OOH advertising as you know it is no longer. New developments in the OOH space are fuelling innovation that is transforming the medium into a powerful tool for marketers of all persuasions.
We’re spending more time than ever outside the home. Around 70% of our time is spent outdoors.
Because of this out of home advertising is becoming DOOH, it’s benefiting from the considerable growth in available data and new technologies.
This growth is driving increased ad spend, better measurement and more interactive and personal ways to engage with consumers with DOOH.
Let’s take a deep dive into the current trends in the OOH advertising space and the positive effect that they can have on your marketing strategy.
What is out of home advertising?
Out-of-home advertising (OOH) is the collective term for any visual marketing or advertising or media that exists outside of the home. It comes in many shapes and forms, but the most common are found on billboards and street furniture (such as bus stops). OOH also refers to advertising in public areas such as stations or transit hubs, stadium screens and the cinema
Innovation in OOH advertising
Optimisation – DOOH to optimize reach
While the majority of OOH inventory is printed, more digital screens are becoming available for advertisers to create more dynamic campaigns.
These screens are producing better optimization and allowing advertisers to create more personalized messaging. They can utilize different kinds of triggers to generate a more dynamic form of OOH advertising.
This innovation is no longer a gimmick, and advertisers have demonstrated how effective digital OOH can be. They have shown that tDOOH is not just effective, but also scalable.
Better data has helped to fuel these innovations. Advertisers can now change media based on the movement of many devices in real-time. The rise in smart devices means that road based billboards can change instantly based on the demographic that will soon be in front of the inventory.
Real-time is important, but in reality, it is part of a growing trend in which the medium is becoming more of a reactive solution. The ever-increasing amount of data that marketers now have at their fingertips is driving this trend. This versatility is driving personalization and producing incredible results for marketers using DOOH to achieve their goals.
Enabling digital buying of media in real-time
Marketing automation has reached the out of home industry. Programmatic buying of OOH media is now commonplace.
The purchasing of OOH advertising was previously a lengthy process between the advertiser, digital marketing agency and inventory owner. With this model, the real-time strategies that now dominate the DOOH world would not be possible. Today this process has become far more efficient.
The buying of OOH inventory has not just become automatic, but it is now available in many of the same interfaces that marketers can buy mobile or display ads. This process allows marketers to activate their campaigns seamlessly across several channels.
This real-time automation also means that It’s also easier to leverage first and third party data sets directly into campaigns, maximizing personalization and increasing ROI.
Attribution and measurement
The out of home advertising industry has focused heavily on solutions to measure the results of OOH campaigns. Marketers can acquire detailed metrics around their digital campaigns down to impressions and conversions. Data has enabled this in the DOOH world.
As a result, brands can now identify the number of impressions an OOH campaign has generated. Measurement has come a long way from surveying consumers; marketers now can say with confidence how many people saw their ad.
But the innovation doesn’t stop there. Smart data can close the out of home attribution loop. Data around store visits or digital behavior can be used to with OOH exposure to provide the kind of metrics that marketers could previously only get on their digital campaigns.
These leaps forward are all thanks to data. Data is pivotal for measurement and driving accountability in the digital advertising space, and this is no different for OOH.
Data in DOOH advertising
For marketers, OOH is now an exciting place to be. Data is fuelling innovation and creating powerful DOOH campaigns.
We now have access to a gigantic data ecosystem which wasn’t available ten years ago. These data sets have enabled advertisers to do more with their activities and campaigns.
However, the advertising is only as good as the data the fuels it. Marketers must be aware of the data they are using in DOOH. Third party data needs to be highly vetted, and direct partnerships with 2nd party providers are a much better solution.
With accurate data at the center of the DOOH revolution, we will surely be seeing more ad spend allocated to reaching consumers outside. This data will enable much better results for marketers and ensure that digital campaigns can be instantly activated in the OOH channel.
What could be considered the first use of the term “business intelligence” was used in a document in the 1800s. It described banker Sir Henry Furnese, who gathered information and acted upon that information to increase his profits.
Now, BI is a system of strategies and technologies that gather and analyze data to provide historical, current and predictive views of business and its operation.
BI can comprise a wide range of data gathering and analysis, including the analysis of structured and unstructured data. This information helps to identify business opportunities and enhancements, providing business with a competitive market advantage.
BI is also most effective when it combines internal data, like financial information, and external data, like market information.
By combining different data and sources of information, businesses can get insight into possible new products, new markets, and analyze marketing efforts, to name a few uses.
Think of it like embarking on a trip without a destination in mind, a map or a GPS to find your way. You wouldn’t set out without the tools to help you get there successfully. BI is the roadmap for your business.
That’s the “why” of BI.
Let’s look at “how,” with 11 steps to implement a successful business intelligence strategy.
1 – Get Buy-In
Whatever the size of your organization, the first step in your BI journey is to get buy-in, starting with an Executive champion.
For one, it demonstrates to the entire business that the BI strategy is a priority. Secondly, the Executive champion can ensure resources are in place, both human and financial, from across the organization.
The Executive sponsor is the liaison with the leadership team to ensure communication lines remain open and that every area is supporting the initiative. Finally, the Executive sponsor can remove any roadblocks.
When considering the best position to be the BI leader, it doesn’t have to be the person in charge of IT. After all, BI is not a technology initiative. The Financial Officer could be a good choice, but the best option is the member who understands the business strategy and will champion BI.
2 – Focus on Team Collaboration
Next, you need to build a team around the Executive champion. Don’t assume that the entire organization will support the initiative. You will need a cross-functional team to bring together all the components of the business. You need to champion team collaboration, with the right collaboration software.
Members of this team will be the champions for your project within each area of the business, so make sure you look for some impressive resumes when hiring for this role. They must understand the BI needs of their area, and be able to communicate the benefits of the project to each area.
They will have to share the information once it’s available and help each area to implement any changes or enhancements that are derived from the data.
It’s essential that each area of the organization dedicate human and financial resources to the BI strategy. While the IT department will be a vital part of the team, they should not be the entire team. Each area of the business must own the program. They know the information that would help them, and they need to be able to access the information when they need it.
Be sure to include strategic, operational and tactical areas of the business. They each require different types of information to enhance their work and improve the business.
