Drowning in Data: Why Less is More When It Comes to Marketing

Woman pointing to graph

It seems almost heretical to suggest that marketers make do with less data, because no marketing strategy can be successful without it. 

But the biggest mistake that most businesses make is collecting too much data or worse, aggregating bad data. This leads to something called InfoObesity, a term that was popularized by Alvin Toffler in his bestselling 1970 book, Future Shock. He was referring to the never-ending stream of voicemails, emails and marketing reports that were problematic even before the digital age.

Now in 2022, data overload has only become more bloated and obtaining quality data is not always as intuitive as it first appears.

For example, research from the Harvard Business Review discovered that data quality is far worse than most companies realize, saying that a mere 3% of the data quality scores in the study were rated as “acceptable.”

Data on computer with red keyboard

But why is this?

Surely, the more data you have on your prospective customers, the more you can tailor your marketing campaign to them, right? Wrong. The truth is that when you are overloaded with unfiltered information, it makes it harder to find the key details you need to craft a meaningful campaign.

While many businesses focus on accumulating data, the focus should be on how to accumulate higher quality data that helps you to connect with prospective customers in a much more in-depth way. 

In this blog, we show you how to do just that.

Why Data Is Important

Hands holding money in front of laptop

Understanding your target audience is essential. If you want to craft a successful marketing campaign that inspires them to buy from you, you need to know what makes them tick. 

Getting to Know Your Target Audience

Woman points to data and statistics on board

Identifying your prospective customers entails understanding who your ideal prospects and customers are, who your competitors are, as well as the core challenges your product or service solves.

Data on your target audience should ideally include the following:

  ● What is their demographic (job titles, industry, location, age range, hobbies etc)

  ●  What is their psychographic information (behaviours, attitudes, lifestyle, personality traits, etc.)

  ●  How much money do they earn?

  ●  Where do they hang out on and offline?

  ●  What challenges do they have that your product or service solves?

  ●  What tone of voice would work best with this audience?

For example, if you have a financial recruitment company that targets high-level finance professionals on one hand and financial institutions on the other hand, your target audience may look like this:

  ●   Job titles: CFOs, accountants, finance officers, bankers, stockbrokers, analysts and clerks

  ●   Companies: Banks, accountancy firms, government departments, corporations

  ●   Salary: £50-£100k

  ●  Demographics: Most likely men between the ages of 35-55, most likely married with a family

  ●  Biggest pain point for professionals: Finding high-level, well-paid executive finance jobs with opportunities for advancement onto more senior, boardroom and/or partnership opportunities

●  Biggest pain point for companies: Sourcing skilled, highly experienced executive-level talent with the right mix of financial knowledge and technical skills

How to Identify Your Target Audience

Two women point to data on computer

Start by analysing your existing audience base.

Why do they buy from you? What makes them buy from you as opposed to your competitors?

The simplest way to discover this information is to survey or interview your customers. There are many ways to do this. For example, you could encourage your customers to fill out a short form. Alternatively, you could interview a few of your customers to find out what they like about your services. Consider offering some light incentives such as a chance to win a freebie or a small discount in exchange for them taking the time to fill out a form or agree to an interview.

Woman with headset sits in front of laptop

You could also read the reviews and analyse what people have said about you. Positive reviews can give you a clue as to what it is that people like about your business. If you have received any negative reviews, read those too to find out what frustrations your customers have and how you could improve. 

Hands type on keyboard with reviews

Next, head over to social media. The social media websites you should go to depend upon which social media websites you set up. Most social media platforms have an audience insights page that gives you more information about the people who subscribe to your website. 

For example, Facebook Audience Insights contains a range of useful demographic information such as gender, lifestyle, location, device used, interests, and more. 

Even if you do not have a large audience, you can use this information to create detailed personas and avatars.

You can also use a similar technique to research your audience when you don’t have an existing customer base. If you don’t have that much data on your social networks, make a list of your competitors and companies that are similar to you. Carefully analyse the likes, comments and level of engagement on their social networks. 

Tools such as BuzzSumo, Hootsuite and Brandwatch can help you to do this. BuzzSumo helps you find your competitor’s most shared content, while Hootsuite Streams allows you to track keywords, competitors, and hashtags across every social network. You can then use Brandwatch to create a social media report and analyse how much people talk about your brand online compared to how much they talk about your competitors.

Doing this helps you to get to know your audience better and make more well-informed decisions.

According to a survey from Deloitte, 49% of respondents say that analytics helps them to make better decisions, 16% say that it enables key strategic initiatives, and 10% say it helps them to improve relationships with both customers and business partners.

So, What is The Problem With Data?

Shadowy man looks at red data on a screen

Disjointed, unstructured data doesn’t give you any information. Instead what it does is waste your time as you sift through excel sheets, pulling together random names, dates, demographics and email addresses. Modern-day marketers simply don’t have time for that.

A survey published by Forbes found that 95% of businesses cite the need to manage unstructured data, while 40% of respondents said they had to clean unstructured data on a regular basis. 

Too much data actually gives you less information. Because not only do you waste time trying to make sense of all the facts and figures, but you end up with data overlap that punches holes in your story.

