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10 Laws of List Generation

Law 1: The Law of Planning (10 Laws of List Generation)

Read the full:  10 Laws of List Generation

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Pants before shoes; 22 pieces of advice from a data strategist

Ok, not everyone puts their pants on first thing in the morning.  I  wander the house a bit my robe.  It’s an analogy I want you to remember.  Whatever you do, there a certain, correct, order of things.  Pants always go on before shoes.

The same concept works for data and sale and marketing initiatives that depend upon data.  There is a certain order of things.  If you want your CRM or Marketing Automation System to run at mythic levels of functionality, learn the correct order of working with data.  Here is the order:

1. Start with a solid strategy

Before diving into the bits and bytes, get a solid understanding of what you want to accomplish as an end goal.  Everything has an order.  Don’t run out and buy tools, data, CRM, Marketing Automation, etc until you understand what the end goal is.

Once the strategy is in place, then it is time to dive into the data.  Following is the step by step sequence that data|z guides our clients through.  These are the major milestones,  within each milestone are multiple subcomponents.

2. Build a set of sequential random samples

You don’t want to test data operations on your live data.  Build a set of sequential random samples.  With a sequential random sample, each larger sample encapsulates all the members of the previous smaller sample.  My samples start with 25, 100, 500, 1000, 5000 and grow exponentially from there.  This makes the process of debugging your data cleanse process naturally.  The 25 sample works?  Great,  now on to the 100 and you already have a baseline that works for the first 25 records.  I have seen brilliant engineers struggle by assuming they have the full picture and jumping right into a sample of 5000.  You can’t see what is going on with 5000 records at a glance.  After 15 years of seeing (and fixing) ugly data, trust me on this one.

3. Make the plan, not technology control the process

When you have a hammer as your product, everything looks like a nail.  Technology vendors are famous hammer swingers.  You are smart. Right?  Make sure you develop a strategy first.  Pick technologies that can conform to your plan, not the other way around.

4. Character level field validation 

Data is uglier than ever before.  Data from many sources and many formats makes its way into your CRM or Marketing Automation System.  Many characters are unseen.  Screen out illegal characters.  Do this first, but be careful.  Look for patterns in the data and always sore

5. Unicode and web character conversions 

We’ve established data is ugly.  Depending on your target system, you need a plan for Unicode characters.  Do you convert them or store them in their original form?   Web characters are ubiquitous garbage in today’s CRM.  Anyone who has pulled data from a webpage has seen a title that looks like:

Vice President of Sales & Marketing

The & is from the HTML in a web page.  The translation table below is a sample of conversions that need to be done. If you don’t do it your “Vice President of Sales & Marketing” will not come up when you build a campaign with filter “Vice President of Sales and Marketing”

6. Field level validation 

Does the phone field contain a phone number?  Does the email field contain an email?  Does the company name contain a location?  When doing field level validation you must both validate and move data to the correct fields.  Once clean, the target system should have field level validation protection on the initial data entry.  A good technique to avoid destroying/deleting data is adding secondary fields from your primary data values, such as Company2, Phone2, Email2, etc.  While doing field transposition (moving data from one field to another), you don’t want to destroy the original data.

7. Field Splitting

What is field splitting?  Field splitting data mines the all fields within a CRM/Marketing automation record.  For example,  you should never encode a location as part of a company name. Data is commonly entered by sales reps in this format.  It is problematic and it wreaks havoc with reporting & analytics.

Examples:
Microsoft – Denver
Microsoft – New York

In my time analyzing data I have seen every ugly data situation that you can imagine.  Try to avoid situations like the location at the end of the company name. Another common data occurs when sales reps add notes at the end of the business name.  Data types should never be mixed in the same field.

8. Field transpositions

Once the components (email, title, phone, location, etc)  are identified with field splitting, the components are placed into the proper fields.  This may seem simple, but in many cases, it is not a simple swap.  What if you find a location in the company name, but a location/city/state already exists in the data record?   You will need a set of rules to handle the field conflicts.

A good technique to avoid destroying/deleting data is adding secondary fields from your primary data values, such as Company2, Phone2, Email2, etc.  While doing field transposition (moving data from one field to another), you don’t want to destroy the original data.

