Analytics Framework

Driving digital sales requires the right combination of quality traffic and customer scale.  The quality of the traffic will impact it's likelihood to convert. 

Traffic:  It’s important to understand  and measure traffic by marketing source (Google, Bing, Facebook, Pinterest, Display, Email, or Organic, etc…), response source (website, phone calls, or store), and device (mobile, desktop, or tablet). 

Note - Depending on product and industry, the idea of driving more traffic via phone calls maybe be an area to explore.  My experience shows that ROI can be 2-4x higher from phone calls vs. web response, both in e-commerce and lead generation. Always test. 

Traffic Analytics:

1.      Review traffic levels and patterns by the following and establish a baseline.

a.      Portfolio

b.      Marketing source

c.      Campaigns

d.      Keywords

e.      Device

f.       Location

g.      Browser

h.      Day parts

i.       Screen Resolution

j.       New vs. Returning

k.   Time period

2.      Understand how much each visit is costing (Cost Per Click).

3.      Understand market opportunity by source and portfolio; how many impressions are available by source.  Ex. How many monthly impressions are there for the term “Mortgage Re-finance” in Google Search vs. Google Display Network. 

4.      Understand click-through rates by program, by campaign, by keyword, by device, etc…  

5.      Pageviews – Review the average number of page views per visitor and understand the type of content being consumed.

6.      Time on Site – Review the average time on site per visitor to understand level of engagement.

7.      Bounce Rates – Review bounce rate % to determine how relevant an experience you’re creating between the Ad  (banner, text ad, post, direct mail, DRTV ad, radio spot, newspaper ad, etc…) and the landing experience (landing page, website, call).

8.      Sync up keyword traffic and conversions and value per keyword; integrate these learnings into a content strategy, prioritize based on value (scale * value per lead), and create new content accordingly.

9. Path analysis - Set up content paths to study the 'clicktrails' taken by your visitors from where they landed to conversion.  Find observations that stand out.  Test design and content adjustments.   

Conversion Analytics:

1.      Define conversion events (goals) – Lead, sale, call, download, email sign-up, webinar sign up, video-plays, and chat session.

2.      Break down visits into ‘stages.’

a.      Visitor arrival – To page, or call.

b.      Visitor acquisition rate – Something signifying visitor has landed at correct place. An example is a 2nd page click from initial landing.

c.      Visitor consideration rate – Something signifying visitor has interest in your product or offering. An example is filling out first field of a lead form or clicking ‘add to cart.’

d.      Funnel % completion rate – Review of how far into the funnel a customer goes.  Example is filling out the first 5 fields of a 7 field form, or arriving at the ‘check out’ process.

e.      Conversion rate – Completion of defined stated goals. 

f.       Abandonment rate – The converse of conversion rates by stage.

g.      Optimize – Once you have enough data, analyze it and identify testing opportunities for each stage, including a plan for different treatments (design, copy, process, order) for each stage in order to optimize your funnel.   Look for opportunities to build your baskets (recommendations), and capture lost leads (email sign up exit pop-ups).


Conversion tools:  Google Analytics, Omniture Analytics, Heap, KissMetrics.

Funnel Analytics:  Heap Analytics.  

Heat Maps: Crazy Egg.

Keyword Research: Google AdWords, Bing Ads, SpyFu, SEMRush, WordStream.

Call Tracking:  DialogTech, CallRail, Calltrackingmetrics. 

A/B Testing:  Unbounce, Optimizely, Visual Website Optimizer.

Reporting & Analysis: Tableau, Ninjacat.