Salesforce, you're not there yet!

What does the perfect CRM for your company look like?  At FindWAtt, we chose Salesforce because it makes the most use of attributes, and is the most customizable.  On the surface, Salesforce seems like the perfect tool for a company as attribute-crazy as we are.  We can create our own custom attributes and values (albeit with some limitations), use formulas to derive values, and create a series of rules to assign tasks to the appropriate person.

The problem is that while conceptually solid, the usability suffers from what seems to be an insufficient amount of structure. Each entity in Salesforce, whether it is Leads, Contacts, Opportunities or Accounts, has their own unique set of fields. I can create a new field for a Lead, but when that Lead is converted into an Opportunity, that new field does not go with it.  To make that happen, I have to dig into the settings, recreate the attribute (under Opportunities this time, instead of Leads) and then specify how those values should map.

Email to Salesforce is another feature that seems very useful, but  falls short of its potential.  Any email sent to a Lead (or Contact) can be Bcc'd to Salesforce and logged under that person’s record. This is very handy for organizing all of your correspondence.  Unfortunately, Salesforce can only match based on ONE of the recipient’s email addresses. If your lead or contact uses more than one email address, you’re out of luck! I know it sounds crazy that someone might actually have more than one email address, but allowing multiple values for any attribute (known as multiple cardinality) is a fundamental thing.

In my dreams I have the option of allowing multiple cardinality for any attribute, and the ability to add any custom field to any entity with the simple click of a button—but Salesforce has yet to make my dreams come true.

As we’ve worked with different e-retailers to create a more effective shopping experience, the question inevitably comes up “How Many Facets do You Need?” The answer of course is, “it depends.” When you have few products in a category you may not need any facets, but when you have many products facets become essential. For 30 products, 2 or 3 facets is usually plenty. But when you have 500 products it might take 6 or 8 facets to enable the visitor to filter the list to a manageable number. Ideally we like to get a list filtered down to 1-5 products. If there are many more than that you start to get too many choices, and decision making becomes more difficult. So the number of facets you need really depends on how well they divide your product listing. It may be that you have 3 facets, but they are not structured in such a way that you can ever get the list down to a manageable number. This is why implementing parametric search is so challenging. In most cases it will not be sufficient to just add filters for Brand, Price, and Customer Rating. What we try to do at FindWAtt is extract as many attributes/parameters as possible, and then calculate which ones are most effective in reducing the product selection per click. This means that it will take fewer clicks for the visitor to narrow down the product list, and get the visitor to a purchase decision more quickly and effectively.

While assisting a friend of mine in shopping for a new car, I recently stumbled across an impressive implementation of faceted search on Kelly Blue Book. It's called the Perfect Car Finder, and allows you to search or browse, and refine your selection based on a multitude of different attributes.

According to the press release in 2007

"...nearly 80 percent of shoppers who visit kbb.com have not yet decided on which model they plan to purchase and less than half know which make they are interested in. With an ever-growing need among vehicle researchers for help in sifting through hundreds of vehicles available today, kbb.com has launched two enhanced online shopping and decision tools to assist car buyers early in their shopping process.

The first of these enhanced tools is Kelley Blue Book's 'Perfect Car Finder®,' which lets consumers search through more than 400 new vehicles and more than 1,100 trim levels of those vehicles by the features and optional equipment that matter most to shoppers. The second is an all-new comparison tool allowing consumers to view an all-encompassing side-by-side comparison of vehicles."

There are basic options for people to shop by Make, Model, Body Style, Size, Price and Gas Mileage. There are also advanced options for people to shop by Engine Type, Horsepower, and Various Interior/Exterior/Safety Features. It's a shining example of how I'd like to be able to shop for many of the products I buy.

Kelly Blue Book is just one example of the strides made in faceted search. Particularly in the Automobile Industry, there are several examples of faceted search. Yahoo Autos Car Finder is another example of a very impressive set of facets on which to refine your search.