9. Develop a Normalization Plan. 

(In the past I’ve called it a Data Plan), but the phrase has evolved.  A Normalization Plan is a set of standards for your data.  For example: for job titles, do you want them to be listed verbose, such as “Vice President of Sales” or compact like “VP Sales”?  Removing extraneous spaces and punctuation is also something to decide upon.  Do you always go with “Incorporated” or “Inc”?

10. Normalize your CRM

Don’t even think about deduping your data before normalizing.  This is a mistake of the uninformed.  If a consultant wants to dive right in and dedupe, they are not skilled in the art of loving their CRM.

Dedupe without normalizing first is akin to putting underwear over your pants.

11. Pick your dupe matching rules

It’s got to be the right time and you must do some testing to make sure you’ve got it right.  Use tools that can provide customizable dedupe logic to match your business requirements.

13. Dedupe

At this point, you’ll have a plan, you’ll know about all the little nuances and dedupe will be natural and successful.

14. Website fill

Why separate “website fill” from data enrichment?  The website of a company is extremely critical and data enrichment will be 2X more effective if the website is filled first.  Most data enrichment is generic and doesn’t do an adequate job with the website fill.  To this day, the best result I have seen in with crowdsourcing.  I’ve seen match rates for D&B data be 40% without and 80% with comprehensive website fill completed first.

Regarding #11 (website fill) and #10 (dedupe).  For optimal results, an iterative approach can be taken.  Dedupe, Website fill and then dedupe again.  This is advice from someone in the trenches.  Dedupe does not work when data is ambiguous.  Filling in the website makes the 2nd dedupe cycle pick up matching records that were missed the first time.

15. Company data enrichment

Account/Company data should always be filled in first.  Not all vendors can do both.  Get a solid plan together.

16. Identify TAM 

The TAM is the total addressable market for your product or service.  ONLY after you have all your current account data cleaned and ready should you seek out new data.  Know what you have first will save you in your list buys.   In some instances, you will need a data acquisition strategy as there is simply no target list for your customer base.  Note that this step is about the identification of your TAM, not buying a list.  Get a full picture first.

17. Account segmentation strategy

Once your data is enriched with Industry, Revenue, Employee count and social footprint and other data points, it is time to segment your Accounts to optimize the outreach strategy.  Don’t forget the time factor.  Data decays; you can also segment data based on your capacity to execute your outreach campaign.

18. Identify contact personas

Who are the people that you sell your product or service to?  Who are the influencers? Where have you had success in the past?

19. Identify outreach methodology 

At this point, you should know your TAM and contact personas. How are you going to engage customers?  Do you need social data?  Do you need direct dial phone numbers?  If you have not done this before, do some testing.  Don’t do an entire Company and Contact data buy before testing your premises.  What really works vs. what you hope works.  This is the biggest sales and marketing process mistake and the ramifications can be dire.  It may make sense to spend $1.00 a record per direct dial phone number vs  $.10 per record for main company lines that end in voicemail 95% of the time.

20. Contact segmentation strategy

Does your team outreach with the same message to all contacts?  That is dark age selling

Tailor your outreach campaign:  battle cards, scripts, voicemail, and email scripts to target specific segments.  A VP of Sales should not be approached with the same messaging as a green sales rep.

Segment your contact data by both Title and Department.  If you can get it, and org chart helps to determine the up, down and peer influencers within an organization.

21. Contact data enrichment

At this point, you should know your TAM.  You should also know your TESTED outreach methodology.  You know what you need in contact data, now work out a plan to build or acquire it.

22. Everything else

When you get your black belt in martial arts, your real journey begins. If you have done steps 1-19, now your sales and marketing are primed and ready to do the cool stuff.

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10 Laws of List Generation

10 Laws of List Generation

If you need to create a list, don’t ignore the laws… the 10 Laws of List Generation.

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Data Potential

Forget what you think you know about Data Quality. It doesn’t matter. What matters is Data Potential

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Finding LinkedIn data using Google; the definitive 5 minute primer

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2cubes
A full access account on Linkedin is expensive.  Google is Free.

Think about it.  Looking to find data on Linkedin, but don’t have a LinkedIn account?  Welcome to the five minute starter course to getting free data… using Google.

