Put Data FirstWhy Data Quality in CRM and Marketing Automation are Top Priority

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Put Data First | ringlead.com

Introduction

What is Data Quality?

How to Build a World-Class Data Quality Strategy

Conclusion

Contents

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Whatever your si tuat ion may be, you wi l l quickly real ize that i t al l comes back to data, because data is the real value in your CRM and market ing automation platform.

We don’t mean to tr iv ial ize the importance of workf lows, automated processes and dr ip nurtur ing campaigns that these systems offer. These features are one of the pr imary reasons that organizat ions invest so much t ime and money into their implementat ion and ongoing administrat ion and improvement, but many are rendered utter ly useless when they come into contact with dir ty data.

Introduction

You’re probably reading this because you:

Have irrefutable evidence that your CRM and market ing automation databases are a mess

You just went through some reports generated from your CRM or market ing automation system and thought to yourself , “Wel l , that doesn’t seem r ight,” or

You’re about to get a fresh start by implementing a new CRM or market ing automation system

A

B

C

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Dirty data has a way of si lent ly inf i l t rat ing your organizat ion, creat ing frustrat ion, ineff ic iency, and loss of conf idence (eg. dismal user adopt ion) in the systems themselves. I t can affect each department and group of stakeholders in a very di fferent way, but unless there is a “State of Our Data” address, the problem is not brought to the forefront of the organizat ion’s col lect ive psyche.

In this ebook, we wi l l show you the importance of managing your data with the same attent iveness that you manage your organizat ion’s other highly- importantstrategic assets.

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You may have heard this term used in the past in reference to a specific initiative, but data quality for CRM and marketing

automation can refer to a variety of different aspects in effective data management. In this ebook, we’ll cover the following

aspects of data quality:

Let’s dive deeper into each of these core areas.

Uniformity: Are certain values uniform across all of your records so that you can easily segment data? Is

there a set of data standards for certain fields? Eg. Are all of your state and country values categorized

using two- or three-letter abbreviations, or do a mixed set of values exist, such as New York, NY and NY?

Accuracy: Is my data accurate and up-to-date?

Completeness: Does each record contain all the possible information that is available to me? Do we have

access to additional firmographic information that can be appended to a record? Do we have access to --

via social or web data -- additional contact data for this organization?

Uniqueness: Is this the sole record in the database, or do duplicate records exist?

Consistency: Are my records accurate, complete and unique across all of my systems, including CRM,

marketing automation, ERP, etc.?

What is Data Quality?

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Uniformity One of the key requirements of a customer and prospect database is to easily segment the records, allowing your organization to interact with one set of contacts differently from others. This can be easy if you have a strict set of values for each field and the input is controlled at the insertion point.

A common requirement is segmentation by job title, but there are simply too many variations on an individual’s job title to try to account for each with a picklist value, so the standard method of insertion is via a regular text field. This creates a pretty big problem for segmentation. We’ve come up with 16 variations of input for one title and this doesn’t account for common misspellings.

Director of MarketingDir. of MarketingDir of MarketingDir. MarketingDir Marketing

Director of Mktg.Director of Mktg

Dir of MktgDir. of Mktg

Director of MrktDirector of Mrkt.

Dir. of Mrkt.Marketing Director

Mktg. DirectorMrkt. Director

Mktg. Dir.

Think about all of the different combinations of text and punctuation for Director of Marketing:

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Building reports, automated marketing programs and workflows to account for all 16 of these Director of Marketing variations can appear to be a daunting task, especially when you expand this thought process to the different types of data that should be used for segmentation (address information including state and country data, industries, organization size, custom fields, etc).

For this particular problem, we recommend adding an additional field, which we’ll call the “Title Bucket.”

Whether you do a manual review and update of these fields, or an automated update, you should end up with a collection of Title fields that correspond with one of your Title Bucket values.

What’s the end goal of this effort? When reporting, you don’t have to worry if you’ve accounted for all of the variations of a given title. When setting up automated marketing programs, again, you can simplify the logic that you’ll need to write.

Vice Pres. of Mktg

Existing Values “Bucket” Value

Director of Marketing

Dir. of Mktg.

