Whatever it is you do, when potential customer are looking for your products or services, you want them to consider you(r products and /or services). If you are not on the first page of Google, you are non existent (Disclosure – author works at Google).

It can however be difficult to get listed on the first page of the Google natural search results. Even if you list in natural search results you can often expand the number of search queries you can appear on, or claim more real estate on the Search Engine result page, by adding a few paid keywords through Google Ads. There are numerous great guides on what a good PPC campaign looks like. I am not going to attempt to write a better one.

One thing I would strongly recommend both beginners and advanced Google Ads advertisers. Try to limit your keywords to only exact match. And make sure you have only a few keywords that are very closely related in any specific Ad Group. This will very likely take you a bit of time to set up. It will pay off in the long run, as advertising through Google Ads is all about relevance. Make sure your keywords, ad texts and landing pages are more relevant and appealing compared to what your competitors do.

Another one I would strongly recommend is to complement that strategy with the use of machine learning. Use machine learning to optimise your creatives, dynamically assign the right amount of credit to each interaction, go into each auction with exactly the right bid, and find the right keywords and audiences. All of this is possible and difficult to beat with human experience and time investment.

If you are struggling to create a healthy ROI, consider hiring expert help. Unsure of how to evaluate the numerous different Search agencies out there? Feel free to contact us for help. We are happy to point you in the right direction.

A Google Ads account is never finished nor fully optimised. You will always be able to test and/or find new improvements. In order to do so effectively, we recommend you install conversion tracking, and install it through a technique called site wide tagging, to mitigate conversion data loss.

Conversion Tracking

Google Ads conversion tracking is a free tool to help you understand what happens after someone clicks on your ad. By knowing which keywords most often lead to conversions you can invest more wisely and boost results.

Conversion tracking is extremely easy to install. Agencies sometimes prefer third party tracking tools as they already manage other digital marketing platforms through these tools. I would strongly recommend you implement Conversion Tracking as well, as this will give additional insights inside the Google Ads account. Also it will enable advanced reporting and bidding tools like attribution modelling, cross-device conversions and customer journeys, in-app conversions and automated bidding that might not be available in their 3rd party tool.

Anyone interested in optimising their Google Ads investment should enable conversion tracking as soon as possible.

How does Google Conversion Tracking work?

• Advertisers add a single snippet of HTML and JavaScript code to your webpage. Specifically, this snippet is placed on the page your customers see after they complete a conversion — the “Thank you” page they see after a purchase, for example.
• Or alternatively, if you have the site wide tag set up, you place an event snippet on the right page.
• Every time a customer clicks your ad on Google.com or selected Google Network sites, a temporary cookie is placed on the customer’s computer so a conversion can be recorded when the customer reaches the conversion page.

Create a Tracking plan

Before setting up Conversion Tracking, you should determine which actions are valuable for your business and what are the outcomes they’d like your Google Ads advertising to drive.

For example, if you sells items on your website you are likely to be interested in measuring the number of purchases that your advertising generated. Alternatively, other advertisers may be interested in how many leads or newsletter signups they generated. While, some advertisers may want to measure how many calls or app downloads their Google Ads advertising provided.

Advertisers should set up Conversion Tracking for all of the valuable actions your visitors can take online, to ensure that you properly measure the value your advertising generates for their business.

Setting Up Conversion Tracking

You can find the full step-by-step instructions for setting up Conversion Tracking here. Advertisers with multiple accounts which target the same market should implement their tag on the MCC level. It is very important that you make sure all your Google Ads accounts are tracked at the highest level MCC account. Failure to do so, will lead to poor bidding, duplicate counting of conversions and very limited value from Google Ads attribution tools. More about Cross-Account (MCC) Conversion Tracking here.

Google Analytics goals versus Conversion Tracking

When you set up a conversion in Google Ads you will asked to choose between conversion tracking and Google Analytics goal imports. I would strongly recommend you use Google Ads conversion tracking. At least until Google Attribution is launched with similar features.

The first reason for this is that (with standard attribution logic) Google Analytics will de-duplicate conversions between digital channels. This means that if for example you run Criteo remarketing ads, and the last click came from Criteo, Google Analytics will not send a conversion into Google Ads. A lot of people think this is an advantage, as the number of conversions you see in each of the platforms you use tend to be closer to the real numbers (when you add them up).