It is not surprising that the Automobile Industry is adopting these techniques. Cars are very expensive and complex products, which have many of different variables that need to be considered. Like computers, they also have a highly structured set of attributes and attribute values, making it a natural fit for high quality faceted search. Add to that the market research by KBB that 80% of shoppers don't know which make or model they want to buy, and guiding the purchase decision becomes critical to success.

Have you seen any other Automobile related sites that employ impressive faceted search? Do you agree that it is a good fit? I'd be curious to see any research into how much this type of shopping interface improves conversion rates in Automobiles.

Welcome to FindWAtt’s Findability Find of the Week, a blog series where we highlight websites or tools that are making great strides in findability.

This Friday’s Find of the Week is the “Guided Search” feature on CompUSA.com. Although this Guided Search is not available in all categories, it greatly improves the shopping experience for products where it is available. To fully understand the effects of this feature, we’ll examine the “before” and “after” options in laptops.

Before: “Subcategory” choices are mixed and confusing

Subcategories address a variety of attributes, from screen size to operating system, in one list. The list is difficult to understand quickly, and forces the customer to read each option carefully.

After: Choices are grouped into clear and logical Attributes

Subcategories have been replaced with a series of filters which are grouped under meaningful headers. The customer can quickly read each section header and determine whether or not he wants to make a choice from that section.

Before: Customers can choose only one “Subcategory”, with unpredictable results

Although these subcategories address a variety of attributes, the customer is limited to picking only one. What if he’s looking for a laptop with a 64 bit OS and a 17” screen? Too bad! Choosing “64bit OS” or “17” and Above Screen” returns 5+ pages of results and no further options for narrowing them down except “New” vs. “Refurbished”.

After: Customers can make choices for each Attribute

After choosing 17”-20.1” from the “Screen Size” Attribute, the Customer is then allowed to choose an Operating System (or various other Attributes).

Before: Number of Results doesn’t add up

The Subcategory “Desktop Replacement” claims to contain 14 products.

But clicking on it returns 16, of which 12 are “New”, 2 are “Refurbished”, and 2 are not accounted for.

After: Number of results adds up and is updated based on choices

“Desktop Replacement” says it contains 15 products, and actually returns 15 products when clicked.

After clicking on “Desktop Replacement”, the remaining filters are updated to show that 10 of these are “New”, 4 are “Refurbished”, and 1 is “Open Box”—all are accounted for.

Although CompUSA’s new “Guided Search” feature is not perfect (for instance, not all numbers of results add up correctly, indicating that some products are missing attributes), it is miles better than the “classic view” with no attributes. Products are much more findable in the new version, and customers have much more information available to them when making decisions, as well as the chance to decide what is important to them on an individual basis.

Do you have a website or tool you think should be highlighted on FindWAtt’s Friday Find of the Week? Leave a comment below or send a tweet to @WikidKandice to submit your contribution!

Welcome to FindWAtt’s Findability Find of the Week, a new blog series where we highlight websites or tools that are making great strides in findability.

This Friday’s Find of the Week is the “Highlight Differences” Feature on BestBuy.com.

Here at FindWAtt, we believe that findability encompasses not only the ability to locate the product you’re looking for, but also having the necessary information to make a purchase decision. Although this information is frequently buried within the product details for a particular item, some retailers understand the importance of having it in a usable format. The ability to compare products is becoming more and more common when shopping online.

However, too many retailers allow customers to view products side by side, but stop there, forcing customers to pick through all available data to figure out the difference between two or more products.

BestBuy.com has gone a step further and introduced a feature called “Highlight Differences.” To use this feature, a customer chooses two or three products to compare, then clicks the appropriate button, and Best Buy shades the product attributes that differ. For example, in the two cameras pictured, one has 3x Optical Zoom, and the other has 20x, so the line for the Optical Zoom attribute is shaded in blue.

This display makes it far easier for the customer to scan the list of attributes and easily identify the relevant differences between two or more products, thereby leading more quickly and easily to a purchase decision. The feature isn’t perfect (for example, some attributes which actually mean the same thing are expressed differently, and are therefore highlighted as differences) but it is a huge step in the right direction.

