Data cleanliness in a CRM is not a one-time effort, but the result of an intelligently designed, automated system that runs continuously in the background. This article is a precise, step-by-step plan on how to build such a system in HubSpot, using automation in Make.com and artificial intelligence to transform data management from a manual nightmare into a strategic asset.
Anatomy of Chaos: The True Sources and Costs of Mess in Your CRM
Before we move on to building solutions, we must accurately diagnose the problem. Understanding where data clutter comes from and how it realistically affects the company's financial results is key to justifying the investment in cleaning it up. This is not an IT department problem – it is a strategic challenge for the entire organization.
Patient Diagnosis: The Most Common Data Ailments in HubSpot
Duplicate Records: This is the most common symptom of chaos. They arise when a lead fills out a form using a different email address, the sales team imports a list without verification, or different salespeople manually enter the same people. The consequences are compromising communication blunders and completely distorted reports that make it impossible to reliably assess marketing and sales activities.
Incomplete Data and Missing Associations: A contact record without a phone number or job title is useless for sales. Equally dangerous is the lack of association between a contact and a company, which prevents Account-Based Marketing (ABM) activities and leads to inconsistent customer experiences.
Inconsistent Formatting and Lack of Standardization: This is the silent killer of automation. If phone numbers are saved in formats like +48 123 456 789, 123-456-789, and 123456789, creating a list for an SMS campaign becomes impossible without manual cleaning. The same applies to country names ("Polska", "Poland", "PL") or job titles, which prevents effective segmentation.
Incorrect Lifecycle Stage Configuration: A fundamental problem is often the lack of a properly defined and automated process for managing customer lifecycle stages. As we discussed in detail in the article on HubSpot implementation for the SaaS model, without this basic structure, the CRM becomes just a digital address book, not a growth engine. This leads to the paralysis of key functions such as segmentation, smart content, or lead nurturing and qualification processes.
Information Paralysis and Hidden Costs: How "Dirty Data" Drains Your Budget
Technical problems translate into measurable business losses. Poor data quality is a hidden tax on every operation in the company. The table below translates abstract problems into specific, severe costs of the mess for key departments.
| Problem (Technical Symptom) | Direct Impact on the Sales Department | Direct Impact on the Marketing Department | Strategic Impact (on Management) |
|---|---|---|---|
| Duplicate Contacts | Two salespeople contact the same lead, leading to chaos and customer irritation. | Inflated database size metrics, double mailings, incorrect assessment of engagement. | Inflated HubSpot subscription costs, incorrect sales forecasts, damage to the brand image. |
| Incorrect Formatting | Inability to use the click-to-call feature, integration errors with VoIP systems. | Failed SMS campaigns, inability to segment by country/region code. | Investments in communication tools do not bring a return, loss of competitive advantage. |
| Incomplete Data | The salesperson cannot prepare for the conversation and personalize the offer. | Inability to perform precise segmentation, sending inadequate offers, ineffective lead nurturing. | Incorrect understanding of the ideal customer profile (ICP), misguided product development decisions. |
| Outdated Data | The salesperson wastes time calling the wrong person, which destroys a potential relationship. | Lowered email deliverability (hard bounces), wasted budget on campaigns to inactive contacts. | Decline in brand reputation (perceived as spamming), inaccurate market data. |
Proactive Deduplication with Make.com: Build Your Cleanliness Guardian
Instead of relying on manual and reactive cleaning, we will create an automated guardian that will watch over data cleanliness right at the gate. The key will be the Make.com platform, as HubSpot's built-in tools, although useful, have fundamental limitations.
| Aspect | Native HubSpot Tools | Approach with Make.com |
|---|---|---|
| Type of action | Reactive (the "Manage Duplicates" tool works after the fact) | Proactive (the scenario works in real-time, preventing duplicates) |
| Matching criteria | Limited (mainly email for contacts, domain for companies) | Fully flexible (email, phone, first name + last name + company, any custom field) |
| Automation | Requires manual review and merging of suggested pairs | Fully automated process of decision logic and data merging/updating |
| Enrichment potential | Limited to merging. | Huge - possibility of integration with AI, external databases, and other systems in the same process. |
Scenario Architecture in Make.com
We will build a scenario that intercepts every newly created contact, checks if a similar record exists, and takes the appropriate action. The process is as follows:
- Trigger: We use the HubSpot "Watch CRM Objects" module, which runs immediately after an attempt to create a new contact.
