Integrating Marketing Efforts with Data and Analytics
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Data analytics has seeped into just about every aspect of business, it seems, and digital marketing is no exception. Using data analytics can help organizations segment target customers and better direct marketing efforts, creating a more level playing field for businesses big and small. Integrating AI into a marketing campaign is as easy as feeding it big data. From there, an algorithm can form a proposed plan, paving the way for a campaign. In short, insight from customers can help form a tailor-made ad campaign.
Data-Driven Insight and Prediction
Data collection in the past regarding shopping habits was laborious and time-consuming. Users had to be tracked, questioned, actively take surveys, and manpower was needed to do all of this. Now, using cameras and mobile devices, tracking data is easier than ever to collect. By 2016, for example, mobile data consumption was shown to be growing year over year by 125 percent, providing an excellent source for data analytics. Scaled up, this data is what’s known as big data.
Big data, or a large collection of data meant for use in predictive analysis, has a vast number of uses, from developing self-driving cars to gimmicks like making a new script for old TV shows.
For marketing purposes, it can help develop new products by looking at what a niche market is using. By analyzing data, an AI can determine the most effective way to market to a specific audience.
The AI, it should be noted, only analyses data and offers action plans. It does not make any decision itself, nor implement any plan or campaign without being told to. It forms conclusions on a massive set of data, and nothing more. Taking the output and making it into an actual marketing campaign still takes a human.
Big Data and Customer Behavior
Data can be collected online, such as customer behavior analysis from a company’s website. Analyzing the data can reveal which products that capture the customer’s attention, what they are saying about certain products on social media, page or site bounce rate, and customer demographics.
Walmart, for example, used data collected from customers’ behavior on its website to better understand their intent. Using this data, Walmart was able to improve the site’s predictive searching function. This resulted in a 10-15 percent increase in completed purchases in 2012. It improved customer experience, something that applies to both B2B and B2C.
It could help a supplier offer a retailer products tailored toward their consumer base, using data to predict what that consumer base wants, meaning more sales for the retailer, and more demand from the retailer to the supplier. AI, predictive analysis, and parsing big data has only improved since Walmart improved its site.
First Tennessee Bank used a different approach to using big data on customers. The bank used predictive analysis to develop hyper-personalized advertising. By sending specific offers to specific customers who, according to the data, would be more likely to make use of the offer presented to them, the back saw a more than 600 percent ROI on their big data venture.
What are your clients and customers reading about in your industry? What captures their attention and will drive them to your content instead of a competitor’s blog? Audience content research, using big data collected from customer behavior in regards to content, can help solidify your brand voice. Analyzing what your customers interact with can help develop a content strategy for your own blog.
For example, if the AI notices recurring topics, it might suggest putting your own spin on the topic. Or, it might identify an author popular with your client base, who could then be brought on as a guest author, drawing traffic to your site.
Keeping the Data Secure
The one drawback to using big data is keeping it secure. While it is best to use anonymous data, there may still be data with personal information attached that your company keeps to feed to algorithms. With many high-profile data breaches in recent years, it’s especially important to keep the information secure — there is now even data breach insurance. With big data comes big responsibility to your clients and customers.
Big data can help a marketing department get a leg up on their competition. It can analyze massive amounts of data, produce conclusions that will help kick off a campaign, and help you better understand who your clients are and what they really want. While it can be daunting collecting the information or figuring out what to do with it, the effort is worth it to gain customer insight.