However, for Google Ads, in the situation described above, it was part of a successful journey and there is no signal back to the Google Ads account that this was the case. I would prefer to get as much feedback as possible on what Google Ads interactions have (partially) contributed to a conversion. The more feedback to optimise on the better.

Second reason is that you can add more conversion options, such as calls, store visits, app downloads and/or cross device conversions, through Google Ads conversion tracking. There will be more on those conversion types later.

Automate Google Ads Bidding

Automated bidding takes the heavy lifting and guesswork out of setting bids to meet your performance goals. Once you have set up conversion tracking well the next step is to automate some or most of your bidding.

There are five different bidding strategies, depending on what you want to achieve. Target CPA (cost per Acquisition) is the most common bidding strategy in Google Ads. It runs on the use of Google’s machine learning technology and advertisers’ conversion tracking data. The algorithm evaluates historical conversion data, leveraging numerous signals, and optimises your bid for each auction to achieve your CPA goal. Target CPA predicts conversion probability of each impression to achieve the maximum number of conversions within your target CPA. Google recently introduced a “Maximise Conversions” big strategy, which if you are constrained by a certain budget, would give the algorithm the most freedom to find cheap conversions.

Google Ads automated bidding is the only way you can adjust your bid for every single auction you enter. Bids are based on numerous different variables around the user, the platform used and the search query that is used.

You can find a lot of answers on the Conversion Tracking FAQ page.

Marketing Attribution is the science of assigning credit to different actors in a chain of events. The science of attribution is an important topic in marketing as device, channel and platform proliferation have made it increasingly difficult to attribute the right value to different marketing activities.

The main marketing attribution challenges that businesses are trying to solve are:

• Intra channel: How are my generic search terms supporting my brand search terms? Or, what is the role of my YT video campaign in supporting my GDN remarketing efforts? How much credit should I assign to my GDN remarketing efforts if I suspect this activity is over valued on a last click basis?
• Multi channel: How is Search benefiting from Display?
• Cross device: What is the effect of my mobile ads on my desktop traffic (conversion rates)?
• Online to offline: Is my online advertising budget driving sales beyond my digital properties?
• Offline to online: Is TV boosting my digital marketing performance?
• Cross platform: How are clients moving between my web and app properties?

The scope in this specific article will be limited to the first two (digital attribution),

How to start with Digital Attribution

It is often difficult if not impossible to do your digital attribution perfectly, but pretty simple to improve your current attribution efforts. As a result of the difficulties involved, many businesses have adopted a simple last click model, where one hundred percent of a conversion is attributed to the last touch point. This is a gross over simplification, but it is an attribution model that has served many businesses well by resulting in significant growth.

Many businesses I have dealt with have made the mistake of trying to go from zero to hero in one go. The best route however to better attribution is to become a little bit better every single week (or month). First of all, you should start by looking at what an investment in improving your attribution might bring you. With other words, is it worth it? Is there a lot of scope for improvement in changing your attribution logic? How much of your time and resources is it worth?

If your customer journey is short, both in length of time and in the number of touch points, often the effect of changing an attribution model is less impactful. A silly example. If ninety percent of your conversions is preceded by only one click, for ninety percent of your customer journeys, the difference between first click or last click attribution is exactly nothing.

In Google Analytics, you can check the Conversions > Multi Channel Funnels section, where you will find five different reports who all try to answer this question in one way or another. Keep in mind that these Google Analytics reports are all cookie based, so they will very likely underreport the complexity of the true customer journey. Google Analytics can’t track between the different devices that may have been part of the customer journey.

To invest or not to invest in Cross Channel Attribution

First, let us start by looking at how complicated the average customer journey is in how many touch points your customers have with with your website (and/or marketing channels) before converting. For longer more complicated journeys the attribution logic can be an important part of evaluating your different marketing efforts. So let’s use the Google Analytics Demo account to look at this for the Google Merchandise shop.

Go to Conversions > Multi Channel Funnels > Path Length report.

You wil see the following graph ->

Please note that in the drop down menu, we have un-ticked all the Goal conversions (like email signup for example) so we are looking exclusively at customer journeys that have resulted in actual purchases in the web shop.

You can increase the length of the customer journey. For some industries this makes absolute sense. If you do a lot of branding through the Google Display Network, you might want to look at impression assisted conversions as well (if you have enabled those).

