Conversion of Abandoned Cart and Abandoned Browse Prospects using Next Generation Big Data Analytics. A US Retail Giant

Transcription

1 Conversion of Abandoned Cart and Abandoned Browse Prospects using Next Generation Big Data Analytics A US Retail Giant

2 Results included a 100% Increase in Our Client's Target Base IMPROVIZING GROWTH IMPROVASING GROWTH Our Client is ranked among America s top ecom retailers. The company is now streamlining its online retail strategy, and aims to grow its e-commerce business at 20% a year. This includes introducing custom-solutioned quick-response retargeting customers who browsed or shopped online, but exited the website without shopping. The DataMetica solution implemented at our retail Client resulted in increasing the target base by 100%, and identifying a particular customer s activities across log-ins with the help of advanced analytics. Custom-solutioned quick-response customer retargeting Identifying a particular customer s activities across log-ins

3 EXECUTIVE SUMMARY Our Solution Helps Increase rate of cart abandonment conversions. Personalized timely using advanced analytics. Our client, an American retail giant (herewith referred to as The Client or Our Client), has a promising e-commerce business, yet stiff competition and the slow maturity of the e-com industry has led to a steady sales decline. Added to this, recession-hit American companies have been spending less online. The Client is determined to move ahead and is working on an ambitious three-year, $5 million turnaround plan that relies heavily on e-commerce, mobile web technology and predictive marketing analytics to achieve the goal of becoming the most engaged retailer in America. Abandoned cart Added items to cart and did not make purchase Abandoned browse Browsed items and did not make a purchase. i One of the ways to do this was to increase the rate of cart abandonment conversions. Our Client has already implemented a Big Data and superior Analytics solution, and the customized cart abandonment conversion solution would tackle the task of ing customers that have abandoned cart and abandoned browse The business objective of course is to win customers back using advanced analytics that would approach them with a personalized timely . The would offer to help sort out the problems that might have led them to abandoning browsing or abandoning their shopping carts. DataMetica has drawn up a use-case for The Client that would gather a complete snapshot of the customer s activities, and the re-targeting solution that would help convert abandoned shopping attempts into sales.

4 THE CHALLENGE To grow, the US Retail Giant needed to be information-driven and proactive. A quick-acting cart abandonment and browse abandonment solution was the answer. The current system at The Client's setup uses Ominiture (an online marketing and web analytics software) to send daily daily extracts (via Genysis feed) to Exact Target (to get a single view of every customer) to calculate the abandons and abandoned items. However, this is the extent of information abandoned carts that The Client has managed to capture. There was a lot lacking. While basket abandonment and browse abandonment are inevitable situations in the E-ecom business, it should be treated as an opportunity rather than a loss. This is because as per industry reports, an effective cart abandonment strategy can recover 18% of abandoned carts, resulting in an incremental of more than a million per day. To capture this opportunity and to get ahead, the company needed a method and solution for browse and cart abandon scenarios. The challenges faced with the current system are Omniture Feed has not been reliable daily. Omniture Feed has a 2+ day lag time (no plans for real time triggers) No method available today to determine browse abandonments

5 THE APPROACH DataMetica has worked out the following broad approach to tackle the challenges 1 2 Define process to identify customer and enrich customer mapping dataset. Define a process and the business rules for generating a file (in the Big Data system) using the available Clickstream data, by identifying Cart Abandonment and Browse Abandonment use cases. 3 4 Define the structure (list of fields) of the target file to be delivered to the Marketing team. Define any dependency, assumption and constraints available.

6 THE SCOPE To enable the US Retail Giant to use the existing technical landscape, but get more out of it by employing Big Data technology, DataMetica worked out the following project scope for the Cart Abandonment and Browse Abandonment use cases. CART ABANDONMENT CASE A Customer (known/unknown) adds a cart on The Client's website but does not make a purchase that day. Such scenarios are marked as cart abandonments. CONDITION A customer adds product(s) to the cart but does not make a purchase that day. When a customer logs-in and adds product(s) to a cart and makes a purchase that very day on same or different device (mobile), this cart and all other carts created by this customer (Source Ecommerce Platform) on that day are not considered as abandoned. If the customer adds a cart and removes all the items from the cart, it will not be considered as an abandoned cart. For partial items removed from the cart, we would like to only show the list of SKUs that were not removed. If a cart is added after making a purchase, and then abandoned, then it is considered as abandoned. NOTE : The logic is to identify if a purchase was made during that day by traversing through the Clickstream logs and identifying if that Cheetah mail ID/cookie id has made a purchase. We did not go into the Source ecommerce Platform to validate this info. Due to latency issues between collection of Omniture logs (every 24 hrs) and Source Ecommerce Platform data, there will be scenarios where customers who purchased after the handshake are not excluded. This is a risk in any cart abandonment campaign. Also, we may have scenarios like the ones mentioned below where incorrect identification of cart abandonment may occur. All the Products/SKUs that have not been removed from the cart are reported as cart abandoned for that customer. If a customer adds SKUs and removes ALL SKUs, he is excluded from this list and is tagged as browse abandoned.

7 BROWSE ABANDONMENT A customer (known/unknown) views a product on The Client's website but does not add it to the cart that day. Such scenarios are marked as browse abandonments. All the products viewed by the customer will be included in the browse abandoned list for the customer. Even if one SKU is added to cart, this customer is entirely excluded from browse abandonment list. Customers who add to cart and remove all SKUs are also marked as browse abandonment. FUTURE SCOPE In future phases the cookie id /browser /device can be mapped to a customer to understand the single screen experience. Mapping of unknown customers with transaction data Classification based on product hierarchy, brand and other product attributes. Identify a solution to bring Source Ecommerce Platform- Cheetah Mail-ID address mapping through Big Data. The future scope of this project will be to avoid/reduce the lag time and enrich customer mapping data(that the current system faces) in reaching out to the customers.

8 THE SOLUTION With the current solution, Omniture merely evaluates individual sessions, and infers if it s a cart abandonment situation. It does not track a user across sessions, even on the same day. It therefore cannot (a) identify if a customer has returned and (b) track a customer across sessions and work out for instance, that a customer who has logged in earlier in the day comes back later and makes a purchase. Further, (c) only cart abandon sessions are tracked, while browse abandon sessions are not. DataMetica s customized solution is able to use advanced analytics that are able to identify all 3 of the above situations. The solution includes Using advanced analytics to match and identify that a customer who logs in via his Cheetah id with his other visits to the site without logging in (by studying the cookie activity) Extending the results that Ominiture throws up, by using that information and identifying future sessions belonging to the same prospect. Ensuring that there is no error. For instance, consider a customer who abandoned his cart in the morning, but returned to shop and checked out the same SKU in the evening. Without a custom solution, Omniture s limitations would entail an to a browse abandon customer, urging him to come back and shop, whereas he has already shopped.

9 RESULT/BENEFITS A three-time increase in conversion of cart abandonment cases. A five-time increase in conversion of browse abandonment cases. IMPACT ON REVENUE There is a proven month-on-month increase in revenue by a million dollars

10 ABOUT DATAMETICA As the emerging leader in the Asian continent for efficient, performance-driven open source Big Data and Business Intelligence solutions, DataMetica uses a focused, highly analytical, use-case based approach and a customer-centric strategy that always works. The strength of its solution lies in the phased approach from Advisory, Platform and Professional Services, right up to advanced support solutions. Customers are treated to DataMetica s home-grown methodologies perfected over several large-scale data management and analytics installations. Contact us for an opportunity to understand the specifics of how Big Data will work for your organiztion! [email protected]