A common misconception in the industry is that incrementality only applies to verticals where customers visit the stores frequently (think QSR, gas stations, home DIY improvement, etc.). However, this could not be further from the truth.

Incrementality Is NOT Simply Pre/Post Exposure Testing

Let’s start with understanding what incrementality is NOT.

Many people mistakenly believe that measuring the effect of ad exposure on visitation can be calculated accurately by applying a pre/post-test. In other words, by looking at the number of visits a given person was making before the exposure and comparing that to the number of visits that same person made after exposure, you assume any difference in visits can be attributed to the advertising campaign. 

Pre/post testing has a number of methodological issues, but for a moment let’s only consider the utility of a pre/post-test in determining the effectiveness of ad exposure in verticals with low customer visit frequency (like auto-dealerships, tourist destinations, etc). Since most people do not go on holidays multiple times a month, or to the dealership enough times in a month, comparing visiting behavior before and after exposure might create a situation where we have nothing to compare behavior with! An approach that used pre/post testing would not be useful in these cases.

Cuebiq’s Incrementality Methodology Goes Deeper

In a nutshell, Cuebiq’s incrementality methodology works around this problem as follows: for every device in the exposed group we look at all the brands they visited in the 12 weeks prior to the campaign, and build a visit profile—for which is simply all the brands a person likes to visit. So, for every device that was exposed to the campaign, we have a series of brands that describe that person, creating a “persona”. Personas can vary widely from person to person. For example, think about the difference between people who only visit fast-food restaurants and bars (Persona A) and people who only visit parks and gyms (Persona B).

We then repeat this process to create personas for every device in both the control and exposed groups. From there, we can identify similar personas (Personas A for example) that exist in both groups. That is, we end up with devices in both the control and exposed group that are very similar to each other in terms of what brands they like (and by extension, their interests), except that one set of personas is exposed, and the other ones are not. The only difference between them is the advertising exposure.

See where this is going? By using machine learning we can predict visits to the POI of interest for Personas A in the exposed group, then Personas A in the control group, and link their visit history to their current visiting behavior. We then use sophisticated machine learning to create a mirror version of exposed Persona A that behaves as if they had never been exposed. This goes beyond a mere look-alike logic and even standard control-group methodologies: you’re comparing exposed devices to themselves, unexposed! 

The difference in visiting behavior between Persona A that was exposed and their mirror version had they not been exposed gives you the increase in predicted visits that happened because of the exposure – that is, the incremental visits!

Putting it All Together For Low-Frequency Verticals

Now let’s go back to our use case of verticals with very low visit frequency, like auto-dealerships and hotels. Remember: the problem with these verticals is that very few customers repeatedly visit an auto-dealership or a hotel throughout the duration of a campaign. 

With our methodology, repeated visit frequency by the same device doesn’t matter! Why? Remember that these personas are composed of many different devices. As long as there is at least ONE customer within that persona that visited a location at least ONCE (in both control and exposed groups), we can establish an average baseline of visitation for both control and exposed groups. Then, we use the same mirror technique to understand the average baseline of visits for the exposed group had they not been exposed.

See how this has nothing to do with how many times a specific person visited repeatedly? It’s all about having at least one device in each persona with a visit so we can establish baselines of visitation for both exposed and control groups, measure the difference, and determine incremental visits.

An Auto-Dealership Example

Let’s look at an example that simplifies our methodology for the purpose of illustration: an auto-dealership is running a campaign to promote people to visit their brick-and-mortar stores. You don’t usually visit these stores. However, all we need for our methodology to work is for you to visit sometime during the campaign.

• First, we create a Persona for you: “Persona A”
• Then, we find devices that look like you (Persona A) in our exposed group
• We also find devices that look like you (Persona A) in our control group

Imagine there are 100 devices that look like you in the exposed group, and 100 devices that look like you in the control group. Even though you only visited once during the campaign, we are able to see that, for example:

• Your look-alikes in the exposed group visited 5 times (so 5/100 or .05 visits)
• Your look-alikes in the control group visited only 2 times (so 2/100 or .02 visits). 

We then apply machine learning to create the mirror version of the exposed group (like we mentioned above), to calculate the incremental effect of ad exposure on devices in Persona A. In this case, the calculation would be something similar to .05 - .02 = .03 incremental visits – or, we can attribute 0.3 incremental visits to the auto-dealership to the ad exposed to devices in Persona A.

If we add all the incremental visits for all personas in the exposed groups across the whole campaign, we get the total number of incremental visits for all of the exposed groups!

So, as long as there are people in both the control and exposed groups that look like you (in terms of which brands you like to visit), we are perfectly able to determine incrementality by roughly following the logic we described above, even for verticals with very low visit frequency such as hotels, auto-dealerships, etc.

Pretty powerful isn’t it?

The post Incrementality for Car Dealerships or Tourism? Yes, it’s a thing. appeared first on Cuebiq.

