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Tips for cleaning up CRM | Marketing-Ideas Marketing

Changes in the Facebook & General Consumer Landscape

One of the primary purposes of CRM management or customer relationship management is to harvest, organize and analyze customer data to better understand who they are and how to reach them. Armed with the right data, your CRM can streamline workflows, improve customer loyalty and retention, and increase profitability.

However, with a constant stream of customer information flowing in, it is important that you perform regular data cleaning to ensure that the information your CRM system uses for analysis and insight generation is accurate and up to date.

CRM data cleanup can be a complicated process – here are some tips to make it easier.

Why is a “pure” CRM important?

A CRM serves several purposes. It helps collect, organize and segment customer data, nurture customer relationships, optimize sales funnel and streamline customer service.

But a CRM’s ability to produce successful results depends on the data quality. With clean data, it can run faster and more efficiently. This results in:

Improved customer satisfaction Better decisions Cost savings

However, when the data is not clean, it hinders your employee’s ability to perform their jobs at a high level. As Forbes notes: 1

“When sales and marketing professionals professionally swim in lots of dirty data, they are removed from making informed data-driven decisions. Only 33% of marketers feel they can rely on their CRM data to make decisions. Poor data quality costs the US economy about $ 3.1 trillion a year. As the old saying goes: ‘trash in, trash out’. ”

To get the most out of your CRM system and its various tools – especially those that rely on automation – they need clean data to supply them. Just as a supercar needs high-quality gas, a CRM requires first-class, up-to-date data.

What is dirty data?

Companies use various channels to collect contact data from customers or prospective customers, including:

Physical store facades Face-to-face contact via sales teams Websites Mobile websites Mobile applications Catalogs Mail orders Call centers

When the information is entered manually, this inevitably leads to human error. And the same can be said for automated data collection and entry – it just happens less often. But even then, if a customer enters incorrect data, there is no way for a machine to know it.

Bad data is a more common problem than you might initially think.

According to the Experian Data Quality survey – which has surveyed more than 1,200 organizations across a range of sectors and company sizes – the average company estimates that 22% of its contact data is inaccurate in some way.2

So how does data get bad?

This typically happens in one of four ways:

Outdated data – Customer information decreases over time. The information they provided a year ago may not be the same today. People move; they get new numbers, change their email addresses or change jobs. According to Data Axle, 3 approximately 4% of mailing list addresses, resulting in $ 180,000 wasted annually on mail that cannot be delivered. Copy data – One of the most common ways in which data gets dirty is when there are duplicates. So even though the information may be correct, a duplicate record skews your CRM’s analysis or results in duplicate contact. Common reasons for this include: Manual error Merge lists Defective CRM software Incorrectly formatted data – The way and order in which data is loaded can be performed in one of several combinations. It can be first name, last name and then contact information. Or last name, first name, contact information. Etc. When data is entered manually, it opens up the possibility for one employee to fill out the forms differently than another. Customers enter their data incorrectly – Similarly, when customers are asked to fill out online forms, they may enter the wrong information in a particular box or misspell.

Ways to clean up your data

It is expensive to have bad data. According to the Harvard Business Review, it costs $ 3 trillion every year: 4

“The reason why bad data costs so much is that decision makers, managers, knowledge workers, data researchers and others have to accommodate them in their daily work. And it is both time consuming and expensive. The data they need has lots of errors, and in light of a critical deadline, many individuals simply make corrections themselves to complete the task. “

But just because it hinders other businesses, does not mean the same thing should happen to you. Fortunately, there are steps you can take for data cleansing, thus optimizing your system and saving you money.

Some simple CRM cleanup tips include the following.

# 1 Standardize the data

First, take precautions to prevent more bad data from accumulating, or at least reduce the rate at which they occur.

Data standardization achieves that.

As Momentum data notes: 5 “If no concrete and strict rules are implemented, employees will enter asymmetric data that will be difficult to adjust. As databases get bigger, this becomes more and more relevant as data standards can be quickly lost. ”

By setting rules and systems for data collection and input in CRM, you can instill good habits that replace fragmented methods. Implementing proper shape validation and data cleansing processes does not completely eliminate bad data input, but it does reduce the frequency with which it occurs.