- Search: The "Search for CRM Objects" module searches the database according to advanced criteria (e.g., the same email OR the same phone number OR the same combination of first name, last name, and company). This is proactive deduplication in action.
- Router (Decision Logic): The "Router" module directs the process to one of two paths: A (no duplicate) or B (duplicate found).
- Action: On path A, a new contact is created. On path B, instead of creating a duplicate, the existing record is updated with new information (e.g., from the form), which also enriches the data.
Artificial Intelligence in the Service of Data: Standardization with GPT
Data cleanliness is also about its consistency. We will use artificial intelligence to standardize company names, job titles, or format phone numbers. Simple rules cannot handle variants such as "Company X Sp. z o.o." and "Company X LLC". A language model (LLM) like GPT understands the context.
Practical Scenario: Standardizing Company Names with OpenAI
In the Make.com scenario, right after the trigger, we add an OpenAI module. We pass the company name to it with a precise prompt: "Your task is to standardize the company name. Remove all legal and organizational forms (Sp. z o.o., S.A., LLC, etc.). Keep only the main part of the name. Return only the cleaned name." Such standardization with GPT allows for much more accurate duplicate searching and the creation of reliable reports. The same technique can be applied to categorize job titles or extract data from notes.
Continuous Improvement System: How to Implement an Automatic Database Audit?
Even the best preventive systems require control. The last pillar is an automated, weekly audit. This is not a project, but a continuous process. In Make.com, we create a recurring scenario that scans the database for anomalies and reports them on Slack or by email.
Example audit queries can search for:
- Contacts without an assigned owner.
- Companies without any associated contact.
- Open deals with no scheduled activity in the next 7 days.
- Leads older than 90 days.
Such an audit creates a data-driven feedback loop. When the report shows the same problem every week, it becomes clear that it is a systemic error in the process, not a single human error. This allows solving the problem at its source by fixing the process, not just the data.
Conclusions: Data Cleanliness is Not a Myth, It's a System
The belief that perfect data cleanliness in HubSpot is a myth is wrong. Cleanliness is not a state, but a dynamic system based on diagnosis, proactive automation, and intelligent supervision. Clean data is the foundation of effective sales, precise marketing, and exceptional customer experiences. Building such a system requires specialized knowledge. If you want your HubSpot to become a reliable growth engine, not a source of frustration, contact us to implement a data management strategy that really works.
Frequently Asked Questions (FAQ)
HubSpot's tools are mainly reactive (suggesting duplicates for manual merging after they are created) and are based on limited criteria (mainly an exact match of the email address). They do not prevent the creation of mess in real-time and cannot handle complex cases like typos or different email addresses for the same person.
No. Platforms like Make.com are no-code/low-code tools that operate on a visual "drag and drop" interface. However, they require logical thinking, an understanding of how APIs work, and a good knowledge of the data structure in HubSpot. Building a solid, reliable system often requires experience.
The costs are both direct and hidden. Direct costs include wasted marketing budgets, inflated HubSpot subscription fees (you pay for duplicate contacts), and the inefficient work time of salespeople. Hidden costs include lost sales opportunities, wrong strategic decisions made based on bad reports, and damage to the brand's image.
Not necessarily. The cost of queries to AI models (like GPT-3.5) is very low and is calculated in fractions of a cent per operation. Considering the value that a perfectly standardized database brings (the ability for precise segmentation and personalization), the return on this micro-investment is huge.
Yes. The described system consists of two parts. Preventive scenarios (real-time deduplication) work on new data. However, you can build a separate, one-time scenario in Make.com that will process the entire historical database, standardizing it and flagging duplicates according to the same advanced rules.
It is a concept in which an automatic data audit is used not only to fix them but to diagnose flawed business processes. If the system reports the same error every week (e.g., a contact has no owner), it is a signal that the process of assigning contacts needs to be fixed, not just individual records. It's about treating the cause, not the symptoms.