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Optimizing eCommerce Conversion Rate using Predictive Analytics

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An eCommerce business heavily relies on consumer behavior for reaching new heights. There are many measures that can be helpful in determining the traffic on the site after the visit, but analyzing the certain data types before their visit is the key. What if you, the owner of the eCommerce store gets to know what your customers are willing to pay for a particular product?

What if you are able to stay two steps ahead in the run all the time; optimizing your customer service efforts to solve the concerns of the customers before they haunt you as customer issues?

Worried about how you will be able to deal with all these factors? Predictive Analytics has the answer for you. It can help you:

  • In improving the customer engagement

It’s important to understand the fact that every customer engages with the retail site in a different manner. One can sign up for your website’s newsletter while other can check out promotions/offers on your website etc. It is a need driven approach.

Predictive Analytics can help you in assessing different customer behaviors by looking at different variables to generate the desired engagement. Different tools are available which help retailers in developing models to track and analyze different customer behaviors. Some of them include SAS, Lattice, Alteryx etc.

Predictive analytics tools also assist investors to understand the market opportunity. This is achieved as predictive tools are used by inside sales representatives to browse through the publicly available information about a lead and compare it with qualities of company’s existing customers.

  • Better Campaign Management to target right customer base

As predictive analytics study the customer behaviors, retailers can get the promotional campaigns right by using them in a correct way. Promotions are a must for every retail business and if it goes wrong, it can mean heavy loss for the company.

Online merchandising strategy requires precise segmentation and targeting. Choosing the right tools is important. Unfortunately, most of the merchants feel that the tools they are using for online merchandising strategy are not satisfactory.

With the help of predictive analytics, it is possible for merchants to correlate data from multiple sources; it enables the companies to create personalized campaigns that are targeted to the individual customer segment.

  • Price Optimization to get maximum profits

Traditional approach has been that the retailers used A/B or Bandit testing methodology to set prices for the products to maximize the profits. The problem with the approach, however is that it is highly manual and is prone to human error.

Predictive Analytics model can help in this scenario. It takes a different approach by utilizing real time pricing mechanism based on several factors , including:

  • Product price history
  • Customer Activity
  • Competitor Pricing
  • Available Stock
  • Order History etc.

Pricing is a sensitive issue. There is a need for close monitoring of the automated pricing set by predictive analytics so that it doesn’t affect retailer’s environment.

  • Proper Stock Management

Like pricing, keeping a close eye on stocks or inventory is also a crucial aspect when it comes to Predictive Analytics. An evolution was introduced by Walmart in the field of inventory management as it asked the suppliers to support real time inventory management, also referred to as Vendor Inventory Management (VIM).

Predictive Analytics helps in smart stock management. If it sees no big demand of a certain product/product category, it automatically minimizes the minimum/threshold inventory. The retailers are benefited by this approach as the funds needed are allocated smartly.

  • Minimizing chances of fraud

Fraud is one of the harsh realities in the retail space. Retailers are facing billions of dollars every year accounting to fraud.

In such situation, if technology is able to reduce losses from fraud, retailers would be tempted to implement that for sure. Predictive Analytics enables a retailer to analyze and understand customers browsing patterns, payment methods and purchasing patterns to detect & reduce fraud. Machine learning to automate fraud detection and prevention is also utilized by some retailers nowadays.

  • Enhanced customer services

Retailers are faced with several questions when it comes to providing customer service. Each retailer has a different question depending on specific customer needs. Predictive Analytics can assist in building out predictive model which is targeted towards specific customer service needs from retailer-to-retailer.

Like pricing and all other models, customer service model will also keep on refining itself over a period of time. It will then be able to provide more accurate predictions which the retailer can use to provide better customer services to its customers.

  • Real Time Decision Making

The most important aspect of predictive modeling is that it allows the retailers to make decisions in real time. This has been made possible as streaming analytics has the capability to generate insights in real time.

Fast paced environments like retail cannot rely fully on historical data only. These real time insightful decisions enable the retailers in launching a promotion at the best time, placing right products at right time and identifying the ones which would generate maximum revenue etc.

Deploying Predictive Analytics into your system

Predictive analytics tools are great way to maximize your returns but it should be noted that having just a tool cannot make you successful. Predictive models become accurate over an undefined period of time and it can cost you lots of money.

One of the best approaches is to utilize the skills of an efficient data-scientist who would help in building accurate predictive models. A need for a skilled developer is also there as he/she would be needed to integrate the predictive analytics model within your platform.

The retailers are left with three options to utilize the power of predictive analytics:

  • Integrate with eCommerce

Several eCommerce vendors are introducing predictive analytics in the form of plugins and predictive tools. It is the best way to use predictive models if you are using any of the ecommerce platforms. For example,  Springbot on Magento platform.

Proper implementation of these predictive tools will give a personalized experience to your customers.

  • Using an open source predictive tool

Some of the open source predictive tools are listed below:

  • R
  • KNIME
  • PredictionIO

Retailer has to put extra efforts to integrate an open source predictive tool into their own environment. This means extra cost, as there would be need to hire skilled personnel for maintaining the same.

  • Buying a full suite

Some of the full featured predictive analytics suites include the following:

  • SAS
  • SAP
  • Predixion

This option is a costly affair but it has many benefits as it has built-in models for fraud, pricing, customer service etc and requires minor tweaking to suit the business needs.

Conclusion:

Predictive analytics can’t be ignored in fast paced retail environment. If not all, selecting the areas which you feel would benefit by using Predictive tools should be tested.

Also, it takes time for any model to predict accurately. So, continuous monitoring is needed after its implementation to benefit from this new feature.  

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Optimizing eCommerce Conversion Rate using Predictive Analytics