What is Data Analytics?
To start, data analytics refers to qualitative and quantitative techniques and processes used to augment understanding of both internal or external data sources to convey information. It is useful in all facets of business, particularly marketing, operations, and business development.
What are some examples of Data Analytics?
Data analytics is a broad sweeping term, but essentially includes any usage of data that can convey important information. At Untitled, we appreciate the expression that most companies are drowning in data, but starving for information. Data analytics starts with taking all of that data and then extracting insights from it. To look at an example tool that is widely used, let’s refer to Google Analytics.
Google Analytics takes all of the raw, anonymous data that trafficks to your website and produces visualizations from a variety of fields that describe that data. Without the platform, it would be difficult to extrapolate useful information the raw, unstructured data that it takes in. The platform is a great example of a tool that helps produce value from the noise that surrounds large quantities of data.
At Untitled we use quite a few different tools to produce data analytics such as Tableau, Periscope Data, Power BI, Python and R data visualization libraries, and Adobe Clickstream among others. The objective is exactly the same as Google Analytics though even if the sources of data are different. We aggregate the information and use visuals to describe what that information is saying.
What if I am not collecting data?
Most companies are collecting data, even if they don’t know it. To start with the fundamentals, most businesses have a website. If this doesn’t apply to your business, we encourage you to visit our page, Web Design In Louisville, to learn more about how we can get you set up with a web property of your very own.
With that said, if you have a website and Google Analytics installed, you are already collecting valuable data that can be used for data-driven marketing campaigns. You most likely also have information on your customers stored in a Shopify database if you are an e-commerce business, or an alternative CRM for most other types of business. This data is extremely valuable when properly analyzed and visualized with data analytics.
If you want to enhance your data collection strategy, Untitled uses a variety of special pixels for this very reason. We also have worked to build pixels of our own enhanced by machine learning to produce information from raw data being collected by a website.
How can my marketing strategy be improved with data analytics?
Data analytics provides pertinent information regarding your customers and the key segments that drive revenue. With data analytics you can derive commonalities among your customers such as their geographical location, demographics and psychographics. Leveraging data analytics to identify trends within these respective categories should inform your marketing strategy and make you more efficient with your advertising spend.
If for example, you found that your average customer is between the ages of 45-55 years old, and you were directing all of your spend towards millennials with millennial based messaging, there would be a disconnect between what your marketing should be, and what it is. With a properly tuned marketing strategy, marketers will be allocating budget much more effectively and net higher performance results. This is all most effectively done through the use of data analytics.
How Can Data Analytics Improve My Operational Strategy?
In a similar fashion to improving marketing budgets, data analytics can assist with helping operational budgets. When we say operational budget, this can range from HR and performance by employee, to the overall efficiency of your supply chain such as improving inventory turnover, or reducing inventory holding cost.
What are the Differences between Descriptive and Predictive Data Analytics?
Descriptive analytics is defined as: a summary statistic that quantitatively describes or summarizes features of a collection of information. Examples of descriptive analytics in marketing would be: less than 10% of your customers buy more than once, this ad campaign resulted in a 7% uplift in revenue comparatively to the last month of sales, or creative set A performed better by a factor of 3 than creative set B in our A/B test campaign. These are analytics that describe data for what it is.
Predictive analytics are different. Instead of being used as a data summary, they are used to predict the outcome of data in the future, or in other words, it is a branch of advanced analytics which makes predictions about unknown future events. Typically, predictive analytics uses historical data, data models, specialized software, machine learning and artificial intelligence to make predictions about what will happen in the future based upon what information we have in the present.
For example, if we collect data on a sample of individuals observing their age and yearly income, we can plot a graph of an audience where the X axis is “Age” and the Y axis is “Yearly Income”. From there, a regression curve can be plotted across the data set. After the data is loaded into the model, upon encountering new data (in this case new individuals), by learning the age of the individuals we can predict with “relative certainty” their yearly income.
This example is an oversimplification of a complex variable (someone’s age relative to their income). However, by utilizing a predictive model that carves down the complexity of a data set, one can begin to use predictive analytics to derive valuable information from unstructured data.
How Can Machine Learning Help My Business?
Machine learning (a component of predictive analytics) will help your business if you have the proper infrastructure in place, with logical ground truth in your data. The industry likes to use a hierarchy of data science needs to identify if your business is ready for machine learning. As you can see, the first four tiers of the pyramid all convey the need for structured, accurate data and stable infrastructure prior to machine learning being a value add or even possible.
Machine learning algorithms are a black-box phenomenon for most people, but must be treated like any other system: garbage in, garbage out. With that in mind, Untitled starts by helping you work your way all the way to the top of the pyramid, regardless of your starting point. From there, exponential gains can be realized by leveraging machine learning as a core strategy within your business.
The Risk Of Not Using Data Analytics
The risk of not using data analytics is drastic for any organization. As more companies continue to leverage data analytics and become more data-centric, your organization will be left behind. The adage “disrupt or be disrupted”, or Andy Grove’s famous saying, “only the paranoid survive” certainly apply here.
Companies that use data analytics for every facet of their business gain a huge competitive advantage over counter parts that don’t. The era of using gut intuition still prevails, however, it should be assisted and perhaps more appropriately for large organizations, guided by data analytics. In war, the opponent that has more information is posed to win, and that’s exactly what data analytics is: information.
The return on investment for Data Analytics
The verdict on ROI for data analytics is out. See for yourself what global technology leaders are saying here.
With facts as straightforward as this, is it worth the risk to not utilize data analytics as a part of your organization’s strategy?
126% Increase In Profit
- Companies using analytics have 126% profit improvement over competitors – McKinsey
$13.01 to $1
- Investments in analytic solutions returned $13.01 for every dollar spent on average. – Nucleus Research
50% Higher Revenue
- Organizations actively using data have 50% higher revenue growth. – Dell Global Technology Index
5x Better Performance
- Top-performing organizations use analytics 5x more than bottom performers. – MIT Sloan Management Review
How To Become A Data-Centric Organization
Becoming a data-centric organization means a lot of things to different types of people. It can most easily be defined as a company that utilizes data in every facet of its business to be more efficient, lean, adaptive and deploys Intelligent Optimization strategies. The reality is that most organizations are not data-centric, but are moving towards it. That is, in part, why Untitled exists. We are a firm to help companies along their data journey to becoming a data-centric organization. To start, a company must undergo a digital transformation: collecting data and leveraging data analytics to derive information for better decision making.
Getting Started With Untitled
Data analytics can be an overwhelming topic to digest, but that is why you came to the experts right? The point to make is that if you made it to the end of this page, and realize from the facts provided within that data analytics should be an essential part of your business strategy moving forward, please contact us to discuss.
No matter where you are in your data journey, even if it hasn’t begun yet, Untitled can help you become a data-centric organization that uses data analytics in every part of your business.