Increase Conversion with Big Data: 9 Predictive Analytics Platforms

Predictive analytics is a technology based on large amounts of data to predict the future behavior of people in order to make optimal decisions. It uses many methods from statistics, data mining, takes into account both current data and data from past periods, on the basis of which it makes forecasts of future events. In business, forecasting models use patterns based on data for a specific period to assess potential risks and opportunities. Models identify relationships among many factors to make it possible to assess the risks or potential associated with a particular set of conditions. The result of using predictive analytics is the adoption of the right (most effective for business) decisions.

How can predictive analytics come in handy for e-commerce?

This article is based on material from Gagan Mehra and written in the first person, we supplemented the descriptions of nine platforms with illustrations and explanatory videos.

Using forecasting models, you can predict the behavior of potential customers, identify the most popular products, understand what guides visitors to the site when they leave, and avoid this, and so on. Using predictive analytics tools will help increase website conversion, which means significantly increase the company's profit.

So, how can predictive analytics be used?

According to a study conducted by Ventana , only 13% of respondents use predictive analytics. However, 80% said this option is very important for their business.

But before we go any further, I want you to keep in mind that just having a platform for working with predictive analytics — an application that records data and builds forecasting models on its basis — is not enough to succeed. As John Elder said, it is incredibly difficult to build an accurate forecasting model - for this you need to make a huge amount of effort, spend a lot of money and time.

To make sure that your investment in predictive analytics is not in vain, you need to work with a qualified data processing and analysis specialist to help you build an effective forecasting model, and a talented developer who integrates it with your platform.

Option 1. Ready-made forecasting tools integrated into the e-commerce platform

With the development of the use of predictive analytics, several developers of platforms for e-commerce sites offer forecasting tools and useful plug-ins in a ready-made form. You should use one of them in the first place, as this is the easiest way to start using predictive analytics in your business, while avoiding the headache with integrating forecasting models into your service.

Here are some examples:

• Springbot on Magento is a good starting option for companies with 25,000 or less customers (rates from $ 199 / month).

A screenshot of the service displays its working scheme: first you need to add your e-commerce store, then the system using predictive analytics identifies the most effective promotion channels and measures the conversion for each of them.



• Canopy Labs offers an automated recommendation system for choosing the right products at the right time using predictive analytics. It also offers the Shopify platform (rates start at $ 250 / month for a site with up to 100,000 customers).

Below is a screenshot from the service, which describes the operation of an automatic system that optimizes sales: the forecasting model monitors customer preferences in real time and, based on them, predicts the best-selling products in each period.



• Custora- A more reliable set of tools that helps to increase the cost of the customer’s life cycle (how much he will bring to the company) and integrates with Shopify (tariffs start at $ 3,000 / month, the number of customers reaches up to a million).

In this screenshot from the service’s website is an example of a loyal customer’s profile built with its help, with a predicted assessment of its life cycle on the website - $ 367.



How does the service build this profile? The picture below depicts a scheme of working with loyal customers: the system identifies them based on their purchases, analyzes their parameters, helps to form marketing communications with them so as to motivate them to buy even more, and the life cycle manager helps determine which customer work schemes effectively, which are not, and restructure communication with customers in favor of the most efficient models.



No matter what stage of development your business is at, the competent implementation of predictive analytics in the platform can help you provide a more personalized approach to each client.

Option 2. Use open source predictive analytics software.

If you already have experience integrating these things internally, then it will be useful for you to find out that there are several open source predictive analytics platforms that will allow you to create more personalized solutions. The following services have similar platforms:

• R

This video is available on how the service works.



• KNIME

The diagram below illustrates how this predictive analytics service works and how it uses large amounts of data.



• PredictionIO

Demo video of this service.



By choosing this option, the retailer takes on the dirty work of integrating an open source solution into his system. This means that you will need to hire qualified personnel who can implement these solutions, in addition, you need to keep in mind that there may be several errors in open source products that need to be addressed before using predictive analytics in your business.

Option 3. Buy a fully functional package

Of course, this is the most expensive option available - a license for one SAS user costs $ 87oo, but they provide the widest functionality for conducting effective predictive analytics. Here are some suggestions from this area:

• SAS

From this video you can find out how the forecasting model is built using SAS.



• Predixion

Examples of those areas of activity for which the service offers ready-made forecasting models is an advantage of this type of platform.



• SAP

Company recorded a beautiful video about the benefits of Big Data in general.



The advantages of such proposals are that they offer pre-built forecasting models for various fields of activity - the fight against fraud, pricing management, etc. They only need to be set up so that they work in retail.

In addition to this, most developers of such services offer consulting services on the use of these tools, instead of having the retailer independently hire IT employees to work with predictive analytics.

Summarizing

Predictive analytics technologies are very important for retailers who want to succeed nowadays, they should not be ignored.

You do not need to use predictive analytics in each case, but you should choose those areas in which the implementation of these tools will give the maximum impetus, thanks to which you will revise your goals for achieving profit, be able to prevent fraud and other unforeseen expenses, optimize customer service, minimize costs and develop intuition .

Remember that you will not see the changes immediately, but after a certain period of time, so it is very important to monitor the effectiveness of a particular model and periodically make adjustments for a particular function.

Source: http://conversionxl.com/predictive-analytics-changing-world-retail/?hvid=352IDw#.