3 – Define A Vision and Goals
Your BI strategy needs to provide information to your business that allows you to analyze where you are, and where you need to go.
The vision and goals of the BI strategy must align with the vision and objectives of the company.
So, the BI team needs to consider: What does it want to accomplish with the BI strategy? What does the business need to grow? What data would help support business growth?
Thinking back to our road-trip example, the vision and goals will define where you want to go.
Your team’s vision could be as straightforward as “providing BI analytics that allows the business to understand and take action to improve key performance indicators.”
Your strategy will then create the map to get you there.
4 – Determine Current State
With your team in place, you can now analyze the current state of the business, from several perspectives:
What data are you collecting? What technology is in place for collecting the data? What sort of collection storehouse is in place for the data? Will you need a new plan for data storage? How is the data shared and used? That includes structures and processes. What works? You don’t have to start from scratch. What doesn’t work? What data isn’t being collected that would help?
5 – Define Key Performance Indicators (KPIs)
A KPI is a measurable value that will show progress towards or achievement of a business objective. KPIs will tell you whether you’re achieving your goals or lagging behind targets. KPIs must align with the company’s overall vision.
For instance, monthly sales growth, monthly sales performance, or the number of new customers in a month are all examples of KPIs related to sales. They must be measurable.
Think about industry KPIs, internal and external data, and any historical data about your company. As well, think about who will be using the information and how you can best deliver it to them.
Each business area will have its own KPIs. To start, you don’t need a lot of KPIs. Choose a few important ones, from various areas of the business, as you can always add to the list later.
This is where it gets good:
Your cross-functional team will be at the table telling you how they use the data, and who will use it, which will inform your strategy and the execution of the plan.
6 – Establish and Document a Common Language
Your KPI list needs to have a “dictionary” that goes along with it. This ensures everyone is on the same page when referring to KPIs like “gross profit.”
A definition of standard terms may seem unnecessary, but it isn’t. It’s part of creating consensus on the data, how it’s calculated, and how it’s used.
Every area of the business must agree on how terms are defined and, more importantly, how data is collected, calculated and analyzed.
Once you reach this stage, you can finally start thinking about a software solution or partner to work with your business.
Choose carefully, using free trials or demos to help make your decision. Look for a partner who lets you start small and scale the project as you get more experience.
You’ll also have to consider things like your budget, and decisions like a cloud-based versus on-site solution.
8 – Phase-In the Strategy
Similarly to identifying a few KPIs and adding later, consider starting small and adding to the project as you progress.
That means you can choose a few KPIs, gather the information, and build reports to share with the organization. Ask for feedback on the reports and make any improvements.
Continue this process by adding more KPIs, developing more reports, and gathering feedback. You don’t have to implement a major project all at once.
9 – Communicate, Communicate, Communicate
We can’t say this one enough!
While you may have buy-in from your BI team, you need to do the work to ensure buy-in across the organization. After all, the best BI data will be worthless if it isn’t understood and being used.
Use your BI team to roll out the results in their areas. After all, they are the best advocates. They understand what their area needs to know, and they understand the BI strategy inside and out. Get the Executive sponsor to be part of those sessions.
Consider doing “lunch and learn” sessions, to explain the process and the data to the business.
Ask for feedback, and listen when you get it.
10 – Review and Revise
Besides gathering and using feedback, you need to continually review your BI strategy and revise it as necessary.
An effective BI strategy doesn’t have an end date. Your business goals may change; new products may be introduced; team members may develop new KPIs.
Keep investing in your BI strategy. Do regular reviews to ensure the data is still valid and useful. Make changes, additions, and deletions, if necessary.
11 – Celebrate
While your BI strategy won’t have an end date, you should take time to celebrate as an organization. Your BI team and their business areas have done a lot of work, and that work has helped improve the business.
Take some time to thank everyone, and have a BI celebration. After all, everyone loves a party!
Implementing an effective BI strategy is not done overnight. It takes considerable effort, time and consideration to be done correctly.
But an effective suite of BI analytics will be valuable to your business in many ways. It can help improve internal processes. It can provide information on efficiencies. It can give insight into possible new products, or adjustments to existing products. It can even provide information on competitors.
Following the 11 steps we’ve outlined will help ensure your business will find success in developing and implementing an effective business intelligence strategy.
Danielle Canstello is part of the content marketing team at Pyramid Analytics. They provide enterprise-level analytics and business intelligence software. In her spare time, she writes around the web to spread her knowledge of marketing, business intelligence, and analytics industries.
Personalization is one of the most exciting areas in the world of advertising and marketing. Today’s consumers expect a much higher level of customization, with companies like Netflix and Spotify raising the bar in terms of what the average consumer expects.
In a year with the advent of GDPR – it’s reassuring to realize that personalized marketing and advertising can be done in an intelligent and insightful way. This is all possible while complying with privacy legislation.
Customer data and insights are at the heart of the future of personalization. We’re beginning to see the benefits of bringing vast amounts of data together to asses analyze and make the right, informed decisions.
For businesses, this translates into a more personalized marketing strategy, product personalization and the ability to adapt to ever-changing trends.
What is personalization?
Lack of contextual understanding for consumers’ behavior has long held back the effectiveness of personalization in spite of a wealth of data, but marketers are finally starting to get a grip on it.
Consumers are demanding more personal experiences, and everyone from retailers to advertisers, marketers and product designers now understand the benefits that personalization can bring for their bottom line.
A lack of context around consumer behavior has previously limited the level of personalization available. Data has increased, but actionable data has often been harder to identify.
As datasets have improved, businesses have become better at understanding what makes good data and how they can use this to fuel cutting edge innovation in personalization.
This ultimately provides better marketing, improved one to one experiences and the ability to predict trends and consumer needs to deliver personalized experiences across the consumer journey.
Understanding your business is the first step of personalization
Personalizing the consumer experience first involves understanding your business. You have to know who your customers are. You have to know what they look like, what they like to do and how they behave in different contexts.
Understanding the context of engagement
The first step involves understanding the context of engagement. Personalization has improved, but with some datasets, context can be hard to discern.
Without this understanding of context business risk poor personalization that consumers will reject and struggle to engage with.
