Increase Conversions with Big Data: 9 Predictive Analytics Platforms

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

How predictive analytics can be useful for e-commerce?

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

With the help of forecasting models, one can predict the behavior of potential customers, identify the most popular products, understand what directs site visitors when they leave, and avoid it, and so on. The use of predictive analytics tools will help to increase the conversion of the site, and thus significantly increase the company's profits.
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So how can you use predictive analytics?

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

But before we go any further, I want you to mean that it is not enough just to have a platform for working with predictive analytics — an application that keeps a record of data and builds forecasting models on its basis — to succeed. As John Elder said, it is incredibly difficult to build an accurate forecasting model - you need to put a lot of effort into it, spend a lot of money and time.

To make sure that your investment in predictive analytics is not in vain, you need to collaborate with a qualified data processing and analysis specialist who will help you build an effective prediction 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 trend of using predictive analytics, several platform developers for e-commerce sites offer prediction tools and useful plug-ins as a finished product. You should use one of them first, as this is the easiest way to start using predictive analytics in your business, while avoiding headaches 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 clients (rates starting at $ 199 / month).

A screenshot of the service displays its scheme of work: 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 automatic recommendation system for choosing the right products at the right time using predictive analytics. It also offers a 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 automated system that optimizes sales: the forecasting model monitors customer preferences in real time and based on them predicts the products most sold in each period.



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

This screenshot from the service site is an example of a loyal customer profile built with it, with a projected estimate of its life cycle on the site - $ 367.



How does the service build this profile? The picture below depicts a scheme for 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 to determine which customer relationship schemes work effectively, which are not, and rebuild communication with customers in favor of the most effective models.



Regardless of what level of development your business is at, competently introducing predictive analytics into 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 with the internal integration of such things, it will be useful for you to learn that there are several open source predictive analytics platforms that 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 data arrays.



• PredictionIO

Demo video of this service.



By choosing this option, the retailer takes on the dirty work of introducing an open source solution into its system. This means that you 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 fixed before using predictive analytics in your business.

Option 3. Buy a full-featured package.

Of course, this is the most expensive option of all available - a license for a single 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 a forecasting model is built using SAS.



• Predixion

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



• SAP

The 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 - anti-fraud, pricing management, etc. They only need to be configured to work in retail.

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

Summing up

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

You do not need to use predictive analytics in each case, but it is worth choosing those areas in which the introduction of these tools will give the maximum impetus, thanks to which you revise the goals to achieve profit, be able to prevent fraud and other unforeseen costs, optimize customer service, minimize costs and develop intuition .

Remember that you will see changes not 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#.