Incrementality is Cuebiq’s proprietary methodology to calculate the portion of visits that can be linked (attributed) to the ad exposure during a campaign. For example, if we measure 100 visits to all of your retail stores during a campaign (and attribution window), our algorithm can determine that approximately 15 of those total visits happened because of ad exposure, so your campaign showed an incrementality effect of 15%. This is a powerful insight that will help you understand how effective your campaign was at increasing foot traffic.

We've compiled a list of frequently asked questions when it comes to incrementality. Read on to learn more!

What are incremental visits?

Incremental visits are visits that happened most likely because of the ad exposure—as opposed to organic visits which would have happened anyway without a campaign. The total visits measured are the sum of organic and incremental visits. We measure incremental visits cumulatively (they can only increase or will remain the same if we don’t measure any in a day) and show new results daily. 

What is the incrementality effect?

The percentage of total visits that are incremental, that is, the percentage of all projected visits that can be attributed to the ad exposure. 

Is incrementality at Cuebiq a pre/post test?

No. A pre/post test is made by looking at the visits of a device previous to the exposure, then measuring the visits after the exposure. For example, if I go to my favorite coffee shop 3 times a week, but after seeing an ad I went 4 times this week, the ad yielded one incremental visit. This is subject to seasonality and other methodological issues. At Cuebiq, we use a control group to establish a baseline of behavior for the exposed group to understand campaign effects.

How does the control group come into play for incrementality?

Our methodology uses the control group to establish a baseline for the visitation behavior of the devices in the exposed group. Our model learns the relationship between a device’s visitation history (12 weeks) and the visits to the brand of interest during a campaign. We then transfer the information learned from the control group onto the exposed group, to see how they would have behaved if they had not been exposed. 

Why is Cuebiq’s solution better than the competitors?

If you wanted to understand the effect of ad exposure on a customer —let’s call her Amy— would you rather compare Amy against Susan in the control group, or against Amy herself had she not been exposed?  At Cuebiq, instead of measuring incrementality by comparing visits in the exposed and control groups, we go one step beyond and compare the exposed group with a mirror (counterfactual) version of itself that behaves as if exposure never happened, so it’s a much cleaner comparison. Additionally, we obtain incrementality results at the device level, allowing us to segment campaign effects by sociodemographics.

How does incrementality relate to uplift?

Uplift measures the percentage difference in visitors during a campaign, whereas incrementality refers to visits during a campaign (and attribution windows).

Can we see campaigns with no uplift but positive incrementality?

Yes. Since uplift measures visitors and incrementality measures visits, a campaign with no uplift but positive incrementality can be interpreted as having increased the visits of the same number of visitors —more visits per visitor.

What are projected incremental visits?

From the impression logs we get from our clients, we match those devices present in our panel. We use this subset of the devices to perform our incrementality estimations. Because these measurements are done on a subset of the total devices in the impression log, to provide numbers for the entire campaign we project the visit results from the matched devices back to the impression log. These are the projected incremental visits.

What is the input for the incrementality model? What is a visitation signature?

Our proprietary model takes as input the visit history of a device for 12 weeks previous to the campaign. In other words, we look at every single brand the device has visited in the previous three months to build a visitation signature. Our main insight is that this signature carries significant information about the device’s preferences for brands and lifestyles, which in turn will affect how effective a campaign is for each device.

Why do we use a 12-week window to calculate the visit history of a device?

Ideally we would like to use a longer time-frame, but with a longer time-frame we run into issues of seasonality (for example, the brands that people visit during the winter are very different that during the summer). The 12-week threshold is a reasonable compromise between how far back in time to look without seasonality issues, and the need for a robust visitation history.

If you’re only looking at a 12-week window, is incrementality still good for verticals where purchases are done with low frequency (e.g. car dealerships, telco)?

Yes. The methodology only requires that people visit the store, regardless of whether they go often or not, because we don’t compare a device’s visits before and after the campaign. As long as there are visits being measured, the methodology is able to measure incrementality.

How should I interpret “loyalty” in the behavioral analysis section of Clara?

Loyalty (the horizontal axis) can be interpreted as the baseline likelihood that a device would have visited a brand store without the campaign. Values to the left along the axis indicate less loyalty, that is, devices that in general would not have visited the POI of interest even without a campaign; values to the right along the axis indicate devices with high likelihood of visiting the POIs of interest regardless of the campaign.

How should I interpret the “incremental visits” axis in the behavioral analysis section of Clara?

Incremental visits (the horizontal axis) can be interpreted as how sensitive the device was to a campaign exposure. If a device is more sensitive to a campaign (up along the vertical axis) it will show more incremental visits (that is, the message resonated with the person holding that device).

What are the quadrants in the behavioral analysis section of Clara?

