28-Oct-2019 04:47 PM Digital Marketing
Before you start any marketing campaign, you do meticulous research to better understand your target audience, your competition and the right industry conditions for launch. But what do you do when all these factors number in the millions? Or if you’re a well-established brand, you might find the need to crunch numbers for billions of determinants, each with millions of variables. So how did data get so big?
Well, data has always been big. Think of a typical shopper’s experience in the supermarket. From the time they enter the supermarket to when they leave the parking lot after shopping, they would’ve generated data regarding POS transactions, coupon redemptions, parking validation, etc. Each of these gives a glimpse into their lives. When pieced together, they even give us a scarily accurate picture of the shopper. Just from this single visit, we know what card/s they use or if they prefer to use cash instead. Coupon redemptions or not redeeming coupons can be tracked and analyzed to re-target them and parking validation data paints a picture of how long a user spends shopping and what vehicle they use.
With the advent of online shopping, brands have scaled up user data collection by petabytes!
In the case of online shopping experiences, you generate online purchase data, click-through rates, browsing behavior, social media interactions, mobile device usage, geolocation data, etc.
All these are enough for brands to predict your next purchase. Or if they can’t, then they definitely know how to pull you into the right circumstances to trigger those mouse clicks.
Traditional data analytics systems are incapable of handling such huge volumes of data. Not only the volume, but the velocity (the rate at which this data is generated) and the variety of data generated are too much to handle. Gartner says that to be called big data these three Vs are essential – variety, volume, and velocity.
Apart from the above three qualities, big data is often messy, unstructured and difficult to wrangle with. Chances are, if you’re a small business just starting to make inroads into your target landscape, you don’t really need to work with huge volumes of data. But once your brand starts making a mark – and you certainly hope it does – you will find yourself cornered and unable to make any sense of the data your customers are generating.
Big brands like Amazon and AliExpress already use big data to provide exceptional customer service. Based on user behavior on their site, Amazon is capable of anticipating the users’ next purchase and can show them highly targeted ads.
According to datafloq.com, “Amazon also uses Big Data to monitor, track and secure its 1.5 billion items in its retail store that are laying around its 200 fulfillment centers around the world. Amazon stores the product catalog data in S3 (object storage service that offers industry-leading scalability, data availability, security, and performance).”
If you’re a brand looking to expand to new geographies, it helps to know and understand your pre-existing competitors. Or if you’re the only player in the new location, you need to build on and analyze data to test for market viability. Either way, companies like SAS have come out with products like SAS® Marketing Automation, SAS® Marketing Optimization, and SAS® Intelligent Decisioning to enable companies to make intelligent marketing decisions that are based on solid data. Getting more campaigns out the door in an automated, trackable and highly repeatable fashion, making the most of each customer contact by determining how business variables will affect outcomes, and enabling analytically driven real-time customer interactions are some promises made by SAS.
The biggest advantage of leveraging the power of big data is gaining a clearer picture of who your target audience is. Sure, you’ve spent countless hours and resources in figuring out your brand’s buyer persona and have already figured out where they hang out the most online. But what your marketing department fails to understand is that people being people, they are susceptible to highly unreliable data reporting. Trying to establish a baseline for your brand audience by conducting surveys on social media or tracking their data (legally or illegally) is prone to errors and can only get you so far. Apart from the data they generate, what gives you a better clue is the data they don’t generate. A user shopping online for a new watch might not have thought of buying a smartwatch. Traditional ads that track user activity will display more ads related to regular watches. But with access to the individual users’ mined data about their fitness habits, food choices and general lifestyle, big data can recommend a smartwatch.
Apart from this, big data also provides a clearer picture of the buyer’s journey from awareness to becoming a return customer. Shopping cart analysis, wherein companies learn about consumer behavior by seeing what the consumer adds into their shopping cart – whether they buy it or not – is giving them the power to predict a customer’s needs.
Combining the data mined about a user with direct customer feedback can provide great insights to personalize shopping experiences.
Big data analytics tools like Mixpanel and Clicktale are helping brands better chart the customer journey while Plotly helps businesses to analyze their data visually.
However, Cassandra is the big big data analytics tool that websites like eBay and Netflix use to help store, organize, and manage large sets of data.
It’s obvious now that big data can do more for you when it comes to marketing. If you’re not ahead of the curve in the marketing landscape, it’s easy to be left behind. While big data may not be the answer to every business marketing, you certainly need to keep an eye open on the recent developments in the field.
If you’re in need of an expert who can help you with what big data can do for you or your brand, get in touch with us at iverbinden.com. We will help you create a brand, connect with your buyers, and captivate your peers.