What do you think of this feature? Have you seen it in use anywhere other than BestBuy.com?

Do you have a website or tool you think should be highlighted on FindWAtt’s Friday Find of the Week? Leave a comment below or send a tweet to @WikidKandice to submit your contribution!

Welcome to FindWAtt’s Friday Find of the Week, a blog series where we highlight websites or tools that are making great strides in findability.

This Friday’s Find of the Week is the Laptop Bag Finder on eBags .

Finding a case to fit your laptop can be complicated. Any site with good faceted navigation will let you choose the material, color, and brand that you’re looking for, but how do you ensure that your laptop will fit in the case you’ve chosen?

Some retailers don’t include sizing information in the faceted navigation, forcing you to delve into the details of any bag you may be considering.

Others allow you to choose by screen size, but don’t inspire confidence with phrases like “max laptop screen size” and “fits most screen sizes”.

EBags, however, has a quick and easy solution to this problem with their Laptop Bag Finder.

One click opens the simple panel where you can choose the brand of your laptop from a dropdown and view a list of models. Selecting the appropriate model is easy—just click through the alphabetical list or start typing the model name or number in the search box and the list will be filtered in real time.

If you don’t know the model of your laptop or it’s not on the list, you also have the option of entering its measurements instead.

Either option neatly filters the available list of products to only those that will fit your laptop, leaving you to browse by color, material, brand, etc. without worrying about size.

This principle could easily be applied to other product categories. Shopping for shoes? Why click on your size every time you change categories--why not enter your size at the beginning of your shopping experience or have the option of entering your foot measurements only once? The possibilities are endless. We’d love to hear your ideas!

Do you have a website or tool you think should be highlighted on FindWAtt’s Friday Find of the Week? Leave a comment below or send a tweet to @WikidKandice to submit your contribution!

I mentioned in an earlier post (Amazon as a Benchmark) how much I both love Amazon and yet find it frustrating because of poor Findability .

I experienced poor Findability (and therefore this new term “web stress”) last week on Amazon as I tried to look for a desktop computer for my colleague Dan Barbata. Dan had been looking forward excitedly to a new computer he’d ordered from Dell about 3 weeks ago but for some mysterious reason Dell cancelled the order – something to do with a parts shortage. Some instant yet painless gratification seemed appropriate and as I’m an Amazon Prime member I thought I’d see what we could get by the end of the week.

Dan’s key criteria are 8GB RAM and a 64-bit CPU to make use of the RAM so he can have lots and lots of applications open at the same time without bringing his computer to a crawl (a problem with 32-bit computers because they couldn’t use more than 4 GB RAM).

First Search Attempt on Amazon

I go to Amazon, search for “Desktop Computer” and quickly navigate to the Desktop Category.

Amazon has lots of filters (a.k.a. facets or attributes) to refine my search:

• Operating System
• Included RAM
• Condition (New, Refurbished, etc.)
• Shipping Option
• Brand
• CPU Speed
• Hard Disk Size
• Average Customer Review
• Green?
• Price
• Seller

RAM is my first criterion but the filter choices provided are useless – the maximum range is 1 to 249 GB which is going to produce one thousand one hundred and forty-nine (1,149) results! Not exactly useful plus how many people nowadays are going to buy a Desktop with less than 1 GB of RAM?

I think it must be 20 years since I had a computer with less than 40 MB of RAM so either Amazon is selling an antique in the above 1-39 MB range or there’s a product data error – of course it’s a product data error:

Note this is a product that Amazon is selling itself and not one sold by an independent merchant through Amazon marketplace where you might think that data quality would be worse.

Second Search Attempt on Amazon

Ok, so now I’m forced to go back up to the search box and try again, this time searching for “8 GB RAM” while staying in the Desktop category. I filter the results by Windows 7 Home Premium and Hewlett-Packard (let’s give them a try after Dell) and Amazon returns 5 results, all of which are sold by Amazon MarketPlace sellers. Notice the reason these show up in the search – MarketPlace sellers put a phenomenal amount of product information into the title - apparently Amazon does not search Technical Details in the full product description which in turn explains why no computers sold by Amazon show up.

