The role of marketing is often seen as a creative one within companies; creating campaigns that share the brand with the world and delight customers.
Although it may not seem like it, marketing analytics is a very important part of marketing.
What marketing analytics is
The marketing analytics discipline works with data collection and analysis in order to understand patterns, evaluate strategies, and make better decisions.
This text is discussing how data can be used to understand a company’s success, if they are going in the right direction, and what trends may be coming up in the future.
In other words, it reports the past, analyzes the present, and guides the future.
Marketing analytics relates to the concept of big data, the explosion of data in the digital world. Companies have access to a lot of data that can tell them how they’re doing, what the market is like, what consumers want, and what their competitors are up to.
Almost anything can be monitored and stored today. On their own, these data points are not helpful to the company if they don’t know how to make use of them.
In other words, marketing analytics involves using data to make decisions about marketing strategy, rather than relying on intuition or gut feeling. This data-driven approach can help a company to track its progress and to optimize its marketing efforts.
Marketing analytics is a tool that helps businesses make better decisions by turning data into intelligence.
While marketing analytics does include web and digital analytics, it is not the same thing.
Although data analytics is used in a variety of ways, all methods have the same goal of using data to improve a company’s performance and future success.
Web analytics is the process of measuring, analyzing and reporting internet data for purposes of understanding and optimizing web usage.
Digital analytics refers to data collected from various online channels, such as websites, web pages, mobile apps, social media, and so on.
This means that marketing analytics includes data from all marketing channels, both offline and online.
How marketing analytics works
Marketing analytics is not just about data analysis. There are steps that need to be completed before tracking and collecting data so that the analysis will go as planned.
The three fundamental steps of marketing analytics are identifying the right data, analyzing that data, and using the insights to improve marketing campaigns.
Track data
Data tracking is the first step in marketing analytics. There is a lot of data available.
The data that you track for marketing analysis should be generated by your marketing channels.
Digital Marketing efforts typically focus on websites, social networks, apps, and paid media.
This means that you should keep track of data on how consumers are interacting with your brand on these channels in order to understand what is and is not working with your current strategies.
To track data, you need to use tracking parameters in URLs and install code on your website. We will look at this more in-depth below.
Collect data
If you track your marketing, you can see how well it is working.
The data you collect should be related to the KPIs you have for your strategy. Your KPIs are the metrics that help you measure your progress towards specific objectives. The instructions given by the manufacturer specify what data should be collected to carry out the analysis.
To help identify these indicators, here are the main metrics you can track:
- website or blog metrics: number of sessions, number of visitors, average session duration, pages per session, traffic sources, bounce rate, conversion rate;
- SEO metrics: organic traffic, SERP position, organic conversion rate, domain authority, page authority;
- paid media metrics: click-through rate (CTR), paid conversion rate, cost-per-click (CPC), cost-per-thousand-impressions (CPM), cost-per-lead (CPL), cost-per-acquisition (CPA);
- social media metrics: reach, engagement, social media traffic, social media conversion rate;
- email marketing metrics: delivery rate, open rate, clickthrough rate (CTR), conversion rate, unsubscribe;
- business metrics: return on investment (ROI), customer acquisition cost (CAC), monthly recurring revenue (MRR), cost per acquisition (CPA), retention rate.
View data
one of the responsibilites of data visualization in marketing analytics is to provide a visual representation of the data.
This can be done by looking at graphs, tables, maps, and other data in performance panels or dashboards.
Plotting data points on a graph or chart allows you to see patterns and trends that would be difficult to spot otherwise. This can help you to find insights in your data. If you only had the raw data, this would be practically impossible.
The benefits of marketing analytics
In today’s complex and competitive marketplace, marketing analytics can be the key to success.
Below, you will see the benefits of marketing analytics that can put your company ahead of the competition!
Measure strategy performance
Marketing analytics can help you understand your marketing performance and identify areas that can be improved.
We can optimize our strategies to achieve better results and our objectives.
Quantify the ROI of strategies
ROI can be a helpful metric to measure in Digital Marketing.
You can evaluate how well your campaigns and strategies are doing by looking at the company’s cash flow, and focus on ways to improve your financial return.
Stay focused on goals
The goal when evaluating strategy performance is to verify if progress is being made towards the company’s objectives.
marketing analytics allows businesses to keep track of their micro and macro objectives.
Understand consumer behavior
Marketing analytics can help you understand your customers better. You can compile data from various sources to get a clearer picture of your audience’s profile. This data can include how people interact with your website and how they respond to your marketing campaigns.
This allows you to create a more realistic buyer persona and develop more personalized strategies.
Monitor competitors
Marketing analytics doesn’t just look inwards. You can also collect data about how well your competitors are doing and what strategies they are using that are working. This way you can be prepared to face them.
Defending strategies
Delivering results is important for anyone who works in marketing. It can be for the company’s CEO, the department manager, the team leader, or coworkers.
You need to be able to analyze data and build efficient reports in order to defend the strategies you have adopted.
Support decision-making
Just like any other analytics discipline, marketing analytics looks at past data to inform future decisions. However, marketing analytics also looks at real-time data to make decisions in the present.
Data analysis is essential in order to make better decisions, create better strategies, and achieve the company’s goals.