There are plenty of forums that go into details, giving specific examples of how to use search strings on Google to find almost anything.  While I will give some search string examples, first, I’m going to teach you how to think about search. Knowing how to think about search will outlast any example I give you.

There are three core concepts to understand when trying to retrieve data from LinkedIn or anywhere on the Internet.  If you understand these concepts, along with the basics of search string design, you can get ALL the data you want from LinkedIn.  Let’s get started and define the three core concepts (it’s real easy stuff).

Three Laws of Internet Search

  1. Law of Specificity:  Simple.  You want to develop a search query that brings back accurate results.
  2. Law of Completeness:  The goal. You need to understand and access all possible results.
  3. Law of Permutation:  Search engines return limited results.  If the size of the total data exceeds what the search engine returns, the search needs to be split into multiple smaller searches.
With the three laws in mind, lets look at some example google searches.   Our first goal is to find people with the current title of “Inside Sales Manager” from Linkedin.  Internet search has not changed much in 20 years (Yahoo 1994,  AltaVista 1995, Google 1998).   From the perspective of a teacher,  I’ll show the evolution people take as they learn how to find things on the Internet.

 starlordPhase One: The n00b

The n00b will typically sit down at the google search box and type:

noob-linkedin-search
Immediately they get excited.  Seven million inside sales managers to sell to.  Right?  No, this is a fantasy.  There are several problems with this search.  If this is you and we meet some day, it will be me laughing with you, not at you.

Here are some of the problems:

Problem 1: Google lies.  Google will never give you 7 million results with a single search.  If you are viewing 100 results per page and then press the click to the 4th or 5th page of results, it is rare that you will get more than 500 results from google.  Below is the exact search done above, looking at the second page of results, you actually get a maximum of 148 results from this search.

google-lies2

Problem 2: Not all results are from LinkedIn.com.  In this example, you can see results from clearslide.com and jobs.net.  This breaks Law #1, Specificity.

Problem 3: You targeted words, not phrases.  The results are from any page which includes the 4 words (linkedin, inside, sales, manager).   There is a huge difference between:  Inside Sales Manager  (no quotes) and  “Inside Sales Manager” (with quotes).

Inside Sales Manager translates to:

Find a page with Inside AND Sales AND Manager on it

“Inside Sales Manager” translates to:

Find a page with the phrase “Inside Sales Manager” on it.

To put it in mathematical terms, actually, nah, forget that.  Just think of it this way.  You are totally screwed if you try searching this way.  It won’t work.  Let’s fix these problems and move on to the Trainee phase.


Yoda-lukePhase Two: The Trainee

You’ve had a few months of search under your belt and you know to put phrases in quotes and to target specific sites with Google search operators.  The problem with the trainee is efficiency.  Most of the mistakes made have to do with inaccurate targeting.

A typical intermediate search looks like something this:

intermediate-linkedin-search

What was done here?  We used the “site:” operator/instruction

Search engine operators include siteinurlintitle and several others.  They are used as special commands to the search engine. Read more:  Google search operators.

In this case, we used the site: operator and we told Google: “Only bring us back results from the website location http://linkedin.com/in “The focus is the “in” section of the LinkedIn vs. the entire site.   In addition, we put quotes around the phrase “inside sales manager”.   This will improve additional search parameters.  I’m sure search gurus are feeling quite smug right now.  If they take the time to read this article, they may also gain some insight. This is a guide to teach you to think, not to just give the answers.

There are additional problems with this search:

Problem 1:  Google is still lying to you.  When you navigate to the 6th page, you will get a maximum of about 540 results (in this example), not 25,600 that google reports.  In fact, for any search that exceeds 500 results, you will not get all the results.

Google is a page ranking engine.

Google will give you the 500 pages that it perceives as the best match.  As a general rule of thumb, if the Google search results are greater than 500, then you should make your search more specific and get the search results below 500.

 google-lies3

Problem 2:  Inaccurate targeting.  You are targeting the right pages, but the wrong part of the pages.  Our goal is to find people who are currently Inside Sales Managers.  This search will find any profile where “Inside Sales Manager” is somewhere on the page.

people also viewed

This search will find people who are currently Inside Sales Managers AND will also find anyone with “Inside Sales Manager” ANYWHERE on their Linkedin page.  In fact, one of the results from our Google search was a Veterinarian specializing in large animals. This is due to the “People also viewed” section of the profile.