Building a detailed view of how your customers use your products, engage with your various touch points and illustrates why they are doing this will provide a solid base for highly effective personalization strategy. It’s also a great case for POS integration, helping you to get a unified view of ever point of customer interaction.
An example of this involves combining data to create a holistic view of your customers. If you are looking at personalizing your brand marketing, it’s not just enough to identify that a consumer fits within the profile of your target audience.
They might not be in the right frame of mind for engagement. Combining profile data with other data sets that can signify intent is a much better way to achieve great personalization.
For example, combining profile data with precise visits data to similar categories of a store can help you to understand the context. From here it’s possible to create highly personalized communication based on real-time consumer behavior.
It’s essential to maintain your personalization strategy so that as things change, you can adapt your personalization strategy.
If you have a physical consumer touch point, changing trends in your area can occur quickly. Understanding these changes can give you an advantage over other brands and retailers in the area.
Visits data combined with demographic data can help to identify who visits your store, your competitor’s store, the area and where they come from.
For example, identifying that Chinese nationals visits to the area are growing month on month can be valuable for your physical retail personalization strategy. You can personalize your retail environment to drive revenue and visits.
Offers, incentives and one to one marketing in retail
The challenge with many marketing strategies is that offers, promotions, and incentives are developed to be one size fits all. Many retailers, for example, will have a single offer aimed at every store visitor.
But each consumer is unique with different personalities, profiles, motivations and brand history. Personalization is valuable in these instances as it helps to deliver the desired offer to the relevant customers.
The aim with marketing personalization is to get get the ideal offer which is most likely to convert a specific customer to the customer is the right moment.
Data is enabling this process already. But using intent data such as consumer location or interaction history and matching this to the ideal offer is improving the level of personalization marketers can deliver.
By combining intent data with other datasets such as store visits or purchase data, retailers can see how each offer affects purchases. This combination means that marketers can understand the impact that each offer has physical store visits or purchases.
With data, retailers can begin to respond to each consumer at an individual level. The data that they use to achieve this will help them to simultaneously optimize these offers and the delivery of these offers to improve their marketing and their bottom line.
For paid media, the ultimate goal is to achieve a one to one marketing strategy. With the rise of technology, it’s now easier and quicker to deliver personalized marketing at scale.
New datasets have developed a deeper understanding of consumers and how they behave in both the online and offline worlds. Using data allows brands to reach consumers with personalized marketing, across many different channels and touchpoints.
Understanding where and how consumers move can help brands to personalize their marketing activity. Location-based segmentation, for example, allows marketers to build more specific audiences, optimize ROI and reduce wasted ad impressions.
Media buying platforms offer many ways to segment audiences, but a rise in unique third party datasets have meant that marketers can segment and fine-tune audiences better than ever before.
The data that marketers now have at their disposal has enabled them to do more than just personalize based on past consumer behavior.
Advanced datasets can take personalization to the next level. Marketing personalization is becoming predictive. Brands and advertisers can now combine multiple data sources to understand how consumers behave on both a micro and a macro level.
Using this information, it’s possible for marketers to become predictive with their personalization.
Marketers can continuously update their perceived customer profiles with data that explains a consumers profile clearly. This helps the business to personalize the consumer journey and remove potential barriers to purchase.
Data is enabling businesses to understand which areas to invest in the future to deliver personalization.
The personalization power of unified datasets
As we’ve already alluded to – the future of marketing personalization doesn’t just involve a single dataset. It’s the combination of many which will bring new levels of personal marketing and brand interactions.
As data increases the trend for unified datasets will do so as well. To create high levels of personalization we need to find an effective way to consolidate the data sets that can fuel personalization.
Data platforms are catching up with the personalization needs of the modern marketer. The infrastructure is advancing to support the staggering growth of data that is available for marketers to drive personalized marketing efforts.
The data is useful to drive marketing personalization, but it will soon extend beyond this into other areas of the business. Data platforms are delivering highly personalized marketing to customers, but they are also having an impact in other areas such as logistics, the supply chain, and product development.
Advertisers use audience segmentation so that they can eliminate any unnecessary spend from their marketing efforts, become more efficient and effective and boost key KPIs.
Using data, advertisers can create much smarter audience segments. They can prioritise the right consumer, with the right ad using the right message.
That’s why we’ve made sure that our precise, first-party data sets are available for marketers and advertisers to use directly in their DMP. Advertisers should be able to do this directly in their media buying solution.
Proprietary location data focused on precision and accuracy
Tamoco’s data is industry leading. We use our proprietary SDK for data collection along with our extensive network of sensors to understand consumer location with higher levels of precision and accuracy.
Detailed visit behaviour
Tamoco’s data methodology is designed to reduce the number of incorrect data points. We understand visits with granular accuracy. False visits are filtered out, and our data methodology is transparent. This methodology means that advertisers can be confident that Tamoco visits data is more accurate than other visits-based targeting solution.
Benefits of using location directly in your DMP audience segmentation
To drive incremental gains to ROI location can signify which of these users are relevant to your campaigns. Location data is real-time and behavioural based. These attributes mean that you can exclude irrelevant audiences, and save valuable marketing dollars in the process.
Advertisers can then tailor their campaigns to users that have physically exhibited certain behaviours, such as visited a specific store or frequented a series of physical locations.
This targeting helps to build relevant messaging and ensures that your segments are squeaky clean in terms of precise targeting. No more wasted budget on consumers that aren’t relevant to your brand campaigns.
Location is a good indicator of intent
Add intent to the segmentation process. Retargeting campaigns are more effective if you can reach consumers when they are in the right frame of mind. Retargeting works well in the online world, but this is often limited to your current inventory.
Consumers show purchase intent in the offline world as well. Visiting your store is a good example. However, by mapping the offline world, advertisers can use location to identify consumer intent in different ways.
Retargeting to consumers who have visited (or are currently visiting) a competitor or a store in a similar category is a powerful way to reach the right audiences.
Using location data to fuel analytics
Using location signals directly in your data management platform enables smarter cross-selling. If you have built up a database of descriptive and behavioural audiences, adding location can provide a better way to upsell new products or promote return purchases.
Using your analytics solution, location data can directly increase how you understand consumer trends, patterns and intent. Location data is a tool for building up a more detailed view of your customers.
