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Analytics in Mobile Retail – The Way Forward!

by (February 19, 2016)

Retail Mobile Analytics
Mobile Analytics in Retail

With Smartphones in the foray, you can see a huge shift in how mobile is being used in Customer Engagement. Being in touch with a customer’s need is of paramount importance and no retailer can afford to disregard a customer’s voice. In keeping with this trend, a retailer must learn to anticipate a customer’s need, beforehand, and serve these unarticulated needs with ease. Analytics can play a critical role in addressing this need.

Retailers are integrating Analytics into their mobile applications to be able to gather, contextually, relevant customer information. There are five major categories where Analytics will play a vital role:

1. Personalization: When you log into the application, you can see products of your liking, based on your previous browsing history, purchases, wishlists etc. From a customer’s point of view, this makes shopping easier, increases chances of impulse buying, provides discounts on products that you have been eyeing.

The upside for a retailer is an increase in spending (where there was no immediate need) and more products being sold on account of lower prices, thereby, providing economies of scale to retailers.

2. Pricing: There are various parameters that the platform will consider such as competition, inventory, required margins etc. To provide this push to customers, to buy the product based on variances in these factors, makes absolute sense to a retailer.

3. Predictive Analytics: For retailers to predict their sales based on factors such as historic sales, consumer demands, trends, seasonal fluctuations, lead times, elasticity of demand etc. is of utmost importance to create their blueprint on planning in allocation, replenishment, assortment, merchandising, price optimization and supply chain decisions thereby, saving costs for the organization.

4. Managing Supply Chain: In order for retailers to manage higher fulfillment, low inventory and faster delivery, there needs to be supply chain intelligence between various stakeholders such as vendors, warehouses, customers etc.

5. Customer Experience: Analysis of customer buying patterns, to provide seamless buying within the application, promotes customer experience. One click checkout services, by big brands like Amazon, Apple, provide better customer service. Another aspect is that of analytics in customer support service analysis of customer support can provide valuable inputs on various issues faced by the customers, which, in turn, can be used to rectify such issues and thereby, lay emphasis on customer service.

Analytics Tools

A quick analysis on various Analytics tools used by the leading ecommerce retailers are as follows:

  • Flurry Analytics: This is a free platform available across all device platforms such as iOS, Android, Windows Phone, and Java ME. Flurry Analytics, predominantly, provides information on User Acquisition. It provides information on Active Users, Sessions, Session Lengths, Frequency, Retention, Audience Personas and Demographic stats. It can, also, provide information on Devices, Carriers, Firmware versions and errors, creation of Conversion funnels etc. However, Flurry does not provide cohort analysis and real-time data.
  • Google Analytics: This is a free platform available for iOS and Android. Google Analytics provides information on User Acquisition such as new users, their source, sessions, returning users, their country / language, app versions etc. It, also, provides a feature to get reports on speed, crashes and exceptions, create goals, track conversions of objectives and see the goal flow.
  • Localytics: This is a Freemium tool which will have limited features in the free version and is available across iOS, Android, Windows Phone and HTML5. Localytics is a friendly tool which provides “help bubbles” to new users. It provides App Usage and reports by location, device, carrier, app version and by unique users. It provides information such as Active Users, Sessions, Session Lengths, Time periods, New / Returning users, Day Part Analysis which provided information on the time of the day when users are most active, Events and Attributes, Annotations that can be left on graphs etc.

Recommendation Engine

A classic case of Analytics in ecommerce is that of a Recommendation engine. A recommendation engine is used to recommend products to a customer based on his/her previous purchases, browsing patterns or products complementary to the one being purchased. The use of a Recommendation engine can increase an average order size by recommending complementary products based on predictive selling or cross-selling.

Case Study- Overstock.com

Overstock.com is an online retailer that sells a variety of discount merchandise, including furniture, home décor and apparel. The company known, predominantly, for its web business decided to launch their mobile application in 2010. In 2013, they decided to integrate Flurry Analytics into their iPhone and iPad applications. This was done mainly to learn the key differences in shopping behavior on a phone versus a tablet and thereby, optimize both to achieve more purchases on each platform.


Some of the study results were that iPhone users are more task-oriented and conduct 4-5x the number of specific product searches per session as compared to iPad users. On iPad, users exhibit browsing behavior and conduct 3x as many scrolls through a list of products.

Based on these findings, Overstock.com was able to redesign features on the iPhone and iPad applications to help users to shop in the preferred way on each device. There was a subsequent 70% increase in purchasers from searches on iPhones, 30% increase in purchasers from product image on iPad and a 25% increase in purchases or the users overall on both iPhone and iPad.

The Future

Mobile is considered to be the most rapidly emerging digital marketing channel. Therefore, the need for mobile analytics is gaining prominence.  Mobile Analytics seems to be the right path to quality measurement and successful optimization. It helps you to build an efficient mobile marketing strategy and learn which parts of your application drive valuable conversions. Mobile Analytics let you know the entire user experience of your mobile application. Developers can create an app experience that is much more useful and engaging for the users.

Have you enabled analytics for your mobile retail solutions? If not, this is the right time to get started!


Alexia Andrews

Pre-Sales Consultant, RapidValue

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