Why your website is so bad at capturing leads and what to do about it

The most important employee in your organization is….

Your Website is your most important employee

It is available 24/7 and (hopefully) takes only a few seconds to find and is easy to navigate.   It tells your clients where you are, when you are open, how to contact you, what you do, etc.  Without it, you have less business.

Doubling Leads Every Year during the Great Recession

During the recession, I became responsible for the website and digital strategy of a company that sold expensive, low-demand products that often required (hard-to-obtain) financing to purchase (custom homes -- what sane person wanted to build a new home during the worst housing market in history?).

The company enjoyed record lead counts during my digital stewardship -- nearly doubling leads year over year.

Don’t Make Me Think

A number of things contributed to this success, but chief amongst them was the advice found in Steve Krug’s book, Don’t Make Me Think: A Common Sense Approach to Web Usability (a new edition has been published since my initial work: Krug, Steve. Don't Make Me Think: A Common Sense Approach to Web Usability. 2013. Print.).

In the second edition (2006) [which is what was available to me at the time], in Chapter 2, “How we really use the web”, Mr. Krug presented three “Facts of Life”, to wit:

1. We don’t read pages. We scan them.
2. We don’t make optimal choices. We satisfice.
1. “Satisfice” is explained as a cross between “satisfying” and “sufficing”
2. We choose the first reasonable option.
3. We don’t figure out how things work. We muddle through.   

He ends the chapter with the following:

If your audience is going to act like you’re designing billboards, then design great billboards.

The home page of your website is a billboard.  

The application of this with respect to evaluating designs is to load a page, take off your glasses, walk to the opposite side of the office and see if you instinctively know the answer to the question: “What do I do next?”

Early this morning, I hit the website home page of five (5) custom home builders (three that I knew from my previous life and two that won organic search -- ironically the search winners were not the ones that were economically most successful/dominant a year ago).  I turned on the “NoCoffee” Chrome extension, used it to blur the pages and scaled to a laptop-width screen.

Here are the results:

The red ovals represent things that look like they may be clickable and answer the question: “What do I do next?”

Guests come to a website with an objective.  The home page is normally the starting point. On a scan level, a user must be able to quickly find the desired information. Otherwise, the user will abandon the site, return to the search engine and visit the competition. An SEO fact that seems to escape most marketers and website designers is that Google measures the rate at which users return to search after visiting a site -- if users quickly return to Google and then go visit another link, your search ranking will be lowered for that query!  A slow and/or difficult-to-use site will steadily lose search rank. Your business will suffer as a result.

Of the sites in the image above,

1. Site #1 - I have no clue what behavior the designer wants from the user.  Is the goal to force menu usage or scrolling? Both are bad UX strategies. See "The Fold Manifesto: Why the Page Fold Still Matters." The Fold Manifesto: Why the Page Fold Still Matters. Web. 11 Apr. 2016. and Wroblewski, Luke. "Obvious Always Wins." LukeW Ideation Design. Web. 11 Apr. 2016.  The site needs clear CTAs above the “fold”.
2. Site #2 - Obvious call to action (CTA) dead center. It looks like it might require some typing and could benefit from more visual contrast/pop. Nonetheless, it is obvious what the designer thinks the user wants to do next.
3. Site #3 - Three (3) CTAs.  This may or may not be better. Two of the buttons may be “below the fold” for many users. Buttons are less frictional, but now the user needs to read each one to figure out which one of the buttons is appropriate.
4. Site #4 - The CTAs match the site theme and blend in.  It took slight thought to find them -- the colors should be adjusted.
5. Site #5 - No clear CTA. The menu is red! Why? I guess they want me to think… ...or go to a competitor’s site.

In 2006, mobile web usage wasn’t really a concern.  The web was awful on phone-sized screens.

The world has changed a lot since then.

150 times per day with an average duration of 1 minute and 10 seconds

We check our mobile phones 150 times per day and the average mobile session is one minute and ten seconds (1:10min) long (see Adams, Laura, Elizabeth Burkholder, and Katie Hamilton. "Micro-Moments: Your Guide to Winning the Shift to Mobile." Think with Google. Nicolas Darveau-Garneau, Lisa Gevelber, Matt Lawson. Web. 11 Apr. 2016.)

Adams et al. argue that to succeed in a mobile world, sites need to

1. Be there
2. Be useful
3. Be quick

Intent Rich

Mobile usage is “intent-rich” (see "How Micro-Moments Are Changing the Rules." Think with Google. Web. 11 Apr. 2016.). To win on the small screen, your site needs to more than have an obvious CTA. It needs to have obvious quick paths to information.

4 New Moments

There are four new moments every marketer should know (see "4 New Moments Every Marketer Should Know." Think with Google. Web. 11 Apr. 2016.):

• “I want to know” moments
• “I want to go” moments
• “I want to do” moments
• “I want to buy” moments

Consider the following blurry home page:

It appears that there are blue buttons with graphical images for location, hours button, ??? & pictures and a red button (probably an RFI CTA).  This website home page feels frictionless and easy to use.  At a glance, “know” and “go” seem satisfied. “Do” and “buy” may or may not but it looks there is, at least, a path in this direction.

