Marketing analytics is collecting, analysing and reporting data about marketing activities. It is used to make business decisions and help marketers understand what customers want. The goal is to measure how well your company meets its goals and objectives by analysing your customers’ behaviour and preferences.

Today there is no arguing that the marketing analytics strategy has evolved by leaps and bounds. Still, analysing previous customer data to enhance marketing decisions in the future continues to be the bedrock of marketing data analytics.

Market analytics is crucial to any effective and successful B2B or B2C campaign. In this guide, we’ll discuss what marketing analytics is, how it works, why it’s crucial for today’s businesses, and how companies use it.

What is Marketing Analytics?

Marketing analytics uses data and statistics to understand, predict and influence customer behaviour. It is a subset of Business Intelligence (BI) that analyses marketing-related data to make informed decisions.

The data used in marketing analytics can come from various sources, including websites, social media, email campaigns and other digital channels. Marketing analytics can use data from traditional sources such as surveys or focus groups and digital sources like web form submissions or customer service tickets.

Marketing data analytics tools like statistical analysis, predictive modelling and customer relationship management (CRM) software can help find insights from data and help make data-driven decisions about where to focus efforts for maximum impact.

One of the pertinent marketing analytics examples is predictive lead scoring, which uses historical data about prospects’ behaviours combined with additional information from online forms or surveys to estimate whether they are likely to purchase from you.

Types of Marketing analytics

The three main types of marketing analytics are descriptive, predictive and prescriptive:

Descriptive Marketing Analytics: This helps understand why a certain parameter or an entire campaign did not work as expected. Descriptive marketing analytics explains what happened over a given period. For instance, it will help determine why a particular campaign keyword has stopped generating impressions that it used to a month ago.

Predictive Marketing Analytics: Predictive marketing analytics leverages the insights gained from descriptive analytics to predict how your campaign may perform. It uses historical data and statistical models to predict future customer behaviour. This type of analytics can be applied at various stages in the customer journey: pre-purchase, during purchase and post-purchase.

The main goal of predictive marketing is customer segmentation. Segmentation refers to grouping customers into groups based on shared characteristics that are important from an operational or marketing perspective. For example, if you have an e-commerce website, you can create different segments based on what products customers tend to buy together. This could help identify new product recommendations or suggest complementary products for cross-selling purposes.

Prescriptive Marketing Analytics: After deriving insights and understanding how the future will pan out for a campaign, prescriptive marketing analytics helps determine the course ahead to ensure the maximum ROI from a campaign. The prescriptive approach uses data to predict the outcome of a campaign before it begins. Looking at past data, one can determine the most likely outcome of a particular strategy or tactic based on previous results. Then, it can recommend what needs to happen next to achieve that outcome.

Prescriptive marketing analytics aims to help you identify the best course of action for your business. You might be looking for insights into increasing sales, managing margins, or reducing costs. These techniques don’t rely on historical data alone. They use predictive models and advanced algorithms to analyse what could happen in the future if your business makes a particular decision.

The Importance of Marketing Data Analytics

In the world of marketing, analytics is a must. It’s not enough to have an idea and hope it will work. You need to be able to track its performance and see if it’s working or not. That’s where marketing analytics comes in.

B2C or B2B marketing analytics strategy is critical for enterprises of all forms and sizes to understand where they should allocate their resources — whether that’s more time and money on social media or email marketing — so they can increase their sales revenue over time.

Online marketing analytics is even more critical today, given that consumers are becoming highly selective in which brands they want to engage with and which brands they want to avoid interacting with. With marketing analytics, you can analyse the effectiveness and performance of your call-to-actions (CTAs), logs, social media channels, thought leadership pieces, website pages, paid advertisements, etc.

Here’s why advanced marketing analytics is relevant for businesses today:

  • Increasing Revenue — Understand how customers behave, optimise your website for maximum conversion, and increase the value of your product or service offerings.
  • Improving Customer Satisfaction — Find out what customers want and provide a better experience across all touchpoints from awareness to purchase.
  • Reducing Cost — Identify operational efficiencies and improve them in campaign delivery to reduce costs without sacrificing results.
  • Gaining Customer Retention — Gain information about customer retention and churn, which allows for identifying customer satisfaction issues and taking appropriate action. This will help reduce customer attrition in future.

Components of Marketing analytics: An In-Depth Look into How Marketing Data is Analyzed

Creating a robust marketing analytics strategy involves a systematic approach to obtaining the correct data pertaining to customer behaviour and market conditions. Then, statistical analysis is applied to identify causal relationships between different data variables. The insights derived from the analysis help an organisation improve its marketing efforts.

On this note, here are the main components of enterprise marketing analytics strategy:

Data

Everything from customer surveys to social media posts can be used as data sources for marketing analytics. The more data sources you have access to, the better your analysis will be because it gives you more information to make decisions about how to reach your customers.

What Kinds of Customer Data Can You Get? 

