Geoffrey Moore once compared companies without data analytics with deaf and blind deer on a freeway. And Moore was quite right too!
Any online transaction between a shopper and a seller is a negotiation. And in every consultation, knowledge conjures power. Right kind of information about the other party gives you leverage. And leverage lets you squeeze a better deal where you reap the benefits.
As a shopper, the sites you visit, the products you browse, the models you put in your cart only to dismiss the window and buy the same item from some other website- that data is significant for a seller. How? Let’s see!
Business Analytics in e-commerce- Can You Optimise Conversion?
To guarantee a sale, the seller must:
- identify the influenceable section of the target shoppers
- figure out what’s important to them, and
- present them with an apt product-deal combination
It’s tough! Buyer’s intent is hard to spot. It’s where data science, analytics, & e-commerce based machine learning come into play.
Data Science in E-Commerce- How Is the Revolutionising Happening?
Insights
Sellers use technology to track customer behaviour without disturbing said customer. If a shopper logs into a store Wi-Fi, the software monitors the sections where they linger longer. Marketing teams look at social media and browsing patterns to gather information on trending items.
- Prediction
Algorithms study the existing data like product attributes, shopper behaviour, and correlations. They try to infer future trends. They make assumptions based on the data they’re fed about the shopper.
- Strategy
Algorithms work to predict what the company should sell when and at what price. The product design, supply-demand forecasting, window of sale, promotional schemes, and personalised shopper targeting are strategized using the learnings of the machine.
- Personalisation
As per a 2014 Infosys report, 78{ed162fdde9fdc472551df9f31f04601345edf7e4eff6ea93114402690d8fa616} of consumers respond to targeted ads that focus on their specific interests with a successful purchase.
Sellers use the data and patterns to revisit their retargeting plan, reach to a client of a particular age, interest, gender, and demographic and offer a deal that suits their present concerns.
Why Is Predictive Analysis in e-commerce Needed?
Shoppers often have the advantage of information over sellers. They know the product details and the shopper credentials. They have market reviews and ratings to turn to. They have options from different sellers for one product.
The seller has no way to know what drives the shopper- price, availability, or delivery window. It has to rely on guessing, and that doesn’t work out well in all cases.
Predictive analysis & e-commerce analytics allow the seller to gather fact-based ideas extracted from previous consumer behaviour using machine logic and data science. It helps them optimise conversion, and offer a better customer experience.
Basic Advantages
- Cross-sell- I see you bought an iPhone 7 recently. Can I interest you in an iPhone case?
- Up sell- I see you browsing a Full HD LED TV. Here, this 4K is the next version and fits your expected price range too.
- Personalisation- I have your location. Would you like to see where our nearest store is? Or if we express deliver to your place?
- Opportunity- I see you are in this country right now that has this upcoming festival that’s apparently a huge thing. Can I interest you in this relevant product that you may need during the said festival?
And That’s Just the Front End
Data science can help e-commerce websites by allowing consumer retention via heightened levels of perception and recognition. But, it can also be used for web analytics, fraud detection, payment, delivery, and other post-purchase aspects that build customer experience.
LSI: business analytics in e-commerce, ecommerce analytics data science, Data Science in ecommerce, predictive analytics in ecommerce
