></ul><ul><ul><li>Accurate data will be the single most limiting factor and often prevent meaningful analysis </li></ul></ul><ul><li>Your use and management of your ad source (as well as overall content management) may vary greatly and will to some extent dictate your results. </li></ul>
  15. 15. Lead:Lease Conversion Metrics
  16. 16. Lead:Lease Conversion Metrics What if this source is a high cost per lease? Remember that some of this traffic originates at the other ILS’s. Ad spend optimization tends to be possible at lower converting, lower volume sources.
  17. 17. Evaluating Leads through Visit Set Ratio (VSR) Questions Answered: <ul><li>If the data in my property management software is less accurate then I would like for making purchasing decisions, what expository metrics can I use to determine lead quality and lease expectancy? </li></ul><ul><li>Is VSR the best way to rank ad sources? What are its limitations? </li></ul><ul><li>How universal is VSR? Is it best used at the property, sub-market, market or portfolio level? </li></ul>
  18. 18. Evaluating Leads through Visit Set Ratio (VSR) Solving The Equation: <ul><li>A sample of 257,000 leads originating over the past 12 months and handled by Level One was reviewed. The ad source, lease status and whether or not a visit was set was analyzed. </li></ul><ul><li>59.8% of the leases generated from all leads came from renters who set a visit during the Level One inquiry. </li></ul><ul><li>As nearly 2/3 of all identified leases originated from a visit set, VSR becomes a prime indicator of the success of an ad source. </li></ul>
  19. 19. Evaluating Leads through Visit Set Ratio (VSR) Limitations of VSR: <ul><li>The average phone call converts to a visit at 30% while emails convert at about 5%; therefore, you have to factor the distribution of contact types when evaluating a source. </li></ul><ul><li>Evident in the previous graph, no source is universally good or bad. VSR across a portfolio is useful directionally and can help determine where to investigate further. </li></ul><ul><li>VSR can not factor renters who use a source but neither call nor email prior to visiting; however, it is unlikely that a source would convert well in this group and poorly from those who first contact. </li></ul>
  20. 20. Evaluating Leads through Visit Set Ratio (VSR) How do I compute VSR? <ul><li>To compute VSR accurately, you must first be able to do the following: </li></ul><ul><ul><li>Record each and every lead (“pitching” traffic lowers the denominator and makes the statistic inaccurate). </li></ul></ul><ul><ul><li>Separate “real leads” from “fake leads”: about 15% of the calls and emails from your ad sources are not leads (some can be as much as 40%). They are family, friends or vendors. To the extent possible, these must be removed from the equation. </li></ul></ul><ul><ul><li>After accomplishing the above, you must have a consistent and reliable way to record the intended visit. It is better to record the intent during the inquiry as the data will contain many more variables if you try and do this on-site. </li></ul></ul>

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