The quadrants divide all the matched devices in a campaign into 4 personas, depending on how loyal (high/low), and how sensitive they were to the campaign (high/low). Please see the definitions for “loyalty” and “incremental visits” in this FAQ for more details about the meaning of these terms. The four quadrants are:

1. Upper left: Low loyalty / high incremental visits. These devices in general would not visit the brand of interest, but they show high campaign effectiveness.
2. Lower left: Low loyalty / low incremental visits. These devices in general would not visit the brand of interest, and show low campaign effectiveness. Basically lost dollars because they never visit, and didn’t care for the campaign messaging.
3. Lower right: High loyalty / low incremental visits. These devices usually visit the brand of interest, but the campaign had very little effect on them. For these loyal devices the campaign message was either redundant or simply not relevant.
4. Upper right: High loyalty / high incremental visits. These devices in general visit the stores of interest more often than not, and the campaign message resonated with them to the point of increasing their visits.

Which quadrants in the behavioral analysis section of Clara are better?

The top two quadrants are in general more desirable because they indicate high campaign effects. Whether it is desirable that these high effects appear on loyal devices or not depends entirely on the goal of the campaign. If a campaign wants to bring in customers that usually won’t visit, an upper left quadrant is more desirable, but if a campaign’s goal is to cater to already loyal customers (for example, for branding) then the upper right quadrant is more desirable.

What is the feasibility threshold for a campaign?

Before accepting a campaign, Cuebiq looks at the structure of the campaign to determine if there is enough data to make the attribution pipeline worth running. We look at things like the number of impressions in a campaign and the number of POIs measured, the number of DMAs. If we determine that there are enough of these, we accept the campaign.

What about the 250 visit threshold to show incrementality results in Clara?

Even if Cuebiq accepts a campaign (see “feasibility threshold” in this FAQ), we still need a minimum amount of total measured visits to guarantee that our results are reliable, that is, they won’t be subject to too much change. We determined that 250 total measured visits (in both control and exposed groups) is a good threshold between stability and how fast we can show results to our eager clients. Because the campaign has been running usually for a few days/weeks before it achieves this threshold, it is not uncommon to see incremental visits jump from 0 to a large number once we populate the results in Clara.

Can you provide more detail about how the methodology works?

We use a neural network with two branches, one for exposed devices and one for control devices. Each branch gets trained separately, and once training is done, the exposed devices are passed through the control branch, effectively reversing the exposure condition and generating predictions of visits for the exposed devices (with their specific visitation signatures) as if they were control. For more information please see our white paper here. 

How do we interpret when the number of incremental visits remains flat in the Clara charts?

This means our methodology is not able to detect an increase in incremental visits with respect to the previous dates.

How do we interpret incrementality by SID?

Incrementality by SID is the simple sum of incremental visits of all devices that fall within the SID.

Please contact business@cuebiq.com for more information.

The post What is Incrementality? appeared first on Cuebiq.

Due to the COVID-19 pandemic, we saw dramatic changes to in-store visitation this past year. It was also a time of transition for Cuebiq as companies across all industries were compelled to pivot their strategies and find new opportunities for growth. 

In developing our 2022 Attribution Benchmarks report, we reviewed the past few years and produced benchmarks that mitigate the impact of COVID-19 by taking a longer view. Despite an unusual few years, our benchmarks can be used to inform decisions and measure campaign effectiveness against others in your industry. 

Now more than ever, location data and attribution metrics are essential to evaluating the success of your campaigns. Using these tools, you can understand actual consumer actions, tie them to ad exposure, and utilize those insights to fuel your strategies moving forward in a post-pandemic world. 

Metrics for Attribution Benchmarks

Here are the key metrics you need to know for understanding attribution benchmarks:

• Brand uplift — the impact of ad exposure in driving visits to store; the ratio of visitation between exposed and unexposed groups 
• Visit rate — identifies the percentage of customers who visited the store of all exposed customers

View Study

Use Case Examples

Our report pinpoints the average range for brand uplift and visit rate across 18 different industries. Therefore, if your visit rate is higher than the average for your industry, you can conclude that your campaign has performed exceptionally well. For example, if you are a brand marketer at Chipotle and your visit rate for a buy-one-get-one-free burrito campaign is 4%, which is well above the QSR industry average of 2.04–3.51%, your campaign is successfully driving customers to store. This could be because you targeted the right audience or because your ad caught them at the right time of day.

The same goes for other industries, such as big-box retailers like Walmart. If you are the brand marketer at Walmart and your visit rate is under 0.5%, so below the industry average of 0.51-0.61%, this indicates that you should adjust your campaign. For example, you could be neglecting to target young parents in your back-to-school campaign or you could be running ads on a platform with the wrong age demographic.

Equipped with this information, you can then upgrade your campaign by customizing your queries, which will allow you to learn more about your customers and make your campaign more applicable to your respective use cases.

For more insights, be sure to check out the full Attribution Benchmarks report.

The post Attribution Benchmarks: How To Evaluate and Upgrade Your Campaigns appeared first on Cuebiq.

We got the opportunity to sit down with Lewis Rothkopf, President of Martin, a media buying platform that solves critical gaps in the existing DSP market. Read on for Lewis’ take on how the advertising industry is changing, Martin’s stance on incrementality measurement, and how Martin is working with Cuebiq to improve the experience of marketers. 