VP of Marketing

V.P. of Marketing

V.P. of Mktg.

VP Marketing

V.P. Marketing

VP/Dir. of Marketing

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Accuracy According to a 2013 Experian QAS survey, 94 percent of businesses believe there is some level of inaccuracy within their CRM systems. When you think about the time, money and focus that is put into CRM, an allowance for inaccurate and useless data is mind boggling.

Think about the time that your organization is wasting sifting through inaccurate or worse, completely useless, data. Inaccurate data leads to:

What can you do to ensure data accuracy?

Wasted sales efforts on useless bi ts of information stored in CRM, leading to discontent and potent ial abandonment of the CRM system ( ie. decreased user adopt ion)

Longer wait t imes for support whi le reps are forced to piece together information whi le on the phone with a customer, leading to decreased customer sat isfact ion

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Completeness At Sirius Summit 2013, Jim Ninivaggi, Service Director of Sales Enablement Strategies at Sirius Decisions cited a study that found roughly 30% of an enterprise salesperson’s time is spent doing research on the Internet. If you think about that in the context of an 8 am - 6 pm workday, that means that 35 days per year are spent doing research.

In Data Driven: Profiting from Your Most Important Business Asset, Thomas C. Redman sums up the advantages of data completeness as “A moat around our business [that] gives us a unique competitive advantage.” Nowhere is this more evident than the aspect of data completeness.

Imagine a situation that puts you against your biggest competitor to close a big deal in a hurry. On January 1st, Acme Inc., decides that they’ll be choosing a solution in 30

days, so they request information from both you and your biggest competitor. Time is of the essence to get in touch with Acme Inc.’s designated information gatherer, John Smith.

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Both you and your competitor originally received the same

basic information: Name, title, email address and a generic

phone number. Because your forward-thinking organization

has incorporated data enrichment tools into your processes,

you were able to (1) extract Acme Inc.’s website from

John’s email, (2) crawl the company website looking for

additional company information, saving you from wasted time

researching hundreds of pages on the company website,

and (3) find additional information including John’s direct

extension, a company description, even a personal bio for

John.

Using the website as a starting point, you can identify this

company’s web footprint, looking to linked web pages on

LinkedIn, third-party trade publications, SEC statements and

more to identify additional decision makers, an invaluable

and time-saving head start for our complex, multi-touch,

sales process.

By obtaining both Bill, Cassandra and Michael’s contact

info through this data append process, you’re able to reach

out to each of them proactively and get them involved in

the conversation. Then, you can turn on your marketing

automation/lead scoring programs to supplement the sales

process.

All of this can be accomplished before your competitor has

even picked up the phone.

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Uniqueness Duplicate records in CRM and marketing automation platforms are a familiar aspect of bad data. The errors and frustration that duplicates cause can be felt across most departments at almost every level.

Reports are skewed, the wrong messages are being sent, and quarrels are created over one record that somehow made it into the system twice and was distributed to two different sales reps.

How are duplicate records created? Today’s CRM and marketing automation platforms come equipped with very basic duplicate identification which is, in almost every case, based on a scan for an exact-match email address. In our example to the right, Joe Prospect used two different email addresses, therefore, duplicate records were created, even though all of the other information was nearly identical.

Now that you have a basic understanding of how duplicate records come about, let’s look at some of the ways that they can wreak havoc on your CRM and marketing automation platforms.

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Inaccurate Lead Scoring in Marketing Automation In the example on the previous page, the lead scoring points for a whitepaper download would be associated with a new lead, joe.prospect2@gmail.com. Those lead scoring points should have been associated with the original record in the system, joe@ABCompany.com. This will equate to hot leads being left behind your marketing automation platform, and not being distributed to your sales reps for follow up.

Decreased Open RatesUsing the same scenario as before, you will be sending emails (via drip nurturing campaigns, one-time email blasts, etc.) to both of these records, joe@ABCompany.com and joe.prospect2@gmail.com. It’s likely that an email open will

register with only one of these records, since Joe Prospect will probably only open one of the emails and delete the others.

Increased Cost of Marketing Automation A key component of your marketing automation platform license is based on the size of your database; in other words, duplicates are piling up in your database and costing you money.

