J.Scott Zimmerman (CEO, Xola) - Hacking Lead Gen Part II

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Scott shows us the technical side of lead gen through segmenting leads and ranking them in your system before reaching out.

What you will learn:
-Internal tools to build for lead segmenting.
-How to teach this to your salespeople.
-How to focus on the right leads.
-Juice all areas of your pipeline.

Visit SalesHacker.com for more sales hacks, tips, and tactics.

Published in: Technology, Business, Real Estate
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J.Scott Zimmerman (CEO, Xola) - Hacking Lead Gen Part II

  1. 1. UNIFIED BOOKING & DISTRIBUTION PLATFORM FOR BUSINESSES PROVIDING ACTIVITIES
  2. 2. PROBLEM NICHE FRAGMENTED SMB’s Narrower focus on niche, geography, or vertical Fragmented markets often lack organized data More challenging to identify and reach
  3. 3. Pre-Qualification Hyper PreQualification can double your success
  4. 4. NEW PROBLEM Marketing guy needs to filter100 specific scenarios around data Marketing guy walks over to engineer and begins to verbalize the 100 specific scenarios around filtering data from database Do the math on time and salaries
  5. 5. SQL String Builder  = PreQual() pq pq.filterCitiesLoose([‘city1’, ‘city2’, ‘city3’]) # String value – Filters cities by loose match pq.filterCityLoose('Tahoe') # String value pq.filterCityPrecise('Tahoe') # String value (city name) pq.filterState('NV') # String value (State abbreviation - XX) pq.filterKeyWord('kayak') # String value pq.filterKeyWords(['farm', 'farmstay', 'hotel']) # Array. Exaple: ['item1', 'item2', 'item3'] pq.filterRank(15) # Integer
  6. 6. Example pq.filterState('FL’) pq.excludeCities(['Key West', 'Miami', 'Naples', 'Saint Augustine', 'Fort Lauderdale', 'Destin', 'Miami Beach', 'Orlando', 'Marathon']) pq.filterKeyWords(['jet boat', 'sailing', 'motor boat', 'yacht']) pq.filterOutKeyWords(['museum', 'convention', 'library', 'market', 'zoo', 'gallery', 'gardens', 'performing arts', 'state park', 'monument', 'stadium', 'arena', 'chamber of commerce', 'nature center', 'visitors center', 'visitor center', 'church', 'shop', 'shops', 'outlets', 'country club', 'college', 'mall', 'falls', 'recreation', 'national forest', 'nursery', 'historic site', 'historic park', 'comedy club', 'farm’])
  7. 7. Output SQL String  SELECT company, url, contact, email, phone, city, state, postal_code, country, rating, rankplace FROM usa_customers WHERE id>0 AND ((company NOT LIKE '%museum%' AND title NOT LIKE '%museum%') AND (company NOT LIKE '%convention%' AND title NOT LIKE '%convention%') AND (company NOT LIKE '%library%' AND title NOT LIKE '%library%') AND (company NOT LIKE '%market%' AND title NOT LIKE '%market%') AND (company NOT LIKE '%zoo%' AND title NOT LIKE '%zoo%') AND (company NOT LIKE '%gallery%' AND title NOT LIKE '%gallery%') AND (company NOT LIKE '%gardens%' AND title NOT LIKE '%gardens%') AND (company NOT LIKE '%performing arts%' AND title NOT LIKE '%performing arts%') AND (company NOT LIKE '%state park%' AND title NOT LIKE '%state park%') AND (company NOT LIKE '%monument%' AND title NOT LIKE '%monument%') AND (company NOT LIKE '%stadium%' AND title NOT LIKE '%stadium%') AND (company NOT LIKE '%arena%' AND title NOT LIKE '%arena%') AND (company NOT LIKE '%chamber of commerce%' AND title NOT LIKE '%chamber of commerce%') AND (company NOT LIKE '%nature center%' AND title NOT LIKE '%nature center%') AND (company NOT LIKE '%visitors center%' AND title NOT LIKE '%visitors center%') AND (company NOT LIKE '%visitor center%' AND title NOT LIKE '%visitor center%') AND (company NOT LIKE '%church%' AND title NOT LIKE '%church%') AND (company NOT LIKE '%shop%' AND title NOT LIKE '%shop%') AND (company NOT LIKE '%shops%' AND title NOT LIKE '%shops%') AND (company NOT LIKE '%outlets%' AND title NOT LIKE '%outlets%') AND (company NOT LIKE '%country club%' AND title NOT LIKE '%country club%') AND (company NOT LIKE '%college%' AND title NOT LIKE '%college%') AND (company NOT LIKE '%mall%' AND title NOT LIKE '%mall%') AND (company NOT LIKE '%falls%' AND title NOT LIKE '%falls%') AND (company NOT LIKE '%recreation%' AND title NOT LIKE '%recreation%') AND (company NOT LIKE '%national forest%' AND title NOT LIKE '%national forest%') AND (company NOT LIKE '%nursery%' AND title NOT LIKE '%nursery%') AND (company NOT LIKE '%historic site%' AND title NOT LIKE '%historic site%') AND (company NOT LIKE '%historic park%' AND title NOT LIKE '%historic park%') AND (company NOT LIKE '%comedy club%' AND title NOT LIKE '%comedy club%') AND (company NOT LIKE '%farm%' AND title NOT LIKE '%farm%') AND (company NOT LIKE '%institute%' AND title NOT LIKE '%institute%') AND (company NOT LIKE '%store%' AND title NOT LIKE '%store%') AND (company NOT LIKE '%memorial%' AND title NOT LIKE '%memorial%') AND (company NOT LIKE '%spa%' AND title NOT LIKE '%spa%') AND (company NOT LIKE '%massage%' AND title NOT LIKE '%massage%') AND (company NOT LIKE '%wellness%' AND title NOT LIKE '%wellness%') AND (company NOT LIKE '%therapy%' AND title NOT LIKE '%therapy%') AND (company NOT LIKE '%golf%' AND title NOT LIKE '%golf%') AND (company NOT LIKE '%links%' AND title NOT LIKE '%links%') AND (company NOT LIKE '%theater%' AND title NOT LIKE '%theater%') AND (company NOT LIKE '%theatre%' AND title NOT LIKE '%theatre%')) AND (URL NOT LIKE '%facebook.com%' AND URL NOT LIKE '%tripadvisor.com%' AND URL NOT LIKE '%yelp.com%' AND URL NOT LIKE '%cox.net%') AND state = 'FL' AND (city NOT LIKE '%Key West%' AND city NOT LIKE '%Miami%' AND city NOT LIKE '%Naples%' AND city NOT LIKE '%Saint Augustine%' AND city NOT LIKE '%Fort Lauderdale%' AND city NOT LIKE '%Destin%' AND city NOT LIKE '%Miami Beach%' AND city NOT LIKE '%Orlando%' AND city NOT LIKE '%Marathon%')
  8. 8. COMPARISON No Pre-Qualification With Pre-Qualifiction Open Rates: 48% Open Rates: 48% Engagement: 4.8% Engagement: 9.5%
  9. 9. JUICE EACH PHASE OF FUNNEL

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