Category: Big Data

What is Marketing Analytics?

Analytics, at its core, is the study of numbers. Numbers provide valuable data and insight into any given marketing strategy by making performance quantifiable. With marketing analytics specifically, the numbers help to track, identify, and understand user behavior. 

Understanding your audience is critical to making sound marketing decisions that deliver the best ROI and drive positive growth. 

In a simplistic breakdown, marketing analytics are intended to perform in two ways: 

1. Measure the effectiveness of marketing strategies and campaigns. 

2. Identify opportunities for improvement that will yield greater results. 

Why Do Marketing Analytics Matter?

Without marketing analytics, a business is operating blind. The marketing campaigns are merely pushed out to the world with little to no understanding of how strategies are landing with your audience. 

 Marketing analytics matter because: 

• It makes the actions quantifiable. Whenever numbers are used, it provides concrete data for the marketing program. For example, it’s easy to notice that overall sales increased after a personalized content marketing campaign. Though, a more effective approach would be tracking the specific percentage (25% e.g.) of traffic that came from a blog published at a specific time (November, e.g.) and converted a specific number of leads (5% e.g.) for a specific product (holiday gift, e.g.).
• It helps plan for future marketing. When you understand which tactics are working, you can strategically plan for future marketing. Not only does this help with planning marketing activities, but also overall budget allocation. 
• It identifies the “why” of what did or didn’t work. After a marketing campaign or strategy has launched, the only way to adequately understand performance is with marketing analytics. The data can be drilled down to track individual messaging across a broad spectrum of outlets, ensuring no approach is wasted. For example, maybe your click-through rates from social media to your website are fantastic, but not converting to sales. With this information, you can focus your energy on shifting the homepage to reduce bounce backs.

How Can Marketing Analytics Drive Growth?

Ultimately, the information provided by marketing analytics is meant to drive growth and provide positive ROI. There are a few key ways that marketing analytics can help drive growth: 

Identify target audiences. One of the most brilliant things that marketing analytics can do for businesses is to segment audiences. The analytics can help identify and group users by:

• Age
• Gender
• Geographic Location
• Income Level

Even further, marketing analytics can identify subgroups or intersections in data sets. For example, a segment could be women, aged 30-45, in Tulsa, OK.

When grouping users together, data can be extracted surrounding how to best target those groups. Marketing analytics can also reveal new groups that are worth targeting. For example, perhaps the target audience is believed to be women aged 30-45 years old, but you find that certain marketing tactics are delivering positive results in teenage boys aged 16-20 years old. Having that information is powerful when driving growth. 

Predict future user trends. Predictive analytics compile past trends and historical data to help determine how users will behave in the future. This can help plan marketing strategies to align with certain seasonal behaviors. For example, sales of certain products are higher during the summer. By targeting audiences in the discovery phase in spring yields the most beneficial results. 

Eliminate what doesn’t work. One of the best ways to reap rewards with marketing is to eliminate the tactics and strategies that aren’t working. The less time and money spent on fruitless endeavors, the more growth can happen. 

Which Marketing Analytics Deliver the Best ROI?

The answer to this question will be different for each business. However, some general items will ensure marketing analytics deliver the best ROI. 

1. High-Quality Data

The power is in the numbers. Data that is quality both in scope and extraction is so important to delivering the best marketing ROI. The best quality data will be: 

• Current
• Consistent 
• Precise 
• Accurate
• Relevant 

2. Combine Past, Present, and Future Data

To achieve a comprehensive overview of marketing analytics, all data should be considered. The past provides insight into user behavior and trends, while the present focuses on relevant current climates. The future is a prediction based on past and present trends, therefore eyes must also be turned toward the future to properly steer marketing strategies.

What Are the Top 5 Growing Data and Analytics Trends for 2021?

Today, data and analytics are constantly evolving the way we do business. Companies are becoming heavily reliant on historical big data to create a foundation for decisions, from driving cost efficiency to determining new strategic directions. These decisions are more important than ever, especially when businesses are battling to stay competitive amongst the digital frontier.

To keep the competitive edge in the turmoil of a global economic crisis, more companies are taking the lead to be proactive in data analytics. The traditional AI techniques of being driven by big data are rapidly transitioning to the class of analytics based upon smaller yet more varied data techniques. Automation technology is the solution to this rapidly growing, complex problem.