Increase predictability
While data analysis can give you a better understanding of trends and patterns, it cannot predict the future. This can be useful in identifying patterns and trends that may predict what will happen in the future.
Predictive analysis helps the company plan for future risks and opportunities.
Let’s talk about this…
What is Predictive Marketing Analytics?
Predictive marketing analytics uses data to make predictions about user behavior, future events, and results. Predictive analytics uses past data to make predictions about customers and marketing results. It is a combination of statistics, predictive modeling, artificial intelligence (AI), and machine learning. You can make accurate predictions about the future by studying patterns in data from the past.
The three main types of business analytics are:
- Descriptive analytics: To predict future events, you can start by looking at descriptive analytics – historical data and performance – to determine what has already occurred.
- Predictive analytics: Next, look at predictive analytics to determine what is likely to happen in the future. This involves looking at past data and using algorithms to predict future events.
- Prescriptive analytics: Finally, you can decide what to do next based on what you’ve already done or what’s already occurred. Determine the best course of action by considering what is most likely to happen.
10 Practical Ways to Use Predictive Analytics in Marketing
Here are ten specific ways to use predictive analytics to enhance your marketing efforts to help grow your business moving forward.
Targeting and Segmenting Your Audience
You can create new campaigns that are tailored to your audience’s stage in the buyer’s journey by using behavioral and demographic information. You can effectively move prospects down the sales funnel and further engage current customers by creating specific, targeted campaigns.
There are three primary ways to use predictive analytics to target and segment your audience:
- Affinity analysis: This method involves segmenting customers based on attributes they share.
- Response modeling: By looking at how customers responded to certain stimuli, you can predict how likely it will be for future customers to react similarly.
- Churn analysis: Also called attrition rate, churn analysis will show you what percentage of customers you lost during a specific period. You can also determine how much potential revenue or opportunity you lost because of losing those customers.
Distributing Targeted Content
It is beneficial to know what kind of content interests your audience and which platforms they use the most so that you can make better choices for your content marketing strategy. You can improve your chances of converting leads into customers by tailoring your content creation and distribution strategies to provide more personalized experiences.
Predicting Customer Behavior
Campaign data from the past can be combined with demographic information about customers to help predict future customer behavior. Customer ratings can help you to gauge how likely they are to make a purchase or take a particular action, so you can plan your marketing efforts accordingly.
Predictive Lead Scoring
If you don’t have the right process in place, you might waste a lot of time and energy trying to reach people who are not interested in what you have to offer. Lead scoring can help you prioritize and qualify leads based on factors such as their interest, urgency, and authority to purchase. This can help you avoid issues down the line.
Lead scoring is a way of assigning values (points) to individuals based on where they are in their buyer’s journey (or sales funnel). A higher score for a lead means they are more qualified. A lead score is generated using data that can include information the lead submits to you, their actions, and how they engage with your brand on different channels.
Lead scores allow you to prioritize which leads are the most promising and likely to turn into customers. This helps your marketing and sales teams focus their attention on the right leads, rather than wasting time on those that are unlikely to convert. Your team can more effectively lead potential customers by predicting their future buying habits and meeting them where they are.
Predicting Customer Lifetime Value
CLV can be predicted using the same methods used to target and segment an audience. Analyzing historical data can help you determine which customers are the most profitable, what marketing activities generate the highest ROI, and which segments of your audience are the most loyal.
Acquiring New Customers
After dividing your audience into segments, you can use customer data to create identification models. The goal is to find potential customers that are similar to your current customer base and target them accordingly. This will turn them into leads and customers.
Determining Better Product or Service Fit
You can better understand what your current customers want from you by using a combination of customer behavior data, lead information, and historical purchase data. By looking at what a customer has bought in the past, you can infer what they might want or need in the future. Finding ways to improve your products and services by considering the wants and needs of your customers is a good way to improve your business.
Upselling and Cross-Selling to Current Customers
Recommended next steps for increasing profits could include using data you’ve collected on customers’ purchasing behaviors to cross-sell or upsell to them. If you want to better market to your current customers, look for patterns in their behavior.
For example, if you own a marketing firm that specializes in content marketing software as well as a related social media tool, you would benefit greatly. After six to twelve months, 40% of customers who subscribe to your content marketing program also subscribe to your social media tool. Your marketing campaign should target content marketing customers who have been with you for six months, in order to increase your upsell rate to 60%.
Reducing Your Customer Churn Rate
Churn rate is the rate at which customers stop using your product or service. It’s commonly expressed as a percentage of subscribers. The percentage of clients a marketing firm loses within a specific timeframe could be defined as churn.
The goal is to have a growth rate that is higher than the churn rate. Predictive analytics allows you to identify potential problems before they result in a customer leaving. You can determine the areas in which your business is doing poorly by identifying trends. If you can spot potential problems before they happen, you can stop them from becoming bigger issues and losing customers.
Optimizing Future Marketing Campaigns
The better you can plan and implement your marketing campaigns, the more information you have. Better targeting and messaging can help you create stronger and more genuine campaigns that connect with potential customers and clients. This should ultimately lead to more successful outcomes.
Predictive analytics can reduce risks and improve ROI. Although these tactics may not guarantee success, they can still be beneficial by providing insight for future practices and decisions.
