The entrance of distributed computing and parallel processing has caused a revolutionary paradigm shift in the marketing industry. This has been achieved by incorporating data analytics and integration of this data to enable huge profit margins.
Data can now be deduced, patterns detected and predictions made; in essence, businesses can anticipate the customers’ needs, concerns and even future complaints. This level of customer interaction in the modern markets is essential.
As a matter of fact, a company in this age must be driven to understand its customers’ needs to be successful. The implication is that the business can stay in race with trends which are rapidly evolving and develop products proactively in response.
This was the case in 2012 when Walmart restocked up to 7 times more strawberry port tarts than normal just before Hurricane Frances. The New York Times states that Walmart had been at the forefront of using predictive technology and in fact had used data worth trillions of bytes worth of shopping history that contained information of patterns obtained when Hurricane Charley struck several weeks earlier.
The implication of such occurrences is that companies have to take a data-driven stance if they are going to gain a competitive edge over rivals. It helps that such data is readily available, which means that its usage is no longer restricted to elite companies at the forefront of data science. Even SMEs are bound to benefit from it.
Why is Big Data Analytics Important for Sales?
Analytics have always been a crucial part of any business’ existence. Data collected over the course of operation helps to explain who buys which product and, hopefully, the data-crunching experts can try and figure out why.
This information can also be used to gain useful insight into seasonal demand and keep on top of trends. Questions that can be answered include what is trending on the market? Why are you gaining or losing customers? What do you need to do to stay competitive?
In areas that encourage consumer engagement, loyalty or help to boost confidence in your brand, the availability and usability of good analytics data is incredibly important.
Improving your pricing strategy: Whenever a new competitor enters a market, their go-to way of gaining a competitive edge is through pricing. Rival organizations then attempt to retain their dominant position via price-matching. On this front, the SMEs are unable to compete because they can hardly shrink their way into profitability anymore.
According to McKinsey, a 1% increase in price for the average business translates into an 8.7% increase in profits. But if it were that simple, every company would do just that – increase their prices to the point consumers can no longer bear.
Big Data analytics is incredibly useful for businesses that want to explore new methods of pricing their products. Dynamic pricing takes into account any number of factors that are fed into it – demographics, time, season, supply, demand and even competitors’ pricing information – and gives suggestions on how to price your products. If needed, these can then be reviewed by humans and published on the market.
Improving brand and product engagement: Along the same rein, big data can also be used to identify and process brand sentiment. For example, it’s possible to use Twitter’s API to scrape data on which phrases occur most often when your brand is mentioned. After which, a brand sentiment score can be generated.
This data can be thought of as a form of feedback for your product, and the insight gained can be used to improve in areas where the company is thought to be weak. Understanding consumer sentiment helps to understand barriers present in the market and increase sales.
Taking the Twitter example a bit further, the company has become an absolute behemoth in the social media space, handling over 70,000 GB of data every second. Access to most of this data is open to the public or, if you’d prefer to gain insight in real-time, subscribe to the company’s enterprise program. Processing such a massive amount of data calls for a big data platform like Hadoop or Spark.
When looking at Hadoop vs Spark, they are pretty evenly matched. Spark gains the upper hand on Hadoop when you need to process real-time insights on this data and send back a response to the customer’s device.
For instance, if you need to strengthen the security checks on your cart and checkout pages, since no customer wants to be kept waiting for ten minutes. Hadoop works better for data you’ve been saving up for years and perhaps you feel you could gain some valuable insights into your current or past consumer behavior.
Generating and retaining leads: Big data serves as an important source of insights into how users interact with your website and brand. It’s possible that the majority of your site’s visitors originate from a social media platform like Twitter. Normal analytics can be useful for shedding light on aspects such as number of visitors, but it takes a lot more information to understand the connection between key pieces of data.
For example, in order to understand that visitors from Facebook spend more money than those from other sites, you’ll need access to a big data platform. Further, maybe visitors sent to your site from a backlinked blog prefer a certain product over the rest. For normal data platforms, generating such links is going to be a difficult task.
Understanding and influencing consumer behavior: A lot of people make purchasing decisions for similar reasons. Buying a keyboard and a mouse together is perfectly natural, after all. It may surprise you to know that, for a computer, gaining such insight is a lot harder. It doesn’t possess the same kind of intuition that allows you to deduce that, after all.
Big data creates an opportunity for businesses to create a sort of intuition for their platform, and takes it a step further. Some insight is notoriously difficult to come across and takes more than intuition to deliver. Walmart’s realization that ‘pampers are often bought together with beer,’ despite being little more than an urban legend at this point is a great example of the same.
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