How Predictive Analytics is Transforming the Retail Industry
Updated: Feb 10
Remember that time when you were browsing through a blog online and that beautiful dress you have been coveting for months suddenly popped up in one of the corners of the screen? How could you possibility resist? You just had to purchase it!
‘What a coincidence!’, you would wonder.
It’s actually smart business.
Predictive analytics, the new holy-grail in town, is a rather clever way to boost your business.
It analyses the data obtained from buyer preferences and past purchasing patterns and gives you pointers on exactly what pricing methods and promotional strategies you should focus on to get
an edge over your competitors. It’s basically a roadmap to profitability.
A large part of predictive analysis employs the use of Artificial Intelligence and machine learning algorithms. E-commerce and retail brands have been using machine learning algorithms to fix prices and forecast demands for years. In 2014, Amazon went as far as to patent predictive
stocking to maintain the edge.
Imagine you are travelling to your dream destination and forgot to pack your charger. You can obviously order a new one and get it delivered to your hotel room. As you search for the charger, the website also shows a pair of noise cancelling headphones that you can get a discounted price. You think, “What the hell!” and buy it.
Now you might think that it’s normal for websites to suggest people to buy things together for a greater discount but a lot more data analysis goes behind showing you those exact pair of headphone that you impulsively bought. The change in location, new address that the goods have to be delivered on, the items you search for, the brand you might prefer, the general budget you maintain while ordering; all this and so much more goes into tailoring content for each consumer according to their needs.
Predictive Analytics is Transforming Retail
It is almost impossible to function in the market without predictive analysis. Due to fast-paced groundbreaking developments in the field, new AI software is coming up every now and then, capable of sifting through massive amounts of data to get more efficient and accurate results. Though the focus of these advances has been solely on the consumer aspect, like reducing fraud through big data analysis and enabling chat bots to augment support services, applying these in the sector of banking, capital markets, healthcare, and transportation has truly revolutionized the industry. By embracing the use of AI and ML in e-commerce, we are looking at newer possibilities and unlimited opportunities. Looks like finding the needle in the haystack is becoming easier by the minute.
The world is moving at a neck-breaking pace and to keep up with is a humongous task. As more players step into the field, the competition gets tougher every second. Predictive analysis helps companies be a step ahead of their competitors as it makes available a huge databank of comparative data which companies can study to modify their strategies and policies to stay ahead of the curve. For example, if you were to have all the possible info about the pricing patterns of your competitors, it would surely offer you a tactical advantage when you price your own products.
Apart from pricing, predictive analysis has lent a helping hand in the domain of recommendations and promotions, fraud management, supply chain management, and business intelligence. Based on information garnered from a consumer’s purchase history, browsing trends and the offers that have worked in the past, predictive analytics can pick out promotion techniques that can help the retailer generate higher sales. By laying out the consumer patterns, it facilitates the entire supply chain process, right from planning and sourcing to delivery and returns.
Retails brand and chains are also coming up with new ideas every day to hike up their profits.
A few years ago, Westfield Malls’ lab worked with eBay to build 10-foot-tall interactive screens in
its San Francisco shopping centre. Shoppers swiped these screens to browse products from
brands like Rebecca Minkoff and Sony, which they could purchase directly on mobile. This not only allowed the brand to build a connection with the consumers but also gave valuable data as to what kind or products are prefers by different genres of consumers.
Datahut and predictive analytics
The raw material required for any predictive analytics model is Data. A part of that data is available publicly on the internet. With the help of web scraping services, Datahut equips companies with impactful data, thus contributing towards the goal of creating data-driven retail strategies.