I got a surprise at a recent ecommerce conference.

For the price of a coffee in the break, I offered to show people how to use Google Analytics Custom Dashboards to see all their key ecommerce goals in one place.

I was shocked to discover that many retailers don’t have any goals set up. So there was nothing to put on that dashboard. They couldn’t take me up on my offer.

Instead, people asked me “why do you need goals in Google Analytics if you’ve already got the transactions and ecommerce conversion rate?”

Here’s my answer. There are many things Google Analytics goals can do. But I’ll confine myself to just one:

Google Analytics goals can help on-line retailers increase their profits.

That’s a big claim.

How can goals lead to increased profits?

Without needing to know anything about your margins, I can show you how using goals can increase your profit by reducing the cost of acquiring new customers.

Getting new customers is vital. No matter how good your retention rate, your business will go down if you don’t attract new customers.

But the cost of getting new customers is high and it’s often not easy to tell whether the money you spend on reaching those customers is paying off or not.

To paraphrase a well known saying, “half the money I spend on customer acquisition is wasted, the trouble is I don’t know which half”.

Let me explain the problem.

When you look at your visits in Google Analytics you can instantly spot two groups of visitors without needing any goals.

1. There’s a big group of maybe about 30% of your visitors who bounce straight off the site. They’re not interested in you right now, or they’ve seen exactly what they need to see on that page and are finished for now.
2. There’s another very small group of maybe 5% or less who make a purchase on this particular visit. You know a lot about these people from the standard GA reports.

But neither of these are useful sources of new customers. What about the other 65%, forming the bulk of your visitors? They didn’t buy this time, but they didn’t bounce straight off the site either.

This group will include people who are existing customers, or people who already know about you from previous visits. You can spot these people because they have come from your emails, or they have come via a brand keyword or ‘navigation’ search.

But it will also include people who have come from generic non-brand searches, or from referring sites, or from other forms of on-line acquisition campaigns.

This last group are really interesting: they’re your potential new customers. You’re probably spending a lot of money on attracting these people, but since they didn’t buy anything this time round, how do you tell whether the money was wasted or not?

Using Goals in Google Analytics will allow you to make an informed decision on which sources of potential new customers are more valuable than others.

Which means that you can cut the money you’re wasting on sources of ‘bad’ prospects and invest your resources in the sources which are bringing you more valuable prospects.

Which means that your cost per acquisition goes down and your profit goes up.

How to Use Goals to Zero In on the Best Sources of Potential Customers

• Create goals for micro-conversions on the ecommerce journey, such as viewing product details, or adding to cart and you’ll be able to identify people who are showing strong signs of intent to buy
• Create engagement goals for people who view more than a certain number of pages. They may not be as valuable as people who reach one of the micro-conversions. But they’re more valuable than people who see only 2 pages, for example.

Use these goals to compare different sources of visitors in the GA ‘All Traffic Sources’ report. The best view for the comparison is the ‘performance view’, so switch over to that and then use the drop down menu on the right to examine the individual goals.

This view of the report makes it easy to spot the sources which are sending you visitors with high conversion rates for these goals. You now know which sources are worth investing in. Sources which send lots of vistitors but with low conversion rates for these goals are less promising, so move your resources away from them.

Using this technique you can get a good idea of which “half of the money” is being wasted and which is bringing you in real potential customers.

Other Ways of Using Goals

In this article I’ve concentrated on just one way retailers should use goals because I wanted to make it clear how you can link this approach to profit.

But of course there are plenty of other reasons why ecommerce sites should use goals. Here are some related articles on the subject

Checkout Funnel Reporting

The classic ecommerce funnel and goal report is the one for the checkout. This report is used to monitor the performance of your checkout system and learn which stages are causing your customers problems and possibly costing you sales. Here’s a power-user tip: for day to day monitoring of your checkout you can create a custom dashboard showing the checkout abandon rates from each stage of your checkout.

Error Reporting

Every error your visitors see is like a slap in the face. You’re investing money in getting people to your site. That money is wasted if the visitors have a bad experience. So you should smooth the path to the cash desk by sweeping errors out of the way. Using goals for error messages is a great way of seeing where your customers are having problems. This approach means you can even use GA Intelligence Custom Alerts to let you know when there’s something wrong and maybe even suggest the cause.

Post from: CxFocus - making Customer experience count

Why Retailers Should Use Goals in Google Analytics

Post from: CxFocus - making Customer experience count

Why Retailers Should Use Goals in Google Analytics

The first thing any GA implementation should include is two profiles: a working profile and a ‘raw’ unaltered backup profile. One day that raw profile will save your bacon.

The ‘raw’ unprocessed profile is one in which the data has not been modified in any way when configuring the profile settings or by adding filters.

You should always have such a set of data available so that you can sanity check anything else you do against a pure source. And you will also have a backup in case anything you do in your working profiles messes up the data.

How to Set Up a Raw Profile

You should not configure the profile to ignore any parameters and you should not configure the default page. Doing either of those would modify the data.

