December 2008

What are your Web analytics telling you?
How to use online consumer data to create customer segments
and deliver more effective marketing messages. By Lydia Martell

We know that our customers are shopping online, according to one study, 80% of consumers research their purchases on the Web regardless of whether they buy online or go to the store. Yet how many of us are using the data generated by traffic to our Web site to gain insights on consumer behaviour and to fine tune our marketing messages to specific target audiences? If we treat everyone the same way, chances are we are not optimizing how we allocate our marketing funds and how we manage the user experience.

As the future of media becomes increasingly digital, it will become even more important for us to understand this data. Every page view, every search query and every item added to a shopping cart can become one more data point (referred to as Key Performance Indicators or KPIs) for us to use in customizing our marketing message and placement.

Contrary to what you might think, you do not need to be very digitally savvy in order to delve into the world of Web analytics. To make it even easier, one of the most useful Web analytic tools is free of charge. Google Analytics is available at www.google.com/analytics.
There are four easy steps to finding meaningful data outlined here below. Later, we will give you a concrete example from Conversys' Web analytics.

Step 1 - Identify your goals
This sounds like a platitude but knowing what you want to achieve before you start goes a long way toward giving you focus and direction. A goal of increasing overall site traffic, versus driving sales to end-of-stock items, will yield very different approaches.

Step 2 - Look for patterns
Review the Key Performance Indicators (KPIs) available to you. For example, Google Analytics provides over 80 different reports ranging from number of visits, average time on site, to country of origin. Decide which KPIs are the most closely related to your goals and observe if there are any patterns, e.g. when one KPI goes up, the other KPI goes down and vice versa. This process may yield surprising results and give you another view of your customer that you had not realized.

Step 3- Add time
Once you have found a distinguishable pattern, confirm it over time. This could be days, weeks or months depending on the data. Take as much time as you feel is necessary. The more comfortable you are with the pattern you’ve discovered, the more chances of success you will have in the next step.

Step 4 – Test your theory
Here is where you get to have a little fun. If you make a change to Web page X, what happens to the KPI? Try to test your theory one factor at a time, or if you feel adventurous, try several factors at a time but always keep a detailed log of what you did and of the outcomes.

Conversys applied these steps to its own Web analytics using aggregate data from retail consumer electronic customers during the busiest time of the shopping year, the Boxing Day period between December 23, 2007 and January 3, 2008. Its flyers received over 350 million hits and averaged 4.5 hits per second during the peak day of December 24, 2007.

Since the goal was to determine which factors influenced sales conversions, it tracked the KPIs which were considered the closest precursors to sales. In this case, the KPIs were “items added to shopping list” and “items added to shopping cart”. Data analysis revealed that customers were adding smaller items to their shopping lists (ex. cameras) while bigger items were being added to their shopping carts (ex. flat-panel LCD HDTVs).

The company could theorize that consumers preferred to print their shopping list and go to the store to buy the smaller items (ex. cameras) while it was more convenient to purchase the bigger items (ex. Flat-Panel LCD HDTVs) online.

For a consumer electronics retailer, a variety of marketing programs could be implemented to test this theory. For example, the retailer could provide printable coupons to ensure that customers adding items to their shopping lists would actually go to the store. Or, they could offer shipping incentives to encourage customers to purchase larger items online.

It did not take a lot of effort for Conversys to develop a simple theory that provided more information about its customers, allowing it to customize the message and potentially improve our goal to increase sales.

Lydia Martell is an interactive marketing professional living in London, Ontario. She can be reached at lydiamartell@gmail.com.