By now, anyone doing paid search knows how critical Google Ads and Google Analytics are to understanding your business. Both track and measure site traffic, which helps you evaluate your current marketing tactics to see which are most effective and which could be improved. When it comes to understanding and improving conversions, Ads and Analytics offer different functionality, and the model that is best for your business will depend on many factors. At the end of the day, it’s all about understanding, which you get from tracking tools, the customer journey and knowing where to allocate your resources around it to grow your business.

Quick Ads and Analytics Primer

Google Ads, formerly Google AdWords, make its money off of people using their platform to advertise (duh!). That said, advertisers also make money off people using the platform (hopefully), so it’s important that you can tie and track that cost back to leads, sales, and revenue.

Google Analytics is the platform that allows you to analyze your website and users, so you can determine how your digital marketing strategies are performing overall. It is a free but powerful tool that lets you track a lot more than just ad data.

Ads and Analytics Differences

While both Ads and Analytics allow companies to monitor website and user data, there are several differences. It’s important to understand these nuances so you can effectively modify marketing strategies for the best outcomes. The major differences include:

1. Attribution Models

One of the most important differences between the two tools, especially in terms of evaluating marketing tactics, is attribution models. Ads uses a last ad click attribution model and Analytics uses a last click attribution model. While this may not seem like a huge difference, it has significant implications for how you optimize advertising spend and assess and improve your conversion funnel. Attribution models and conversion paths, which measure how someone becomes a customer, are covered in more detail below.

2. Data Calculations and Recording

There are numerous data discrepancies between the two tools which result from varied calculation methodologies, data sampling issues and types of data recorded. Some of the discrepancies include:

  • Certain transactions are recorded in Analytics and not Ads and vice versa
  • Goal conversion is calculated differently
  • Ads allow flexible conversion counting
  • Ads discounts invalid clicks whereas Analytics does not
  • Analytics has data sampling issues
  • Analytics can’t track all Ads conversions
  • Ads tracks clicks (multiple clicks in one browser session is multiple clicks) whereas Analytics tracks sessions (multiple clicks in one browser session is one session)

These discrepancies don’t mean that one is better than the other, they just highlight how important it is to understand what you are measuring. They also caution businesses when combining or comparing reports from these two tools.

3. Timing

The two tools can give different conversion dates. Analytics records the conversion when it happens. Ads record the conversion when the last ad was clicked prior to conversion. There are also different processing times for receiving this data. Ads can show conversion data daily whereas Analytics requires a 72-hour processing window.

How To Optimize Ad Spend

When it comes to your marketing budget, digital is sure to be making up a bigger piece of the pie each year. With the emergence of Ads and Analytics, you have copious amounts of data from which to develop sophisticated customer acquisition strategies and conversion funnels. Many businesses use multiple marketing channels to try and convert target audiences into paying customers.

The various types of marketing channels can include organic search, paid search, direct, social media, email, referral or affiliate. Your conversion funnel is likely to include many different channels and touchpoints. Consumers tend to visit sites multiple times before fully engaging, so sample conversion paths could look like:

  • Paid Search > Organic Search > Email > Direct
  • Referral > Organic Search > Email > Paid Search > Organic Search
  • Paid Search > Paid Search > Paid Search
  • Email > Organic Search > Paid Search > Direct

Basically, there are three general parts to conversion: initiating, assisting and completing. You can use various forms of attribution modeling in order to understand which part of the conversion path, but you always want to know which specific keyword, ad, location, device, etc played at least some part of the conversion in order to fully understand and leverage your ad spend.

It’s important to  understand how these different channels work together and where your conversions are coming from. Attribution models can help you better understand your conversion funnel and where to best allocate your marketing budget.

Importance of Different Attribution Models

Analytics’ default attribution modeling is last click, meaning the last click before the purchase or conversion goal gets 100 percent of the credit. With this model you won’t be able to glean any insights about other parts of the conversion funnel. Ads’ default attribution modeling is last ad click. Under this method, the last adword, or paid search term, that was clicked before reaching the conversion goal gets all of the credit. Again, there are limitations in this method in that it obscures all but the last ad click.