Conclusion

Your website lets you down. It could do a much better at encouraging prospects to begin the journey down your sales funnel.

Home pages that require thought introduce friction, impede usage and frustrate users. If your competition does a better job on its homepage, odds are good that it will rank better in search, that it will gain more customer eyeballs and that you will lose business to them.

Fix your home page. Sell more.

Copyright © 2016 Stand Sure. All rights reserved.

Unsupervised Machine Learning

Tay

Many readers will probably be familiar with the recent news story about Microsoft’s AI bot, Tay, that was taught to say hateful things by users (see Microsoft silences its new A.I. bot Tay, after Twitter users teach it racism).

In some news reports, it was mentioned that Tay is an example of unsupervised machine learning.

What is Unsupervised Learning?

In a nutshell, unsupervised learning means “let the data do the talking”. It’s about discovering patterns and relationships.

Common approaches

• Cluster analysis which seeks to group similar data together
  
• Latent Variable models which seek to transform the observable variables into latent/hidden variables that are inferred (i.e. not directly observed)
  

Examples of systems using Unsupervised Learning that you already know

• Google’s understanding of foreign languages (see Wikipedia's article on Google Translate) and synonyms (see How Does Google Use Latent Semantic Indexing (LSI)? [from 2005],
• Amazon’s “you may also like…”
• Netflix’s recommender system

Two places your business can quickly benefit from unsupervised learning

Lead Scoring

The problem

About half the companies with which Stand Sure works have some form of lead scoring in place. Lead scoring normally means using data to prioritize which prospects and opportunities get salesperson [human?] attention.  Prospects with a score better than a certain value get called on; those below the threshold may get email marketed but generally do not get as much human attention [unless there are insufficient “good” leads].

Every customer lead scoring program that we have encountered to date suffers from the same problem: unreliability -- the scores do not align with outcomes and sales loses faith in the scoring.

The reason for this unreliability is that organizations tend to score leads using explicit factors (some form of BANT (budget, authority, needs & timing) or RWA (ready, willing & able)).  These factors are all necessary for a sale, but they are not sufficient for predicting that one customer is more likely to buy than another. Indeed, in a lot of organizations, the data for these factors is not collected until after a customer has interacted with a salesperson.

Consider, for example, a lead scoring model that based on historical data produces results like the following, where 1=sale and 0=no-sale:

Actual	Model
1	1
1	0
1	1
1	0
0	1
0	0
0	0
0	0
0	0
0	0

The team behind this model reported to management that it was 70% accurate, which is true.  What it failed to report is that the model misclassified 50% of prospects that went on to buy (a 50% false negative rate (FNR)).

Still, 70% sounds pretty good…

When Stand Sure looked at the model, we computed a Coefficient of Determination (R Squared) value, which measures how much of the variance in the actual data is explained by the model.  In the example above, the R2 value was 8⅓%, meaning that the model correctly explained reality very rarely -- a coin toss would have done a better job. (See the Google Sheet at https://goo.gl/iNVIF8 for more statistics from this model).

The solution

1. More variables
1. sign-up page
2. session count
3. Sign-up day of week
4. Sign-up time of day data
5. the number of pages viewed in the sign-up session.
2. Clickstream cluster analysis
1. ALL web users were assigned to data-driven groups
2. For web users who signed up and became leads, cluster assignment was added to the data (it turns out that users that behave the same way online tend to have similar behaviors offline -- in this case, the leads from certain clusters were MUCH more likely to buy).
3. The company set unit sales records.

Conversion Rate Optimization (a.k.a. Fixing bad website User Experience (UX))

The problem

Most websites ____! Erm, most websites are designed from a vertical-centric point of view and not from a customer-centric POV [if you are contemplating a website design update, it is generally best to NOT work with someone who makes websites for others in your industry -- you will end up in a “sea of sameness” and a set of pages that sells well to people in your industry].

Anecdotally, 90+% of the pages on most sites are visited by only a small fraction of users.

Users get confused & frustrated and leave.

The solution

1. Association analysis (this technique is normally used for shopping cart analysis -- what items are commonly bought together)
1. Pages that commonly were consumed together were identified
1. Where it appeared to the business that the group of pages indicated confusion, content and navigation changes were made
2. Pages that had high site exit rates were simplified and users were encouraged via navigation cues to get back on a non-exit trajectory.
2. Once the user experience (UX) was improved via the incremental changes suggested by association analysis, clustering analysis was performed.
1. The conversion rate to lead for each cluster/group was assessed.
1. Personas were created to describe the groups based on what content was most consumed by the members of the group.
2. The page content and navigation was updated to better align with the perceived user intent.
1. New lead collectors were created to more closely align with the user intent
2. Downstream email marketing campaigns were designed to keep providing the members of each group with useful content
3. Outcome - exponential lead growth [the image below shows quarterly lead counts -- the digital marketing budget remained constant throughout the period shown]