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Revenue: Google Analytics Ecommerce

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Revenue experiments are focused on buying funnel optimization, and on increasing LTV (lifetime value) and ATV (average transaction value), for example, using upselling, changes in the range of goods and monetization models of products and services.

Google Analytics Ecommerce has a powerful set of reports. The maximum effect can be achieved if you configure Enhanced Ecommerce on the website (read more here).

Example 1.

The online buying process of tours resembled the process of purchasing a plane ticket: choosing dates, entering passengers data, then inputting the payment data, and this process was too protracted for users. That's why experiment with a single click payments implementation exceeded all expectations. In this case, the user doesn't need to choose a date and fill in travelers info. He only needs to specify the number of travelers and to pay for the trip. All other data can be specified after the purchase or during the week. How did Tom's team evaluate the success of the experiment? Of course, the key parameter is the Ecommerce Conversion Rate.

(Ecommrce Convertion Rate)

And as a consequence - revenue

(Revenue)

He can also delve deeper and track Conversion Rate at every stage using funnels, which we’ve already spoken about in the Activation.

Example 2.

The success of the previous experiment inspired the team to get rid of selecting the number of travelers as well. After analyzing the most popular tours, a little experiment was chosen to be implemented: create a tour for couples only, and only for the city of Paris. This segmentation gave an increase in Conversion Rate and profit for this city, which let to increase in the number of cities participating in the experiment.

The effect of the experiment can be calculated like that:

(Product Revenue) (Product Category=Paris)

Example 3.

The majority of purchases are made not on the user’s first day, but during the week. Tom's team knows that if they encourage the emotional, spontaneous purchases, they can increase their Conversion Rate and revenue. To increase the number of purchases made on the first day, they’ve chosen to show the coupon to the new visitors, which is valid for one day. How to measure the success of this experiment?

For this, you can use the dimension called Days to Transaction. We can measure the increase in sales happening on the first day,

(Revenue) (Days to Transaction=1)

as well as how revenue decreased or haven’t changed on other days.

‍(Revenue) (Days to Transaction≠1)

You can also see how much you’ve earned because of the coupon.

(Revenue) (Order Coupon Code=BayToday_34232)

Example 4.

Selling related products or upselling can raise the Average Transaction Value and increase the revenue. But what extra things can be sold, in the already packed journey, where everything is included: hotels, itineraries, and attractions. The team had a lot of ideas, but they all haven't been scored high enough during the discussion. Until a simple idea appeared; to sell discounts at restaurants along the route. For $15 you receive 10% percent discount, that for sure will pay off if you go in. The most amazing thing is that this item costs almost nothing for Tom's team, except actually sending a special coupon to the traveler. Restaurants were more than happy to give discounts in exchange for a steady customer stream.

Thus, while purchasing a trip, there was an option to include a discount for two restaurants, for just $15.

To measure this experiment, you can track changes in Average Transaction Value or revenue.