Here are five of the biggest analytics trends to look out for in 2021.

1. Why Analytic Automation with Advanced and Scalable AI

With the demise of “big data” and the pivot to “small data” analytical techniques, AI systems will be required to do more with less. Advanced AI has the unique ability to analyze complex data sets and quickly detect trends and patterns that would be challenging or easily overlooked by the human eye. Analytics automation provides the opportunity for corporate analysts to focus more on high-value targets to drive top-line growth. Intelligent automation is the “engine” for today’s complex and challenging data-driven decision-making.

2. How Xops Delivers Automated Analytics for the Enterprise 

Xops has an objective to increase efficiencies with an economy of scale by deploying DevOps best practices. The end goal is to reduce cross duplication of technology automation processes while maintaining high levels of reliability, accuracy, and reusability. The key components of Xops (data, machine learning, model, platform) scale with DevOps best practices to maximize the value of analytical information. Xops promise to automate and accelerate the collection, collation, and identification of data elements, ultimately helping organizations keep their competitive edge.

3. What Dashboard Automation Promises for the Organization

The rapid movement to deploy data automation solutions that deliver insightful user-created data has tremendous implications. Analytical data traditionally would have to be delivered by IT or a data expert within the organization. The promise of analytical data generated on-demand by anyone ranging from marketing, human resources, or even finance will shift organizational agility, delivering insights faster and more effectively to the company as a whole. The impact on an organization from decentralized to on-demand data delivery can be dramatic.

4. Why Cloud Services Are Rapidly Growing for Analytical Data

With the advent of increasingly complex and larger data sets along with their intrinsic values, cloud services are rapidly becoming the repository of choice. Data is stored outside an organization on remote, secure servers. Extremely valuable information is better protected and in case of a disaster, data can be recovered much more efficiently. Scalability for the enterprise is more fluid with the cloud hosting services.

5. Why Data Analytics Has Become a Core Business

Data analytics has transitioned from being a secondary support function to mission-critical for an organization. The buy-in for data analytics has widespread support from the board room and the C-suite for its tremendous potential. The positioning of data analytics as a core business function, though, does not come without cost. Often, businesses may underestimate the complexity of data collection and analytics, missing valuable opportunities. Many times, partnering with AI-powered tools such as Inzata will immediately shorten the ramp-up time to deliver valuable data assets.

Conclusion

These data and analytics trends are being driven by analytics automation platforms. Technology is rapidly advancing, and organizations embracing it first will enjoy a competitive edge. Previously, a few gatekeepers generated the data analytics and insights based upon specific data requests from corporate analysts. With the trend for insight generation being pushed down to the individual, data analytics becomes available to all. The ability to create insights on-demand by the end-user will inevitably lead to a leap in corporate strategic planning, decision-making, and the ability to maintain a competitive edge.

Information is power, or so the saying goes. Yet, that power rests in large part on how recently that information was collected. Let’s say you want to buy a house, you take the tour and like what you see. Before you make your final decision, though, you want to see a home inspection report. Do you want to base your decision on a home inspection report from six months ago or the one the home inspector will finish an hour from now? How about an inspection report from one month ago? One week? 

Information is constantly changing, anything could have changed within the past few weeks alone. To make effective and accurate decisions, you need to be working with the most current information. That desire rests at the heart of real-time analytics and the competitive advantage it offers.

What is Real-Time Analytics?

Businesses are inundated with data from countless sources. They get data directly from customers, aggregate data from social media, traffic data from websites, and even from marketing tool suites. Real-time analytics takes all of that data, processes it, and provides up-to-the-moment results. 

The kind of results depends on the analytics software you use and the settings involved. For example, some businesses prefer that the software provide answers only when queried on something specific. Others prefer that the software offer real-time alerts or trigger real-time actions for certain pre-set results.

Why Does it Give You a Competitive Advantage?

No business can know with certainty what data its competitors possess. You also can’t know with certainty how soon they’ll analyze that data and put it to work. Leveraging that real-time information lets you adjust tactics, change orders, or even create promotions in response to new trends before your competitors do. 

