You may wonder why people just don’t use reporting instead of analytics?! These two are often used interchangeably to mean the same thing. But the truth is, they are completely different. There are actually too many different forms of business intelligence. Marketing Analysis is quite a powerful way to transform all marketing data into something genuine. With some data-driven insights, brands and firms can make informed decisions, tailor their strategies, and maximize their influence. In this article, we will dive into the power of marketing analysis and also, we are talking about how data-driven insights fuel growth.
What is Marketing Analysis?
You may have started spending some money on marketing. You’ve made a few tweaks here and there, but all of a sudden you ask yourself, What’s next? What creative should you create? What audiences seem interested in your products or services? What keywords are people converting to on your website? And these are all the right questions.
The act of gathering, reviewing, and analyzing data in order to comprehend important facets of a market, such as consumer behavior, market trends, the competitive environment, and the success of marketing initiatives, is known as marketing analysis. It’s a crucial tool for companies trying to enhance their marketing tactics, find growth prospects, and make data-driven decisions. Businesses may learn more about what motivates their audience, how they engage with brands, and which strategies work best by doing marketing analysis.
Different Categories of Marketing Data
There are three types of marketing data that you need to be working with and need to understand.
- First-party data
This is data collected directly from your customers and interactions. First-party data is highly reliable and relevant, offering deep insights into your audience’s preferences and behaviors.
- Second-party data
This is someone else’s first-party data shared with you, usually through partnerships.
- Third-party data
Gathered by external organizations from various sources. Third-party data offers broad market insights and can help identify new customer segments. However, it may lack the precision and relevance of first-party data and comes with more significant privacy considerations.
For example, cookies are a way to collect data and with all the recent changes around the deprecation of third-party cookies, it’s important that you know which data you’re working with.
Setting Yourself up to do Marketing Data Analysis
The most important first thing to do as a marketer is to create a reliable automated data pipeline that brings all of the data you have available into one place. Then, you need to ensure that the data you have is clean and correct so you can create some basic dashboards or reports to have, at the very least, a basic overview. This enables you to report on your numbers and answer basic questions about your marketing efforts when they inevitably come from management.
Having all of your data accessible in one place also makes it easier for you to do more advanced marketing data analysis on an ad hoc basis as questions come up, without having to make requests to data teams and wait for tickets to be resolved.
Advanced analytical techniques
Advanced analytical techniques are a nice Segway into a couple of examples of advanced analytics techniques.
Customer Segmentation
This involves dividing your business’s customer base into groups of individuals with similar characteristics or behaviors to tailor marketing strategies more effectively. But in order to apply customer segmentation, you first have to analyze your customer base to understand how you will segment them and what you intend to do with that segmentation. Think about the different segments you could create. Then, ask yourself, “If I had these buckets, what would I do differently for each group?” Different creatives, different landing pages, different messaging, different keywords, different products or services.
Predictive Analytics
This uses historical data and statistical algorithms to forecast future behaviors, trends, and outcomes, helping businesses anticipate customer needs and make proactive decisions. If you can do some form of reliable predictive analytics, then you’re in the top 1%. So, don’t pressure yourself too much in this particular area, but if you do get there, good for you!
Sentiment Analysis
Often applied in social media monitoring and customer feedback, this assesses the emotional tone behind textual data to understand consumer attitudes, opinions, and emotions towards a brand or product. it’s only really useful for the biggest of the biggest global brands. And if you’re not Coca-Cola or Adidas, it’s worth the compute power.
Customer Lifetime Value Prediction
customer lifetime value prediction estimates the total revenue a business can expect from a single customer account throughout their relationship, enabling companies to identify high-value customers and allocate marketing resources efficiently.
Leveraging Data for Strategic Decision-Making
We all need to be strategic about what we do, because otherwise, what are we doing? Just doing stuff and hoping it works out? Let’s be real. It’s 2024 and your marketing strategy needs to be informed by data, It can’t actually be your conviction. But the more you know, the more conviction you can build around your strategy. Marketing data analysis can help tell you how you can spend your marketing dollars more efficiently. There are four key areas you need to be able to turn to data to aid in your decision-making. 1. Targeting, 2. personalization, 3. channel optimization, and 4. budget allocation.
- For targeting, you need to understand who buys your product or service so you can find more of them out there and target them with your marketing.
- Personalization is about analyzing what resonates with your target audience. For example, what creatives perform the best? What content pieces have the most conversions? What organic social posts have the most reach and engagement?
- Channel optimization is about evaluating each channel’s effectiveness in terms of reaching your target audience, engaging them, and driving conversions. You need to know what role each channel plays in the customer journey and how to get the most out of each channel.
- Budget allocation requires you to have a good top-level overview of your marketing spend by channel. Then lean into the channel mix to figure out how to distribute that spend across your channels. Paid ads and content are great, but they’re only as good as what you put into them. So, if you put sub-par creatives into it, you’ll get sub-par performance out. So, be sure to allocate a budget for creatives, too.
Tools and technologies powering data analysis.
Naturally, you can’t do marketing data analysis without tools. create a reliable data pipeline, get all of their data into one place, and create basic automated reports. You need tools for all of this. So you should Have a playground where you can analyze your data.
There are two common ways to achieve everything on this list;
One is referred to as the modern data stack. The other is out-of-the-box software. Many companies do opt for the modern data stack but it can be costly to build and maintain, requiring a ton of developer and engineering time. Alternatively, you can opt for vertical software to solve these challenges out of the box for you. Funnel happens to be just one of those tools. Using Funnel, you can achieve one, two, and three with just a few clicks, and for number four, you even have options for where you want that playground to be.
You can either explore the data in our Google-Sheets-like Data Explorer, or you can simply choose a few fields and actually send them to a Google Sheet to slice and dice however you want. Or, if you’re a data warehouse user, you can always export years of transformed historical data to a data warehouse and do your analysis there.
There are alternatives, especially in the data ingestion area, but no platform offers the depth and reliability of connectors because it turns out those APIs can actually be a little tricky to build and maintain.
Pay Attention when doing analysis!
Getting started with data analysis is one thing, but is a whole different ballgame to be good at marketing data analysis. So, to help you avoid tripping up pay attention to these three things;
- double-check your numbers before you present an analysis.
- Number two is the data you’re looking at the same as what someone else is looking at.
- take care when presenting your numbers. You might think the analysis is the hard part and the presentation is the easy part, but how you present your numbers really affects the impact that they will have. So be sure to follow data visualization best practices.
Bottom-line
Marketing data analysis can and should inform your marketing strategy. You can’t do marketing in 2024 without doing some kind of data analysis. Tools are essential for doing marketing data analysis. And finally, remember, marketing data analysis isn’t just about numbers. It’s about unlocking the story behind your marketing efforts. Take your numbers, contextualize them, and discover the stories they tell.