- +1 (925) 292-6668
- Contact Us
Jeff Bezos, the CEO of Amazon, became the world’s richest person after the Amazon’s earnings report was made public this month. Partly, the credit for it goes to big data technologies. Because Amazon was the first company to anticipate the potential of big data, it was one of the first online shopping sites to try innovative techniques to boost sales, such as product recommendations, drone delivery programs, and near-home warehouses to drastically cut shop-to-delivery time. All these things combined led to it becoming the top online shopping store in the world.
But, if Amazon can do it, surely any other store can, as long as they leverage the full power of big data in the right way.
A few years ago, we didn’t even know that the things we do on a regular basis can be translated into important information. But today, big data is not only a buzzword but the essence of every new technological innovation. Velocity. Volume. Variety. The merger of these three words has completely changed each industry. Whether it is construction, retail, manufacturing, healthcare or even transport, big data coupled with artificial intelligence and deep learning is revamping each sector drastically.
For the big data analytics in retail industry, a huge success came with the emergence of product recommendation systems. Due to the use of huge databases that could track user shopping patterns, developers were able to introduce product recommenders. The product recommenders are ‘you may also like’, or ‘inspired by your browsing history’ as seen on Amazon.
These big data insights also let the category managers know firsthand what the hottest products are in their categories. These coupled with the past year’s data-sets, allow the managers to know what products to keep in the inventory, what type of users are they going to have in the near-future and best ways to keep the retention rate high.
Recently, Target, a chain of shopping stores, targeted pregnant women with baby product ads after determining the type of products they were buying through their platform. Though the act itself is rather controversial, it points towards how clever strategies can have a long-term effect if used wisely.
They can offer optimized prices if the customers are reluctant to buy a product because of its high price. This all is done by calculating their-onsite behavior, their product choice and the checkout page leaving pattern.
Another example is Jet, a shopping store, who’s online success entirely relies on its price optimization techniques to attract customers. It is probably known for its amazing feature ‘price drop as you shop.’ The feature decreases the amount you have to pay if you follow its provided options. The options include pay by credit card, opt out of free returns, use a single shipping method and so on.
Some stores also recognize the pattern of returning shoppers through the databases and provide more coupons and wholesale offers to them whenever possible. This increases retention, reliability and solves users’ actual problem i.e. getting products for nominal prices right at their doorsteps.
Boston Retail Partners says that even brick and mortar models are trying to delve into this tactic and they are employing beacons, Wi-fi, and reservation systems to improve in-shop experiences to identify each customer and his needs for a more personal sales pitch. Some even use the smartphone numbers and link it to each customer to make a persona of about things he will like and his buying pattern.
One major flaw that retail stores had before big data technologies became regular was how to improve customer retention and decrease costs. Keeping customer sales executives was noteworthy but it wasn’t actually fruitful. Huge amounts were being spent on it. And, still, many customers felt disgruntled by the representatives who couldn’t answer properly, and especially when these customers had to stay in the queue for far too long. But, with the emergence of chatbots, the process is now much more streamlined.
Major retail stores now use custom bots that are trained on sets of supervised learning algorithms that help them get better in answering customer queries. The best thing about these bots is that they get even smarter with every question answered. This will mean intelligent systems that will eventually become personal guides for each product. Top brands such as Uber, Kayak, Expedia and many others are now using these chatbots on their website and facebook pages to answer user queries 24/7.
Most ecommerce stores have even started using AI-powered visual bots to solve an age-old problem of finding a product name by its description. Let’s suppose you saw a black leather wallet in someone’s hand and you liked it. But as you don’t know what brand it is, how will you describe it to a shopkeeper? The best way is to snap a picture of it – may look a little awkward, so use your espionage and photography skills to capture the pic. Now, just add the photo to an app and it will tell you what type of wallet it is, its price and where you can buy it online. Nice, no?
One of the first store Neiman Marcus has brought this idea to reality. And, the store says that it has improved the retention ratings of its app by more than 40 percent through these bots.
Other stores that are using visual bots to keep users engaged with them include Asos, Nordstrom and Urban Outfitters.
Where big data has changed the way for customers to engage with content on retail stores, it has also improved how retailers interact back. Previously, we were seeing advertising that was intrusive, irrelevant and bombarding. But now, thanks to ways we can use data analytics in retail, the advertisements are more sober, precise and accurate.
Netflix has saved billions of dollars in yearly revenue thanks to in-product adverts. It uses data analytics and artificially intelligent algorithms to target its users with personal show recommendations. These are so accurate that Netflix doesn’t have to promote its shows outside its own medium.
Buzzfeed, a popular content website, has never used advertisements on its platform. It instead relies on native-style sponsored content that the users can easily digest. Sometimes, these users don’t even know what they were reading was actually sponsored content. It relies on its in-house tool ‘pound’ to calculate how many times a story is shared. And, based on those insights it develops more similar stories that can ignite emotions in the visitors, thus making them share it.
We offer a fully custom solution built to your needs, covering everything from inventory management to payment processing.
When you visit a product on a website and then visit Facebook, do you start to get advertisements of similar products from that website in your news feed? This is programmatic advertising. Facebook can target users based on their browsing history. It was made possible by Facebook through its Facebook Exchange (FBX) advertisement system. Websites drop cookies on user’s browser once they start browsing it. And, when they later scroll their feeds on Facebook, they are shown retargeted ads by those websites to remind them to buy those products.
Let’s declutter these big data facts to summarize impact of big data on E-commerce and retail:
Big data is changing the world pretty fast. According to futurists, we are going to see more technologies being introduced in the next 10 years then what we saw in the previous 20 years. Therefore, it is the right time for ecommerce and retail sectors to tap into these technologies and work on improving customer experience to get better returns.