How to power your marketing with b2b big data

Big data has gone from ‘the talk of the town’ to an accepted staple in b2b marketing. But when it comes to making it work for your campaigns, there’s much to consider. From issues with collecting, standardising, and interpreting data (not to mention keeping it secure) without the right know-how, b2b big data could be no more than a big nothing.

We've put together this guide to help you make the most of your b2b campaigns. It takes a close look at some of the issues, how you can mitigate them, and how you can get the most out of your big data.

But first, let's look at what happens when you get it right.

Benefits of b2b big data

It’s hard to overstate the impact of effectively leveraged b2b big data. With oodles of customer and market data at your fingertips, big data offers a specificity that b2b marketers of old could only dream about.

Here are some of the key advantages:

Enhanced customer segmentation and personalised campaigns

Have you ever received an email from a large company with a customer database of millions and felt like they were speaking directly to you? It’s not an eerie coincidence or marketing wizardry—it’s customer segmentation using big data.

Here’s how it works:

• Data Collection: Gather diverse customer data from multiple sources, such as transactions, interactions, demographics, and behaviours.
• Data Analysis: Analyse the collected data using advanced analytics tools to uncover patterns, trends, and correlations.
• Segmentation Criteria: Define criteria based on the analysis to segment customers into groups with similar characteristics and behaviours.
• Segmentation Utilisation: Tailor marketing strategies and messages to each segment's specific needs and preferences to improve engagement and effectiveness.
  
  And, hey presto!
  
  As if by magic, customers receive what feels like a personalised email in their inbox. And according to the research, personalised is precisely what you should aim for. A survey by Sinch Mailgun found that 35.2% of email unsubscribes can be attributed to a loss of interest in the offerings or irrelevant content.

So, when it comes to keeping your customers engaged, customer segmentation for personalised messaging using big data is a big win.

Improved marketing ROI through data-driven decisions

When making decisions in a complex, dynamic business landscape, sometimes it feels like a crystal ball would come in handy. Then, you could know where to invest your budget to get the biggest bang for your marketing buck and make confident, informed decisions.

While a functional crystal ball might be out of reach, the next best thing is big data.

By optimising marketing efforts through data analysis, businesses can allocate resources more efficiently, maximising return on investment (ROI) and minimising wasted ad spend.

Real-time monitoring and optimisation

The ability to monitor customer behaviour and market data as it rolls in is a game changer. It allows you to make adjustments on the fly based on current context and eliminates guesswork.

Imagine two campaigns, one using real-time monitoring and one without it.

A b2b landing page is loading slowly due to large image sizes, resulting in increased bounce rates. The campaign using real-time monitoring will diagnose and resolve the issue before conversions take a nosedive. The other campaign continues to bleed conversions while you’re waiting for a report. This is the power of real-time monitoring and optimisation.

Challenges faced with b2b big data

Despite the myriad benefits, there are just as many hurdles to overcome to make your b2b big data work for you. Here are some of the common challenges companies face.

Data quality and accuracy

Since the earliest days of computation, people have conflated “data-driven” with “accurate”. 

Consider this quote from Charles Babbage (often referred to as “the father of the computer”), in 1864:

“On two occasions I have been asked, "Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?"... I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.”

Today, this problem is captured in the acronym GIGO (garbage in, garbage out).

Unfortunately, data quality remains a major issue to this day. According to Gartner, poor data quality costs organisations an average of $13 million USD every year. So before you rely on your data, it’s essential to make sure it’s accurate, consistent and up to date.

Data privacy and compliance

Unless you’ve been living under a rock, you’ll no doubt have heard about the major data breaches in recent years. According to a report published by IBM Security, the average cost of of a data breach in Australia in 2023 was AUD $4.03 million. This is an increase of 32% since 2018. In contrast, organisations that invest in security AI and automation save an average of AUD $2.14 million.