  • First-party Data (Directly from the organisation’s users): It is the most valuable kind of data you can get since it’s the most reliable, and it will be “the source” of data in the future, given the rise in objections to third-party cookies. For example, customer touchpoint/event data, email marketing data, customer purchase data, etc.
  • Second-party Data (Data that you get from another organisation): Data such as survey reports, etc., are helpful, especially if your organisation also shares a similar target audience. You can also collaborate with that organisation to benefit both businesses.
  • Third-party Data (Data rented and sold by other organisations): Even though the third-party data is large in volume, it is often unreliable because it comes from something different than a trusted second-party organisation.

Data Preparation 

Data preparation is the process of transforming raw data into a format that’s more easily consumed by marketing data analytics software. This is a critical step because it can make or break an analysis, especially when working with large datasets.

Data preparation aims to ensure that you have the most accurate and valuable information for your analysis. This can mean cleaning and organising data sets, transforming or combining them with other sets, or aggregating them over periods.

Another reason that reinforces the importance of data preparation is that raw data from different sources often vary in structure and quality. For example, if you wanted to analyse how many customers visited your website on each day last year, you would need to combine all the daily traffic counts from your web server logs into one place so you could create a single report for each month instead of hundreds of reports for each day.

Typically, the process of data preparation involves the following tasks:

  • Data Accumulation – The most pertinent data is accumulated from data lakes, operational systems and other relevant data sources. The onus lies on the data science experts to determine and confirm that the data collected applies to the objective of executing marketing analytics.
  • Data Profiling – This is the process of verifying the structure and quality of your data. It involves examining different variables and features to ensure that they are consistent across all records, even if they are not explicitly tied to each other. It is a critical stage that helps you spot errors in your data set before you begin analysing it.
  • Data Cleansing – Data cleansing is the process of checking, correcting and enhancing data quality. This can be done by identifying errors or missing information in a dataset, removing duplicates, and ensuring it is free from anomalies.
  • Data Formatting – In marketing analytics, data formatting refers to transforming raw data into a format that supports analytical procedures such as statistical analysis and machine learning. For example, a table containing customer information might include demographic variables such as age and gender and purchase history information such as product category codes or brand names. These variables can be combined into new variables, such as “age-gender groups” or “category-brand groups”, for subsequent analysis.
  • Data Transformation – Data transformation is changing data from one format to another. This can be done to clean up dirty data and make it usable and cognisable or transform it into a more appropriate form for analysis. Generally, it involves changing the format, structure and content of the accumulated data.
  • Data Validation – This process involves conducting data quality checks to ensure that it meets a certain standard. The data validation process is done by comparing the data to other sources, checking for duplicates and removing wrong entries from the database. The final data is then stored in a data lake or other data repository for further use.

Methods of Data Collection

The first step in any analytical process is to gather relevant data from various sources. This may include internal data from customer relationship management (CRM) systems or other databases and external data such as third-party surveys, website analytics and social media monitoring tools. Data can also be collected manually through interviews or surveys. Here are the different methods of data collection to pave the way for robust enterprise marketing analytics:

  • Surveys – It is a straightforward method. Ask your customers whether they like your product, why, what could be improved, etc. This gives you first-hand information that can be a goldmine of insights to turn your future campaigns around.
  • A/B Testing – In simple words, divide your audience into two segments, create two versions of marketing material and share one version with each of these segments. Launch/finalise the version that performs better. When this process is followed for more than two segments of the audience for more than two versions of the marketing material, it is called Multivariate Testing.
  • Organic Content Creation – Analyse your audience’s interaction on your blog, website visitor data, social media content, email newsletters, YouTube content, podcast content, etc. You will often find new audiences and customers for your business from organic content.
  • Paid Advertisement Interaction – Many tools can help you understand the kind of people you attract through your paid performance campaigns. It will help you know whether your message is resonating with your target audience or is attracting a different segment of customers you were not targeting initially, etc.

Data Cleansing

Once collected, data must be cleansed to ensure that it is accurate and consistent across all sources. Data cleansing is essential for marketing analytics because poor-quality data can directly impact your business. If you have inaccurate information about your customers, you might be wasting money on campaigns that won’t generate sales or leads.

However, it must be noted that data cleansing is about more than just removing insufficient data. It is about ensuring a precise balance between different data types.

For example, if you have an address field, it is supposed to be in a specific format (street name, city, state). If some elements of this format are missing in some entries or contain invalid characters, this can cause issues.

Manual or automated methods can do data cleansing by using advanced marketing data analytics software. Manual methods include:

  • Data Profiling: This involves examining the data to determine if it is missing, duplicated, contains errors or is of poor quality. The results of this process can be used to identify problem areas in the database that need to be addressed through further analysis or automation.
  • Data Scrubbing: In this process, data that does not conform to the standards set for an organisation’s data model is manually removed or modified. For example, an address field may be entered in a way that makes it difficult for software to read it (such as misspelt street names) correctly. In that case, this field needs to be scrubbed so that software can correctly interpret it during subsequent analysis processes.
  • Data Matching and Merging: This process involves comparing information from one source to another to identify duplicate records and merge them into a single record (usually with an identifier such as a customer ID).

