How does poor CRM data hygiene affect your marketing strategy?

Poor data hygienic can make or break your CRM marketing efforts. Brand reputation would be at stake, Automations cannot be leveraged and sales team might be demotivated to give their full potential.

Poor CRM data hygiene significantly impacts marketing: Inaccurate Targeting Misguided audience targeting. Hampers personalized communication. Reputation Damage: Harms brand reputation with outdated messages. Resource Waste: Targets inaccurate or non-existent contacts, wasting resources. Missed Opportunities: Incomplete data leads to missed sales opportunities. Workflow Disruption: Disrupts marketing automation and workflows. Compliance Risks: May lead to non-compliance with data protection regulations. Limited Analytics: Compromises accuracy in marketing analytics. Customer Dissatisfaction: Incorrect personalization causes dissatisfaction. Decision-Making Impact Regular updates ensure sustained success. #CRMDataHygiene #MarketingImpact

One of the biggest mistakes I see teams making when it comes to the data in the CRM is to not establish a clear data strategy with data governance and owners. Without these, Marketing Ops and Sales Ops professionals get stuck in an endless cycle of inputing, updating, verifying, de-duping, cleaning, etc., etc. and never realize the benefits and ROI of the data. How many times have you heard Ops folks complaining that their data quality is poor and therefore can never unlock the insights that the business needs?

Having a clean CRM is essential to any company. We understood this early at Dropcontact: our proprietary algorithms clean, update, verify and enrich your CRM data (our solution is natively integrated with HubSpot, Pipedrive and (soon) Salesforce). They also merge duplicates. All this in real-time.

These are the main three advantages of good CRM data cleaning: 1. Better Decision-Making: Clean data leads to accurate insights, enabling more informed and effective marketing and business strategies. 2. Increased Efficiency: With up-to-date and precise data, teams spend less time on data correction and more on impactful activities, boosting productivity. 3. Stronger Customer Relationships: Accurate data allows for personalized communication, enhancing customer satisfaction and loyalty, and fostering lasting relationships.

Without good data hygiene, the data becomes useless. It can't be trusted, used, or leveraged to run the business, provide any meaningful insights, or make any recommendations or decisions. You can't have a view of the business (current, past, or future) without quality data in your CRM.

Well, at least from my experience, data hygiene is important because first party data is often reused for the purpose of developing look-a-like audiences, retargeting audiences, nurturing drips, etc. Thus, if your CRM data is invalid, it has the potential to perpetuate invalid data points that create inefficiencies is audience targeting.

Agreed, but more than that, poor data hygiene causes: - Departments and companies set goals based on bad data, which means goals may be impossible to achieve or a waste of time because most customers/leads have bad data, so our predictions, analyses, and conclusions will be wrong. - Bad data means useless reports and wasted time analyzing data. What does this mean? Incorrect estimation of cash flow, hiring, revenue, department performance, and activities to improve it, conversion and campaign goals are the best ways to make the future of your business unpredictable and dangerous. - A mess in your personalized and automated campaigns. Incorrect names, segments, or subscription types cause customers to treat communications as spam.

Without a fully operating CRM, several problems can emerge: - You don't have a full or accurate vision of your number of contacts - If several team members are collaborating in your CRM, they might contact the same client twice because of duplicates and they'll lose credibility - Your sales team can lose a precious amount of time if they clean your CRM data manually.

Poor CRM data hygiene means poor insights/feedback for marketing teams to act upon. This affects overall metrics and you are never presented with the actual data at any point.

You should have a dashboard that gives you an overview of how many records are missing fields, bounced, unsubscribed, etc. I would also include when the record was last updated and active. This will help you analyze your data health and show when you need and what you need to update.

Aside from measuring the quality of the data, gaps in the data, or otherwise, another way to look at your data hygiene is to understand what's missing from a business perspective. What gaps are there to having the insights and analytics you need to measure performance against/ towards your objectives/ targets? What gaps are there to producing the metrics that are meaningful for your business? What gaps are there preventing the team from being efficient and effective? The answers to these questions will not only assess the overall "rating" of your data, but will help you identify the most critical areas of focus and prioritize accordingly.

The first place to start is to review processes and identify where data flows into the CRM. You can lean on marketing automation platforms to keep data consistent and validated prior to it syncing with the CRM. Tools like LucidChart help with the mapping of these datapoints but others are available. Beyond this, you can pull reports within the CRM to look for blank fields, old data, flagged records and so on. It's always important to look for the root cause of the problem instead of fighting fires and trying to update information as you go.

Create a report in your CRM that shows Leads created this year. From there, group the rows by a field (ie Lead Source) that you want to assess. Group the columns by the Created Date. Group the Created Date by the calendar month. Hide the detail rows. Now, you have a report that shows you the good data and the bad data, grouped by when they were created. Repeat this process for other important fields that you want to assess.

If you are in the B2C space, you will end up accumulating millions of unique data instances for your users. The more data you collect the tougher it becomes to maintain hygiene in your CRM as too many people work on the database making incomplete, error-prone entries. Some workarounds are 1. Set up quarterly, bi-annual data audits to ensure data sanity and identify common errors or leakages. 2. Try to automate the data collection process as much as possible to avoid human error 3. Use AI and bots to perform regular basic checks on your database 4. Regular controlled purges of data that is irrelevant or dead 5. Enrichment tools to help you update old records 6. Build a DIY solution that gives your customers a chance to update their data.

