Google Analytics Ecommerce for Online Marketers

Let’s review Google Analytics E-Commerce as a feature before we dive right into how it can help your business. The E-Commerce feature added to Google Analytics integrates into your website relatively easily. If you’re not familiar with some basic Javascript, then you’ll need your webmaster or a developer to help you add the feature to site. In its most basic form, Google Analytics Ecommerce allows you to track near real-time sales data for particular orders, products and categories of merchandise. This feature in and of itself wouldn’t be particularly extraordinary to most etailers who have access to real time reporting anyway, except, for its priceless integration with the other features that Google Analytics has to offer: Traffic Sources, Visitors, Content, etc.

Combining ecommerce data with traffic source data is one of the most powerful tools available to an online marketer, whether in an established company or a small start up. Prior to the introduction of the ecommerce feature within Google Analytics, marketers were forced to mark actual conversions with a rough “average order value” size which greatly limited the power of the data. Let’s take a look at a hypothetical example of how the new feature enhances a marketers power:

With Google Analytics and Adwords, you could previously (before the Ecommerce feature) tell that a keyword like “chocolate candy” had a conversion rate of 2%, generating 2 sales for every 100 unique visitors through paid search, while “chocolate covered truffles” had a conversion rate of 2.5%. Without knowing the actual value of those conversions on aggregate, the data could only be used as a rule of thumb. Let’s assume that you bid $0.50 per click on each of these keywords and that your net margin on the sale of all chocolate is 80% after accounting for overhead, etc. Lets also assume, although untrue, that the advertiser competition and number of clicks and sales generated under this scenario are equal and statistically significant indicators of actual values. The best an online marketer could do under this scenario would be to try to estimate average order value by looking at the product mix in the particular category, which may not be that different, and try to drive down the average cost per conversion to a level that appeared as if it was profitable.

With Google Analytics ecommerce, this is no longer the case. The marketer now has the power at his fingertips to tell at the keyword level the actual value of visitors. Let’s return to the example above and make some hypothetical assumptions. If the average order size for the keyword “chocolate covered truffles” is $50 and the average value for the term “chocolate candy” is $25, then the marketer is spending $25 to make $25 in sales for the term “chocolate candy” and is actually losing $5 every time an individual makes a purchase. The recommendation for this key phrase would be to: lower the bid, enhance the landing page for optimization, and test different pricing strategies to optimize the mix of variables. For the purposes of this analysis we’ll assume that conversions only generate one lifetime purchase, and so the lifetime value of a customer is only equal to their first purchase. If we return to our example, the key phrase “chocolate covered truffles” would spend $25 to bring in $62.50 on average, yielding a profit of $25.

There are many other complexities that are not accounted for that must be taken into consideration in the real world. For example, many of the most profitable key words are the “long tail” words and phrases that have high conversion rates, but don’t occur often enough to gather statistically significant data with regards to per visit average values. In these instances, it’s best to remain conservative and apply a bidding strategy that’s more likely to be profitable in the long run. Depending on your cash flow, it’s also worth while to determine the average number and value of lifetime purchases for individuals that came to your site from paid or unpaid search. The ecommerce data can also be used in assessing which words in natural search should be targeted through SEO efforts and vice versa for keywords where you’re already ranked high in the SERPs.