What this graph tells you is that 55.21% of the Google Merchandise store transactions that happened in the specific time period you have selected (1st August – 1st of September for example) have had one touch point (usually a website visit; could be an GDN impression or Rich Media touchpoint as well) in the last 30 days prior to the transaction date. However, those 55.21% of the transactions represent a mere 33.62% of the revenue in the month of August.

This means two thirds of the Google Merchandise revenue is the result of customers journeys that consist of more than one touch point. Would a last click model do justice to all touch points in those customer customers journeys?

Secondly, let’s look at how you would value different marketing activity under two radially different models. To see the scope for improvement, I like comparing first click with last click models, and seeing how large the difference is in how you would valuable your marketing channels and/or campaigns. I chose the “last non direct model”, because conversions assigned to Direct are not traceable to any marketing activity, and want to see where I can influence results by changing my marketing mix. I add the linea model as an “in between alternative”, because there is room to add a third model.

So, I used the GA Demo account again. I select only transactions as the conversion types and increase the look back window to 90 days to make sure I see as much of the (same device) customer journey as possible. I change my primary dimension from “MCF Channel Grouping” to “Source / Medium”, to make these insights a bit more specific to the types of marketing activities that one runs. This results in the overview below.

You should focus on the last second last column. That percentage is the percentile difference in the number of conversions credited to the difference marketing activities or channels if you assign away from the last click and credit all the first click. That is the level you have to play with on a channel level.

Usually, you find display or upper funnel activity being undervalued on a last click basis. On the contrary, brand search, remarketing activity and affiliates are usually over valued on a last click basis. If you have a lot of channels where the difference between the models is huge an investment in better marketing attribution could result in significant efficiency gains.

It is good to be aware of how your marketing attribution logic affects your optimisation efforts.

Google Analytics Marketing Attribution shortcomings

Keep in mind that Google Analytics is cookie based and unable to follow users across different devices. In reality, customer journeys are often longer than Google Analytics is able to record.

Google Analytics is pretty amazing at reporting on the effectiveness of your marketing channels. Google Analytics is a webanalytics platform, so often a visit to your digital property is required. The free version of Google Analytics is able to record impression level data from GDN and YouTube (without a click resulting in a visit to your digital property), but not from other third parties (such as FaceBook, TubeMogul or AppNexus).

If you invest heavily in branding and prospecting, you should consider upgrading our Google Analytics to the paid version, and channeling your Search, Video and Display efforts through a platform such as Google Marketing Platform (GMP).

This will give you a lot of additional insights into the role the different marketing channels play in your acquisition efforts. Also, GMP has fantastic feedback loop that allows for an automation in bidding based on advanced data driven attribution models. This sort of technology comes at a price and is generally only worth it for very large advertisers.

If you are not in that club, no worries. The easiest gains often come from in channel optimisations.

Google Ads data driven attribution is the methodology of algorithmically assigning credit to different actors in a chain of Google Ads search clicks leading up to a conversion. The usual attribution challenges provided by device, channel and platform proliferation are non existent as Google ties these touch points together. And there is only one channel and platform, Google Search.

The default model in Google Ads is last click. This is a gross over simplification, but it is an attribution model that has served many businesses well. Especially when you are up against competitors with a much larger market share the last click model can be very effective. You are, as of early 2016, also able to select traditional non-last click models such as linear, time decay, first click or position based. These models are known as “rules-based models”, since they’re based on a simple rule to assign the corresponding conversion credit.

No model is perfect. But all of these “rule based models” models are, in ninety-nine percent of use cases, inferior to the Data Driven model. The Data-Driven Attribution model in Google Ads provides an Markov chain based attribution model that values all Google Ads search clicks on the conversion path appropriately, based on each search click’s contribution to driving the propensity to convert for a specific user.

Search Ads 360 Attribution

Whereas switching to a new attribution model is a turnkey approach in Google Ads, Search Ads 360 has a lot more to offer in terms of customising bidding strategies and attribution. As a result of all those cool extra options, there are a lot of things that can go wrong as well.

The first thing to nail is your labeling of the account. Group every set of keywords that has a similar function in your Search strategy together under one label. The most common way to do it is to go for a “brand”, “brand generic”, “generics” and “competitor” grouping. But it depends on the size of your business and the number of clicks and conversions you are measuring. If you are one of the large product comparison sites, you might want to have that grouping set up for every product line (insurance, mortgages, utilities, ect ect). If you office one product and ninety percent of your investment in Search is on twenty or so keywords, you might want to stick to a simple “brand” and “generic” grouping.