For example, consider a conversion funnel that looks like the following:

Referral > Organic Search > Email > Paid Search > Organic Search

In this situation, Analytics would give all the conversion credit to the last organic search click, whereas Ads would give all the conversion credit to the paid search click. A sub-category of last click is non-direct last click. This particular measure gives credit to the last marketing activity that led to a conversion. In the above example, paid search would be given full attribution for conversion.

Additional attribution models include:

  • First Click: In contrast to Ads and Analytics, this method puts all of the importance on the first interaction a potential customer has with your content. It is not the most efficient method, but it does favor strategies based on attracting top of the funnel (TOFU) or initial customer interest.
  • Linear: This attribution model gives equal credit to all touch points from initial to assist to completion. While this method incorporates more parts of the conversion funnel, it may overvalue unimportant touch points and undervalue important ones.
  • Time Decay: This model gives more credit to the clicks that happen closer to the conversion. This is a more efficient method but may overvalue the last touch points.
  • Position Based: Also known as U-shaped, this attribution model gives 40 percent credit to the first and last interactions and 20 percent to those in the middle.
  • Data Driven: This method is relatively new and not available to all customers. It relies on an algorithm to assign conversion credits, meaning it is essentially a conversion probability model. All parts of the conversion path are valued but the algorithm is capable of analyzing what has actually happened in the past along with counterfactuals (what could have happened) to identify the ad clicks that lead to conversion.

How To Decide Which Attribution Model is Best

Deciding between attribution models ultimately depends on your business model, target audience, industry, sales cycle, financial goals and advertising goals. There is no single perfect model, and you may find yourself using different models as your business and objectives evolve. In general, here are some considerations for when the different models are most appropriate:

  • Last Click: If you are selling products or services that don’t require a lot of thought, or are frequently impulse buys, this method could be appropriate. For example, many customers do not give much thought to their toothpaste selection, so a fast moving consumer goods company could use last click to track marketing spend on their items. This method is also relevant if you have a lot of leads in your pipeline, but you want more information on what is driving the final conversion decision.
  • First Click: When you are a new business, it’s important to focus on growth and building brand awareness. In this scenario, you may want to focus on first click attribution, so you can track how successful you are at customer acquisition.
  • Linear: If you’re in a line of business, such as customer service, where each touch point with a customer is equally important, then linear attribution could fit your needs.
  • Time Decay: This method gives strong insight into when customers engage with your campaigns, which could be especially useful when evaluating time-sensitive promotions. This is also appropriate to use with longer sales cycles.
  • Position Based: If your business model or advertising objectives are set to attract users at the first and last touch points, this could be a good method to use. It gives you an end-to-end picture of your conversion funnel while recognizing that the most important interactions are the initiating and completing actions.
  • Data Driven: If your business is already receiving significant ad clicks and conversions every month, then a data-driven approach may be appropriate. The predictive algorithm is better for more established businesses with higher traffic.

Even though Google Ads and Analytics have default last ad click and last click attribution models, you can change these settings, so you are using the appropriate measurement tool for your business. In Ads, you do this by clicking on Tools > Measurements > Conversions > Name of action whose attribution method you want to alter > Selecting attribution model > Done. In Analytics, you can compare attribution models by going to Conversions > Attribution > Model Comparison Tool. Analytics typically has more options for attribution models and even allows for custom attribution models.

Ads vs. Analytics: Which is Better?

When it comes to Ads versus Analytics, they are so different that there is no judging one as better than the other. Each system is designed for different purposes, and each can be beneficial to your business. Making sure you have clear marketing goals and objectives can help you leverage each tool to its fullest capability. Understanding what data are captured and evaluated in Ads and Analytics will ensure you have the proper framework for evaluating digital marketing campaigns and optimizing ad spend. Utilizing different attribution models that are appropriate for your business is the first step in gathering the information you need for these decisions.