What challenges are brands and agencies facing in 2021?

It's impossible to discuss day-to-day challenges that marketers are facing without first reflecting on the human toll that COVID has claimed. Marketers are people, too, and they are experiencing the same fears, anxiety, and sadly, loss that so many have endured. 

At the same time, the world continues to revolve, and marketing remains the engine of the consumer economy. Virtually every discussion with agencies and brands centers around two areas: measurement (i.e., is my marketing working, and where, and why?) and the forthcoming depreciation of third-party cookie and certain device-based identifiers.

Although these changes will require that marketers and their solution providers evolve to adapt to a new, better, more privacy-focused reality, we reject the notion that anything "apocalyptic" is about to take place. Offering consumers more control over their data, while continuing to support interest-based advertising, is a win for everyone, and we embrace it.

Can you talk about a project your team has worked on that’s been particularly rewarding when addressing any of these industry challenges?

We have this great client called Brooks Running. A super sophisticated and forward-looking brand, they make awesome athletic gear (some of which I own myself!) We're big believers in the notion that measurement needs to be causal in order to be valid — that is to say, vanity metrics are mostly irrelevant and can actually harm a marketer's efforts if they optimize to things that don't really matter much, like CTR.

Brooks understands this and has been a leader in championing incrementality measurement, so that they can better understand the real-world impact that their marketing has on their business. We recently demonstrated with them that "always-on" lift measurement in our platform using a sophisticated methodology consistently shows lift in purchases. 

How important is the ability to customize measurement and targeting in today’s increasingly complex marketing landscape?

Really important! At Martin, another thing about which we feel really strongly is that marketers should be able to easily utilize one of their most precious assets, that is, their own data, to make their efforts more successful. This can take the shape of onboarding CRM files to anonymously target (or anti-target) their existing customers online. It can also be used to customize the bidding algorithm with fine-grain control, based upon the marketer's objectives.

How does being an independent DSP allow you to help your clients achieve their goals more effectively?

Look, we're not naive — we know that there are others that have been at this for a long time, and that are much bigger than us. But we see that as a strength that powers several of our most important competitive differentiators!

Being an agile, nimble, scrappy, customizable, and strategic technology partner that is willing to quickly adapt our product to meet our clients' unique needs is why we are succeeding. We are able to make and act on decisions quickly. The most junior media buyer at any of our clients has a direct line to our CTO. We listen to our customers. And we hear them. And then we help them win.

How is the use of visitation and mobility data improving your ability to serve your clients?

Here's where partnering with Cuebiq provides us with a not-so-secret weapon: being able to kick off Cuebiq-powered visitation studies from right within our platform is pretty groundbreaking, I think. Again, it all comes back to measuring the incremental impact that a campaign has had on a marketer's business: did our algorithm reach the right set of consumers, and cause them to visit a retailer's location? Cuebiq helps us answer that, and we're excited about the ways in which it's driving the industry towards being more privacy-focused, with consumer consent and choice at the center.

As you look to the near future (12–24 months), how do you anticipate the advertising industry will change? What excites you most about where the industry is going?

Honestly it's difficult to consider anything beyond tomorrow as being the "near future" these days! But COVID, thankfully, will become under control and consumer migration and purchase patterns will likely revert to their pre-pandemic norms. The ways in which we address consumers and attribute lift may change, but it is indisputable that marketers will continue to demand more precision in how they are able to target as well as a greater understanding of what is driving their success. 

I'm excited to be part of a company that is purpose-building marketing tech entirely anew — not on top of an older codebase — to meet the evolving needs of marketers today and tomorrow. I'm excited to partner with forward-thinking companies like Cuebiq, who do things that we don't, and do them really well. And I'm excited (and a bit misty-eyed) to enter my third decade in digital marketing surrounded by a team of experts who fight like hell every day to make this industry a better, safer, and more accountable place.

Check out this blog on incremental impact to learn more about how incrementality is key to marketing measurement.

The post Partner Spotlight: Q&A With Lewis Rothkopf, President of Martin appeared first on Cuebiq.

After a never-ending last year, it’s finally 2022 — and this new year brings a host of new opportunities. We know that the last few years brought myriad changes, and you may now need to make last-mile tweaks to your measurement campaigns or build custom solutions altogether to address new problems. Not sure where to start? Here are three ways you can evolve your marketing measurement to fit your needs:

1. Incorporate Your Own Measurement Methodology

You are likely familiar with the methodology of your existing measurement partner. But what if your brand has its own approach to defining major methodology components, such as how the control group is defined or how uplift is calculated? It’s time to look for a measurement partner that provides a platform, which will enable you to incorporate your own methodology, so you can create a custom solution that fits your needs. Say, for example, you wanted to define your own baseline for measurement — a flexible measurement solution in a platform environment will enable you to do just that.