That lets you collect on the immediate benefits of reduced waste, better marketing, and an uptick in revenue. It also helps solidify your business as being on top of what is happening in the world. Customers like businesses that either predict or seem in close tune with the market. Every time you seem on top of your game, you cement your business as the one people should turn to first. 

What is the Cost of Working with Outdated Data?

So, what are the pitfalls of working without outdated data? For some functions, such as forecasting, data that is a little out of date probably won’t change the results by a significant amount. 

For any business that must respond quickly to rapidly changing trends, the cost can prove to be high. Let’s say a trend highlighting the value in some otherwise obscure IT function is rapidly developing in a specific industry. If you run a managed IT service company, recognizing the trend quickly lets you update your service offerings, set pricing, and shift your targeting strategy. If you don’t catch that trend quickly, you can lose out on potential revenue from new customers and expanded services for old customers. 

Use Cases

One of the most obvious use cases for real-time analytics is monitoring your IT infrastructure. Immediately you are able to gain visibility into the capacity, availability, and performance of your infrastructure investments. This lets you respond to any issues as soon as they arise. Getting the information tomorrow won’t be any help to your service levels. 

Another common use case is for digital marketing efforts. Let’s say that you offer a rewards program for customers. When they are shopping online or in an app, it’s a golden opportunity for some personalized marketing based on their previous purchase history. Real-time analytics can alert you or your automated marketing system that the customer is browsing the store. That lets you deliver customized discounts, coupons, or personalized promotions when someone is most likely to buy.

Real-time analytics is a powerful tool for carving out a competitive advantage. It helps keep your company at the forefront of changing trends. It also helps your business adapt faster when the unexpected happens. In turn, you reap short-term as well as long-term benefits in terms of cost-savings, revenue boosts, and customer conversions.

Half-Life of Data Mean?

The term “half-life” was originally coined by scientists studying the amount of time it takes for at least 50% of a substance to undergo an extreme change. When studying analytics and data science, the term often comes up. 

While the half-life of data isn’t as exact a measure as the half-life of substances, the implications are similar. In this case, the half-life of data is referring to the amount of time it takes for the majority of it to become irrelevant. This is an exponential curve downwards, meaning that data is at its peak value when first collected, then accelerates in loss of value over time.

In a recent study, researchers highlighted the issue that administrators often underestimate or misunderstand the half-life of their data and the implications it carries. 

What Are the Three Business Categories of Data?

Nucleus Research found in their study that businesses driving decisions with data fall into one of three categories: tactical, operational, or strategic. The half-life of data varies by the business data category.

These categories were self-identified, and no real-world business is only one of these categories. Companies in the study were asked to select a category based on four factors: their suppliers, their markets, how regulated they are, and how much they depend on intellectual property.

Tactical

According to the study, the tactical category contains companies who utilize data to influence their processes in almost real-time. Because data received is extremely valuable when first received, then rapidly declines in value to the company, this category has the steepest downward curve of data half-life.

This category emphasizes how important it is for companies to have technology that allows them to act as quickly as possible on actionable data. The study found that, on average, the half-life of data in this category is a mere 30 minutes. That means data is losing a majority of its value in the first 30 minutes after collection!

Operational

The study indicates that companies using data for operational purposes generally require it to make decisions that could be anywhere from a day to a week. This is a mid-level category with a half-life curve that goes down exponentially, but far more slowly than data of companies in the “tactical” category.

Nucleus Research found that data in this category had an average half-life of 8 hours but ranged widely among companies, from one hour to 48 hours.

Strategic

Companies falling into this category use data for long-term processes and plans. Strategic data’s value is the most distributed, losing value very slowly over time. The half-life of their data is a small-slope linear graph. Strategic data’s average half-life is 56 hours and widely variable.

What Are 3 Ways to Speed Up Conversion from Raw Data into Actionable Insights?

Here are three ways to divert data from silos and process it into valuable and actionable insights for your business.

Ask Good Questions – In order for raw data to be valuable, it must have a defined purpose. Meeting with all stakeholders and determining what specific question you’d like answered, then identifying data that must be collected to answer it instantly increases the value of what you’re already collecting.

Use Segmentation – If possible, differentiate among types of clients or users as much as possible. This will create more individualized and accurate insights.

Create Context – Data silos happen when large, ambiguous groups of data are collected. Ensure that everyone understands what each piece of data actually means to instantly add value to logged data.