There are lots of tools that you can integrate with your ecommerce sites that will ensure its growth. However, given the rise in the need for predictive analytics to know more about the customers, you should focus on the different predictive tools and plugins that are available to integrate with your platform to make the best of it.

Why? The simple reasons to use these tools are that it will ensure larger traffic and more sales but most importantly these tools are the easiest of all to use without requiring banging your head against the wall as you will not need any painful integrations.

The most commonly used predictive analysis tools that are used by most of the ecommerce platforms are:

• Springbot on Magentofor companies having 25,000 customers or less
• Canopy Labs offered on the Shopify platform for companies having about 100,000 customers
• Custorais a good tool to integrate with Shopify if you have more than 1 million customers.

When you use these types of predictive analysis tools and recommendation engine you will be able to offer the right products at the right time irrespective of where your ecommerce business is at. All you need is to ensure proper application of the predictive analytics into your ecommerce platform so that you can deliver more bespoke experiences according to each customer.

Few other options

Well, you do not have to restrict your choice to the above tools only for ensuring better predictive analysis and provide better customer experience s you yourself can experience when you visit sites like Nationaldebtrelief.comfor a debt relief option or to any other online store to buy any other product.

• Using an open source predictive analytics product is a very good and useful option provided you already have in-house technical team. There are many open source predictive analytics platforms you can choose from that will allow you to build more customized solutions. These platforms will need to be implemented in theenvironment correctly by skilled resources as there could be a few glitches otherwise.
• You may also buy a full featured suite if you want a more expensive option. These tools will however provide you with the highest functionality for predictive analytics thereby balancing the cost factor. You can choose from a wide variety of packages that comes with built-in models for various areas such as pricing management, fraud and others. This tool will need only a few minor tweaking in your ecommerce environment.

If you are new to predictive analytics then you are advised to take help of a consulting service to choose and deploy the tools. They will help you to understand the basics and how to get started before you choose any of the above approaches.

About the data sources

Now you know about the different approaches and tools top use for web analytics you must know about the data sources as well. This will help you to achieve the fundamental goals of web analytics which is ideally to collect and analyze data that are related to organic traffic to your site and their usage patterns.

Typically, the different data comes from four different sources primarily and it depends on you which one you will use for your analytics.

• You can use the direct HTTP request data that comes directly from the HTTP request messages send called the HTTP request headers.
• You can also use the server generated and network level data that are usually associated with the HTTP requests but are not a part of an HTTP request. To use this you will need successful request transmissions such as the IP address of the requester.
• Another data source that you can use effectively is the application level data that is sent after a HTTP request. This is usually created and processed by the different application level programs such as PHP, JavaScript, and ASP.Net. It includes sessions and referrals as well and is normally captured by the internal logs and not the public web analytics services as it is done for the other sources.
• Lastly, you can use any external data that can be combined with the on-site data. This will help you to enhance the website behavior data and interpret usage.

Typically, the IP addresses play a significant role in such predictive analysis tools to work. These addresses are generally associated with the geographic regions of the ISP or Internet Service Provider as well as other different factors such as the open and clickthrough rates of emails, lead and sales history, and data of the direct mail campaign or any other types of data that is required.

Log file analysis

These tools will also help you in log file analysis of the web servers that record the transactions. These log files can be read using web log analysis software to collect data to determine the popularity of the site.

• Back in the early 1990s all website statistics typically revealed the number of client requests or hitsmade to the web server. At that time this was a far too reasonable approach because most of the websites then entailed only a single HTML file.
• With the passage of time when images were introduced in HTML the websites contained multiple HTML files and therefore such count turned out to be less useful. In order to make a better analysis two units of measure were presentedin mid 1990s to gauge the data more accurately to find the quantum of human activity on the web servers.
• Then the search engine robots and spiders were introduced in the late 1990s. With it the web proxies also came to existence that dynamically assigned IP addresses for larger companies and ISPs. With such data complexities it became more difficult to identify the number of unique human visitors to a given website. The log analyzers helped a lot by tracking visits by the number of cookies and not by the requests from the known spiders.

All these tools and sophisticated predictive analytics technology that evolved helps the retailers to survive and succeed in today’s competitive environment.