2. Build Custom Insights To Optimize Your Media Spend

If you need to gain a deeper understanding of your customer, it may be time to innovate and build your own custom insights. Customizing your queries will allow you to learn more about your customers while still utilizing the underlying components, such as data or exposure methodology, that brought you to the provider in the first place. For example, if you want to know the average number of ad exposures it takes to drive your consumers into a location, you can do a custom analysis using exposure and visitation data to understand what your optimal exposure rate is. Armed with this information, you can tailor your media frequency cap to match the needs of your audience specifically. A flexible platform environment will enable you to use existing measurement assets such as location data, in a way that can inform your future campaign effectiveness.

3. Deliver Campaign Reporting in Customized Formats

When you go to export your data, working in a flexible platform environment will enable you to deliver campaign reporting in a customized format and cadence. Say, for example, that you want a specific channel or analysis to appear first in the report and you want to receive the data every two weeks — you can absolutely do that. 

The opportunities for customization of your measurement solution in a flexible platform environment are limitless. You can customize processes that will make your results more applicable to your respective use cases. In these changing times, it’s important to have a flexible measurement solution that solves your advanced marketing needs.

To learn how Cuebiq can help you customize your measurement solution, chat with an expert on our team.

The post It’s Time To Customize Your Measurement Solution appeared first on Cuebiq.

It’s an interesting time to be a marketer in the world of offline attribution. Reporting that was once limited to end-of-campaign validation has been transformed by better data collection techniques and artificial intelligence (AI) modeling. By embracing up-to-the day campaign monitoring and the concepts of predictive and prescriptive analytics, offline attribution is now an actionable science, complete with optimization recommendations to increase return on ad spend.

Through descriptive, predictive, and prescriptive analytics, you can now think about campaigns in new ways, allowing you to do the following:

1. Anticipate outcomes by evaluating cross-channel KPIs on a daily basis
2. Optimize media in real time to improve results before it’s too late
3. Decrease media costs while getting better results (aka improve ROAS)

Let’s take a deeper look at how each type of analytics works and how marketers can leverage the insights to strengthen their strategy. 

Descriptive Analytics: What Has Happened? 

Descriptive analytics looks at data to answer, “What has happened?” So for attribution, it means monitoring a campaign’s delivery metrics and quantifying campaign performance in driving consumers to store.

Consumer behavior changes each day, so when measuring attribution, it’s important that you use data that was collected daily for real-time results. Also, as more and more marketers run integrated campaigns, you’ll need a solution that looks at each channel individually while also measuring the effectiveness of cross-channel buys.

In Cuebiq’s platform, descriptive metrics include Uplift, Visit Rate, Projected Visits, and Cost per Visit to answer questions such as:

• Is my campaign driving to store?
• How many users went to store?
• Which channels are most effective?
• What did it cost to drive a visit?

For example, if an automotive group runs a regional campaign to support a weekend sale, they can use a simple uplift report to measure whether the marketed dealerships saw an increase in traffic week-over-week and whether their increase was higher than that of regions without an active campaign. Furthermore, Cuebiq can report on which dealership saw the greatest traffic per DMA, suggesting the greatest responsiveness to the campaign long before sales data is available.

Predictive Analytics: What Could Happen?

Predictive analytics uses data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. 

As incrementality becomes a priority for marketers, predictive analytics has been getting more buzz as a way to determine if and how much your campaign is changing consumer behaviors. 

Incrementality using AI is generally calculated by first looking at a user’s past behavior to predict future behavior in the absence of exposure to advertising. The prediction also considers a control group to take into account variations like seasonality. The next step is to measure the actual visits that took place after campaign exposure. The difference between the predicted and the observed visits is the number of incremental visits. Performing these calculations at the consumer level is the best way for a marketer to get actionable insights. 

Predictive analytics is the mechanism by which Cuebiq reveals Incremental Visits, Cost per Incremental Visit (CPIV), and Customer Acquisition Cost. Leverage these metrics to answer the following questions:

• Is my campaign changing behaviors?
• What is the incremental impact for each consumer/segment?
• What did it cost to drive additional visits?
• Do I spend more attracting new vs. returning customers? 

For example, if a quick-service restaurant brand has just introduced a coffee menu, their Brand Managers would want to know how well the menu launch campaign is performing. Is it driving visits among new customers? Or are loyal consumers visiting more frequently? Furthermore, what is it costing them to attract each new visitor? Marketers can leverage consumer-level incrementality to quantify these questions and more.

Prescriptive Analytics: What Should We Do?

Prescriptive analytics is all about providing advice using AI-based models to predict the possible outcomes of various courses of action, then scientifically determining which solution will likely yield the best results. By quantifying the decision-making process, marketers can trust they’re making the best choice before putting their precious budget behind it.

Prescriptive analytics is versatile and can be applied to all levels of media planning — from your targeting strategies to the channels and publishers you select, even down to the creative types you use. Likewise, recommendations can be used for in-flight optimization or to inform future campaign strategy, depending on when a marketer reviews results and how flexible their media buys are. There are multiple “levers to pull” when optimizing your strategy, including mid-flight adjustments, so it’s important to clarify your campaign goals (and limitations) from the outset.