The above search finds exactly what you told it to find.  It’s ok, you are still in training.

Problem 3: Bad mixed with good.  Good results are mixed with bad results.  The problem is Google limits the total results to approximately 500 (sometimes a bit more).  If you have 50% bad results, then you are getting 50% less results.  That means 50% of the results that you will never see.  Most people stop here in their evolution.  Once they get some results, they assume success and work with what they have.

If you can master accurate targeting then you will be on your way to becoming an expert.


bladerunnerPhase Three: The Expert

You’ve got a few years under your belt and you’ve evolved.  You’ve spent time in the forums, went to conferences and know two or three theories regarding the best search strings for getting data from LinkedIn.Here is an expert search string:
 site:www.linkedin.com “know in common” “Inside Sales Manager * * * * * * – Present”This is a good search string.  The results will be very accurate, but not yet perfect.

What was done right, and why:

Success 1:  We picked the right pages.  Adding the “know in common” is targeting pages that are profile pages.  This is important because job pages, article pages and many other page types exist on LinkedIn.  These can cause false positives in search results.  The alternative would be to do search that looked like this:

site:www.linkedin.com/in OR site:www.linkedin.com/pub “Inside Sales Manager * * * * * * – Present”

This technique (using site:www.linkedin.com/in OR site:www.linkedin.com/pub) has been floating around for years.  People keep copying without questioning, so it is still out there. This targets both the “in” member links and the “pub” public links. However, why use it when something simpler works better?

Success 2: We are picking the right parts of pages     What is:    * * * * * * – Present   ?

The asterisks are wild cards.

IF you knew:

  1. The exact name of the company   AND…
  2. The month and year the person started working at the company AND…
  3. The exact title of the person you were looking for

THEN:  You might type something like:

site:www.linkedin.com “know in common” “Inside Sales Manager at TargetCorp  July 2010 – Present”

However, when doing a search, you typically don’t know this information, so the asterisks are used to “fill in” the missing information.

See the searches A-E below.

A site:www.linkedin.com “know in common” “Inside Sales Manager at TargetCorp July 2010 – Present”
B site:www.linkedin.com “know in common” “Inside Sales Manager at TargetCorp * * – Present”
C site:www.linkedin.com “know in common” “Inside Sales Manager at * * * – Present”
D site:www.linkedin.com “know in common”  “Inside Sales * * * * * – Present”
E  site:www.linkedin.com “know in common” “Inside  Sales *  * *   * *  *  – Present”

Search A: is narrow and exact.  The title, company and start month/date are specific. The results will be Inside Sales Managers that work at TargetCorp from July 2010 to present.

Search B: Will include the results of search A.  In addition results with ANY start date will be included.  The two asterisks are used to search for any month and year.

Search C: Will include the results of search A and B.  In addition results from ANY company will be included, not just from TargetCorp.  The three asterisks are used to search for any month, year and company name.

Search D: Will include the results of search A-C.  In addition, the search will not be limited to “Inside Sales Managers”, but anyone with “Inside Sales” at the beginning of their title. The five asterisks are used to search for any month, year, company name and title that starts with “Inside Sales” and will pick up company names that are one and two words.

Search E: Will include the results of search A-D.  In addition, the search will not be limited to “Inside Sales Managers”, but anyone with “Inside Sales” at the beginning of their title. The six asterisks are used to search for any month, year, company name and title that starts with “Inside Sales” and will pick up company names that are any length.

Does it stop at (6) asterisks?  For all practical purposed YES.  In extensive testing the data experts at Broadlook determined that anything past six asterisks hits a point of diminishing returns.

Using more than 6 asterisks in google queries yielded less than 0.03% additional results when total search results were less than 500.  Based on the extremely low “net new” results, it is not viable using more than six asterisks in a search string.