Something I like doing is just exporting all fields, order by ID or name. Then run a simple formula that count if not blank. And then have. % of populated for each field. Then: 1. Review what has no data at all and are empty 2. Review the fields that are named around the same 3. Make note of picklists Then go through and start to clean / sunset fields. Special note: Remember there's often times down stream impacts to other systems that MIGHT be connected to these fields. And you'll want to verify there's no down stream impacts.

Additionally it's important to map out what your marketing stack looks like and the data flows that occur between them. This will help to uncover where the data is coming from, which systems hold what data and what is passed between systems. From there you are able to put a plan together on what platform does what, when and how to avoid duplication, overwriting and unnecessary data flow processes that lead to corrupt data.

Data hygiene starts with the inputs – ensuring you know the datatype and destination. Having a data dictionary is key and supported by defensive methods to prevent dirty data coming in. Create processes that flag bad data for correction and provide a process for redirecting that data back into workflow. Having a routine data audit on the existing data is also helpful and will allow quicker identification of root cause failures.

Removing duplicates in data is an important process in data hygiene. In this fast-paced world, data duplication is common but one needs to be vary of it to make sure we do not waste time with unwanted data.

I always aim to identify where data actually enters the system. This is where you can standardise data across the organisation. For example, you have a website with forms. This is where you would pay close attention to field validations, formats, languages and so on. It might be that you want phone numbers to include the area code. Amend your forms so this is a dropdown with the phone number. Within the CRM itself you should be using validation rules and automation to ensure human behaviours are managed well and the team are using the CRM in the way the organisation wants them to - no individualised approaches to anything!

Don’t keep old leads. I’ve seen this for years and it’s just getting worse. My team does a LOT of system migration work and marketers really like to move over their entire database. I always suggest running an analysis of who is engaged and who isn’t. For example, if you can create a list of prospects created over 2 years ago, who have been emailed more than once in the last year, and still have no activity, it may be time to send them to your recycle bin.

You need a dedicated team to look after the CRM. This is so important because if there isn't a resource looking at the system, no one will proactively be looking after your revenue machine! This team will identify the processes and governance needed to run a healthy CRM that enables your sales, service and marketing teams instead of hinder them. One thing I really like is to allow Leads/Contact to actually update data themselves. You can do this with forms on a marketing automation platform like Account Engagement. You would allow prospects to proactively enter information into forms that sync with the CRM. This approach relies on a strong content strategy so ensure you are producing valuable content people want to download!

Establishing data entry standards (and rolling these out to the team) is really important. This means setting rules for how data is entered (e.g. date formats, capitalisation) to maintain consistency. CRM platforms like HubSpot provide tools to help standardise data entry, which is useful in maintaining consistency across different data points. Additionally, data cleaning should be done regularly (I tend to do it weekly) to remove or update outdated, incorrect, or duplicate information. It is important to use the automation features CRM platforms offer to reduce the need for manual input. With the advances on AI and machine learning, data hygiene processes such as identifying and correcting data inconsistencies can also be automated.

A few more tips to maintain data hygiene: 1. Remove unsubscribed data, keep them in a separate list if required. 2. Discard old leads. It is not as useful as you think. 3. Discard hard bounces. 4. If the data is unengaged for a while, send them a double opt-in email to make sure they want to receive emails from you. If they don't, you know what to do.

Data governance includes the before mentioned processes and policies, but also includes roles & responsibilities. You need to have clear R&R expectations for who enters the data, manages the data, edits the data, etc. Wherever possible, you want to leverage automation and integration to minimize manual entry, thereby reducing manual errors, redundancy, and time wasted.

Using ChatGPT’s Advanced Data Analysis tools is a great way to help with data cleanup. But before uploading any files into it, be sure to anonymize the data as much as possible to protect people’s privacy and keep their data secure. A good alternative to uploading all that data: If you’re looking to clean up a certain field, give it some dummy data that represents the typical cleanup work that you need to do and ask it to create an Excel or Google Sheets formula that will help you with that.

The frequency of data cleaning depends upon the number of data points in the system. It can range from daily to even monthly.

It is always easier to regularly maintain your customer data rather than do a complete overhaul every 2-3 years. Stay on top of your data. If something seems wrong, it probably is. Fix it.

Data hygiene & quality in CRM has one fundamental issue – OWNERSHIP. Is it sales? Is it marketing? Is it a data owner? Reality is if all teams are aligned to the same goal (Revenue) then actually everyone wants to own data quality and contribute to it. Reality is data hygiene & management isn't a one time task, it's an attitude towards accurate reporting, segmentation & targeting & a feedback loop where all parts of the business contribute equally.

Si la base de datos no está limpia, estaremos perdiendo tiempo y dinero. Un grave error es la duplicidad de datos y la mala clasificación de los datos. Tengo el claro ejemplo de un CRM al cual acuden muchas personas y siempre comenten el error de crear un nuevo cliente/contacto sin revisar previamente si este ya existe, cada uno con unos datos de contacto, etc Finalmente ?Cuál es el dato correcto? ?Con cuál te quedas?