These insights can be used to inform future segmentation and predict which audiences are more likely to convert at a specific stage in the buyer journey.
Using location to build lookalike audiences
Reaching new customers that are currently outside of your customer data set can be challenging. It’s something hard to know if the process of building lookalike audiences is reliable.
Using location data, it’s possible to build real-world behavioural based audience segments. For example, by taking an audience that converts highly, we can understand similar consumers based on how they move and behave in the real world.
This generates lookalike segments that are based on current real-world behaviour rather than vague similar interest data. Ultimately it will build segments that are more likely to convert.
Example segments and audience segmentation strategies
Some of our location-based segments are available already. Here we will look at some familiar audiences segmentation use cases using this data.
Women’s clothing stores
Brands looking at segmenting their audiences based on consumer interests can use location to refine their audiences. Let’s look at how this would work with an audience based segment.
You already have a pre-built audience that is relevant to your women’s clothing brand.
Using location-based filters directly in your DMP you can further filter this audience to reach the most relevant users.
You can filter based on the number of visits to women’s clothing stores. You can set the time period for these visits.
This will segment your audience based on those that have physically visited a clothing store in your defined time period.
Drinking places (alcoholic)
Using location, you can build retargeting audiences in your DMP to maximise your ad budgets.
If you are looking to retarget consumers based on their behaviour, then location can help to define the right audience.
Set your audience to include those that have visited an alcoholic drinking place.
You can filter these visits based on past visits, or on specific dates or days of the week.
This can help you to build incredibly specific audiences – such as Friday night venue attendees.
Speciality food stores
As previously mentioned, location data can be helpful to build new lookalike audiences based on consumer behaviour. This method can help you to create unique lookalikes based on actual measured real-world behaviour.
We can build an audience based on visits to speciality food stores. Here we have our seed audiences that consists of consumers that we know have visited a health store at some point in our defined time period.
We can do one of two things here:
Move the identifiers into our current lookalike modelling solution. This will create a new audience based on a unique seed audience.
Use a location-based lookalike solution. This will use the audience to match with devices that have exhibited similar real-world behaviour.
All of the above segments are readily available in leading DMPs and other media buying solutions.
How to activate Tamoco’s precise location data
Our data is currently available through DoubleClick, AppNexus, AdForm. Here you can begin segmentation immediately using Tamoco’s location data.
We can activate these segments instantly in The Trade Desk, Adobe Marketing Cloud, Facebook Advertising, Sizmek, Beeswax, Widespace and BrightRoll. Please contact us to enable this.
Want something more custom?
We can build custom segments on demand with our team of data scientists. These can be fed into the above solutions. Here are some examples of what our team can provide for your campaigns.
Brand affinity – we can create segments that are based upon brand affinity to your brand, a competitor or another relevant brand.
Detailed visits – Our team can help segment audiences based on verified visits to any physical POI, venue or location.
All of our data solutions can be fed into your current data or targeting platform. Our team of data scientists are ready to support your integration and take your marketing to the next level.
So as a marketer you want to know how location-based marketing can help you to reach your marketing goals?
It’s time to take a serious look at location. Big data is tearing up the rulebook in a number of different industries. This trend continues with data-driven marketing becoming the new normal. Mobile has changed many things, but it’s having a huge effect on the way that markers are using data to reach their goals.
The missing link in this equation is location data. The rise in mobile adoption has provided a much better and accurate understanding of how audiences behave in the offline world. This location data is allowing marketers to do incredible things, based on cold hard evidence.
We’re going to look at some examples of location-based marketing. Get ready to see how you can use location-based marketing to create effective campaigns. Learn how to use location data to provide powerful insights and measure attribution with precision.
What is location-based marketing and big data marketing?
Location data can be seen as a branch of big data. When the term big data is used people generally think first about quantity. Whilst this probably has to do with the reason that the terms exist, big data isn’t really about quantity.
We think that data is big in the sense that the impact is big. We think of location data as big because of it’s quality in both application and insight.
With that in mind, we can define big data as the collation of data from multiple sources. To inform better decision making, powerful targeting, and improved attribution.
Location data is big data that uses information about a person or group of people’s movement or behavior. This is used to understand wider trends and patterns. Location-based advertising and marketing use this data to fine-tune marketing efforts. But it is also used to generate better engagement and get valuable insights into customer behavior.
How can my business use location data and location-based mobile marketing
For marketers, it has sometimes been difficult to understand the benefits of location data. Especially whilst trying to get around the technical side of how it works. In the beginning, many companies had inaccurate data sets. But now the science behind location data has advanced greatly. This enables marketers by providing quick and reliable results. All by incorporating location data into their marketing strategy.
These uses are now much more accessible and easily combined with existing marketing efforts. Plug and play location-based marketing is now available. With this in mind let’s look at some of the key marketing practices that benefit from location data.
Audience segmentation is a key challenge for any marketer. In order to optimize marketing dollars, it’s important to make sure that you are reaching the right people. It can sometimes be difficult to get this right, and often involves a lot of hypothesizing and testing as well as optimization.
Location can help to build powerful audience segments as it’s a key indicator of intent. For example, let’s say you make the active decision to walk into a specific location in a shop. It is then likely, at some level, for you to be interested in some of the products in that location.
Location can also be used to build historical audiences based on location history. This means that you might target a group of people who are health nuts. You’ll have many ways of doing this currently. But location adds something that isn’t possible through traditional targeting options.
You can build an audience of people who visit gyms twice a month and have been to a dedicated health and fitness store in the past three months. If you have a strong idea of the type of target audience your product sits well with then location is a powerful tool for identifying custom segments.
An important point to make is that these audiences can then be used in the way that best suits your needs as a marketer. You can target them through social media ads or send the data straight to a trading desk, DSP, DMP or other ad network. You can use it to overlay custom audiences to understand overlap. You can even use location to see the accuracy of your existing audiences and targeting.
An example is a location-based campaign that targets users when they are close to a physical store of venue. When the user enters a pre-defined location they are given a message that informs them of the CTA that is nearby.
This can be in the form on a push notification via a third party app on their phone. But it’s also possible for marketers to feed this real-time data into existing media buying tools that can deliver ads via other programmatic media. As long as this is real-time the audience is still in the relevant moment and therefore effective.
