Segmentation and personas are areas of interest to traditional marketing departments. But now with web analytics, consumer segments can be set up and analysed real time and/or historically with many of the standard web analytics tools such as Google Analytics, Omniture’s Site Catalyst or Discover, Clicktracks or Webtrends.

If you look at the average conversion rate for your whole website, on it’s own although useful, would considerably more insightful if you could filter the effectiveness of different segments / people / campaigns - see the conversion rate of .
What do I mean by a consumer segment?

It could be PPC (pay per click) campaign traffic, or seo traffic, or email campaign traffic. Or a segment where people clicked on a banner or landing page on the website.

You can then easily compare the average conversion rates of these different segments and also bear in mind actual amount of traffic from each segment. For example, email campaigns might have a conversion rate of 4.2% compared to PPC traffic with a 2.6% but there might be significantly more PPC traffic than email traffic in which case, better to spend one’s time and efforts optimising PPC than email campaigns.

I’ll post some more on segmentation later this week. Thanks so much for reading!

If you are a company with a sizeable email marketing budget and list you will be interested in increasing sales or increasing leads. But what should you key performance indicators be?
Some choices for a ecommerce-driven business are:

Sales (total sales, sales by campaign/type/segment/cell, per e-mail delivered)
Profit/margin per e-mail sent and campaign level
List growth/churn (size of database, growth month over month, churn rate month/aggregate)

Some companies are more about lead generation than pure sales. They want to get a lead into the hands of a partner or call centre or salesperson–so they are about efficiencies, rather than sales and/or profit margin. There choice could be:

1. Cost per lead
2. Funnel movement (how leads migrate through the funnel stages)
3. Total reach (how much of the prospect database can be reached at any given time). This includes churn metrics as well.

Notice that the primary KPIs for the two instances above are not about click-through rate or open rate.

When the real focus of the program is purely branding, the metrics seem simpler, but with a deeper definition:

1. Response rate (both open and click). Brand marketers are increasingly looking at the total reach of their base and less at campaign-over-campaign results. They want to count how many eyeballs were exposed to the brand each month, the resulting interactions and incremental Web exposures.
2. Site visits and site journeys. Site traffic statistics driven from e-mail are becoming increasingly valuable to see what content and interests are driven from each campaign/program and business division. Depth of site exploration… List growth. See how email traffic performs against searches made, types of products bought so you can define your personas against email marketing.

All brands place increased value on the size and relative growth of the database since it represents a potential share of market and voice. It is essentially the lifeline to reach a mass of customers in an efficient manner. This, combined with qualitative feedback, is quite useful in measuring brand attitude, awareness and level of involvement with the brand.
So also for example in this context thinking about how to encourage higher subscription rates to an email newsletter via personalised call to actions based on site activity or cookie id to move more people from your bottom left to top right (great customers).
Here are some standard KPIs but obviously every company’s KPIs will be different and will need to evolve over time as well.

E-mail KPIs:

E-mail sent/delivered
Campaign over campaign impressions/opens (total/unique/% rate)
Campaign over campaign clicks (aggregate/unique /% rate)
Click to open (% who open the message and click through)
Churn rate (% unsubscribe/opt out/undeliverable)
Send to a friend (viral rate)
ISP domain response (open/clicks)

Commerce-Driven Program KPIs:

Total sales (campaign, month, quarter, segment)
Profit/margin per e-mail sent / source
Sales per e-mail sent
Cost per e-mail sent
Average Order Value (AOV)
Number of orders
Conversion rate (to open/click through)
Number of site visits (page rank)

Demand Generation

Number of leads (by product/client type)
Number of downloads
Site visits
Page Rank
Cost per lead
Conversion rate
Membership/subscription/database growth

Web Focused

Cost per visitor
Number of leads by entry page
Heavy user share
Average page views
Stacking effect
Length of site visit by source

What do you think? Have I missed some that you think glaringly obvious…
Thanks so much for reading

Emetrics has come to a close after a few hectic days in DC, interspersed with seeing the Dalai Lama in George Town, the solar powered homes exhibition on the mall, the White House, hours chatting in the omni shoreham lobby bar and swimming in the invigorating heated outdoor pool at sunrise.

That asides, what has been going on? Or as my American counterparts would say, what are some of the key takeaways in terms of consumer understanding and behaviour. I’ll do another post about Google Analytics and Microsoft’s Gatineau this weekend.

Jim Novo of Drilling Down fame, spoke about speaking the “exec level” language that CEO/CFOs understand. If we think about our sales pipeline, it is the predictive/future likelihood to happen that execs are interested in when it comes to understanding our online data, sales and consumer behaviour so you can focus your efforts, marketing spend and optimisation efforts where they will have the most impact. Which are your dreck customers, your former best customer, new customers and best customers - map them out on a two dimensional value map with an XY slope.

Use recency, frequency and latency (you can even begin looking at these with Google Analytics) to understand your best and worst customers and grow your best customers. And importantly, build your predictive customer performance pipeline with your CEO/CFO so that they understand it, help you build it - which helps significantly with buy-in. Buy-in let’s face it, can be the biggest obstacle to taking action in any company.

Joseph Carrabis, the web analytics association new anthropologist and neuro-behaviourist on the scene, spoke about really taking advantage of our hard wiring to make our audience do and think what we want them to think or do. As human beings we all apply our own stereotypical and prejudiced frame of reference to everything into which we come into contact. For example in the context of images on a webpage, which image and at what position and angle will trigger what emotions or thoughts at a subconscious level. If an image is positioned at an angle, it implies motion. A photo of a couple, an elderly man, a teenager and early thirties woman will also, all provoke different inferences from one’s audience. To illustrate this, Carrabis engaged 50 of us in a persona exercise where we had to sit down after he namecalled six photos to tell him which one we thought was the Economics professor in Beijing. Interestingly, most of us thought the middle-aged conservative looking white man, was that character - and we were right. The key thing being the inferences that we draw.

In terms of multi-variate testing, the weather channel, used a variety of different images, a couple, then a man and also a woman to see which image was working more successfully in terms of optimising the page for it’s audience and hence having the highest conversion rate. Interestingly, the web page version withe the image of a woman on her own had a much higher conversion rate than other versions tested. This can be linked back to Carrabis point about the power of assocations, inferences and our pysche hard wiring on what we think about images, positioning and sound on a webpage.

Neil Mason, a fellow Londoner, talked about segmenting one consumer segments into tribes (richly developed personas in other words), using datamining to provide statistically robust anomalies, patterns, associations that stand out from a business commercial perspective and use these to identify key drivers for purchase and identify the most valuable consumer segment. For example, with a case study on the Royal Mail, segments included price finders (10%), cottage industrialists (2%) and regular posters (1%) - which were the most valuable segment. They also indentified that visitors who “saved a quote” on their first visit were signifantly more likely to become and continue to buy from the website and be the website’ most commercially valuable segment (worth most money). They used these consumer segments to drive email marketing segmentation and discovered that emails sent 4 to 5 days after their last visit were most likely to convert. Less than 4 days was too soon (the visitor was still thinking about it) and more than 5 days and the conversion rate began to drop. It’s all about the timing - oh - it’s recency again.

 ”Where are most valuable customers, how should I optimise my site?”

Thanks for an interesting emetrics everyone and I look forward to meeting those I met again soon