As soon as those label groupings have had twelve hours to set in, you can create a Data Driven Attribution model. Make sure you select the right floodlight tag(s). If you have multiple tags for different value drivers, you can combine them in the same model and set up another specific model for each of the main ones. You can choose what models to use for what bid strategies. Your brand campaigns would want to take all value drivers into account, where as you campaigns aimed at selling “product A” should perhaps use the model created for “product A”.

Should I just switch to bidding on a data driven attribution model?

You should. But, you might want to test how different valuation of certain campaigns, ad groups or keywords looks under another attribution model first. You can do this in three different ways. First one is perhaps the simplest one. You can go to the report shown above in Google Ads (Tools > Attribution > Attribution Modelling), and compare the model you are considering versus the default last click model. You can do the same in Google Analytics and select only Google Ads traffic. On Search Ads 360 you can create custom columns to evaluate how the activity in specific Bid Strategies is affected when switching from last click to DDA bidding.

Our advice is always to keep budgets constant and change the (CPA) bidding to reflect the shift in conversion credit under the new model. This way, you spedn the same money but get more through better and smarter bidding.

Google Ads Conversion tracking and/or Floodlight versus Google Analytics goal import

If you are serious about improving your Google Ads attribution importing Google Analytics goals is not best practice. I would strongly recommend installing the (cross account) Google Ads Conversion Tracking pixel (through site wide tagging) if you have not done so already. This allows for the attribution logic to take all Google Ads search clicks across different devices into account.

An example. Customer A has visited your website twice via Google Ads Search click before he booked a hotel room. First on his mobile on his way home from work in the train. After discussing it with his partner at the dinner table, he books on his laptop at home. If you import Google Analytics goal, you will see two customer journeys, if you have Google Ads Conversion tracking installed, you will be able to see this as one customer journey and connect both clicks to the sale.

A significant step forward that takes a lot of the guesswork out of your more upper funnel mobile search investment.

Remarketing is the marketing technique where you show your ad(s) to visitors who have (recently) previously been to your website. This marketing technique is a simple and cheap way to target visitors that have already been to your digital property (website or app) and might not have converted. The longer the customer journey, the more sense it makes to remind people of your USPs, or to up or cross sell certain products.

There are a few ways in which you can create your remarketing audience lists.

Google Analytics Remarketing

Remarketing with Google Analytics combines the power of Google Analytics with the reach of the Google Display Network. Using the 250+ native variables of Google Analytics, marketers are able to create sophisticated user segments, which can then be retargeted on GDN (also via Similar Audiences and or via RLSA on Google Search) just as normal Google Ads remarketing lists. Users can create remarketing lists in the Google Analytics interface which are linked to an Google Ads account. Once created, a remarketing list can be used in the Google Ads interface just as a normal remarketing list generated with the Google Ads remarketing tag. GA Remarketing lists automatically show up in the “Shared Library” section of Google Ads and generate Similar Audiences lists just as Google Ads Remarketing lists.

Please see the Google Help Center for more screenshots and how-to’s information for a simple setup. If you want to take you remarketing strategy to the next level, take a look at advance strategies for audience segmentation in our Google Analytics start guide. If you sing in on that page, you will find a guide to creating test audiences that you can use to create A/B test to determine incremental effect of any audience strategy. You can also play around with Custom Dimensions to collect for example to favourite airport, peak versus non peak travelers, or family non family data points and use these in your segmentation strategy.

If that is not advanced enough, you can use GA’s Data Import feature to pull in customer value data like churn propensity, customer value, lifetime purchases, etc. to build lists which can be used for remarketing. These lists are also ideal for generating Similar Audiences for example for “Gold Members”. Get started on that here.

Everyone should try to use a Google Smart lists in their Remarketing strategy. It uses Google’s Artificial Intelligence techniques to determine your most valuable visitors characteristics and it selects the 15% of cookies that are most likely to convert on a subsequent visit.

If you are using the DoubleClick platform you might want to consider upgrading to Google Analytics 360 in order to use your GA audiences for your remarketing strategy.

