How to Fill Data Gaps in Google’s Online-to-Offline Platform

Data gaps happen. As marketers, we do our best to mitigate those gaps and secure attribution accuracy. Our ultimate goal is to provide customers with interconnected, well-rounded experiences. But regardless of the methodologies we use to gain actionable insight—such as paid analytics platforms like Google Store Visits—data details still fall through the cracks.

Data: The New Champ of Intelligent CX

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To attain the highest degree of attribution accuracy, you need to be aware of existing data sources and the data you’re missing. The deliberate prioritization of these data gaps and attribution accuracy offers several benefits:

  1. A direct connection between digital and offline marketing spend.
  2. Effective implementation of omnichannel, nonlinear marketing campaigns.
  3. An ability to influence customers throughout all stages (find, buy, ask, and advocate) of the customer journey.
  4. A customer’s notable transformation from average consumer to brand advocate.

When data gaps aren’t mitigated, marketers and brick-and-mortar associates could miss out on conversion opportunities and experience a negative impact on their bottom line.

Data Gaps: Causing a Rift in Attribution Accuracy

While Google’s Store Visits platform offers unparalleled access to consumer preferences, Google’s offerings are as limited as they are informative.

Don’t miss out on conversion opportunities. Prioritize attribution accuracy to improve your customer experience and your bottom line.

The constant shift in trends, customer habits, and preferences enhances the volatility of attribution accuracy and data aggregation. That is, if you know how and where to fill in the data gaps. For that reason, our data science team identified four common obstacles to Google Analytics and some innovative solutions. Now you can focus on filling the data gaps rather than getting hung up on the details.

Obstacle 1: Disabled Geolocation Services

When a customer opts out of Google’s geolocation services, your ability to attribute their online-to-offline interactions is hindered. Reasons to decline location services vary from person to person, but recent articles indicate that customers feel betrayed by companies’ lack of respect for their privacy.

Graphic representation of a cellphone with a Google map app open and a large question mark locator icon

Solution: Create a Brand App

Customers are more likely to share their geolocation information within the ecosystem of an app. And they’re more likely to share this information with a company they know and trust. If your brand doesn’t already have an app, you’ll want to start your efforts there.

Next, you’ll want to ease friction and re-establish trust with your customers through transparent, honest messaging and privacy policies. Trust is a key component to acquiring customer data. In fact, 72% of customers in the United States expect the private data they entrust a brand with to be protected. Unfortunately, customers have come to expect less than the best from large companies like Google.

A brand app provides a tailored-to-fit shopping experience and helps you refine and enhance your attribution accuracy.

A brand app bypasses Google and allows customers to be more in control of their data. Additionally, it enables you to attain a higher degree of attribution accuracy, which helps to rebuild trust with customers.

Ideally, these measures will persuade customers to share their geolocation with your brand app. Of course, some customers will still opt out of your location services, but you can still gain insightful data. Once you’ve aggregated consumer data through the app and related marketing campaigns, you can refine and enhance your attribution accuracy and update marketing campaigns to fully reflect customers’ habits and desires.

  • Resource Recommendation: AppInstitute is an easy start for marketers or business owners looking to create a brand app.
  • Solution Difficulty: Difficult. Creating an app can be difficult, and some customers will continue to decline geolocation access regardless. You’ll still need to create location estimates, just as you would with Google.
  • Benefit for Customer: A brand app provides a tailored-to-fit shopping experience.

Obstacle 2: Paid-For Channel Data Only

Google’s platform provides great insight, but Google is going to do its best to keep you in its ecosystem—after all, you are a paying customer. As a result, the data insights and information sets you get from Google are inherently biased. They can even prevent you from seeing the full picture data can provide. For this obstacle, there are two solutions worth implementing.

Two bar graphs

Solution A: Extrapolate and Compare Data against KPIs and Other Metrics

Extrapolating and correlating a variety of data sources allows you to attain a higher degree of attribution accuracy. Rather than relying solely on Google data, use it as your baseline and compare it against other digital channels like organic traffic and social media interactions.  

In order to succeed, you’ll need to maintain a healthy balance between data sources and data correlation pools to avoid perpetuating bias within your own statistical models.

Maintain a healthy balance between data sources and data correlation pools to avoid perpetuating bias within your statistical models.

Aggregate data from all sources. Utilize your company’s CRM system to acquire and correlate data. From there, you’ll be able to translate the data and make rational decisions that connect your digital and offline marketing spend and reflect customer behaviors and needs accordingly.

  • Resource Recommendation: Salesforce CRM allows you to store, access, and correlate high-value consumer data without negatively impacting the customer experience.
  • Solution Difficulty: Moderate. Data aggregation, sorting, and correlation takes time. Be patient as this process could take up to a month.
  • Benefit to Customer: Varied data allows you to accurately mirror and cater to customer pathways.

Solution B: Utilize Google’s UUID

Google’s unique user ID program can seamlessly connect with CRM data to identify repeat users interfacing with your brand, app, or website over multiple sessions or devices.

To start, you’ll need to identify CRM data and ensure you have enough of a baseline to identify repeat IDs or trends. Next, merge that CRM data with Google Store Visits and create unique user IDs. These unique IDs allow you to identify repeat visitors who are using a range of devices or platforms.

With UUID, customers can shift between devices and take their time shopping–without being disconnected from their search intent or the products they’re interested in.

