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Personalization and Recommendation Solutions as a Transformational Strategy for Retail Sites

Online technologies for e-commerce sites in line with the overall trend are also shifting. Transition to mobile computing is one aspect, the other is more new applications for improved customer engagement. These new technologies are helping retail sites to serve their customers better, attract more customers and increase conversion rate.


The problem is most e-commerce sites owners view the technologies and applications as supportive elements and rarely differentiate them from their value perspective. Some apps and plug-ins are necessary requisites for running an online retail shop. Beyond that, the onus is on the consultants and IT executives to guide the sites through the intricacies of technology selection and implementation.


There are three types of application a retail site’s technology infrastructure is built on. Process automation apps, for example, the shopping cart app. It automates the process of purchasing a product for the buyer. The analytical apps that facilitate analyzing, interpreting, and understanding the data which are invaluable support tools for the business. Google Analytics, as an example, is widely used for the purpose of tracking traffic patterns and acquiring marketing insights. There is also a third type which is a combination of these two categories of apps. For example, online Customer Relationship Management (CRM).


Any of these types of applications, in essence, might demonstrate transformational capability. Transformational applications are solutions that make a profound impact on the business by bringing some sweeping changes, not just in the refinement or streamlining business processes, but radically shifting the way the business is conducted. Transformational applications are often tantamount to the implementation of strategy innovation that creates new business model opportunities.


One such application, which many e-commerce sites have started to implement lately, are the recommendation and personalization solutions. These apps can be considered as the hybrid type because they serve both purposes. At the same time, at the present level of industry acceptance of this technology, for many e-businesses these solutions are proven to be truly transformational, bringing immense changes to their prior business model and profit margin.


In a competitive marketplace where rivals are using every possible technological breakthrough, it is important to deploy solutions that are strategically aligned with the e-commerce site’s needs.


Visitors of the many e-commerce sites today are getting personalized shopping experience. On any page wherever they go, they are viewing displayed offers which correspond to their interests.  On the product pages highly relevant similar items are displayed. While a product is added to the cart, the page shows complimentary items. For the site, these pages are critical touchpoint of interaction with the visitors.


A comprehensive understanding by the retailer of visitor’s touchpoints within the browsing period and accordingly reacting encourage the visitor to make a positive purchasing decision. Throughout the browsing time, the visitor goes through several of these key touchpoints.  For delivering the service that the visitor expects and improve the probability of conversion rate, the site needs to have the capability of reacting on each touchpoint by the visitor’s expectation. At the minimum, the retailer needs to meet their expectation and even better if the expectations are surpassed by providing very accurate personalized recommendations.


Three critical areas recommendation solutions help companies reach these objectives:


Converting visitors to buyers: A visitor is a potential customer of the retail site provided she finds the product or service she is looking for with her set criteria. The recommender system, analyzing her browsing data and past interaction with her along with several other vital data, displays her the most attractive and relevant items, which often result in a purchase.


Customer loyalty: Visitors when consistently receive recommendations that meet or surpass their expectations tend to come back that allow the retailer to increase sales from returning clients.


The growth of a transaction value: Automated cross-sell and up-sell offers, produced and suggested by the recommender system enhances the value of each transaction.


Recommendation solutions are around for quite some time. However, based on advanced machine learning technologies, the new generation of recommendation solutions are increasingly becoming apt in delivering accurate recommendations.


Amazon, Facebook, Ebay, Google, Netflix and all other major players use various recommendation and personalization tools depending on the type of interaction with the visitors and required conversion. The spectacular growth of Amazon and Netflix — the two behemoths in their distinct area — to some extent can be attributed to the successful adoption of recommendation solutions. When Google shows an ad to a visitor, it uses recommendation solutions to do it. Facebook’s uncanny ability to show your long lost friends is also the result of recommendations algorithms.


Most vendors provide present recommendation solutions as a Software-as-a-Service.  Being API-based engines, implementation of a recommendation solution does not require a significant investment. As a result, even small retailers can deploy them and reap substantial benefits.


Retail sites have to realize that competitive advantage comes from superior capabilities and competence developed by the company which is hard for rivals to imitate. Being ahead of the competitors in technology selection that improve customer engagement, developing a closer relationship with the client by delivering desired services and understanding their deep and serendipitous needs beyond their won expectations are factors that will play a significant role in achieving the firm’s strategic goals.
Author: Mitt Nowshade Kabir, CEO of Trouvus

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