Google Remarketing Pixel

One large disadvantage of the Google Analytics remarketing audiences is that they are and will likely be cookie based for quite some time going forward. If you have a simple strategy (based on combinations of recency, frequency and purchase behaviour), you might want to use the Google remarketing pixel. These lists will target users across the different devices they are logged in one (if they have agreed to being targeted), hence creating more reach across all devices your visitor uses. You have less options to customise the lists, but you will find extensive segmentation (churn propensity, customer value, lifetime purchases, last purchase) works well only for the large advertisers.

However you create your audience lists, make sure you (minimise or) exclude the possibility that one cookie or user is in more than one audience lists you target simultaneously. That is really important in order to make sure your data is reliable.

Other vendors that have a large following are Criteo, AdRoll and/or Facebook. Make sure you create extra reach for your remarketing efforts via text inventory that is exclusively available through GDN. And target text only in order to prevent bidding against yourself. That Google Ads text activity will give you some ammunition to question the CPA pricing some the the black box vendors out there will charge you.

Dynamic Remarketing

If you offer a wide range of products, you could greatly enhance the effectiveness of your retargeting strategy by making sure you have a dynamic creative. That creative could be based either on the different parts of the site that have been visited and /or a dynamic feed you have created. You can read more about setting that up a feed to power you dynamic remarketing here (for Google) or here (for non Google feeds). No need to be intimidated by the language, it is a pretty simple process.

Customer Match

You can also create audiences list from email lists that you might own. That can be done through both Google and FaceBook. These audience lists can then be targeted with specific offers or more generic branding activity. For example, you could split the email lists you have in three. One with regular clients that have purchased more than twice in the last year. Another list with clients that have purchased once, and perhaps one that have not purchased for the last year. Match rates between your email lists and Google or FaceBook cookies are usually far from the 100%. But once you have a match you are very likely to have all devices operated by that user in your audience list.

Cross device attribution is attribution where a marketer is able to connect a customer journey. For true true cross device attribution a marketer does not completely depend on cookies to connect interactions with his or her digital assets and/or marketing campaign, but employs a third parties device graph to identify users as they switch between devices.

How a device graph is important for attribution?

According to some recent studies, more than ninety percent of internet users use more than one device to complete a task over time. Now-a-days, customer behavior has become increasingly cross-device. A customer may search for an item on their smartphone and place the order from their laptop. This has made it increasingly difficult for marketers to recognise their customers. One had no other option but to treat one customer as three or four distinct people.

So what’s the solution? It is none other than using an attribution solution with a solid deterministic device graph that allows for customer journeys to be connected across devices. Any attribution or automated bidding solutions that is unable to track users as they move across devices to complete a task is going to provide a sub optimal result.

Imagine you run a site that sell novels. You advertise it on the Guardian and Google Ads. One of your customers clicks on your ad while using Guardian mobile app in office but doesn’t buy any book immediately. Later after reaching home, he researches your product through Google on his desktop. He clicks on one of your Google Ads and immediately buys your product from his dektop.

Without cross device attribution, all the credit will go to the ad that the customer clicked on his desktop and you will not be able to see the full customer journey (the click on your ad while using the Guardian app).

Cross device marketer helps the marketer to recognise the same customer whether that interaction occurs on a desktop, smartphone or tablet.

Working of a device graph

A device graph works by linking devices together. It links an individual to all the devices he or she uses whether it is his desktop, laptop or smartphone. The more (login) data a device graph provider has, the greater the certainty is that that device graph can make accurate connections between devices and users and the more reliable the recommendations are that come using that specific device graph.

Different types of data matching methodologies

There are two types of data matching methodologies- deterministic and probabilistic.

Deterministic matching uses information such as log in data or hashed email address to recognise individuals; no matter whichever device they are using whether it is their laptop or smartphone. For example when a customer purchases something online and puts his information such as name, address etc, it is deterministic data. Then when he will work from another device with same information, the brand will be able to know who that person is on both devices.

On the other hand, probabilistic data uses things like IP addresses, location data and browsers to recognise individuals. Weather forecasting is common example of probabilistic data. If weather forecast is showing eighty percent chance of rain then it means that in the past when there were same conditions like this then it rained eighty percent of the time.

So which is better from both of these- deterministic or probabilistic? The best answer is to ask yourself that which method can give you positive market returns. Most companies claim to be deterministic but very few have the data to provide the reach that large advertisers need.

Understanding what data sets are used is important. And make sure you agree with the way data is collected and managed so you don’t find your ads remarketing to users across different devices that do not want to be exposed to your brand.