These kinds of campaigns get much higher engagement and conversion rates. Marketers can use location data to ensure that their real-time targeting is effective. With location, you’ll also get insight from these kinds of campaigns. These insights can help you to optimize your entire marketing department.
This is an area where location data is offering unique insights for marketers. The ability to measure the effect of advertising in the offline world is a relatively new concept. Especially at a level that rivals the detailed insights that are readily available in the digital realm.
OOH, real-word adverts are big business for marketers. But there’s always been a problem – how do you measure the results? It’s difficult to attribute store visits or purchases to OOH. It’s also difficult to understand exactly how many people are exposed to advertising in the first place.
Location data makes these insights accessible. By listening to areas around OOH it’s possible to measure how many people have passed or remained close to the OOH advert. From this data, you can create insights on how many people have been ‘exposed’ to the OOH ad. Of course, this isn’t perfect as there’s no guarantee that everyone walking past saw or understood the message.
But location data makes it possible for marketers to then measure how many of these people perform the desired goal. This may be that they visit a retail store associated with the ad. This is an effective tool for marketers to be able to measure, test and optimize OOH advertising.
Measuring experiential or other offline advertising
Of course, this tech can be used to measure other forms of advertising. Take experiential, for example. Usually, these campaigns end with the consumer leaving with a sample of some kind. But attribution doesn’t come easy and many campaigns end with the basic insights. These are usually how many people visited the experiential stand, or how many samples were handed out.
But location data enables marketers to then say, with great precision, this many people engaged with our experiential stand. You can then identify the percentage of these people that visited the store within a certain period of time.
You can also generate a dynamic QR code to track, analyze, and retarget your customers. All you need is a dynamic QR code generator.
These insights are invaluable. They provide marketers with the opportunity to get digital insights on traditional offline marketing campaigns.
Measure the effect of digital advertising on offline goals
Location data is a powerful tool to associate online digital advertising to offline conversions.
For example, if you have a Facebook campaign you will have an idea of how many people saw your ad and even how many of these clicked your ad. But if your conversion is in the offline world, ie visiting a physical store, then this is where your campaign traditionally ends.
Sure there are some things you can do to pick up customers on the other side, like offer codes or loyalty schemes. But none of these will offer the same precision or reach as location data-driven marketing attribution.
Using location data marketers can understand the offline effects of digital advertising.
For marketers, getting those insights on a micro and macro level are crucial when creating your strategy. In terms of insight, data is the new normal. You want to base your marketing decisions on data that is accurate and instant.
Understanding the customer is critical to any marketing department. Location intelligence is a powerful tool in the area of customer analysis. Try enriching customer data with demographic and anonymous lifestyle information. This allows marketers to create more effective databases and be better placed to predict where best to spend the marketing costs.
For brands with a physical venue or store location data can provide powerful insights into business performance. Understanding footfall and trends can help to inform on the ground business strategy. For example, retail location data can also help strategize how best to compliment physical retail stores with digital advertising.
Combine this with the ability to see data on competitors and other physical location and you have a powerful toolkit that marketers can use to put data at the centre of their decision making.
But analytical intelligence can be one solution to the personalization problem. Identifying the location of a customer can help brands and marketers to customize their message so that it is personal.
This could be a simple as including terms like welcoming back in your messaging. Or you can create entirely different communication for customers that are in different locations or have demonstrated previous patterns of behavior.
Communicating with your customers in this way can help to build stronger relationships and increase brand loyalty. This allows you to communicate with the right customer when they are in the right place with the right message.
How does location-based marketing work?
Location data is sourced from mobile devices. Sensors are used to understand and pinpoint these devices. This process is anonymized so that the user’s personal details are kept private.
These sensors come in a variety of forms – from beacons to Wi-Fi to geofences. Using a combination of sensors allows for greater accuracy and better scalability of data.
This means you’ll need to understand what types of location data there is. Some are more precise than others. Some are real-time and others are delayed.
Generally, a data provider that can explain to you their methodology and is transparent about their data sources is a good start. Look for sensor-driven data sources such as beacons, GPS or wi-fi. First party data sources are much better than third-party, where the provider cannot validate the accuracy.
Can ensure that data is collected in a safe and secure way
Does your data provider have the correct opt-in procedures? Do they comply with current data collection legislation? These are all important questions that any good location-based marketing company will be happy to explain to you.
It was Unilever’s Keith Weed that pulled no punches toward the digital media industry as a whole this week. His message was clear: “Clean up or get out.” It was made abundantly evident that Unilever was no longer ready to use its $8.5bn marketing budget to prop up the industry. Especially as it has been worryingly dismissive about their level of accountability on everything from data protection to trolling.
Calling out the obvious culprits in GAFA, the broad-reaching topics that were referenced should have sent a direct message to the industry as a whole – to have more of a moral conscious and get its act together. But what’s the big deal all of a sudden?
Well, for starters, this isn’t the first time that the issue of digital platforms misusing their power, reach and influence has been brought up. Nor is it the first time that a large media spender has threatened to pull their budgets if these aforementioned platforms didn’t get their act together. It isthe first time that we’ve had a consecutive series of events in recent months. That gives Unilever and the wider media community a chance to bandwagon and create more of a stand against this ongoing farce.
A drive for transparency
Keith’s message also did something quite novel. It gave some clear guidelines on what Unilever wanted to see in order to give it the comfort that appropriate steps had been taken to make progress in this area. Namely:
1. Responsible platforms: Unilever will not do business with a platform that does not protect children, or which create division in society and promote anger or hate.
2. Responsible content: Unilever is doubling down on its commitment to responsible content, initially by tackling gender stereotypes in advertising through the Unstereotype Alliance;
3.Responsible infrastructure: Unilever will only work with organisations that are committed to creating better digital infrastructure, such as aligning around one measurement system and improving the customer experience.
One of the best things to come into the industry is this collective drive for transparency, which lies at the heart of the problems this sector is facing. It’s convoluted, unnavigable and untrustworthy. It’s losing credibility at an alarming rate and until now it has been sufficient to ‘talk the talk’ and not need to walk the walk. The status quo has been enough to demonstrate you’re doing something about it without actually doing something about it. Well, 2018 certainly feels like the year this will finally change.
