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Rules to Get Clean, Profitable Salesforce Customer Data

Incomplete and/or dirty data makes life really hard for Salesforce admins the world over. It also leads to missed opportunities and sales. Yet it’s really quite easy to fix.

So why, in the second decade of the 21st century is manual data entry the top way users get their business card data into Salesforce? A whopping 70% of Salesforce find missing data in their records. It wastes their time cleaning up the mess, and loses sales opportunities.

Manual customer data entry is like letting your 5-year-old make breakfast.

Cute, but dangerous. And messy and hard to swallow. But, at least it’s a healthy exercise. Manual data entry is not.

Start by getting your company family going with automation. Then follow through on rules, like tough love. There may be some complaining, but it’s good for your org and the bottom line.

You can get cleaner, richer, awesomer Salesforce customer data if you:

  • Stay ahead of dirty data
  • Automate the entry process (scanning)
  • Add context and fill empty fields proactively
  • Eliminate duplicate records (dedupe)
  • Watching it like an eagle (monitoring)
  • Standardize
  • Cleanse
  • Educate your team
  • Invest and never stop the kaizen

Stay ahead of dirty data

Gartner reported that poor data quality costs U.S. businesses anywhere from $9.7 million to $14.2 million annually. Especially if you’re a small business with limited data admin and accounting resources, are you tracking the value and opportunity loss of your CRM data?

Dirty data is any data that takes away from the data integrity (accuracy, consistency, reliability) of the dataset. Types of dirty data include:

  • Typos, dupes, erroneously parsed data
  • Data that violates business rules
  • Consistently generated system data (that may be working under its own rules)
  • Incorrectly collected or calculated data

When we get into numbers and stats, it’s getting into data science, and that’s beyond the scope here. Our main concern is cleaning up the dirty data that people introduce to Salesforce.

The thing about cleaning data is that it’s not inevitable if you set data entry rules for every point of entry. These include: constraints, systematic checks, and integrity checks.

We’ll get to the cleansing in a bit, but scanning contact data can be part of this preemptive process. Let’s go there first.

Automate the entry process (scanning)

As noted above, businesspeople continue to enter customer data manually. Stop this madness! A business card may seem something from the manual era, but if data is the 21st century currency, this card is gold.

So start the flow of data from “name cards” to CRM with accurate scanning. Plenty of apps are out there, but only a small handful are Salesforce-optimized. Scan to Salesforce leads the way. With accuracy over 99% and recognition of 16 languages, this app is about 60× faster than typing in customer data by hand.

It also eliminates human error that results from tired eyes and clumsy fingers. Scan in cards. And if you’ve added other integrations such as for your webforms and other places you acquire data, that helps, too.

Add context and fill holes/fields proactively

Not only can a scanning app help with accuracy, it can help fill data holes. Scan to Salesforce, for instance, lets users add context, such as where they acquired the card, and when. This is in addition to the extensive and accurate contact information that business cards offer compared with someone who jots their contact on a napkin or fills out a webform.

A business cards provides any and all of:

  • Person’s name
  • Foreign language version of their name
  • Job title
  • Department
  • Company
  • Address
  • Office and personal phone numbers
  • Fax number
  • Email address
  • Company URL
  • and sometimes even their social accounts and a photo of them

It’s almost guaranteed accurate (because who puts typos on a business card?), too.

This means a far more complete customer record is going into the Salesforce Account. And that means there’s less filling in and cleaning up to do. You can also sync it to remove dupes and default to the newer record.

Watch it like an eagle (monitoring)

It’s quite common for all forms of data quality to degrade exponentially. This is especially true when multiple people and devices are inputting it. Too many cooks dirty the data.

While updating existing records, users sometimes replace valid information with erroneous data, or simply change/delete information by accident.

New records, whether entered manually or imported, invariably contain a certain number of problematic fields, despite an administrator’s best preventive efforts. Continually monitor the database to identify and correct erroneous data. We’ll get to this more in … The Cleansing.

Standardize

Standardization is another critical step in making sure you get data quality and integrity (yeah, basically the same thing). When entries are not done in a consistent way, data is harder to segment and otherwise use for specific objectives. If you have to combine datasets, it’s also rather nasty.

As mentioned, this starts with checking data at the entry points. If there are limited forms of data, such as contact entries, then establishing the fields is a relatively painless first step to standardization.

Data like name order (many Asian names put the last/family name first), job title, industry, state/prefecture, etc., and country, can affect lead scoring and change how you do your nurture messaging. Choose and effectively communicate your organization’s standards.

You can also implement a normalization matrix to map the values.

Cleanse (clean) dirty data

Admins develop their pet methods or have a solution for cleaning, or cleansing. It largely depends on the nature of your data. The typical steps are:

  1. Preemptive measures, as mentioned above – standardize and use reliable tech to eliminate human error.
  2. Watch for input errors, also mentioned above; i.e., typos, whether human- or machine-introduced. i.e., CAPS LOCK is your enemy.
  3. Remove outliers – This is less of an issue when we’re only talking about customer data, but outliers can still exist in clear misspellings and bogus data. Spotting and cleaning up is a pretty easy manual process. But it doesn’t make it any less annoying.
  4. Search for missing values – These “gaps” can be avoided at the earlier stages so admins can save manual review time. Apps can help with the search process, too.
  5. Scrub the dupes – Scan to Salesforce helps with finding duplicate customer records when you add new ones that actually aren’t new. After that, Salesforce help itself offers good assistance and AppExchange has dedicated tools just for deduping.

Validation is a useful additional step.

Naturally, as data cleansing is no small issue, there are LOTS of solutions. AppExchange lists many data cleansing solutions. I can’t speak to one in particular, but AppExchange reviews are usually quite useful, so read them all and caveat emptor. Got a favorite? Leave us a comment.

Educate and enforce

Whether you’re an admin, a manager, or an end user, you’re taking the lead by reading this article. Use every chance you get to explain the organization-wide benefits of high-quality contact/customer data.

Make data integrity part of the company’s mission. If you’re a small business, this can set you far ahead of the reactionary competition.

Both employees and individuals rely on this data to inform their actions and prove their worth. Provide training and enforce your standards.

Ways to do this include:

  • Defining required fields and proper entry, as well as entry paths.
  • Selecting default values for auto-population where you can.
  • Using field dependencies and workflow rules.
  • Imposing restrictions on who can create Accounts, Contacts, and Leads, and who can initiate Pardot Campaigns.
  • And then validating all your data and seek out the source of errors.

Invest and never stop the kaizen

If you have an MBA, or a Prius, you may know kaizen. But the concept goes beyond your education or your ride.

Kaizen is the art and science of continual improvement. It’s a Japanese term comprising two characters:

改 – kai, which means revision

善 – zen, which means goodness

Together, they imply the benefits of constantly making things better, rather than making big changes or throwing it all away and starting over. In business, kaizen became a trend following the Toyota process of (this article gives great detail and history) of consistently making small changes for the greater good.

These changes occur horizontally and from the bottom up, where every user naturally has a voice and has input.

Modular systems like SaaS, in this case Salesforce, promote both kaizen of getting the most out of the system and kaizen of the system itself.

User education on clean data and data utilization will lead to more deals.

Integrations and add-ons will help you get more and better data. See this article for recommendations.

A little pain, a little expense, and joy awaits

Do this stuff and you’ll have a happy admin, happy sales reps looking for prospects, happy marketing staff running campaigns without rejected email, happy CS with complete customer records. Everyone’s happy. We want you to be happy, too.

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