Multi channel marketing is one of the biggest challenges facing Marketing teams. It is more commonly referred to as cross channel marketing, and can best be described as the science of determining the role different marketing channels had in driving your KPIs. Driving your main KPIs involves marketing activity that is not under the same cookie pool, across different devices, involves touch points on web and app, and should even both offline and online marketing efforts.

Duplication between marketing channels

Let us start with an example. A typical customer journey faced by a cycling brand that involves four different online channels (or platforms), followed by an online conversion (all on the same device).

Interactions with your marketing activity for this specific customer journey start through a DSPs interest category campaign (and was clicked on). The cycle enthusiast visits your site, scrolls through the latest racing cycles on offer, and decide she needs to do some more thinking about the purchase. She is not heavy internet users but she sees the same ad in her Facebook timeline through your Facebook interest category campaign a week later.

You (the brand) start retargeting this prospect after the first visit, reminding her that the offer for extra discount lasts until the end of the month for a co-marketing campaign with a large online retailer.

Our cycling enthusiast has decided on a racing model but is unsure about the brand and performs several searches to find out information about your cycle brand and and alternative racing cycle brand. In this process you pull her back to your site twice through Google Search.

Your Criteo retargeting campaign, using a dynamic feed to show our cycling enthusiast several racing cycles, gets seen a few times over the next five days. Eventually, our travel enthusiast gets her pay check in and decides she can afford to buy the racing cycle. She does a search for your retail partner (Michel Racing Cycles), clicks on the Google Search ad, and purchases the racing cycle.

Here is a visual of that customer journey:

There are four different platforms here:

• Facebook and Criteo will each take 100% of the credit for this sale. Both independently of whether our cycling enthusiast clicked on the ad or not.
• Google will take 100% of the credit for this sale (only after a click on the ad).
• The DSP interaction (lets assume DV360) and the Google Search interactions can be measured through the same interface in DV360. So this platform would be able to show you a customer journey of four touch points, and you could choose how you would want to allocate between these four interactions. One caveat, if you would use Search Ads 360, you would be able to see a visit but not an impression (in SA360).

Michel’s Cycling Shop is a sophisticated advertiser using the Google Marketing Platform, Facebook and Criteo. It ends up with a sale attributed fully to each platform. How should one go about trying to create order in this attribution nightmare?

Adoption of more unified measurement approach

There are different ways in which one can track this multi channel marketing activity. The most common option is Google Analytics. The vast majority of businesses out there use it for this purpose and it would track all click activity. You could add a custom channel groupings to label marketing channels in a way that would make sense for your business. For the customer journey above you would be able to see all clicks (with a standard setup) but none of the impressions. And since all these interactions happen on the same device, we can map this journey quiet well using Google Analytics.

Multi Channel Tracking Tools

Alternatively, you could use Floodlight click trackers to get the Criteo and FaceBook ads in the tool. This would allow you to record all the clicks of activity that you can’t measure through the Google Marketing Platform (GMP). The big advantage is the cross device element of GMP, allowing you to tie devices back to users and connect customer journeys as consumers move from one device to another.

Alternatively one can use alternative web analytics packages such as Adobe, IMB Digital Analytics or Mixpanel to record and analyse the way prospects find your site. These products would have similar capabilities to Google Analytics.

Or you could take it one step further and involve a specialist player. The main multi channel marketing tools in the United Kingdom are, in order of market share, Visual IQ and Convertro, Google and FaceBook attribution are alternative tools that can provide interesting insights.

Our brand is around supporting your organisation to become a bit better at Marketing every single week. A brand is a promise. Your brand is what tells the story of your organization. It reflects the personality and values of your organization; what you stand for and what you are offering. Your brand is not your logo, it is not your tagline, it is not your website or your app. All of these things are reflective of your brand.

Branding or shaping your brand means creating a positive image and reputation for the product and company as a whole. Creating recognition of your company and products and provide meaning for the brand. All of this with the goal of giving customers a preference for your product(s).

What can your brand do for you?

• Shape expectations
• Signal a certain quality level
• Build relationships with customers
• Affect customer retention
• Make consumer decisions easier

As such, brands become increasingly important in a saturated market with many competitors and are a source of competitive advantage that affects your bottom line. Digital branding is an important part of your brand strategy and should be used to fuel data gathering, testing and learning. Digital branding should be used as the “tip of the spear” of any branding strategy. Curious to learn more? Contact us and let us show you.

