The Superbowl. It is the culmination of a football season and the most watched football game each year. But for football fans and non-fans, part of this event’s draw are the commercials. Every year, the “big boys” – Coca-Cola, Anheuser-Busch, Frito-Lay, and more – put their marketing and advertising teams to work to come up with amazing commercials. For days afterward, these commercials are analyzed and voted upon by a loyal following.
For average businesses, however, this type of marketing is an impossible dream. They have to use more conventional methods, most of which involve developing customer personas, trying to develop relationships with customers, providing responsive customer service, and creating online advertising and content marketing which is, actually, pretty budget-friendly.
Hacking Through a Noisy Environment
The problem with internet marketing, or marketing of any kind for that matter, is that the environment is noisy. Marketers are throwing a lot of “stuff” out there and hoping that some of it will stick with some of their target audience members. And consumers are growing tired of the growing disruptions of marketing of products and services that don’t meet their needs.
Many marketers have also come to use data analytics to keep track of the success of specific campaigns they run – tracking consumer response and the number of conversions that can be attributed to each strategy. And this can drive marketing decisions that they make for future campaigns. It’s a good use of “science” to make marketing strategies more effective.
Before and After-the-Fact Analytics
There is data and then there is big data. Conventional analytics is “after-the-fact” analysis. But what if bigger amounts of data could be gathered and gathered before marketing campaigns were ever crafted in the first place? This is the promise of this new big data science. Here is an example:
A bank has had a portfolio of loan options for consumers – auto, home, personal, business, etc. – for a number of years. Now it must compete with a host of new loan products from a host of new lenders, especially online lenders, and business is falling off. How does it determine how to alter it current products, perhaps design new ones, and market those effectively, in order to gain a good market share? Big data analytics can do this.
With the right software and tools, and the right questions, data can be gathered from all over – from the competition, from social media, from consumer review sites, etc. – and all of that data can be organized and synthesized to provide answers that will drive the loan products the bank will offer, to whom, and even how and where they will best be marketed.
The idea of big data analytics is that it is supremely scientific. It deals only with factual information and is based upon actual consumer behaviors that are gathered from multiple sources and then presented in reports that businesses can use to predict how consumers will act in the future. Armed with this knowledge, business owners, managers, and marketers can make smart decisions about what they offer, when it is offered, to whom, and the best places for their campaigns.
6 Ways in Which Big Data Analytics Can Boost Marketing
Consumers looking for products or services often conduct generic searches. And, according to Matt Kirkman, Director of Grapefruit Digital SEO Agency,
“...consumers rarely go beyond the first page of a search result, in fact, beyond the top five results. What big data analysis can bring to the table is not just the most commonly-used search terms, although that is a critical factor, but also the demographics using those terms, where they hang out online, and the most common needs they identify related to products or services. All of these can be used to craft content that search engines will find valuable.”
More Precise Definition of the Ideal Customer
Marketers spend a lot of time developing a target audience persona. Some of this may be based upon current customers, and some may be a “guess and test” strategy. Big data takes the guesswork out. Companies can see who is buying similar products/services, what and where their related conversations take place, what other websites they visit, the social media platforms they use, and, when, where, and how they prefer to be contacted.
Anyone who has shopped for a product on Amazon or ordered a movie from Netflix, will find suggestions for additional purchases or other movies they might like. This is the result of big data and machine learning, gathering information on previous customer behaviors to predict those of future customers. Marketers for any business can use such data to suggest additional products or services too.
Another aspect of personalization is targeted marketing based upon predictive analysis. When leads come in to sales teams, they can be categorized as hot, warm, or cold, based upon how similar leads have behaved in the past. This allows sales teams to spend their time more wisely relative to those leads.
Better Content Marketing
Marketers have used analytics to measure responses to their content – blog articles, social media posts, etc. The right big data analytics tools, though, can now crawl through related content all over the web and analyze responses to it all. This provides marketers with topics, with “pain points” to address, and with offers such as loyalty programs and discounts.
Managing a Reputation
New analytics tools also allow businesses to monitor any mention of their brands or products/services from any place on the web. They can then immediately access those mentions and respond accordingly. When compliments are given, that behavior can be reinforced with coupons, discounts, etc. By the same token, negative comments can be addressed publicly with resolutions that both satisfy the unhappy customer and let others know that a business is serious about its customer service/relationships.
Because of big data algorithms and analytics, it is possible for businesses to rapidly obtain information on competitors’ pricing, price changes in the sector as a whole, and even price points at which customers may be turned away. Pricing optimization is a huge factor in marketing and sales, and the ability to get information and suggestions based upon actual data relieves marketing departments of a lot of manual research, which may not be comprehensive.
This list of six benefits is only the beginning. Data science is relatively new, and promises to be the single most important factor as businesses look to design products and services that consumers really want and need, to find their ideal customers, and to market their brands most effectively.