Third Search Attempt on Amazon

I don’t give up but only because I’m already thinking of writing this post. I cancel my 8GB search, filter by Hewlett-Packard and Windows 7 Home Premium and click on the products to get to the product description page, scroll down to Technical Details (what a pain) to find the RAM. After 4 or 5 tries, I eventually find a Hewlett-Packard computer sold by Amazon that could be delivered by week’s end. But I’m not going to suggest buying from Amazon to Dan because there’s no way we can compare the features of all the computers they stock that have the basic criteria (8 GB RAM, 64-bit CPU) – we simply can’t find them.

Search & Navigation and Product Data - Why doesn't Amazon do a better job?

This incident is not a one off - I frequently experience problems with Amazon's Search & Navigation and Product Data and it mystifies me why this should be the case with the largest and most successful Internet retailer. In this case they clearly have RAM as a separate attribute in a structured field – albeit with some data errors – but the ranges are messed up. In other cases I’ve seen key criteria “just sitting there” in semi-structured form (i.e. contained in text in a bullet point under Technical Details) that are not even addressed in product filters (a.k.a. faceted navigation). What’s particularly intriguing (frustrating from a consumer perspective) is why terms in the search box are not being run against structured values. For example if RAM is a separate attribute in a structured field, this presumably means that the value “8 GB” is associated with the HP Pavilion P6320F computer that I eventually found. In which case why did this computer not show up when I searched for “8 GB RAM?” And why is Amazon missing the key attribute of “64-bit?” Admittedly it’s only been a year or two that such computers have been available but they’re pretty common now and 64-bit is a mandatory requirement now for anyone who’s in the market for a high performance computer with lots of memory. Even if “64-bit” is not part of any structured data provided by the manufacturer, it’s a trivial text mining job to pull it out.

Perhaps I’m being overly tough on Amazon. When we see Findabilty problems on small to mid-size retailer’s sites, we tell them about them and only write about what we’ve seen in a disguised form. But I think Amazon is fair game because they really ought to be top of the heap. The really big driver of this post though is that Amazon is my primary online store and they’re giving me lots of unnecessary web stress.

Customers get stressed if you make them work harder than they should have to on your website and your performance metrics such as bounce rate and conversion rate will suffer.

 CA (formerly Computer Associates) recently commissioned a study Web Stress - A Wake Up Call for European Business in which 13 volunteers wore skull caps to track their brain waves as they tried to search for and purchase a laptop PC and travel insurance. The study was conducted in Scotland by British usability experts Foviance. See some highlights here:

Brain wave analysis indicated that shoppers had to concentrate up to 50% more when using poorly performing websites, leading to greater agitation and stress.

It’s a given that when customers get frustrated they give up or go elsewhere – CA’s own (2009) estimates are that poor performance leads 40% of people to go to a competitor’s site and another 37% to give up entirely. What’s fantastic and exciting about the notion of tracking web stress is that this type of research gets us on the road to being able to measure usability; i.e. as well as experimenting with usability changes and their impact on conversion rates we’ll be able to:

1. Put an actual number on a website’s usability using an index or score.
2. Measure the impact on this Usability Index of making specific changes (e.g. improving Findability , changing design, streamlining checkout process).
3. Most excitingly, to create industry wide metrics that indicate the relationship between the usability index and performance metrics such as conversion rate. These metrics can then provide rule-of-thumb estimates as to how much an ecommerce site’s conversion rate is being held down by poor usability and what kind of improvement could be achieved if the site could move into the usability sweet spot.
   • My own guess is that the relationship between usability and website performance is something like an S-curve. Wouldn’t it be great if a retailer could find out they were at position A and by moving to position B they’d double their conversion rate?

   

   • This isn’t the whole story to conversion rate of course (other factors come into play) but adding this kind of knowledge to industry wisdom would help us all in a big way.

