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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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.

 Phase One: The n00b

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

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.

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.

 

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.

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”

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.

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.

Phase Five: The Automator  

 

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

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15 minute free data|z consultation

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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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