It can seem like hard work to maximise sales from e-commerce but there are plenty of tools available these days to make ecommerce success more achievable. Here’s a post contributed by the team at Swift ERM that will give you some food for thought.
Data-analytics have come of age, and if it remains simply a myth to you, then don’t be surprised to see your competitor’s success glide easily past you, onward and upward, while you continue to ponder.
As an ecommerce retailer, you want to be able to predict what your customers are looking for when they land on your site so you can get them to it as fast as possible.
By using predictive analytics it is all done for you, preferences and history in real-time.
Behind the scenes, the predictive search runs algorithms that continually analyse data based on buyer behaviour to show the best results to the consumer.
BETTER TARGETED RECOMMENDATIONS AND PRICE PROMOTIONS
Recommendations are very important for an ecommerce business but it’s not always easy to get them right.
Predictive analytics can maximise sales from e-commerce promotions by using data from multiple sources to work out a personalised recommendation that will work for a particular customer or a segment.
It makes the challenge easier by using machine learning to understand a consumer’s behaviour, including their purchase history and the performance of different products on the site, to determine the most relevant recommendations with a higher probability of generating a sale.
It’s the same with promotions: predictive analytics identifies those promotions that have worked best in the past and then offers them in real-time based on the consumer’s browsing pattern.
Macy’s has reaped the benefits of predictive analytics that results in better targeting of registered users of their website.
Within 3 months of implementation, Macy’s saw an 8-12% increase in online sales by combining browsing behaviour within product categories and sending targeted emails for each customer segment.
Predictive analytics looks at pricing trends together with with sales information to determine the right prices at the right times to maximise sales from e-commerce while boosting profits.
Pricing is managed using a predictive model that looks at historical data for products, sales, customers, competitor pricing and product pricing trends.
Based on this model, the price for a given product and customer can be predicted at any given time.
Online giant Amazon is a huge user of predictive pricing.
Fraud is a reality for online retail and billions of pounds are lost every year from this crime. Any technology that can reduce losses from fraud is good news for retailers.
If fraud has become your nightmare, predictive analytics can lower credit card chargeback rates (the demand by a credit-card provider for a retailer to make good the loss on a fraudulent or disputed transaction) and reduce overall fraud by analysing customer behaviour and product sales – and removing products from the assortment that is most susceptible to fraud.
The fraud management predictive models identify potential fraud before the customer completes the purchase transaction, resulting in reduced chargebacks and also reduced administration time.
Predictive analytics software comes with pre-built fraud models for a specific industry, such as online retail, making it easy to deploy.
Predictive analytics allows a retailer to analyse browsing patterns, payment methods and purchasing patterns to detect and reduce fraud.
Some retailers are even experimenting using predictive analytics with machine learning to automatically define rules to detect and prevent fraud.
Predictive analytics has become essential in the fight against fraud as new types of fraud are unfortunately being created on a daily basis.
SUPPLY CHAIN MANAGEMENT
Predictive analytics helps understand consumer demand so you can effectively manage the overall supply chain process.
This includes planning and forecasting, sourcing, fulfilment, delivery, and returns.
If a retailer can predict the revenue from a specific product, say in the next month, it results in improved stock management, optimised use of available warehouse space, better use of cash flow, and avoiding “out-of-stock ” items.
Walmart recently introduced predictive analytics with models for supply chain optimisation.
A better understanding of consumers leads to better service overall – offering the products they want at the price they want and with effective after-sales service.
Predictive analytics makes this possible by capturing customer information, analysing trends, and developing models that identify what a customer might like.
At times, consumers may not be able to say what they most like but predictive analytics can still recommend the right products.
As a result, data gathered through predictive analytics helps build a culture of better decision-making.
OPTIMISE PRICING TO MAXIMISE PROFITS
Traditionally retailers have used A/B or Bandit Testing to set prices for different products and come up with the optimal price that results in maximum profits.
The downside is that each price is set manually and can be prone to human error.
Predictive analytics builds a model to support real-time pricing that uses input from various sources such as historical product pricing, customer activity, preferences and order history, competitor pricing, desired margins on the product and available stock to optimise pricing and maximise profits.
Predictive Analytics Technology is Critical for Retailers
In today’s online retail environment the use of predictive analytics technology to maximise sales from e-commerce is critical for retailers to succeed.
It might be that not every use of predictive analytics is relevant to your business but you can pick the areas that will create the maximum impact by reviewing your desired targets.
Do you most need it for increased revenue, fraud prevention, optimised customer service, cost savings or better insights into customer behaviour?
Predictive analytics can produce a huge competitive advantage for an online retailer, though the models have to be thoroughly tested before they are deployed.
Also, periodic human intervention and supervision are required to ensure the models have not gone awry; all models have some margin of error.
The benefits of using predictive analytics to maximise sales from e-commerce are many and once deployed (with continual monitoring) it will be exciting to see how much your business will benefit.
There are many types of predictive software available, this list would be a good place to start.
This article was contributed by the team at Swift ERM who help ecommerce retailers to increase sales and profits through their advanced data analysis marketing software.
You can find out more about Swift ERM here : swifterm.com