Examples of company names that would return using the (6) asterisks technique:

Microsoft
Broadlook Technologies
The Container Store
1st Reverse Mortgage USA

people-also-viewed
Don’t get hung up on the exact string “know in common”.  The idea is picking a phrase that is unique to profiles.  While I was writing this article (Feb 20, 2015), the search string still worked, but when I navigated to the LinkedIn Profile, the phrase “know in common” was not on many of the Profiles.  How did this happen?  Google has a cache of the last time it crawled LinkedIn.  The google results are not real-time, but results in the cached Google index.  In the future, you may use “People Also Viewed” for targeting profiles.  The lesson: don’t get hung up and any portion of the search string and learn the underlying concepts.  That is being an expert.


morpheusPhase Four: The Guru

To understand the training and examples that float around the Internet, you need to understand that most of the search string research was done by recruiters. Recruiters are always looking for the exact, right-fit candidate.  Generally, the average recruiter can run circles around the average sales rep when it relates to finding stuff on the Internet.

In addition to the search queries shown above, recruiters add additional phrases to find specific skills,  membership in groups and target locations.  Recruiters are continually creating the next perfect search string for the next specific candidate they are looking for.  They are creating the searches that the experts are memorizing.  So becoming a Guru is understanding the addition parameters needed for higher specificity.

Examples:

“Greater Chicago Area”   – add this to a search to get someone from Chicago  (Linkedin does NOT list it as Chicago, IL)

“logo * Marketo Certification Group” – add this to find members of the Marketo Certification Group.

Guru’s blaze their own trail.

Advice to becoming a search guru:  Simple.  Start with the end in mind.

Find a page that has exactly what you are looking for.  Next, figure out what makes that page unique.  Modify your search string to find your specific examples.  Occasionally, you will be brilliant the first time and get exactly what you are looking for.  In fact, Gurus nail it the first time more often than not.  The path to Guru passes through many, many iterations, testing new ideas, failing, sleeping on it and trying again until you succeed.



hal-2001Phase Five: The Automator  

What is an Automator?  What does the Automator bring that the Guru is lacking?Hint:  It’s much more than a search string.  It’s about the nature of the process.How do you generate large quantities of data for sales? Recruiters never tackled this problem because they don’t have to.  They deal in the world of the “purple squirrel“; the world of the specific.In a lead generation setting, we want ALL the data.

“Give me every person who currently holds a Vice President of Marketing title.”

In order to do this, you need to become a master at knowing the 2nd (Law of Completeness) and 3rd law (Law of Permutation).

For example:
There are currently over 1200 Linkedin locations and that is just for the US.  Here is an example of six of those areas.

 linkedin-locations

The Automator:

  • Understands what data completeness looks like.
  • Formulates a plan that will acquire all the data.
  • Knows when Google is lying to him.
  • Knows that typical searches will return a maximum of 500-600 results.  When a search exceeds 1000 reported results, he formulates a plan that uses multiple search permutations to get to the data.
  • Already has a list of the top 1200 Linkedin locations
  • In a few hours, he can formulate a plan that an intermediate or even N00b can follow
So what does the final search string look like?  Hint:  It is not a single search string, it is multiple.

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How can we help?” field_type=”text” field_id=”Message” required_mark=”on” fullwidth_field=”on” _builder_version=”3.0.86″ checkbox_checked=”off” checkbox_options=”%91{%22value%22:%22Message%22,%22checked%22:0}%93″ radio_options=”%91{%22value%22:%22Message%22,%22checked%22:0}%93″ select_options=”%91{%22value%22:%22Message%22,%22checked%22:0}%93″ min_length=”0″ max_length=”0″ allowed_symbols=”all” conditional_logic=”off” conditional_logic_relation=”off” conditional_logic_rules=”%91{%22field%22:%22name%22,%22condition%22:%22is%22,%22value%22:%22%22}%93″ use_background_color_gradient=”off” background_color_gradient_start=”#2b87da” background_color_gradient_end=”#29c4a9″ background_color_gradient_type=”linear” background_color_gradient_direction=”180deg” background_color_gradient_direction_radial=”center” background_color_gradient_start_position=”0%” background_color_gradient_end_position=”100%” background_color_gradient_overlays_image=”off” parallax=”off” parallax_method=”on” background_size=”cover” background_position=”center” background_repeat=”no-repeat” background_blend=”normal” allow_player_pause=”off” form_field_text_shadow_horizontal_length=”0em” form_field_text_shadow_vertical_length=”0em” form_field_text_shadow_blur_strength=”0em” /]

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Donato Diorio, Principal

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Phone: 414-899-4204

Email:  donato@dataz.co  

 

Connect with me:

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