From there, you can provide tailored digital marketing experiences through the brand app or targeted promotional links. Ultimately, this technology enables your customers to shift between devices—without sacrificing their service pathway.

  • Resource Recommendation: Merging your company’s CRM with Google UUID enables you to attribute multiple sessions (via devices or storefront locations) to one user.
  • Solution Difficulty: Moderate to difficult. Manually identifying and creating a unique UID can be complicated and time-consuming.
  • Benefit to Customer: Customers can shift between devices and take their time shopping—without being disconnected from their search intent or the products they’re interested in.

Obstacle 3: Inconsistent Data Trends

Customer habits are predictably unpredictable—so their corresponding data is irregular in nature, too. These inconsistent data trends present a variety of problems, such as inaccurate attribution predictions, misinformed marketing campaigns, and skewed in-store statistics.

Solution: Create Secondary Data Sources

The more data, the better. Creating and correlating multiple secondary data sources (with customers’ online activity) is the best way to gain a higher degree of attribution accuracy.

Secondary sources, such as beacons or door counters, offer means for manual data accumulation and empower your stores to capture customer footfall on a regular, consistent basis. Keep in mind that your customers might be beacon-shy: as with geolocation services, customers have to authorize beacon activity. Trustworthy privacy policies and time-dependent correlation analyses are ways you can begin to offset this data hurdle.

The more data, the better. Create and correlate secondary data sources to gain a higher degree of attribution accuracy.

Additionally, know that beacons and door counters are an expensive investment and require maintenance, so you’ll want to allot a budget for these devices. Some related technologies, like iBeacons, can relay a beacon transmission through a URL and forgo consumer authorization.  

  • Resource Recommendation: Estimote captures in-store actions or events and transforms the secondary data points into actionable, data-driven insights.
  • Solution Difficulty: Moderate. Beacons are expensive to purchase and maintain, and some customers may not authorize them.
  • Benefit to Customer: Secondary data sources help you provide a more well-rounded, interconnected customer experience.

Obstacle 4: Store Visit Lag

Not all conversions happen overnight. Some customers take up to a month to purchase an item or service, while others wait less than 24 hours. Google provides access to such data with its Time Lag pathway, but it doesn’t always provide adequate insight into the store visit time lag. This obstacle sheds light on the disconnect between user experience, ad spend, and customer conversion.

Solution: Establish Statistical Likelihood of Initial and Secondary Revisit

Establishing a statistical likelihood of an initial visit bridges the gap between a lag in online interest and an actual store visit or a conversion opportunity. This, in turn, sheds light on the customer’s journey of choice. From there you can evaluate and analyze all customer pathways for effectiveness and common characteristics.

Furthermore, knowing how long it takes for customers to go from click to bricks helps connect digital marketing spend with in-store promotion success. Measuring these customer trends and establishing predictability models opens up new doors for digital and in-store marketing capabilities and offerings.

Measure customer trends and establish predictability models to improve your digital and in-store marketing capabilities and offerings.

Once you’ve established a probability baseline, your digital marketing team can create targeted ads, custom mobile marketing campaigns, or elevated in-store experiences that draw customers to your brick-and-mortar space. This insight also allows you to cater to specific audiences and untapped customer bases and appeal to existing loyal customers.  As a result, these audiences can easily convert when given the appropriate amount of support and marketing efforts.

Before you jump to implement this solution, check your resource capabilities—this approach can be both time-consuming and expensive. In order to establish a secondary revisit, you’ll need to install hardware (such as beacons or facial recognition software) that identifies customers or the devices they bring into your brick-and-mortar locations.

  • Resource Recommendation: Conduct a regression and correlation analysis to predict visits and establish likelihood.
  • Solution Difficulty: Moderate to difficult. Installing recognition hardware will be expensive and require extensive resources. And, as with any data aggregation, creating a baseline will take time.
  • Benefit to Customer: Customers can walk into a store and easily find an item they saw in a promotional ad online.

Data: The Common Ground for Attribution Accuracy

Data provides truths otherwise unseen, allowing you and fellow marketers to better understand your customers and make rational, data-driven decisions regarding campaign strategy, self-solutions help, or sales support. That said, each time you fill in a data gap, two smaller gaps appear. The search for truth is an ongoing process.

In order to get the most out of your search, you’ll want to access those data truths and insights using the solution suggestions we listed throughout this post. By doing so, you’ll be able to achieve four major insights:

  • Prioritize and connect digital and offline marketing spend.
  • Create go-to market strategies that reflect the nonlinear customer journey.
  • Influence customers at pivotal decision-making points both in-store and online.
  • Invest in the future of your customers’ brand advocacy experience.

While the ever-shifting customer journey does offer its fair share of complications and obstacles, it also presents marketers with fresh opportunities to learn and better understand both potential and existing customers. And after all, knowledge is power. You just need to know how to wield it—ethically and smartly, of course.

Ultimately, attribution accuracy can be difficult and time-consuming. It requires expert guidance from data science professionals, patience, and access to multiple resources. At Clearlink, we have everything and everyone you’d need to get the ball rolling on your data aggregation and online-to-offline strategy.

 

Monique Seitz-Davis

With ten years of professional marketing experience, Monique brings a passion for telling brand stories to her writing for Clearlink. Prior work includes collaborative projects with companies like Merrell, Backcountry, Cotopaxi, and Wit and Delight. When she’s not copywriting, you can find Monique trail running, rabble-rousing with her pups, or practicing her bird calls.

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