Predictive analytics have been around for quite a while now but the take up has generally been low, perhaps because of the complexity and inhibitive costs. However, with Big Data, things are changing rapidly, as more affordable and less complicated solutions are now available that can be used by companies of all sizes.
Predictive analytics allows ecommerce companies to:
- Know what your customers are most likely to buy in advance
- Determine the highest price a customer will pay for your product
- Target recommendations and promotions
- Practise better price management
- Reduce fraud
- Improve supply chain management
- Enhance business intelligence
- and last but not least ....make the most money on your sales
Know what your customers want and what they will pay
As an ecommerce retailer, you want to be able to predict what your customers are looking for when they land on your site. A predictive search will determine this by analysing their past click-through behaviour, preferences and history in real time.
Behind the scenes, the predictive search runs proprietary algorithms that continually analyse data based on machine learning to show the best results to the consumer.
This cloud-based solution is easy to deploy and can work with multiple ecommerce platforms. As different customers engage with a retail site in different ways predictive analytics helps look at all the different variables to generate the desired engagement from the customer. This could mean signing up for a newsletter, clicking on a promotion or some other form of engagement.
It also gives you insight into the highest price your customer is willing to pay for a product.
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 changes that by correlating data from multiple sources to determine a personalised recommendation that will work for a particular customer or a segment.
Predictive analytics 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 identify those promotions that have worked best in the past, and then offer them in real-time based on the consumer’s browsing pattern.
The Shopify App Store contains an app that enables retailers to predict customer behaviour and increase sales by recommending the most suitable products. This app defines a unique predictive model for each online retailer based on its product type, customer base, and sales forecast.
Macy’s has reaped the benefits of predictive analytics by using a solution 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 analyses pricing trends in correlation with sales information to determine the right prices at the right times to maximise revenue and profit. 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 – say no more!
Fraud is a reality for online retail and billions of pounds are lost every year from this crime.
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 are 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 solutions come with pre-built fraud models for a specific industry, such as online retail, making it easy to deploy.
Any technology that can reduce losses from fraud is good news for retailers. Predictive analytics solutions like those found in IBM’s SPSS suite, allow 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 acquired Inkiru, a predictive analytics startup with models for supply chain optimisation.
A better understanding of consumers leads to a 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, reviewing trends, and developing models that identify what a customer might like.
At times, consumers may not be able to vocalize what they most like but predictive analytics can still recommend the right products. Intelligence gained 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 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 is required to ensure the models have not gone awry; all models have some margin of error.
The benefits of using predictive analytics in ecommerce are many and once deployed (with continual monitoring) it will be exciting to see how much your business will benefit.
Confessions of the Pricing Man: How Price Affects Everything, Hermann Simon, 2015
Oxford Handbook of Pricing Management, Ozalp Ozer and Robert L. Phillips, 2014