All the fast growing online businesses out there measure incrementality. They learn fast what works well, and what works better. As mentioned before, many (online) marketing platforms, publishers or tools overstate the importance of the role that media played in driving your KPIs. This is usually because they measure correlation and not causation. Many of the attribution solutions do not have base line built in (what if I stopped advertising altogether). Nor do any provide an easy framework to test causation.

Keen to know what your marketing investment is actually driving? Really keen? You will need proper test and control groups and execute tests on a constant basis. Let me talk you through one easy setup, and one hard one.

Incrementality testing on RLSA and or Remarketing

Google Analytics randomly assigns each of your users to one of 100 buckets. For any given user, the User Bucket dimension (values 1 to 100) indicates the bucket to which the user has been assigned. By including a User Bucket condition in audience definitions, you can create multiple audiences that are identical in composition except for their User Bucket values. You can then compare the effects of different campaigns on identical audiences.

For more on best practices to set up Google Analytics audiences please go back to our Google Analytics audience chapter here.

For example, you might create two remarketing audiences that share the same Age, Gender, and City, but are differentiated by User Bucket. You can then run a different version of your remarketing campaign for each audience to see which version is most effective. Or run nothing on part a specific percentage of the User Bucket to find the base line of users that would come back and convert even without being remarketed to.

Just keep in mind that the User Bucket is cookie based and not user based. You might want to combine these random user list techniques with the Google Ads remarketing list. This will have a way cleaner test with a lot less overlap between test and control groups.

Famous eBay Paid Search Incrementality tests

The most famous examples of testing incrementality are probably the researches done by eBay. There are two major ones that have been publicised and discussed widely. The first one, claiming that Brand Search “did not work” and that “generic search value was mostly in new user acquisition”, has been widely critiqued. Here is an interesting quote from that report:

This paper reports the results from a series of controlled experiments conducted at eBay Inc., where large-scale SEM campaigns were randomly executed across the U.S. Our contributions can be summarised by two main findings. First, we argue that conventional methods used to measure the causal (incremental) impact of SEM vastly overstate its effect. Our experiments show that the effectiveness of SEM is small for a well-known company like eBay and that the channel has been ineffective on average. Second, we find a detectable positive impact of SEM on new user acquisition and on influencing purchases by infrequent users. This supports the informative view of advertising and implies that targeting uninformed users is a critical factor for successful advertising.

eBay very recently published another Search advertising research. Here is a small recap of that research:

Paid search, also known as Search Engine Marketing (SEM), allows advertisers to target users of a search engine with relevant ads. It is broadly adopted by advertisers due to its superior capability to drive users, traffic, and conversion compared to other marketing channels. It also provides direct consumer behavior metrics such as ad impressions, clicks, website visits, and subsequent conversions for advertisers. However, the true effectiveness of paid search has been hard to measure, as the sales led directly by paid search ads might lack causal effects.

What are the incremental sales or user acquisitions truly driven by paid search campaigns?

The answer typically involves detecting small signals out of large noisy consumer behavioral data in a controlled experiment.

At eBay, we’ve continued to test and learn. In this latest study, we have developed a hybrid Geo+User experiment approach, and conducted the first-ever long-running field test over one and half year to measure the incremental impact by Google paid search campaigns on one of the largest e-commerce platforms: the U.S. eBay marketplace. Among our findings:

• Paid search drives statistically significant sales lift to the U. S. eBay marketplace.
• Paid search is also an important source for acquiring new users. The user acquisition lift is higher than the immediate sales lift from paid search campaigns.
• The long-running test reveals a strong seasonality trend in paid search effectiveness. Paid search campaigns made the biggest difference on sales during holidays.
• It appears that ad spends need to reach a certain threshold to enable the overall effectiveness of paid search. However, further increasing spends beyond that threshold increases cannibalisation of other marketing channels or organic sources. Advertisers are encouraged to run experiments regularly to guide marketing investments.
• Natural search performance experienced nearly double-digit gains when paid search was turned off completely. Nevertheless, natural search cannot fully substitute for paid search traffic. Natural search and paid search shall work in a complementary way to enhance brand presence and promote customer conversion.

Published at the 2016 ACM Conference on Economics and Computation (EC’16), the full paper can be accessed here. It is a fantastic example of how to measure incrementality (if you have the resources that eBay does).