Don't Let Bad Data Derail Your AI Success

If you're wondering how to improve AI accuracy with clean CRM data, then you're in the right place. Artificial intelligence has gained considerable momentum, touted as a transformative technology with the potential to optimise diverse operational facets, encompassing customer interaction and predictive analytics.

But hold on a sec. Before we get swept away by the AI hype, there's a rather crucial ingredient we need to talk about: your data.

The quality of data used for training and operation directly impacts the effectiveness of AI.

Think of it like this: you wouldn't try to bake a cake with rotten ingredients… Would you?

The same goes for AI. Reliable data is paramount for AI to deliver accurate and actionable results.

During a previous webinar, Prepare Your Data for AI, we asked our attendees about their current perception of their data quality within their CRM. 

• 63.16% of respondents rated their CRM data as "Fair," indicating the presence of inaccuracies and gaps.
• 10.53% described their data as "Poor," characterised by inconsistency, outdated information, and unreliability.
• 26.32% considered their CRM data to be "Good," noting minor inconsistencies.

This paints a picture, doesn't it? Then, when we asked if they’d trust their data to train their AI models, 70% of attendees said they were unsure, which speaks volumes about the underlying confidence in their CRM data.

The Tangible Business Implications of Substandard Data

The consequences of neglecting CRM data quality extend beyond mere inefficiency. Poor data integrity carries tangible business implications, including:

• Reduced Productivity: Sales and marketing teams expend valuable time rectifying errors instead of focusing on revenue-generating activities.
• Inefficient Resource Allocation: Marketing campaigns directed by inaccurate or outdated data lead to wasted resources and a diminished return on investment.
• Compromised Customer Experience: The use of incorrect customer information can erode trust and negatively impact customer relationships.
• Lower CRM Adoption Rates: When users lack confidence in the accuracy of CRM data, they become reluctant to utilise the system effectively.

AI's effectiveness is fundamentally constrained by the quality of its input data. Incomplete or unreliable data will inevitably lead to compromised analytical insights, potentially biased outcomes, and increased costs associated with rectification. Bad data leads to bad AI outcomes.

The Impact of Duplicate Properties in your CRM

At Six & Flow, we've encountered numerous instances where substandard data has led to negative business outcomes. For example, one of our clients had multiple CRM properties that captured essentially the same information about a contact's gender. These included "What is your gender?", "Are you male/female?", and "Biological sex".

The presence of these redundant and slightly differently phrased fields resulted in inconsistent understanding and use of this data within the organisation. This, in turn, created confusion within the CRM, making it difficult to obtain accurate data. Attempting to capture the same information in multiple ways can lead to conflicting responses or overall inaccuracies.

Often, we see that if a CRM has multiple, redundant fields, it causes confusion for users entering the data, potentially leading to errors and the need for manual correction later.

It may seem like a minor issue, but when you have multiple duplicate properties or are working with a large dataset, the wasted time, effort, and inaccurate results add up quickly.

If the client were to try and segment their database or run reports based on gender, the inconsistent data across these multiple fields would lead to inaccurate results and potentially flawed marketing or sales strategies.

Ultimately, prioritising data quality provides the essential foundation for AI to deliver its promised value, enabling organisations to achieve more effective and efficient business outcomes.

Strategies for Preparing your Data for AI

Now for the good news! When you get your data house in order, the benefits for your AI efforts are well worth the effort.

Think about being able to:

• Target the right prospects at the right time.
• Spot upsell and cross-sell opportunities you might have missed.
• Gain proper, insightful knowledge about your accounts.
• Plan your territories and route leads with precision.
• See your sales team actually embrace the CRM because they trust the information.
• Get better feedback loops on your tools, leading to continuous improvement.
  
  

It all boils down to this: clean, accurate data empowers AI to deliver results.

Where to Begin?

The first step is to understand how your different teams are using your HubSpot data and ensure everyone is aligned.

It might sound obvious, but different departments often use the same data points in slightly different ways, leading to inconsistencies. If you’re launching a new go-to-market strategy, examine the data points involved and align them with core business objectives across teams.

For example, Marketing’s objective might be to advertise new services, while Sales’ objective is to score and route leads. Even though both teams have different objectives, they ultimately need the same data points.

Ensure you have a single source of truth, so Sales isn’t using one Revenue Property while Marketing uses another.

Then, assess the completion rates of your core data points.

If you're missing crucial information in a large number of records, your AI models won't have a full picture to learn from.

We recommend exporting all of your properties into an Excel spreadsheet to analyse their completion rates. Often, we find that custom properties have lower completion rates.

Low completion rates can highlight areas where your data capture processes might be falling short. On the flip side, understanding completion rates can also help you identify data points that aren't really adding value, allowing you to declutter your system.

Ultimately, assessing your CRM data is about ensuring it's accurate, complete, and relevant to your business objectives. Because when your data is in good shape, your AI has a real chance to shine, delivering the insightful outcomes you’re after.

Conclusion

AI is only as good as the data it learns from. If your CRM data is inconsistent, incomplete, or outdated, your AI models will struggle to deliver meaningful insights and accurate predictions. Poor data quality doesn't just affect AI, it impacts sales, marketing, customer experience, and overall business efficiency.

The good news? With the right approach, you can clean up your CRM, align your teams, and establish a reliable data foundation that empowers AI to drive real value. By prioritising data accuracy, removing redundancies, and ensuring a single source of truth, you'll unlock AI's full potential—helping your business make smarter decisions, improve efficiency, and ultimately achieve better outcomes.

So, if you're investing in AI, start by investing in your data. Because when your CRM is in top shape, AI becomes a powerful tool rather than a costly experiment.