Welcome to FindWAtt’s Friday Find of the Week, a blog series where we highlight websites or tools that are making great strides in findability.

This Friday’s Find of the Week is the color family filter on Endless.com.

Endless sells shoes, bags, and other accessories, and like most other online shoe retailers, they have the typical filters: Category, Brand, Size, Width, and Heel Height. Where Endless really shines, however, is in their Color Family filter.

Because there is such a wide variety of colors (especially in apparel), using it as a filter can present some challenges.  One common tactic is presenting all color names with no normalization. This leads to too many options requiring too much work on the part of the customer.

For example, here is the color filter from another online retailer who sells shoes. In order to view all blue shoes, the customer has to click Blue, Denim, Light Blue, Navy, and Turquoise! This means ten clicks to view all blue shoes (since the customer has to click to clear the selection each time in order to get back to this list.)

Endless has an elegant and intuitive solution to this problem: rather than displaying dozens of color names, they represent color with swatches, which the customer can then click on to see products in that color.

For example, clicking on the blue square returns the following products (and many more), despite the fact that the “official” colors for these products are Turquoise, Jetblue Quasar, Teal, Navy, and Onyx.

The solution Endless has implemented is well suited to customers’ needs. Color is a very visual attribute, and presenting a visual representation of color that doesn’t require reading an entire list is both intuitive and efficient. The word list of colors above contains only 50% more values than the swatch list from Endless, but takes far more time to process and allow the customer to reach a decision.

Which interface do you prefer? Have you seen this tactic used by other retailers? How could a similar idea be applied beyond just color?

Do you have a website or tool you think should be highlighted on FindWAtt’s Friday Find of the Week? Leave a comment below or send a tweet to @WikidKandice to submit your contribution!

E-commerce websites contain lots of product information that is underutilized. I know this professionally because structured product data is our business. Macys.com, is a favorite shopping site of mine, and was the obvious place to go when I needed to get a blazer in a hurry for our trip to the IRWD conference.

Normally when I shop online, I have some familiarity with the product I'm buying. But in the case of blazers, I was clueless. I didn’t own a blazer and didn’t know any of the relevant attributes. All I knew is they needed to match the various slacks I own, and of course I wanted something with style.

Unfortunately, after searching for "Mens Blazer" and refining by Blazer > Mens, I became stuck. I was presented with a list of 134 blazers and limited options for navigation.

I could narrow results by Brand, Special Size, Mens Waist Size (which seems wrong for a jacket), and Color, but I wasn't ready to make decisions based on these attributes. I wanted to get a sense of the Materials, Styles, and Patterns that were available.

Being in the product data and attribute business, I knew this information was probably available to me, just not easily accessible and usable. To get at this unstructured information I would have to click on the product details for each item.

I found one blazer that looked pretty clean, and looked at the attributes:

- Material: Linen; cotton

- Style: Two button front; Flap pockets; Side vents

- Lapel: Notch

It has some features that I like. But are there any others like this one? What other kinds of materials do they have? Do I have to buy a blazer that is Dry Clean Only? What about patterns?

I had recently read a Smashing Magazine article about How Hard It Can Be to Shop at Macy’s. They were shopping for bed sheets, but experienced the same limited filter options. I could have left the site, but I was confident that Macy's had a good blazer, and I was determined to buy one. Macy’s faceted navigation was failing me, so I took matters into my own hands.

I grabbed all of their product data for blazers, and ran it through our system, to bring out all of the unemployed attributes and attribute values that were present. Look at the rich structure that was produced:

By adding important attributes to help filter results, I turned a potential hour long shopping session into a quick, focused buying session. I gained confidence that Macy’s had what I wanted, and spent time purchasing rather than exploring. I ended up going to the store to make sure the blazers looked good on me (decided to buy 2), but that is the subject of another post.

If Macy’s were to make these attributes available to everyone how much of an improvement in key shopping metrics (bounce rate, conversion rate) do you think they’d get? And how much would the clickstream of the attributes chosen by consumers to refine their search be worth to Macy’s in terms of tailoring their website to meet customers needs?