In Cuebiq's platform, prescriptive analytics is the foundation of two tools. The first is Budget Allocation, which makes recommendations for the optimal media mix. Second is Behavioral Effect, which looks at the impact your campaign is having on consumer behavior to validate targeting strategies or recommend audience segments that will yield a higher impact for your marketing dollars. Specifically, these tools can help you answer the below questions:

• How do I maximize ROAS?
• Which channel combination works best?
• Who should I target for my next campaign?

For example, a Retail Brand Marketer evaluating a digital + linear TV campaign wants to check the cross-channel effect on store traffic as well as each channel’s individual impact. Budget Allocation does the hard work of comparing spend to performance and making recommendations for how to reallocate budget to the channels and even creative types driving the most in-store activity.

Cuebiq believes that the best type of campaign reporting is actionable and delivers insights that you can use. Read more about how artificial intelligence fuels the most sophisticated measurement in the advertising ecosystem through predictive and prescriptive analytics.

The post The 3 Types of Analytics You Need to Measure Attribution appeared first on Cuebiq.

As marketers, we’re constantly wondering how our brand is resonating with consumers (do our ads work?), whether we're investing in the right mix of media channels (for example, is OOH a good medium for my brand?), and what our ROI  is (did we spend wisely?) These are just some of the questions every marketer ponders as they build, plan, and execute their media budgets across today’s complex advertising ecosystem. 

According to The Institute for Practitioners in Advertising, a “one size fits all” media plan just doesn’t work anymore. While broad channels like TV and OOH are still very effective, marketers must be more nimble and develop more varied media plans if they want to engage with multiple consumer audiences. As tactics become more complex, it's more imperative to evaluate and invest in the right tools that not only provide insights but also help guide strategy.

At Cuebiq, our mission is to help marketers understand whether their advertising is changing consumer behavior (we can tell if you if your are ads driving incremental visits), which channels have the biggest offline impact (we can help you build the best media mix for your audience), and how to increase return on ad spend (we provide campaign and budget recommendations in flight).

We built our Measurement solution so that marketers can prove the value of their efforts in the offline world and increase their return on ad spend.

Learn More About Our Methodology

Do your ads drive new visits to store?

Understanding the impact of your ad dollars in the offline world is essential when your main objective is driving consumers to store. Knowing the total uplift of your campaign helps you prove success, calculate ROI, and optimize tactics based on campaign learnings. But let’s take it a step further and talk about incrementality. At Cuebiq, we’ve created a state-of-the-art incrementality methodology to give marketers a more granular look at campaign performance.

Incrementality enhances how marketers build, plan, and activate their media spend across their various ad partners. By taking a deeper look into uplift, incrementality provides you with the ability to see if your advertising is changing consumer behavior — and if your ads are increasing the rate at which consumers visit your brand. Incrementality allows you to break down incremental visits generated from your advertising at the consumer level, so you can enhance your consumer segmentation and media targeting tactics. 

With incrementality comes actionable insights, such as in-flight recommendations for customer segmentation, budget allocations, and cross-channel performance.

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Are you investing in the right media channels?

With incrementality and offline measurement, you can use real-time performance insights to understand how each channel in a media mix is affecting the other, so you can optimize your entire media mix. Through a cross-channel measurement lens, you can drive a higher ROI by allocating proportional credit to each marketing touchpoint across all channels. This reveals which combinations of media do best, so you can change your spend accordingly. 

With these types of insights and campaign metrics, you can create more impactful and diverse media plans that reach and engage all types of consumer audiences.

What is the ROI of your efforts?

And finally, what every marketer really cares about … ROI. 

As a marketer, I know the importance of accurately calculating ROI for marketing and media budgets. But ROI is even more important when it comes to advertising, since budgets worldwide are expected to keep increasing. But as budgets increase, so does scrutiny regarding budget allocation, which is why accurate ROI reporting is critical. 

Leveraging an offline measurement solution allows you to measure the ROI of your ad dollars and see the impact that your media budgets have in the offline world. 

This way, you can demonstrate the value of those dollars in driving consumers to store. With incrementality, you can now calculate the cost per incremental visit much more easily, giving you the ability to optimize your strategies. This helps you not only prove value (ROI), but it also enables you to take the necessary steps to increase your return on ad spend.

Ready to activate incrementality or learn more about Cuebiq? Connect with one of the experts on our team to get started today.

The post Enhance Your Advertising Strategy With Offline Measurement appeared first on Cuebiq.

We got the opportunity to sit down with Supriya Goswami, VP of Marketing at InMobi, a leading technology company that help brands understand, identify, engage, and acquire customers. Read on for Supriya’s take on challenges the mobile advertising industry is facing now, InMobi’s stance on data and privacy, and how InMobi is working with Cuebiq to help brands make the most out of mobile opportunities.

What is the biggest challenge the mobile advertising industry is facing right now?