Off the top of my head I cannot think of any filters that you would dare to apply to this profile. You don’t want to filter anything out, and you don’t want to use filters to change data.

So definitely NO excluding of IP addresses, or changing all the URIs to lowercase. This raw profile is precisely the one you would want to cross-check against when debugging such filters.

What to Do if You Only Have One Profile

If this is the case, I recommend editing the name of the profile to make it clear what it is. It’s probably called something like ‘www.mydomain.com’, which will sort nicely to the bottom of most lists. So I would just add a function and warning to the end: ‘www.mydomain.com (raw data – do NOT change)’

If you haven’t got a profile like this already, you need to set one up as soon as possible. I recommend sticking the date of the next day at the end of the name when configuring this profile so that you can see at a glance when the data begins.

Setting up Goals in a Raw Profile

I’d recommend having at least that goal configured here. The ‘Reverse Goal Path’ report will then have a full, un-modified set of data to work with when you need it to go looking for internal sources of bad links. But I would also configure such a goal in one of the main working profiles these days and configure an Intelligence alert for its conversion rate. This is data is something you need to have in front of you, not hidden in a profile you seldom visit.

How the Raw Profile Can Help You or Save The Day

You’ll need the raw when configuring filters which do things like include or exclude visits of some kind. By comparing the new filtered figures with the un-filtered version you will be able to sanity check whether the difference is credible or whether you need to re-check your filters.

You’ll also find the raw profile useful if you find a situation in which a parameter you have stripped out by using the ‘ignore parameter’ setting suddenly turns out to have a use. For example, you would normally strip session ids out of a working profile. But it’s conceivable that at a later date you might want to be able to find all the sessions which encountered some error page and create a segment in order to track what other aspects those visits have in common.

Finally, you’ll also be very grateful for the raw profile if you ever make a mistake with a filter and discover days later that it has trashed some relevant data. You won’t be able to undo the damage in the working profile, but at least you’ll have the raw data to fill in the gaps in an external report…

Post from: CxFocus - making Customer experience count

Why you need at least 2 profiles in Google Analytics

Post from: CxFocus - making Customer experience count

Why you need at least 2 profiles in Google Analytics

Why would you want to set up more than one profile for each website in Google Analytics?

1. There are things which GA advanced segments cannot do
2. You should always have two sets of data

One of the classic uses of profile in the early days of Google Analytics was to look at chunks of traffic. Segmentation. So when Advanced Custom Segment appeared the days of using profiles to look at the behaviour of different types of visitor seemed over.

Advanced segments have many advantages over profiles for this purpose.

You can see the different segments alongside each other in the same report in order to make comparisons. This is particularly useful when looking at charts.

You’ll find this a great way of establishing which type of traffic was responsible for a change which you have noticed in the overall numbers.

Techniques like this allow you to see things at a glance in an illuminating way. Try setting up a segment for ‘medium contains email’ and then comparing it with the default ‘search’ segment (or, better still, a ‘medium=cpc|ppc|organic and keywords=your brand terms’ one). You may well find that surges in email traffic are also matched by simultaneous rises in navigational search visits. This would suggest that your emails are reminding people to visit the site, via search, even if they’re not clicking on the email itself. Those emails are worth more to you than the basic clicks suggest.

Custom Advanced Segments are also much better than the old ‘profiles’ approach, because you can think up new ones on the spur of the moment and they will be applied to all your historic data.

I used to love the old Clicktracks system because it allowed me to do something similar. If I suddenly thought “I wonder if…” and wanted to delve into the data, I could set up a set of rules-based conditions and use them to segment all my data. Except in those days you could go for a coffee, or maybe lunch, while the data was processed. GA does it so fast some people even use the ‘test’ feature in segment configuration to extract a set of numbers and never bother to save the segment.

But…

There are some things which GA advanced segments can’t do

The most limiting of these is that you cannot segment funnel reports. So you cannot use segments to see how different types of visitor behave in your checkout or your other micro-conversion funnels.

This is a significant loss.

Common sense says that an existing customer returning because of an email about a very strong offer is going to have a very different reaction to your shopping cart page from someone landing from a generic search and who has never heard of you, let alone already been through your checkout. So how would you set about optimising the shopping cart page for that last group when their data is mixed in with all the others?

Segmented profiles also mean that you could give different people access to only the data which is relevant to their work. For example only the email traffic, or only the unpaid search traffic. Even if the ‘different people’ is actually just you with a different hat on, it can be useful to have this task-specific view of the data without all the other noise.

So, although Advanced Segments are one of the greatest features in Google Analytics, I think every site should still set up a series of profiles, rather than just rely on the basic two.

Two?

Why you need at least two Google Analytics profiles

Yes, two. You should always have one set of raw, unmodified data being captured alongside your main working profile so that you have something to cross-check against. Or fall back to.

But I’ll go into more detail about what you should and shouldn’t do with your raw profile next time in a post explaining how to set up a raw profile in Google Analytics.

Post from: CxFocus - making Customer experience count

Setting up more profiles in Google Analytics

Post from: CxFocus - making Customer experience count

Setting up more profiles in Google Analytics