# 2 Solve the small formatting problems

When it comes to data, minor issues can cause major headaches – especially when there are thousands (or millions) of data points.

Take capital letters, for example. When filling out forms, people are not allowed to use uppercase and lowercase letters in first and last names; others can enter their name in capital letters. This may not seem like a problem, but sending out marketing emails and the email incorrectly activating the individual’s name can remove the sense of personalization.

Another common problem with manual data entry involves zip codes, namely zip codes that start with a zero. According to Thomas Bonneau of GB Sterling: 6

“[I]If you have a bad data file, you may be ignoring almost 10% of your nationwide data set … If you have a data file in Excel that contains zip code and the column and column are formatted correctly (especially zip code or text), everything looks great , if you have a zipper with a leading zero (for example, 02739). However, when you save this file in a CSV file to import into your CRM and reopen it again for editing, the leading zero falls because CSV ignores any previous Excel formatting that retained this zero. ”

On the surface, these are small problems, but these small flaws can damage your bottom line by wasting valuable time and resources. By resolving these issues before importing them into your CRM, you save money and time and reduce employee frustration.

# 3 Clean duplicates

No one wants to be targeted twice by a company – even one they are a loyal customer to. It’s starting to feel like spam.

If your customer list is not long, duplicate cleaning can be done by hand. Just do it regularly to prevent the problem from building up. But if you are a large company, automation can help. Most CRM systems have automated features that allow you to specify rules and conditions that detect duplicates.

From there, be sure to set your system to automatically block duplicate content from being entered.

# 4 Archive your data

Do you have data that you do not currently use but may need in the future?

A common problem with companies is that they are required by the compliance rules to store historical data. The fix for this is to archive this information. This way, you can ensure that data is stored securely but no longer affects the system. This frees up storage space, speeds up processing times and makes it easier to search current records.

Similarly, older data that you can delete should be deleted.

# 5 Consolidate fields

In some cases, there may be multiple data entry fields that contain similar, if not redundant, information. By reducing input options, you reduce the chance of double or inaccurate data entry.

# 6 Enrich your data

In some cases, data can be considered dirty when it is incomplete. A zip code may be missing. Or you may have an email address but no telephone contact. Highlight your missing information contacts and see if you can fill in the topics.

# 7 Outsource your CRM efforts

One of the easiest ways to manage your CRM data, especially if you are a large company, is to outsource the process to a data recovery company. These experts have the skills, experience and tools needed to clean up data.

Many of these devices also provide data enrichment services. This means you can clean up and improve your data all at once. By outsourcing, you can save your internal teams time and money and allow them to focus on what they do best.

CRM cleanup made easy

Better data means more useful insights. By performing a regular CRM cleanup, you can ensure that your system is powered by actionable customer information.

From there, you can jump to the next item on your to-do list. Do you want to use CRM data that can be used to improve your marketing efforts?

We can help with that.

Here at Marketing-Ideas, we create a holistic picture of your marketing strategy and then apply proven and true techniques to help you achieve your KPIs – including using your now pure CRM data effectively. Contact today to get started.

Sources:

Forbes. Best practices for data hygiene. https://www.forbes.com/sites/falonfatemi/2019/01/30/best-practices-for-data-hygiene/?sh=1fde3d072395 Økonsultation. The cost of bad data. https://econsultancy.com/the-cost-of-bad-data-stats/ Data Axle. Infographics: How Data Loss Affects Your Sales and Marketing Strategies. https://www.data-axle.com/resources/blog/infographic-how-data-is-tanking-your-sales-and-marketing-strategies/ Harvard Business Review. Bad data costs US $ 3 trillion a year. Https://hbr.org/2016/09/bad-data-costs-the-us-3-trillion-per-year Momentum Data. 9 Tactics for cleaning up CRM data for sales productivity. https://momentumdata.com/9-crm-data-cleanup-tactics-for-sales-productiveness/ Incycle. The ultimate checklist for cleaning up CRM data. https://blog.insycle.com/the-ultimate-crm-data-cleanup-checklist

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