For example, two customers might have the same backgrounds but different needs, expectations or transaction histories. This means that the data you have may not actually be that useful, unless you are able to put the data you have in context. 

According to IBM, poor data quality costs businesses worldwide anywhere between $9.7 million and $14.2 million yearly (which equates to £7.15 million and £10.47 million respectively).

Yellow data on a screen

Unstructured data makes it difficult to leverage the information you do have. This is a significant problem for the majority of companies. Jumbled information isn’t just time-consuming, it also requires talented employees and staff members to interpret it all. Finding these people can be difficult for many businesses.

In fact, just 1.9% of marketing leaders reported that their companies have the right talent to leverage marketing analytics, according to a CMO Survey.

How to Tell If You Have Low-Quality Data

Hands typing in front of two screens

While the cost of low-quality data is clear, separating the wheat from the chaff is less straightforward. 

In a Harvard Business Review, data experts Tadhg Nagle, Thomas Redman and David Sammon recommended the following suggestions to determine whether you have low-quality data.

  ●  Start by gathering a list of the last 100 data records you used or created.

  ●  Then, prioritize between 10-15 key data points that are important to your company.

  ●   Next, team leaders and managers should go through each data record and identify any noticeable errors and analyse the results.

  ●  Once you sift through all of this data, the quality should be clear.

How to Get The Competitive Advantage via Data

Man looks at data on large computer screens

Before collecting any data, the first step should be to define the business problem that you are trying to solve. For example, you may be trying to reach out to a new customer segment, increase existing customers, or perhaps you are seeking to improve sales for one particular product or service.

Whatever the case may be, the first step is to understand your objectives before deciding what data is needed. Next, you need a plan.

So what you should do is create an integrated 360-degree view of the customer that considers every customer behaviour from the time the alarm rings in the morning until they go to bed in the evening.

Make a list of all of the attributes your ideal customer would have, to create your customer personas and avatars. Every potential engagement point, for both communication and purchase, should be captured. Only then can you completely understand their customers via analytics, and develop customized experiences to delight them.

One handy tool to create these personas is Hubspot’s ‘Make My Persona’, which is a step-by-step wizard that will walk you through the process of creating a useful client, customer, or user persona for your business. 

It is also important to understand how algorithms and data correlate to your business problems. Develop an instinct for mapping the variation in the data to the business questions. Essentially what this means is that you want to consider all of the data points you have and analyse whether it actually helps you achieve your objectives

Let’s go back to our earlier example. So two customers might have the same backgrounds but different needs, expectations or transaction histories. They are both white-collar professionals, but one is a homeowner, the other is not. 

Your target audience is white-collar professionals that own their own home. In that example, your next step, in that case, should be to analyse whether the variations in needs, expectations and transactions will give you the information you are looking for. 

But when you are analysing the data, your focus should be on communicating insights and interpreting the data, rather than just looking at the numbers. 

How Successful Companies Use Data


Amazon website

Amazon is a giant in the world of eCommerce. This is in part due to its bloated database. Big data is behind some of their most successful initiatives including dynamic pricing, which allows Amazon to change their prices up to 2.5 million times a day, in response to consumer shopping patterns, competitor pricing and product popularity. 

It also uses its database product to create algorithms that make product recommendations based upon previous buying patterns and searches. 


Hand points remote at Netflix screen

While Amazon is the giant of the retail world, Netflix is the giant of the online movie streaming world. This is in large part due to the way they use data and AI. They collect data on every aspect of a customer’s experience to make movie recommendations. This includes things such as what movies a customer watches, how long they watched for, and if they binged watched it after pausing.

This creates the ultimate personalization plan, which helps to enhance their offering and is the reason why their customer retention rate is 93%.


Accuweather screen

But big data doesn’t just help you to retain customers, it also helps to predict the weather. Accuweather, one of the world’s best-known weather forecasting companies, created an online platform for developers where they could purchase API keys and implement it in their own projects and businesses that benefit from their weather data.

Quality vs Quantity

Hand draws quality vs quantity on screen

Marketing and data analytics has come a long way over the years. The introduction of technology such as AI and big data makes it possible to analyse and interpret customer data at a scale never seen before.

However, it has also created new challenges as companies struggle to cut through the noise of data analytics. In the olden days, just having data was enough to give you the competitive edge. The more, the better.

Nowadays, it is not so simple. We live in an age where data quality matters more than quantity.

According to insights published on Forrester, 60-73% of data is unhelpful and therefore goes unused in analytics, while 65% of companies report having too much data to analyse.

Some claim that data is the most valuable currency in marketing and almost every business and organization relies upon it. We disagree.

Quality data is the most valuable currency in marketing. Quality data is defined as that which is accurate, complete, consistent, reliable and up to date. It makes the difference between powerful and successful marketing campaigns or campaigns that flop. 

To obtain quality data you need time, tools, and talent. In other words, you have to invest in the right staff members and the tech that is going to help you build and obtain good quality databases in less time. While the cost of achieving this may seem steep at first, the investment will be worth its weight in gold when the time saved, customer retention and acquisition all increase exponentially. 

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