Retailers shift their ad spending from TV, radio and print ads to digital ads.
To be successful, web stores must think about what makes good retailing: satisfying customers` goals. Few are doing so.
Online shopping is now almost 7 years old but many top U.S. retailers are still struggling to deliver a fulfilling virtual shopping experience. According to a Forrester Research study of online retailing in October 2000, web site usability barriers cause 65% of shopping trips to fail.
If web sites were real stores, district managers would be asking store managers tough questions about the abandoned shopping carts and angry, confused people at the Customer Service desk. The problem with Internet stores is that unhappy customers are virtually invisible. Unless managers profile their on-line customers’ habits, these lost sales only appear as successful sales on competitors’ balance sheets.
Money to be made
There is money to be made online, despite dot-coms’ recent plight. U.S. online retailing will grow 64% this year to $74 billion according to Forrester. Traditional retailers who are successful online are using this opportunity to regain market share and build a solid source of revenue. To survive, retailers will have to better understand what their brands and services mean to shoppers, and provide at least these same services and features on their web sites, in a clear and usable way.
The tricks of successful off-line retailing are well-known: train sales personnel to help customers make purchases, display accessories near featured products and showcase new items upfront. So it is surprising that some of the oldest and best-branded retailers do not provide features to help shoppers find gifts and do not offer product accessories on their web sites. Failing to help the customer make the purchase turns away a huge percentage of sales.
Retailers can overcome this failure not with technology alone, but by going back to thinking about what makes good retailing: satisfying customers’ goals. At Quidnunc we start the requirements process by understanding customers’ goals, and we use this to guide the design. We call it Scenario Modeling and we use it to prioritize features against the business’s objectives, so we know which are most valuable to the success of the web site.
Business objectives for retail sites might be:
- to capture new customers from gift shoppers during the holiday season
- to increase the browse to buy ratio
- for a new web site, to attract and impress those checking out the site after a run of radio or TV commercials
- for an existing site, to ensure that customers continue shopping here, rather than elsewhere.
Narrow the search
The first two objectives are the most common. Many of us shop for gifts on the Internet. Let’s look at the goal of one such scenario: Shopper wants to buy a gift for her 10-year-old nephew. She doesn’t know what the cool new toys are this season, but would know a suitable gift if she saw one.
This scenario should be familiar to many of us: we know who we’re buying for, but sometimes have only a vague idea of what that person might like.
One major retailer’s web site has a gift search facility, but it doesn’t let our shopper choose the age; the options are just Child, Him, Her, Housewarming, etc. It also asks the shopper to choose an interest area: Technology, Sports/ Outdoors, Family Fun, etc. Choosing from this list means our shopper pretty much has to know what she’s looking for. The result is a lot of work for our shopper, who will have to search almost all interest areas and will have to look through hundreds of products for all kinds of age groups.
A much better gift finder would ask the shopper for the information she knows: the age and gender of the recipient. The site then would display suitable gifts, or at least prompt further exploration. Toysrus.com (hosted by Amazon.com) does well by asking for age range, and then shows possible matches from the list of top sellers, editor’s choices and customers’ recommendations. From here, there are links to browse by type of toy (action figures, dolls, games, etc.) or price range.
Once our shopper starts to look at a particular item, Amazon.com’s “customers who bought this item also bought...” feature really helps; a product that is in the right general direction will lead our shopper to other possibly suitable items.
This whole experience is rather like the helpful sales assistant who asks what you’re looking for and shows you suitable items. Then, based on your feedback the sales clerk shows you other items that might be right or that will prompt further feedback.
The scenario also tells us what is missing from Toys R Us/Amazon web site: the ability to choose by gender. Of course, not all toys are gender-specific and our society is moving away from the girls=dolls, boys=cars assumptions, but our aunt gift-shopper is probably going to play it safe with something that she knows is in the mainstream for a nephew.
Tying this back to our business objectives, we can see that gift shoppers who come to the site and find it helpful are likely to make a purchase, and are also likely to come back. New shoppers who find the site unhelpful may never return again.
The next most important point in the common shopping experience is deciding if the current item is the right one. This time let’s switch our shopper to looking for clothes, which are notoriously hard to sell over the web: Shopper wants to buy a button-down shirt. He wants something in cotton, blue or light gray, with a funky pattern that is machine washable. He likes shirts that offer the option of using cufflinks as well as buttons, but hates very pointy collars.
Fussy isn’t he? The web site that helps this gentleman will have to offer a full description and a picture that allows him to check out details he cares about.