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Consistency Many modern, technology-enabled organizations are using more than one software platform to manage their customer and prospect data. It is crucial to keep your data in sync across your email, ERP systems, CRM, marketing automation platform, and more.

If your data quality plan is limited to one platform, you’re only solving part of the problem. Let’s look at a few scenarios where our objective is to prevent duplicate records in both our CRM system and our marketing automation system.

As you can see, scenarios A and B are problematic, as data can enter from the CRM or the marketing automation system, and because the unique identifier is an exact-match email, duplicate records can easily be pushed from one system to another.

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We’ve broken a master data quality plan into four main

stages: consult, clean, protect and enhance.

The first stage of our strategy assesses your problem

areas, thereby cleaning up the problem. The second

and third stages will become part of your employee’s

processes and should be ingrained in the culture of your

organization.

Stage 1: ConsultStart the process by clearly outlining some of the biggest

pain points that you feel within your organization. Once

you have assembled this list, speak with someone or a

team of experts who have experience in identifying and

resolving data quality issues.

For most of us, when the Check Engine light goes on,

we don’t try to diagnose and fix the problem ourselves.

Instead, we take it to a specialist (a mechanic). The

thought process with your business tools should be no

different. A team that has domain expertise and has seen

thousands of systems plagued with dirty data, can quickly

diagnose your issues, saving you from hours of frustration

and wasted effort.

Stage 2: CleanOn Day 1, you will need to figure out how to clean up the

current mess in your database. Don’t get overwhelmed.

You can break this down into three phases: normalization,

deduplication and enrichment.

Normalizing your data is the first step in this process.

How to Build a World-Class Data Quality Strategy

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By normalizing as much of the data as possible, it makes

the deduplication process more efficient.

Next, a massive scan for duplicate records is in order.

Start your deduplication process by looking at data in

each table, which is commonly referred to as an “object”.

The most common process is to start with a Lead-to-

Lead report, followed by a Contact-to-Contact report, and

finally an Account-to-Account report, in order to simply find

duplicates within these siloed tables.

After you’ve completed the deduplication process in

each table, expand your search by leveraging cross-

table reporting, sometimes referred to as “cross-object”

reporting. Here you’ll be comparing Leads with Contacts

and/or Accounts. After you’ve tackled the main tables/

objects, you can advance to other standard objects (i.e.

Opportunities, Cases, etc.) and then custom objects.

Stage 3: ProtectOnce you’ve cleaned your data, the goal is to protect

your CRM and marketing automation platform (and all

your hard work) by putting tools and processes in place

to enforce normalization. This prevents against the

creation of duplicate records and provides constant and

never-ending enhancement of the

database. In other words, it will

never becomes stagnant.

Without a protection plan, you will

inevitably be back in cleanup mode

somewhere down the line (it could even be next month).

As Benjamin Franklin said, “An ounce of prevention is

worth a pound of cure.”

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Stage 4: Enhance Now that you have a uniform and deduplicated database,

you can begin to enhance your data by appending

available company and contact information.

A common deficiency in CRM data is that the address

information is sparsely available or nonexistent.

Appending address information allows for initiatives

such as in-person appointment setting, regional territory

management for sales, or geo-targeted marketing

campaigns.

Appending contact information can be just as important.

In sales, many times you are dealing with multiple

decision makers. As we showcased in the completeness

section earlier in this ebook, appending additional contact

information can be a key strategic advantage over our

competitors.

+

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I t ’s important to remember that dir ty data can be a big problem, but can be easi ly solved.

Analyze the problem and try to hone in on the areas that are causing the most pain. Then get in

touch with a team that has experience in resolving these types of issues. At RingLead, solving

di ff icul t data qual i ty issues has been ingrained in our DNA since 2003, when Steve Lehr created

the f i rst sui te of dupl icate prevent ion apps for the Salesforce platform.

Conclusion

I f your CRM report ing seems “off” , i f your market ing campaigns are

less than impressive, i f your sales team is underperforming, then this

is your system f lashing the Check Engine l ight. More often than not,

dirty data is the root cause.

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Joe Fusaro, Author

Amanda Nelson, Editor

Rian Nei l , Designer