While big data and marketing might seem like two completely different entities, they are more connected than you may realize.
Data is more readily available than ever before. Businesses are collecting more data than they know what to do with but with all this information comes the need for knowing how to use it properly.
Business owners rely on marketing techniques to grow their business. With the right programs, they can use all this information to make data-driven decisions in relation to their marketing plans to improve the customer experience and increase conversions.
This article will demonstrate how big data is redefining marketing and a few examples of how businesses can benefit from its application.
Types Of Big Data
An article by Analytics Magazine explains that there are three types of big data that are important for marketing purposes:
1. Customer Data
Customer data is the category that businesses are most familiar with. This data includes how customers behave on their website or with their content and data recorded in relation to transactions and purchases.
It also includes tracking sources of traffic, such as through social media, referring links and points of sale.
2. Operational Data
Operational data refers to metrics that measure how a business is meeting its goals. This includes budgets, resource management and allocating necessary resources to the right people.
3. Financial Data
Financial data refers to the profit and loss information, including the business’ revenue, number of sales, cost of sales, expenses. It covers all objective data types that measure financial records.
These data types help with data-driven marketing, which is as follows…
Big data is behind the latest marketing technique, known as ‘data-driven marketing’. According to ComboApp, data-driven marketing is defined as follows:
“The methodology of extracting actionable insights tied to consumer behavior from large data sets in order to predict consumer behavior in relation to new products, marketing positioning and users’ likelihood of interacting with a brand”
The reason behind it is simple: marketers are facing high expectations from decision makers.
They are expecting results quickly and as such, these data-backed metrics can help marketing teams to increase efficiency from their efforts.
Big data has lead to some tactics used in a variety of marketing campaigns that are now considered standard.
For example, the use of first names in emails is an example of data-driven marketing. The personalization of the email connects with the recipient with the content, making them more likely to take action, such as reading a blog post or buying a product.
With all this being said, while marketers are collecting this data, they need the right software to analyze it. After all, the data is only useful if they know how to use it.
Big data software gives businesses vitals insights to help them make key decisions. These are known as analytics tools.
This article by Digital Authority shows that nearly 85% of businesses say they use social media analytics, while 81% use email marketing software and 77% use analytics tools such as Google Analytics.
Forbes Insights shows how effective big data and analytics can be:
“Market leaders in ten industries Forbes Insights tracked in a recent survey are gaining greater customer engagement and customer loyalty through the use of advanced analytics and Big Data.
The study found that across ten industries, department-specific analytics and Big Data expertise were sufficient to get strategies off the ground and successful; enterprise-wide expertise and massive culture change was accomplished after pilot programs delivered positive results.”
So, now that data-driven marketing has been discussed, here are four examples of it in use.
Optimized Price Points
Research by McKinsey found that 75% of a business’ revenue comes from standard products. Furthermore, 30% of all pricing decisions made fail to deliver the best price for those products.
However, with the use of big data, the study found that by simply increasing the price of standard products by just 1%, those same businesses could increase their operating profits by more than 8.5%.
Predicting Purchasing Patterns
Along with big data, predictive analytics tools can predict future outcomes, including purchasing patterns.
An early example of this came from Target when, back in 2012, they correctly predicted a teenager was pregnant by simply analyzing her buying habits - before she knew she was pregnant herself!
Better Customer Engagement
Big data improves the way customers interact with a business. A study by Forrester shows that 44% of B2C marketers use big data and analytics techniques to improve customer response, while 36% of the same marketers are using analytics and data mining for deeper insights.
This will enable them to develop customer-focused marketing strategies and improve the relationship between the consumer and the business.
The better the relationship, the more likely they become loyal to the brand and convert on future product or service releases.
Spencer Stuart found that 58% of Chief Marketing Officers (CMOs) say that along with marketing campaigns, search engine optimization (SEO) and big data are areas of the business that are achieving the most success (check out this in-depth SEO guide here).
The same study shows that 54% of these CMOs believe big data and analytics play a large role in their future marketing plans and strategies for years to come.
Using The Right Data
This is just as important as using big data altogether.
Heard of the phrase ‘garbage in, garbage out?’ In the case of data-driven marketing, it couldn’t apply more.
While there is a case to be made that all data is good data, not all data is the right data.
Marketers need to be sure that the data they are building their strategies on will help achieve the business goals.
Big data is redefining marketing. Businesses have the ability to collect vital information about how customers interact with their brand to improve user experience and grow the bottom line.
It’s vital that marketers know what they are using the data for. More importantly, they need to be using the right data to deliver the results those above are expecting.Updated Date: 05 June 2019, 05:42