Cleaning up the digital supply chain
As a company that works in the world of location data, serving the digital platform industry including partners such as Unilever, we’ve seen how important transparency, relevancy and security are to every single part of the chain. We’ve placed these issues at the heart of what we do at Tamoco. We ensure that consent is properly attained. The data we collect is legitimate and accurately attributed as precisely as is possible. In doing this we hope to bring back some integrity and structure to the industry.
Keith was right – consumers do not care about third party verification. The tools that advertisers such as Unilever use for personalisation, contextualisation and measurement aren’t important if the data underpinning them has not been obtained properly, and utilised in a regulated and transparent way.
This is why GDPR is coming. Not to scaremonger the public or to shut down companies trying to use data and technology to improve services. GDPR exists to stamp out the types of unscrupulous digital platforms that misuse, mislead and misrepresent. It’s these platforms that make life harder for the industry as a whole. By promoting transparency, relevancy and security we hope to claw back some of the trust lost by the industry. At Tamoco we are excited for 2018 to become the year of transparency and control.
There have been issues with data accuracy in mobile targeting in the past. Targeting the right person in the best moment is still the appropriate goal for marketers. But to do this effectively, the data that fuels campaigns must be reliable.
If marketers don’t work from reliable data sets then the information is useless, and mobile targeting will be more of the same.
We’ll look at a few fundamentals to look for that will ensure your mobile targeting campaigns are powered by quality data. We’ll also discuss the effect this will have on different mobile targeting channels.
When we talk about data we look for the following attributes:
First-party – is the data from a first party source. Second-hand data that is unverifiable is not helpful as it could be inaccurate or out of date.
Sensor-driven – this means that the data sets are sourced from accurate sensors. Precise and reliable data sets are sourced from multiple sensor types to ensure accuracy and scale.
Real-time – Datasets must be immediate in order to verify accuracy. To achieve effective personalisation and mobile targeting, action must be taken based on data sets that are real-time.
Programmatic advertising & location data
Location is a fantastic trigger to help fuel mobile marketing campaigns. That is if you can get the moment right.
Programmatic has always had its problems – automation is difficult to get right. A lot has been said about programmatic and it’s effect on delivering relevant content to the right person at the right time.
We’ve all seen and remember poor individual use cases of programmatic advertising. Mobile programmatic targeting has taken the plunge and aims to put data at the centre.
However, problems of accuracy remain if the data that is being used to fuel programmatic advertising isn’t accurate.
Data is derived from accurate, sensor-driven networks
The data is first-party
Without this programmatic mobile targeting will be ineffective. Outdated data sets can mean that you completely miss the relevant moment to target audiences.
It can also mean that your attempts to personalize the experience miss the mark. We all know how important personalization is to the modern marketer.
There are a lot of companies that claim to provide accurate data, but these are rarely meeting the three conditions we just discussed.
Often the data is third-party, it’s driven not by accurate sensors, but vague lat/long indicators. It’s often not live or real-time either.
So make sure your data partner can deliver on those three points. This will allow your programmatic mobile targeting to truly feel personal. It’s also important to get a good understanding of what stream processing is.
Location-based social media marketing
With organic social media becoming more obsolete, more brands are looking at increasing their ad spend on social to ensure that they reach audiences constantly. One way to do so is by using social media management tools that automate posting regularly on all social channels for maximum reach.
Social targeting options allow for geographic targeting. But the accuracy of these targeting options is yet to be verified. How do you know you aren’t targeting a user who checked in there over a week ago?
Let’s analyse current social media mobile targeting options in relation to our three commandments.
Real-time – Facebook’s geographical targeting feature is rather vague when talking about its geo-targeting options. Or at least when talking about the speed of the targeting. “people recently in this location” is how they describe it. But there’s little in the way of how recent.
Now, this is useful for some advertisers, but to create a truly personalized and real-time experience, it has to be instant.
Accurate sensor-driven – Again it’s hard to tell exactly how Facebook sources its location data. We suspect that a large proportion of data is derived from check-ins on the Facebook platform. This does raise some issues – it relies on the user selecting the right location, for example.
Social is a powerful channel for targeting users. But the potential is even greater if brands can accurately target users in real-time. It’s even more effective if this is done in relevant locations and with personalized content. This can even be leveraged to create social proof.
In order to achieve that, the data that fuels social targeting and retargeting needs to be accurate.
Social platforms have always focused on personalization of the news feed. But this highlights some of the problems with facebook ads – they aren’t relevant.
Facebook has spent so much time personalizing the organic news feed but then delivers any ad at any time.
The same can be applied to attribution. Post mobile targeting attribution is valuable for advertisers and marketers. It’s important to measure attribution, especially in the offline world. Mobile location data has been instrumental in this. Closing the online to offline attribution loop is now possible thanks to device location data.
But again, to truly understand physical conversions, marketers need accurate and real-time data sets.
Mobile targeting is a powerful tool for marketers to reach users with personalised messages, at the right moment. Location data and location intelligence helps provide the context that mobile targeting takes place.
Whether programmatic or social, mobile targeting requires data. This data must be accurate, real-time and first-party to ensure that location-based mobile marketing is effective.
Precise data is now available at scale. This means that marketers now have a powerful tool at their disposal, as long as they utilize the right data sets.
Location-based marketing is the practice of using physical location to inform and optimize advertising, communication, targeting, loyalty and attribution.
This sometimes also known as location-based advertising or proximity marketing. At the most basic level, it means creating a one-to-one relationship with the customer. Emphasis is placed on communication in the right place, at the best time and with the relevant message.
A history of location-based marketing
Despite popular opinion, location-based advertising has been around for a while. Sure, it hasn’t always been backed by the smart technology in your mobile phone. Buy it has existed in some form. Local marketing strategies have been a key part of marketing since the practice began.
It might not have been as effective, but brands have been trying to target users based on location since well before most of you reading this were born. Advertising space has always been purchased based on its location. Be it a metro station in an affluent Paris Arrondissement. Or a teenager holding a sign advertising bagels in a certain street in NYC.
