n t i a l . Background Yourdesignerwear.com is an online web shop selling online clothing products and accessories via internet to Indian woman. The web shop has been active for about 3 years now and it is a market leader in online woman cloths for Indian woman. Challenge The Leadership team at Yourdesignerwear contacted Liftsuggest for use of Liftsuggest to generate cross sell & upsell recommendations on the online shop. The goal was use via Google Analytics data and implement Liftsuggest product recommendations to improve the average order value. Approach Liftsuggest team went back to work & knew it was the product recommendation implementation for this web shop to get the revenue increase rolling. Lift suggests is simple an API that uses Google Analytics data as back end. The API when requested with a given SKU call, it responds with the related SKU that has highest probability of conversion for the given product SKU. The API is now converted into a form of Add-on for Zen Cart, Magento, OsCommerce, Joomla & WP e-commerce. Yourdesignerwear.com was on Zen cart so it was a non-technical implementation using a module of zencart. Both the teams decided that it would be wise to place the recommendations on the shopping cart page as that’s the place where consumer provide the intent to shop the product. In addition to the product recommendation API, Liftsuggest also offers product bundling opportunities. Product bundling is a strategy to increase the revenue by providing a specific offer of a product bundle at a discounted rate. The discount rate is determined dynamically by the bundling Algorithm provided by Liftsuggest team. We decided to implement both recommendation & bundling algorithm on the e-commerce web shop. Solution Implementation The solution process started with different design concepts presented to the client based on our learning as to what works on recommendation space. Once they decided to go with the specific design, it was a one click module implementation for Zen Cart.
n t i a l . The Liftsuggest API & the module for zen cart generates these recommendations automatically & displays it on the shopping cart page. To measure the improvements Liftsuggest provided add-on JavaScript that enhanced the tracking of Google analytics via Custom Variable. This resulted in simple report suggesting the amount of additional revenue that was generated using the recommendation engine & visitor behaviour
n t i a l . The Liftsuggest bundling API as it is currently in private beta uses a collaborative filtering process to upsell 2 or more products & by providing additional discount. The bundling works as shown in the image below. The small block below the “add to cart” button is the bundling box that offers a specific discount to the visitors who are seeing these products. The bundle is also generated via custom scripting & it is based on Google Analytics data. The great thing about bundling API is that it is a self-learning , so only those bundles which has highest profit potential will only be displayed based on the purchase propensity of the visitors.
n t i a l . Results The average order value for last 6 month was around $171 per transaction which increased to $207 per transaction in April, being the second full month of Liftsuggest implementation. This results is the increase in revenue to the tune of about 21%. After adjusting for seasonality affect this % increase is actually 29% instead of 21%. There was evident increase in the average order value of the e commerce store increased by about 29%(after adjusting for seasonality) for consecutive 2 months starting from March’11. The increase in average order value was accompanied by increase in the number of items being shopped for every transaction. In addition to the increase in revenue from the recommendations that were placed on the shopping cart page, bundling also contributed separately to increase the revenue. $- $50 $100 $150 $200 $250 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Avg. Order Value Chart 2010 2011 2009
n t i a l . About Author: Ravi Pathak is a co-founder of Liftsuggest – A product recommendation engine based on Google Analytics data. Ravi is responsible for product position & marketing of Liftsuggest. Ravi has more than 8 years of experience catering to different roles as web analyst to the client manager for Tatvic. Liftsuggest is the outcome of his belief of generating action out of available data. Ravi can be contacted at [email protected] & you can keep in touch via twitter @liftsuggest Ravi can be found blogging at Liftsuggest.com/blog.
marktimemedia
aleyda
lindahogenes
marketingsoph
codingconduct
destraynor
helmedeiros
ivargrimstad
tanoku
baasie
konakalab
mayatellez
