The term Big Data has been around since John Mashey started popularizing it, back in the ‘90s. But, it was only in the early 2000’s that Doug Laney gave the concept a tangible definition, characterized by the three V’s.
- Volume: which characterizes the amount of data that is available to a business organization; collected from multiple sources including sales, social media channels, surveys, internet and mobile traffic, etc..
- Velocity: which refers to the rate at which the data is generated and processed to enable businesses to grow and develop.
- Variety: which characterizes the varied formats in which the data is available to businesses – both structured and unstructured.
To put this all into a single perspective: big data is essentially the large volume of data that is available to businesses on a day-to-day basis.
The Trouble with Businesses and Big Data
The concept of Big Data isn’t merely about the volume of data: it’s about what businesses choose to do with it. It’s about using advanced tools to analyze and process the data that has been amassed, and glean reliable conclusions and insights from the analysis to inform better decisions for the growth and development of a business.
However, owing to the sheer amount of data and the analytical tools needed to handle this data, most businesses feel overwhelmed, under-informed and unsure about how, and why, each and every bit of data matters; as such, they choose to limit their analysis and interpretation to a narrow scope that presents only a fraction of the overall picture of their market potential and performance.
In order to properly justify their marketing spend and to ensure that they reach their target audience at the right time, businesses need a more structured approach towards analyzing and calculating their Marketing ROI (MROI). To do that, businesses not only need to conflate their end-of-quarter/year results like strategic returns, economic value and payback period with preconditions like baseline spend and thresholds of various channels, they also need to understand and factor in their audience’s buying behavior. Enter iNBOUND iNTELLIGENCE and math marketing.
The basis of the concept lies in the fact that there is a science to the way marketing works. This is not to say that intuition and creativity don’t factor in, but that marketing as a whole requires a combination of precision and intuition; and math marketing contributes to the precision-half of the equation.
As the name implies, it centers around deriving precise mathematical formulae to exploit big data to optimize a business’s MROI; to better understand how their marketing efforts are actually impacting their bottom line and using that knowledge to further optimize and improve those efforts.
A major advantage of applying math to marketing is that it allows marketers to “score” their incoming leads based on actual customer behavior and engagement. Marketers can then use this score to track and predict behavioral patterns of their incoming leads. These patterns and insights, in turn, can then translate into better ideas that drive growth, increase traffic, generate more leads and finally, increase sales and revenue.
How Does Math Marketing Help?
At this stage, math intelligence allows marketers to improve their customer acquisition strategies by providing insights such as:
- overall cost of customer acquisition (COCA) to gain a clear picture of how their investment dollars are distributed
- behavioral patterns of each of their incoming leads and the likelihood of their conversion
- behavioral patterns of converted customers such as their product history, product usage and social media interactions
- track signals of intent of purchase for those outside a business’s buyer personas, such as gift buyers
Determining Patterns In Audience’s Behavior
Manually dividing an audience into meaningful segments based on a few select demographics can be tricky, especially if the data on some of the customers are incomplete. Ideally, marketers need to be able to use all available data to have a broader idea of their audience’s behavior.
You can solve this with the use analytical tools like Google Analytics, HubSpot Marketing and Sales Reports and Kissmetrics to discern patterns and trends in the data that’s been acquired, along with powerful data-chokefull artifical intelligence tools. With these tools, marketers can set various parameters and conditions to identify similarities and differences in various data sets to derive patterns and trends that may not have been otherwise obvious.
Statistics and Probabilities derived from Artificial Intelligence
One of the main principles here is the ability to derive various statistical probabilities in audience behavior and marketing outcomes.
- Frequency Distribution: At the risk of sounding cliché, sometimes it’s possible for marketers to miss the forest for the trees: focusing on specific details instead of looking at the overall picture. Frequency distribution allows marketers to identify how often a specific event occurred over a given period of time, and predict the probability of that trend in the future.
- Descriptive Statistics: By looking at the overall range and the central tendency of given set of data, marketers can identify the average value, the amount of difference between values and the amount of deviation of each data point from the norm.
- Correlation: Using correlation, marketers can identify relationships between two actions or trends. For instance, the correlation between the download of an e-book and the probability of a customer converting after they downloaded that e-book.
- Cross-Tabulation: This is another comparative method which allows marketers to establish relationships between two data sets. For instance, the relationship between various age demographics and the kind of content they consumed.
- Text Analysis: Using tools like Wordle or TagCrowd, marketers can turn text-only answers in surveys into quantifiable data. These tools generate word-clouds based on frequency of word-occurrences – with the frequencies indicated by the size of the word in the cloud.
Marketers can generate predictive marketing models from data; thereby, allowing them to optimize and generate better business outcomes. For instance, using historical outcomes on previously released content, marketers can predict how similarly produced new content will perform in terms of audience engagement and consumption.
Predictions can also help determine the ROI on marketing efforts based on the gross profits or revenue generated during a campaign. Likewise, marketers can also determine the Customer Lifetime Value (CLV, sometimes also abbreviated as LTV), which determines the profits generated by a customer over their lifetime with the business.
In addition to helping businesses proactively tweak their marketing campaigns for better outcomes, predictive analysis can also help businesses report and resolve any issues that can derail the campaign. No matter the issue – whether it’s a dip in conversions or a decrease in web traffic – math marketing can contribute at every step of the resolution by identifying:
- What happened,
- Why it happened,
- What is happening now,
- What might happen,
- What will happen.
Balancing the Math and Magic of Marketing
As we’ve said before, marketing is a mixture of precision and personalization. While the precision of mathematics can help generate statistical and predictive models of customer behavior, it cannot translate the fickleness that comes with human emotions and interactions.
And, for a campaign to truly reap dividends, marketers need to work in a personal component that is difficult to quantify with analytical software. A part of this can be resolved by interacting with customers and listening in real-time to what they’re saying about the business. However, there is still the need for a creative and intuitive genius to create marketing content that will allow customers to remember and identify with a brand or product. When done right, that ability to resonate with customers’ needs and emotions will translate into increased sales and revenue.