Location-based marketing has developed a lot since then. The underlying technology has advanced at an alarming rate. The ability to understand where audiences go and the ability to market to these is improved.
IP addresses and targeting over Wi-Fi
Location-based marketing has always been around. But, it did up its game in the 90’s once the internet found its way into most family homes.
The dial-up broadband revolution had begun. Most families now had internet access in their home through (a large) desktop computer. For marketers, this meant that advertising could be delivered to a user based on their IP address. Thus marketers could now target based on location, usually an area the size of a postal area.
There are a few problems though. Unless you have a very specific audience in a single geographical area, it’s difficult to ensure that you are targeting the right people. By today’s marketing standards, an entire postal area is just not specific enough.
Another problem that remains is the lack of ability to understand who is on the other end of the screen. When IP targeting was becoming popular, many families had a single computer. The whole family used this. Dad to research which TV to buy next. Mum to book the family holiday. Kids to play online games. This means that ad dollars could easily be wasted. As advertisers were under the impression that they were delivering highly targeted ads.
Location-based mobile advertising was the next advance in location tech. Phones become truly mobile in the early 2000’s. They were used by the masses and even your gran had one. This meant two things:
It was now possible to advertise to an individual based on physical location whilst they were on the move.
Location-based marketing companies now had a means of segmentation down to a single person.
This was fantastic and all, but the main issues were still accuracy. Whilst the ability to target an individual on the move was well received, issues still remained. The area in which location could be accurately placed was still too large. Mobile targeting via phone masts was a vital step on the road to personalization. However, those in pursuit of accuracy were still frustrated. It often requires a form of phone validation to ensure the right person is reached.
GPS, geofencing and geotargeting
In the late 2000’s smartphones really took off. The first smart devices appeared and with the first iPhone, the race to create powerful mobile devices went mainstream. This led to rapid growth of mobile device adoption with GPS capability.
This changed mobile targeting for good. It was now possible to use a device’s precise GPS satellite positioning to understand device location. This process is known as geofencing, geomarketing, or geotargeting.
A geofence is a virtual boundary that is defined in order to perform a specific response once a device enters or leaves the defined area. More advanced geofencing is possible. An example is focusing on dwell time. Triggering a response when a device is within a geofence for a minimum amount of time.
The geofence allows for mobile targeting to occur on an individual level anywhere. It means that audience segmentation can occur based on individual movement. But it did more than previously possible. It was now far more accurate. This meant that personalization (relevant to location) was now available. Location-based marketing was now personal and precise.
Satellite problems and indoor confusion.
Location-based mobile targeting improved in accuracy with the geofence. However, on a precision basis, it’s still not perfect.
GPS has issues in some indoor spaces. So it might be incredibly effective at understanding where a person is in their car whilst driving. As soon as they enter an indoor space, GPS can be temperamental.
Some may argue that this level of detail is neither here nor there. But the problem arises when location data providers can’t differentiate between accurate data and inaccurate data. Advertisers and marketers don’t know when the data fuelling their campaigns is incorrect.
Beacons – iBeacon and Eddystone
You thought that it was location-based marketing you were doing before? How very wrong you were. Beacon adoption was a trend that changed location-based marketing and advertising even further.
Thanks to mobile devices and mobile targeting, advertisers would now focus all efforts on accuracy.
Beacons were the natural next step in this journey. Beacons are small Bluetooth devices that can interact with a mobile device. This interaction allows the device to understand exactly where the mobile device is with an accuracy down to 1 meter. Not only does it do this, it can measure positioning on a vertical basis.
There are two main types of beacon technology; iBeacon and Eddystone. These devices are deployed everywhere from shopping malls to sports stadiums.
This was the true beginning of proximity marketing. Delivering personalizable content with accuracy in precise micro-moments. These moments are relevant to the time, place and person. Beacons also allowed for accurate attribution in the offline world.
But what about scalability?
The problem with Beacons is that they require hardware to be deployed. Unlike Geofencing, where geofences can be set around any place that satellites can reach. Beacons must be physically deployed inside a store, or inside a football stadium. So these individual businesses can enjoy a high level of location-based marketing. Once a person leaves these areas they cannot get the same level of location-based insights.
Enter the network approach
The solution to this problem? The proximity network approach. To maintain beacon based, accurate levels across entire cities, it’s essential to collate this location based hardware. That’s what Tamoco’s proximity network is. It’s a complex network of location-based sensors. This allows for mobile targeting, location-based, marketing and location intelligence and insights at a consistently high level. Moreover, this is available whether the device is indoors, outside, on the first floor and so on.
One issue that many proximity marketing companies have had with location-based advertising is with scalability. Creating a proximity network of location allows for this scalability.
Sensory agnostic and a view of the future?
At Tamoco we take a sensory agnostic approach to location-based marketing. We believe that as technology advances, we need to able to adapt to the changing was that advertising, targeting and marketing will change.
A good example of this is the emerging tech around AR. An AR headset or device is still a sensor. Location is a means to an end for targeting through this medium.
By taking an agnostic approach and ensuring our network approach, we ensure that advertising and marketing can keep up with the cutting edge trends. Moreover, we can ensure that this is available with a precision and scale that those original metro advertisers could only dream of. Using location to improve marketing is here to stay.
Marketing attribution has always been a tough area for marketers and advertisers. Attribution modelling has undoubtedly provided huge value. However, the ability to measure the effect of channels (or touchpoints) on the customer journey has often been fraught with difficulties.
Return on investment has been difficult for a variety of reasons. Understanding the entire customer journey has been problematic. This is especially true in the physical, offline world. Some offline marketing channels have also not been trackable. Attribution solutions have struggled with the multi-channel and real-time aspects that are crucial to understanding the full marketing picture.
Location intelligence has grown in terms of accuracy and scalability. This presents an opportunity. Some of the problems with attribution modelling can be solved with the application of accurate location data. Could this be the solution to the problems that limited marketing attribution in the past?
What is marketing attribution?
To understand the problems and the effect that location data can have it’s important to understand marketing attribution.
Attribution is the practice of allocating purchase revenue to the marketing touch points of a customer. In other worlds – understanding the effect of marketing efforts and channels on the purchase decision of customers.