I think the biggest issue is in how to reach the right audiences at scale in a way that respects user privacy and autonomy. As a lot of the data that marketers have long relied on begins to either go away or face renewed scrutiny, marketers need to rethink the ways in which they understand and identify their target markets. Today’s consumers are mobile-first and highly fragmented, which means that it’s more critical than ever for marketers to utilize the strategies, data, and methodologies still available to them to make the biggest impact.

Can you talk about a project your team has worked on that’s been particularly rewarding when addressing this industry hurdle?

In 2018, InMobi acquired Pinsight Media, which was the former data arm of Sprint. Between this data, data from our own consumer-facing platforms, and data from our SDK, we’ve been able to bring to market powerful insights around location, app affinity, interests, and on-device behavior, among other metrics. Through this, marketers gain the key insights they need to really understand, identify, engage, and acquire today’s mobile-first consumer.

How has the increased use of data to inform advertising targeting and optimization changed your business?

Our business has always been informed by data. The biggest changes we’ve seen of late with our business are in terms of how data can be applied to strengthen mobile advertising. For a long time, programmatic in-app advertising was split into two camps: pure branding and performance (i.e. app downloads). What we’ve been seeing more recently, and what we’ve been talking about with our customers more recently, centers around unique and different ways to both think about and measure in-app advertising. With data and with the right partners, we can show how in-app advertising can help drive in-store visits, online purchases, and other actions that have a direct, positive impact on the business. Also, data helps brands see how in-app advertising works in tandem with their other marketing efforts to drive success.

We can't talk data without touching on privacy. How has InMobi helped advertisers prepare for future privacy regulation and industry self-regulation?

On our end, we have been unequivocal with our partners about the need to respect privacy and to do things right by the consumer. For example, when GDPR was first signed into law in the EU, we flatly refused to work with any app that didn’t have expressly given opt-ins from consumers. While others in the industry were playing things fast and loose, we were steadfast in our privacy-first approach. We take the same approach with CCPA and will approach any forthcoming privacy initiative with the same level of rigor. But, we also take key steps to help our advertiser partners both gain the data they need and run effective campaigns in a privacy-compliant manner. Whether by supplementing an advertiser’s data with our own first-party intelligence or by helping advertisers think differently about their campaigns, we work hand in hand with our partners to ensure compliance while also driving measurable results.

What excites you most about your partnership with Cuebiq? And how will offline attribution help InMobi’s advertisers prove ROI?

We’re really excited to work with Cuebiq to help our demand-side partners connect their digital marketing and advertising efforts with offline sales and results in a privacy focused manner. For a long time, the main mobile advertisers were various app publishers, from social media networks and rideshare apps to mobile games. Thanks in part to our partnership with Cuebiq, we can open up the world of in-app advertising to retailers, CPG brands, and others who rely on brick-and-mortar sales and visits. As time spent in app rises, we’re excited to work with Cuebiq to help a wider array of brands make the most out of mobile opportunities.

As you look to the near future (12–24 months), how do you anticipate the mobile advertising space will change? What excites you most about where the industry is going?

In the next one to two years, expect budgets to move away from mobile web as advertisers further embrace mobile in-app advertising. I also think more advertisers will move beyond top-level metrics and instead look to further tie their online efforts with offline sales and other business-level metrics. I’m excited for the industry to be thinking differently and for working with advertisers to materially boost their business.

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The post Partner Spotlight: Q&A With Supriya Goswami, VP of Marketing at InMobi appeared first on Cuebiq.

You heard it here first: Marketers are now location data experts. Don’t just take our word for it; we have the research to back it up. Cuebiq has again partnered with 451 Research to commission a new series of research papers on how marketers really feel about location data. When we commissioned this research last year, we saw interesting findings around the increase in location data usage and its potential. However, this year’s research shows a major change in how marketers view themselves when it comes to evaluating and investing in location data solutions.

The first paper in our new series, Finding Real Enterprise Value in Location Data, details the relationship marketers have with location data. While last year’s research showed that marketers overall planned to use location data more, this year’s findings showed a change in how marketers view offline intelligence. According to 451 Research, marketers are now using location data to solve challenging problems such as complex attribution, audience creation and targeting, and analysis of trends of large groups of customers.

When asked which attribution features would have the most positive business impact on their companies, 71% of marketers cited the ability to estimate the total incremental visits generated by a campaign and calculate ROI for it.

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Maturity Levels Matter When It Comes to Location Data

Another way to think about how marketers use location data to solve problems is by assessing their maturity levels. Let's look at the five stages marketers place themselves in terms of how fluent they are with using location data: 

• Stage 1: The initial planning. These companies are just getting started, either by exploring different options or putting together ideas or proofs of concept to determine what might work in their organization. 
• Stage 2: Having a defined set of goals and plans, focused on an organized process for leveraging location data, and a solid understanding of the benefits (and how to measure them). 
• Stage 3: Having a set of managed processes that is based on a formal program for incorporating location data into marketing plans. 
• Stage 4: These businesses have a strong, quantifiably measured program, with metrics that measure business outcomes. 
• Stage 5: A fully mature, leading-edge company that uses location data for sophisticated tasks like attribution, audience targeting and analytics, and considers itself to be best in class among its competitors.

