Article

Why Keeping B2B Profiles Up-to-Date Boosts Sales & Marketing Success

Best Practices: Data Quality

Sales and Marketing Success

Keeping B2B buyer profiles up-to-date is essential for maintaining effective sales and marketing strategies. Unfortunately, keeping profiles up-to-date is tedious and often neglected. Failing to update our customer or buyer profiles does your sales and marketing teams a tremendous disservice. Your teams could spend thousands of dollars and weeks of focus trying to get in touch with a great lead from last year, completely unaware that the person changed jobs three months ago – all because their profile within your CRM and marketing automation systems wasn’t up-to-date. People change jobs and companies go throughM&A all the time – and your sales and marketing teams need to stay informed. That’s just one of the many ways your teams will lose with outdated buyer profiles. Having accurate, up-to-date data seems like a no-brainer, right? Let’s consider why many sales and marketing teams don’t always have the data they need.


It takes a lot of buying signals to build the buyer profiles that our sales and marketing teams need to win business. Firmographics, demographics, technographics, intent, 1st-party data, predictive and engagement. Generally speaking, we can’t get all of those signals from one place. Instead, we’re forced to buy numerous sets of static data from several vendors, then blend it all together. Buying multiple sets of data from multiple sources is inherently expensive, and manually combining that data with our first-party data is time-consuming, cumbersome and error-prone. We might jump through those hurdles at first, but the problem arises when we need to update it. 


A static data set is just a snapshot in time, and there’s no way to tell when data has changed to the point where you need a new snapshot. This means that in order to ensure our buyer profiles are up-to-date, we need to regularly buy all of our data over and over again, continuously blending it all together with the old data. That’s going to be very expensive, and it’s going to require a lot of time and effort dedicated to data unification. In many cases, by the time you’ve enriched your new data with the old data, that new data has already become old data. Naturally, many companies will decide that their slightly old data is good enough because they can’t justify the cost of constant data purchases or the time spent unifying it.


Not updating your buyer profiles is a huge mistake in a world of data-driven decision making, where all of the decisions you make depend on having accurate, complete and up-to-date data. Even the best predictive AI models will point you in the wrong direction if the data being analyzed isn’t correct. Simply put, if you want your sales and marketing teams to have the tools they need to reach out and close business, you need to give them buyer profiles that are dynamically updated.


Let’s look at 10 common strategies for ensuring that your buyer profiles are current and accurate, then consider a more realistic solution for automating the entire process.

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9 Buyer Signals Every Revenue Team Should Be Tracking

Revenue teams operate inside a signal-rich environment. Buyers research, evaluate, and compare vendors across many channels before speaking with sales. That activity leaves data behind.

Most organizations collect fragments of those signals across marketing automation, CRM, web analytics, product tools, and third-party platforms. Few teams unify them. Fewer teams activate them in real time. The result: revenue teams operate with partial visibility into active demand.

According to Gartner research, B2B buyers spend only 17% of their purchase journey meeting with suppliers. The rest occurs independently through digital research and internal discussions. Signal visibility determines whether revenue teams recognize demand early or respond too late.

This eBook outlines the nine buyer signals every revenue organization should track continuously. These signals help revenue teams identify active buying groups, prioritize accounts, and accelerate pipeline.

When unified through a modern data intelligence architecture, signals shift go-to-market from reactive execution to signal-driven engagement.