In short, preventing consumer data breaches isn’t just a matter of ethics - there are enormous costs involved. The good news is, investing wisely in data security has a tangible impact on your bottom line.

Integration of data from multiple sources

When integrating data from various origins, issues concerning data quality often arise. These issues typically involve dealing with missing, incomplete, or conflicting data, as well as variations in data formats and structures. As above, this can lead to data quality and accuracy issues, leading to poor decision-making.

For example, let’s say you’re collecting date fields from multiple sources.

One source collects dates as dd-mm-yyyy and another as dd/mm/yy. Depending on how this data is handled, it could render some of the data ‘null’, and therefore unusable.

Worse, your data could be misinterpreted depending on the formatting. Let’s say you’re collecting date fields from across the globe. One source collects dates as dd-mm-yyyy, and the other collects as mm-dd-yyyy (as is common in the US). Without accounting for how different countries use this format, serious errors can be made, leading to misleading or incorrect data.

To ensure seamless integration from multiple sources, it’s important to put data consistency measures in place (more on this later).

Skill gap training for data analysis

Unless you’re a statistician or a computer programmer, working with data can be unintuitive. There are often skill-based or conceptual errors that can arise when non-technical people work with big data that impede good decision-making.

If you’ve taken a Statistics 101 class, you might have heard of this famous error in statistical reasoning:

A study finds a strong correlation between ice cream sales and drowning incidents. This correlation leads some people to interpret the findings that ice cream sales cause drowning incidents.

In reality, the variable driving both these increases is heat.

While it may seem obvious, a surprising number are stumped by this problem. Worse, with the rise of big data in modern business, this style of faulty reasoning can bleed into data-driven decision-making, and it has real-world consequences.

In an article for the Harvard Business Review by author of Thinking, Fast and Slow Daniel Kahneman, data-driven decisions in business are frequently undermined by reasoning errors like these, and with big data a staple in decision-making, it’s coming at a substantial cost.

Strategies for effective management of b2b big data

While there are hurdles to overcome, with the right plan in place, you can use big data to empower your next b2b campaign.

Here’s how:

Data collection best practices

Circling back to GIGO, effective use of big data in B2B marketing begins with input. Ensure that your data collection is:

• Consistent
• Secure
• Compliant
• Transparent

This is the first step toward ensuring data quality. You could do everything else perfectly, but if your data isn’t collected properly, it won’t be useful to you.

Data cleaning and standardisation

After putting measures in place to minimise input errors, data cleaning and standardisation will always be necessary.

To effectively clean and standardise your data, follow these steps:

• Identify and handle missing or duplicate values
• Validate data formats and resolve inconsistencies
• Ensure data integrity by correcting errors and outliers
• Use data profiling to understand distribution and patterns
• Standardise formats and codes across datasets
• Implement data quality checks to monitor ongoing data
  

By following these steps, you'll have a reliable and accurate foundation for making informed business decisions based on accurate data.

Using predictive analytics for forecasting

Now that your data is clean, it’s time to use predictive analytics for forecasting.

By using statistical models and machine learning algorithms, you can identify patterns and trends in your data that would be difficult to discern using traditional analysis. This enables you to anticipate future outcomes and trends and proactively optimise your business strategy in response. You may have in-built predictive analytics in your marketing platform, or you may have a team of data analysts to run bespoke analysis.

Cross-department collaboration for holistic data usage

So, you’ve collected, cleaned and analysed your data. The next step is to interpret it and put it to use.

As we've established, big data analysis can be daunting for those without a statistical background. But data-driven decision-making shouldn't be the sole domain of technical experts. Getting the best decisions requires cross-pollination of ideas and influences.

That's why you need to bring together data analysts with technical know-how, business leaders with market insight, and subject matter experts with boots-on-the-ground experience. This fusion of perspectives will give you a 360-degree view of your data, and help you make decisions that drive real results.

While the prospect of a crystal ball for your next marketing moves might be appealing, in lieu of such an invention, the next best thing is b2b big data.