Touchpoints can cover a wide range of customer interactions. Understanding the effect of these on sales or other valuable metrics allows for the optimisation of marketing channels, activity and budget.
What is offline location-based attribution?
Location-based attribution is the use of accurate mobile device data to fill in the gaps in traditional attribution models.
Smartphone adoption has grown rapidly. Understanding the where and how people move becomes scalable and precise. Customers rarely move without their mobile device, and this is the key. Higher levels of attribution precision are possible. Connecting the online and offline worlds becomes easier. Customer journey mapping and various touch point measurement is improved.
Until recently it has been impossible to understand the offline world. This has meant that advertisers have often been unable to attribute sales in physical stores and locations to a specific channel.
As smartphone adoption has grown using a device location has proved extremely useful in connecting the two.
Mapping the customer journey – cross device attribution
Basic attribution models have chosen to measure first touch or last touch. Much has been written about the failings of each. The choice lies in ignoring either early, top of funnel activity. Or failing to consider later, bottom of funnel activity that helps to move the customer along the buyer journey.
So the natural next step is to focus on multi-touch attribution. Focusing on touchpoints throughout the customer journey is important. But it requires accurate measurement across channels to be effective. The problem is that multi-touch attribution models don’t always incorporate what is happening in the offline world.
Location data allows a complete understanding of the customer journey. This means that it becomes possible for businesses to say the sort of thing like – okay this person saw our Facebook ad and has now completed a purchase. Previous attribution models would then attribute this purchase to the Facebook ad and not demonstrate how to generate leads on Facebook. But a more holistic view of the individual customer might point out that actually, the customer had visited the physical store previously.
This ability to model attribution across the online and the offline leads to a clearer picture of attribution. It allows brands to be better informed about the effect digital has on physical and vice-versa. Your customers exist across multiple marketing channels, so your attribution should too.
Previously brands have tried to close this gap by using various methods to map the offline customer journey. This usually took the form of a promotional code, which allows the brand to understand which channel had the desired effect. But whilst the picture is slightly clearer, it is not enough to be able to inform marketing budgets. Or to provide a clear understanding of the customer journey and the customer experience.
Only location intelligence can provide these insights. And it can do this across the online and offline world with a sufficient level of detail. Location data is versatile, quick and accurate. This makes it the perfect tool to help close the offline to online attribution loop.
Location data connect online advertising to the offline world. This allows for attribution in physical locations. This allows brands to measure store visits and link this offline activity to other digital touch points. It allows for more accurate customer journey mapping. This data can even be used to understand external offline touch points, such as OOH advertising. Already a complete picture becomes available.
Attribution has always had its problems. But brands and marketers should understand and implement insights from customer data points. In this way, location data provides a better understanding of the offline world. It allows brands to measure touch points more accurately. It allows them to map the customer journey in greater detail. And most of all, it allows them to measure the effects of cross-channel marketing in detail.
Mobile advertising is growing incredibly fast. Smartphone adoption has never been higher. Accordingly, marketers are adopting a mobile-first approach. As marketers look to target audiences on mobile devices, they will need to adapt to stay effective at targeting the right person, with the right message at the right time.
Mobile targeting is set to pass other channels as more budget is being allocated to reaching audiences on the move. To stay ahead here’s how to improve mobile targeting and advertising using location data and today’s location based marketing technology.
Location intelligence is an effective method to improve mobile targeting
Location data is a useful tool to help target mobile devices effectively. It allows in-depth segmentation of mobile audiences. It also facilitates real-time message delivery based on a device’s location.
This level of accurate data insight improves the effectiveness of mobile targeting. Location based marketing has been proven to increase engagement and conversion rates. This is because mobile targeting occurs contextually, at the best possible moment.
Location data is used to build complex mobile audiences profiles. This is achieved by understanding these anonymous location signals. Mobile targeting campaigns should feel personal and relevant to the user. This is achieved by applying offline behaviour to the targeting process.
Adding location intelligence to mobile targeting means less chance of delivering content that is irrelevant or annoying. In a world of increased ad-blocking, users want marketing to be helpful. Location based marketing allows mobile targeting campaigns to be just this. They reach users in the best moment with personalized and useful relevancy of the content.
Personalize mobile content and combine this with relevant mobile targeting
Location intelligence allows brands and marketers to target users in real-time with accurate and personalized content. Ensure that mobile campaigns combine accurate targeting with contextual content relevant to the user’s location. Personalization should extend beyond simply addressing a user by name. It should be personalized to a level that reflects the location and situation of the mobile device.
Personalization is key to successful mobile targeting. Improving the relevancy of the content will improve the success of campaigns. High-level mobile personalization is achieved with accurate location data. Understanding the situation of a mobile user allows content to be targeted and personalised. Users are more likely to interact with content that is targeted to their situation.
Make sure your data is accurate and precise
Using location intelligence to target mobile users in the right micro-moment will improve campaign performance. Targeting devices at exactly the right time ensure that you will reach the user in the best place at the right moment. This requires the location data that fuels your campaign to be precise and instant.
Ensure that your location data partner that can source accurate data in real-time to inform your mobile targeting campaigns. Ask how your data is sourced. Using third-party data may not be helpful as it could be incorrectly sourced or out of date.
There’s a lot of bad location data out there. Effective mobile targeting should be based on first-party data sourced from precise sensors. For example, at Tamoco, we source location signals across multiple different sensor types. This helps to understand device location with accuracy. It also ensures that errors in location data are not included in data sets. Data is also sourced from first-party sources. This means that there is little chance of data being out of date, or lose accuracy through the reselling of source data.
Marketers should take accuracy and precision seriously. It means that mobile targeting campaign budgets are as efficient as possible. It ensures that brands and advertisers don’t waste time and money targeting the wrong audience. Each advertising dollar goes further as brands get better, more accurate mobile targeting.
Apply mobile targeting and attribution across channels
Use location intelligence to understand if your mobile targeting is working. Location based marketing provides accurate attribution after devices are targeted. This allows marketers to understand campaign performance in the offline world. The aim of your targeted mobile campaign might be to send users to a specific store. It makes sense to measure the performance of this using location based attribution.
Many mobile marketers aren’t aware of the huge potential for location in closing the online to offline attribution loop. It can help brands to make better mobile targeting decisions over time.