Sometimes it can be hard or expensive to onboard new tools and technology. However, 75% of marketers said that increasing revenue across channels was one of their highest goals in executing an integrated marketing program — and offline intelligence is key to doing that. In order to be successful in leveraging location data, it pays to be an expert (and we hope all our readers are at stage 5). Yet no matter what your current maturity level may be, don’t worry; we at Cuebiq are here to help. Set up some time with us for a quick consultation on location data and the impact it can have on your advertising.

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Use Cases That Have the Most Positive Business Impact

When asked by 451 Research which solutions they desired most, the majority of marketers cited attribution — with 81% saying they are keenly interested in using offline attribution to measure cross-channel behaviors and outcomes. Other use cases include customer profile enhancement, audience segmentation, real-time metrics, competitive analysis, and more. To find out how marketers ranked each use case, download our full study.

In order to take advantage of the benefits of location data analysis, marketers still need to change some of their thinking and processes. While marketers are now for the most part experts, it’s up to them to spread the word about the value of location data to their peers. This is especially important when trying to implement location data solutions across an entire organization. We at Cuebiq hope you enjoy this new research from 451 and that you as marketers continue to spread the word about location data!

The post Marketers Are Becoming Location Data Experts According to 451 Research appeared first on Cuebiq.

As I sat down to write this blog about marketing in 2020, I reflected on 2019 and looked at all the great things my team accomplished and the challenges we overcame. I searched for and eventually identified the one variable that had the greatest impact on our ability to achieve our goals: making data-driven decisions.

What I found was not surprising but at the same time very impactful — our success wasn’t based on talent only (and believe me when I tell you I work with very talented marketers!) It actually stemmed from our willingness to leave behind “gut feelings” and embrace data-driven decisions (I know this doesn’t sound groundbreaking, but it’s hard). As marketers, we all need to be on the lookout for new trends and emerging tech that we can leverage to both improve our decision-making skills and help measure success.

Investing in Your Marketing Stack

Over the past year, my team and I combed the market for new learnings and technology, so that we could enhance our marketing stack to infuse our decisions with high-quality data. As the media landscape has evolved, we as marketers have had to change. We no longer can rely on traditional planning or metrics but are tasked with pushing boundaries and experimenting with new ideas. This makes sense because technology has changed the way we consume content and how we are exposed to advertising. Because of that, marketers have shifted away from traditional strategies for planning and measurement.

Clicks, viewability, and engagements are not a thing of the past but are a stepping stone to understanding true advertising performance. And marketers on the whole are aware of this … according to a recent study Cuebiq commissioned with 451 Research, Getting Real Enterprise Value from Location Data, marketers are finding more and more value in location data. Like my team, marketers in general are looking to enhance their tech stack. In terms of media, they are using location data to fill in the missing holes of the consumer journey, so they can better measure the success of their advertising dollars.

Using Offline Measurement in 2020

Marketers aren’t just using location data more; their expertise in using it has increased dramatically in the past year. If you’re familiar with location data, then you understand how important offline measurement is when measuring the success of your cross-channel advertising.

According to 451 Research, marketers are using location data primarily to solve complex problems and use cases like offline measurement. While understanding “total visit uplift” (total visits to a store generated from advertising) from a campaign is great, marketers are pushing the boundaries and want to understand more granular metrics. 71% of marketers say they will leverage location data to understand the incremental lift driven by their ad campaigns so they can calculate the true ROI. If you can unlock incrementality, you can understand whether your advertising is actually changing consumer behavior by driving additional visits to store, and to what extent. Marketers want more granular metrics so they can understand the full impact of their marketing dollars in the offline world and remove guesswork or gut-feeling decisions. 451 Research found that 65% of marketers want to focus on tying store visits driven by advertising to the incremental revenue their marketing dollars are generating — true ROI!

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Increasing Your Return on Ad Spend

As marketers continue to grow their expertise in location data and other emerging technologies, their needs will become more complex. A huge reason that location data has become more valuable is that it now shows “incremental lift,” which enterprise brands and marketers can easily activate for value. Incrementality is crucial because it helps calculate true ROI and provides recommendations on how to increase return on ad spend.

For the first time in the location data industry, advertising impact is being calculated at the consumer level and not as an aggregate measure for the entire campaign (as has been the standard until now with total visit uplift). This development means that marketers can now integrate data-driven activations into their strategies to increase their return on advertising spend (ROAS) by lowering their cost per acquisition (CPA) — again going from “gut feelings” to data-driven decisions.

So, as you enter this new year and the new decade, I hope that one variable driving your success will be location data. It will enhance your marketing stack so that you can better understand the offline impact of your marketing dollars.

To learn more about location data and how you can leverage offline measurement, set up a meeting with one of the incrementality experts on our team!

The post Offline Measurement: The Solution to Your Marketing Problems This Year appeared first on Cuebiq.