In marketing and analytics, experts usually run scripts to automate the data cleansing process. However, not everyone in an organisation will necessarily know how to set up data-cleansing automation scripts. In addition, it is quite a uphill task for any marketing analytics company to scale up the data cleansing process through scripts alone.

  • UI Automation – This led to the emergence of some of the best marketing analytics tools in the form of UI automation. Any UI-based marketing data analytics software eliminates the need to delve deep into codes and simplifies the process of data cleansing.
  • Machine Learning Models – A well-programmed machine learning (ML) model is one of the best ways for automated data cleansing. You can generate histograms and run column values against trained machine-learning models. This flags data value anomalies that do not match other values that populate the column.
  • Data Duplication – An ML model can segregate data records into a cluster based on their commonalities and perform record linkage. To perform this function, the ML model is trained on non-deduped sets of data. These datasets feature labels for non-matched and matched values.

What is Marketing Analytics Software Used For?

A goldmine of data is useless until you derive insights from it. Today, data gathered from the above sources is enormous and a heterogeneous composition of variables. It is humanly impossible to determine the underlying meaning of such data.

The answer to this challenge lies in marketing analytics software. The zeal of organisations to deliver the best campaign results has resulted in the mushrooming of some of the best marketing analytics tools. These tools analyse sales data and customer information. Some of the best marketing analytics software allows you to view reports on sales, customers, products and more. Marketers can use this information to decide what products to offer, how much inventory they need, and which marketing campaigns work best.

Here are some uses of marketing analytics software:

The benefits of marketing analytics include determining whether or not a website is meeting its goals. For example, if you want more people to sign up for your newsletter, but only 10% are signing up after visiting your site, you’ll want to know what’s causing this issue so you can fix it.

Identify which keywords convert best for your business and focus on those when building content for search engines like Google and Bing. This will help increase traffic from organic searches rather than paid ads (which cost money).

Track customer behaviour on social media platforms like Facebook and Twitter so you can see what posts get shared most often and what kind of content excites people about your brand.

How Does Marketing Analytics Help Businesses?

Marketing analytics is the heart of modern marketing. It’s the science of measuring, analysing, and interpreting data to gain insight into what drives customer choices and behaviour.

A well-programmed and Cost Effective Marketing Analytics Tool will deliver you data insights that will help you make better decisions, improve campaigns, and build stronger relationships with customers.

Here are some ways that AI marketing analytics, such as marketing mix advanced analytics, can help your business:

  • Know which messages work best for your audience. You can use data from past campaigns to predict future results and avoid wasting money on ineffective advertising strategies.
  • Determine when people are most receptive to your offers. You can determine when people will respond favourably to your promotional offers or sales pitches by tracking customer behaviour through website visits, conversion rates, and other metrics.
  • Measure ROI from specific campaigns or initiatives to determine their effectiveness more accurately in different organisational activities (such as sales).
  • Analyse customer lifetime value (CLV) — how much money each customer spends over time — to determine which customers are more valuable than others and should be rewarded with preferential treatment (e.g., more personalisation, gifts or discounts).

How to Choose Marketing Analytics Software?

There are a lot of factors that go into choosing a marketing analytics tool. You need to consider how you want to analyse your data, what kind of reports you need, and how easy it is to use. Business owners in the highly competitive telecom space will need real-time marketing analytics to improve customer retention. Meanwhile, multi-country retailers will require omnichannel marketing analytics to uncover actionable data insights.

Here are a few questions you can ask before you decide on the right marketing analytics software:

  • What type of business do you have? SaaS, B2B, B2C, e-commerce, etc. It determines the kind of data sources you may have and the reporting you are looking for.
  • What are the current marketing data sources you have? How are you expecting it to increase/decrease in the short term?
  • What does your marketing tech stack look like? Is the marketing analytics software you consider compatible with your current marketing tech stack?
  • What kind of visualisation, customisation, and reporting do you need for your business?
  • What is your budget?
  • What are your goals? The complexity of the marketing analytics software will depend on the plans.
  • Does your team/organisation currently have someone who knows marketing analytics? If yes, do they know how to use marketing analytics software? If not, can they be trained in that software? What kind of training will they need, etc.?

Conclusion

It is a fact of the modern world that most businesses have data at their disposal. They can run and customise reports, pull the information into visuals and other dashboards and even analyse their customers’ habits. However, only a few businesses take advantage of these tools. Why is that? Is it simply apathy towards analytics? Hardly.

The reality is that marketing analytics is often challenging to implement due to the lack of knowledge and understanding of marketing measurement tools and marketing mix analytics. You can partner with a pioneering B2b marketing analytics company, like Analytic Edge, to make things easy.

We deploy and provide hands-on training on various highly effective marketing analytics tools. Our hand-held guidance will help you to independently run complex software tools such as marketing mix advanced analytics tools and pre-determine the quantitative outcome of your campaign.

Request a demo, and our expert will connect with you to help you understand how our cost-effective online marketing analytics software will help you bring value to your table.