Give them what the ‘real-world’ can’t

Enhance the experience of shopping with features that add value not easily found in the physical environment. Our applications should reduce the amount of thought and effort it takes to plan shopping trips, manage expenses, checkout, or search for and explore products. This leads to more enjoyable and valuable experiences for the shopper, making our applications far more likely to be used.

1 Lower the cost of search

Reduce the amount of time, thought (cognitive effort) and physical effort it takes shoppers to find what they need. Features that help match products to shoppers’ goals, like going “low-carb” take much of the thought and effort out of choosing the right product. Any feature that can minimize this “cost” is worth the effort. The more tedious the search for products the less shoppers tend to buy.

2 Give them smart, relevant recommendations.

As noted in “Provide Hyper-Relevant Experiences”, smart recommendations come from a deep understanding of the individual shopper. Shoppers consistently and continuously tell you what they like and don’t like, whether they know it or not. Their shopping lists, ratings, reviews, account preferences, shopping history, and returned purchases give you a lot to go on.

Consider the following:

  • Take advantage of everything you know about the shopper to help them make better decisions. If you know their 13-item shopping list contains 10 organic items, show them organic alternatives to the other 3.

  • Stray away from ‘sponsored’ or ‘featured’ products unless they match what you know of the shopper. Since shoppers will likely be on small screens with limited visible space, the stakes are even higher than with traditional eCommerce experiences. Every time you present an irrelevant recommendation, shoppers sees less value in your application. Over time they will begin to ignore all recommendations and could stop using the application altogether.

  • Leverage recommendations of personality matched influencers. We trust others like us more than we do the people selling to us. Shocking! Personality-matched recommendations can come from a single person (person-to-person or trusted expert recommendation) or from a like-minded group (group-to-person or endorsement). Use the powerful influence of social networks of like-minded individuals.

  • Understand shoppers sensitivity to price. Some shoppers are very price-conscious while others aren’t. Know where the shopper falls on this continuum and recommend accordingly. Generally speaking, deals and special offers are attractive to shoppers. Just make sure that no matter what the deal, the product you recommend is relevant to that shopper.

3 Track their progress and spending for them.

When you track the progress of shoppers, you can proactively check off items in their shopping list and calculate the total amount of money they should expect to pay. These features are very attractive to shoppers, but be sure to allow them to override the actions of the application at any time. Functionality like this helps reduce stress by instilling a sense of security that they have not forgotten any items while allowing them to accurately and actively control their spending.

4 Provide a quick checkout.

If you know the contents of the shoppers’ carts, you can accurately calculate the total cost of these items. If the shopper provides a preferred payment method in their profile, allow them to check out without having to wait in line to rescan all of the items. As noted in ‘Be fun, smart, attentive and efficient’, this service is extremely attractive to shoppers, especially those who are time-sensitive.

15 references informed this principle

[1] Adomavicius, Gediminas & Tuzhilin, Alexander, Context-aware recommender systems, Proceedings of the 2008 ACM conference on Recommender systems - RecSys '08, 335, 2008.

[2] Buttussi, Fabio & Chittaro, Luca, MOPET: a context-aware and user-adaptive wearable system for fitness training., Artificial intelligence in medicine , Vol. 42, No. 2, 153--63, February 2008.

[3] Ebrahimi, S, SmarterDeals: A context-aware Deal Recommendation system based on the SmarterContext Engine, 2012.

[4] Jacobs, Sunelle; Van Der Merwe, Daleen; Lombard, E & Kruger, Nadia, Exploring consumers preferences with regard to department and specialist food stores, International Journal of Consumer Studies, Vol. 34, No. 2, 169-178, 2010.

[5] Kourouthanassis, P & Roussos, G, Developing consumer-friendly pervasive retail systems, IEEE Pervasive Computing, 32-39, 2003.

[6] Kourouthanassis, Panos E.; Giaglis, George M. & Vrechopoulos, Adam P., Enhancing user experience through pervasive information systems: The case of pervasive retailing, International Journal of Information Management, Vol. 27, No. 5, 319-335, October 2007.

[7] Park, CW; Iyer, ES & Smith, DC, The Effects of Situational Factors on In-Store Grocery Shopping Behavior: The Role of Store Environment and Time Available for Shopping, Journal of Consumer Research, 1989.

[8] Reischach, Felix Von; Eth, Z; Michahelles, Florian & Schmidt, Albrecht, The Design Space of Ubiquitous Product Recommendation Systems, Proceedings of the 8th International Conference on Mobile and Ubiquitous Multimedia, 1-10, 2009.

[9] Roussos, George & Moussouri, Theano, Consumer perceptions of privacy, security and trust in ubiquitous commerce Personal and Ubiquitous Computing, Vol. 8, No. 6, September 416-429, 2004.

[10] Savage, Norma Saiph & Baranski, Maciej, I’m feeling LoCo : A Location Based Context Aware Recommendation System in Location-Based Recommendation System, Springer Berlin Heidelberg, 2012.

[11] Consumer Choice Behavior in Online and Traditional Supermarkets: The Effects of Brand Name, Price, and other Search Attributes, A. Degeratu, A. Rangaswamy, J. Wu, International Journal of Research in Marketing, Vol. 17, No 1, P. 55-78. 2001.

[12] Consumers and Pervasive Retail, P. Kourouthanasis, G. Roussos, ELTRUN-Athens University of Economics and Business, 2000.

[13] Building Consumer Trust in Pervasive Retail, G. Roussos, Nov. 2004.

[14] Intelligent Cokes and Diapers: MyGrocer Ubiquitous Computing Environment, P. Kourouthanassis, D. Spinellis, G. Roussos, G. Giaglis, 2004.

[15] Checkout Optimization: 70 ways to increase conversion rates, eConsultancy, 2010.

© 2014 - Jonathan Morgan | @promorock | LinkedIn