How Accurate Geolocation Data Fosters a Better Customer Experience
Having the most reliable data the first time you ask for it is a no brainer for the consumer, but its obvious importance is often overlooked by the provider. Data quality should be a dominant component to support a business’ reputation. But what if the data were slightly off? What implications does that have?
Standardizing the Definition of Data Quality
Now that companies are using data to drive marketing strategies, product development, and other key business decisions, stakeholders need to know more. They need to know whether data represents an intent signal or an interest signal. They have a right to know the honest origins of the data they’re using — whether it’s been pulled from bidstream or it’s truly opt-in data from a reliable publisher. They deserve to know that the data they’re using has been collected in a privacy-safe manner and if permission has been ethically obtained. Furthermore, business users should have some transparency around modelled data and declared data. They should have visibility into what’s inside each segment.
The Essential Data Requirements for Successful Developers
Leading brands and local businesses alike rely on innovative business and consumer data to market their products, sell directly, and advertise through multiple channels, including social media, display, email, and direct mail.
Tech companies rely heavily on data to support search and navigation, location analytics, risk assessments, and more.
While use cases might be different, here’s what all companies and their product developers should look for when evaluating the data that will fuel their solutions.
Data Enrichment, Your Business, and Your Career
Creating great customer experiences is ultimately what matters most, and this requires a single customer view and data enrichment techniques for a deep understanding of your customer. Organizations that rely on only first-party data are at a disadvantage. They risk missing out on valuable new information as time passes. For example, did your customer just move to a new state or buy a new home?
Beyond the Data Label: The Next Phase of Data Assessment
This past summer, the IAB Tech Lab launched a much-needed transparency initiative in the form of its Data Transparency Standard, establishing baseline statistics on the objective attributes of a given data segment, such as refresh cadence, ID types, and segmentation criteria. The Data Label should be a big relief to data buyers, including agencies and advertisers, that are trying to wrap their heads around audiences, but it’s still only the starting point when it comes to assessing digital audiences.
For as much as the Data Label injects transparency into audience construction, it only addresses objective attributes, and not the qualitative aspects of the data segment. To get the most value out of their data investments, advertisers and agencies need to go further and develop their own standards for evaluating data, especially when it comes to quality.
The Risk of Emphasizing Data Quantity Over Quality
The privacy movement heralded by January’s implementation of the California Consumer Privacy Act has shone a spotlight on the ethical issues surrounding data collection. But digital marketing insiders know that ethics is not the only issue plaguing data-driven business.
Ensuring the quality and accuracy of data is a major challenge for marketers, data brokers, and consumers. Drew Kutcharian, CTO and co-founder of audience platform DISQO, checked in with Street Fight to provide his vision of the data quality-quantity balance and how privacy legislation will affect it going forward.
With Investment in Geo, Foursquare Focuses on Improving Data Access
When it comes to location data specifically, Senior VP of Product Josh Cohen is seeing Foursquare’s partners put more emphasis on the quality of data. The company’s partners are developing more sophisticated understandings of the range of data quality when it comes to location, which means Foursquare has to dedicate more resources to make sure new industry-wide expectations are met.
The Rise of First-Party Data: Why Quality Matters Over Quantity
For years, digital marketers have paid hand over fist in the digital gold rush for data. Instead of a tangible product, tech companies earn millions in revenue from the data they collect on previous, current, and future digital consumers. But digital marketers seeking to gobble up as much data as they can for their campaigns — while not stopping to consider the source of or methods used to collect it — are taking the wrong approach. The age-old mantra of “quality over quantity” has never been more relevant in online advertising, and marketers must quickly and fully embrace first-party data or risk their digital campaigns (and bottom lines) falling flat.
Why Your Location-Based Ad Campaign Isn’t Working (And How to Make It Better)
Many low-accuracy solutions produce horizontal location data only – location in multi-story buildings is not even a possibility. The result is that advertisers are designing campaigns with the equivalent of one hand tied behind their back, generating two-dimensional campaigns for a three-dimensional world.
What advertisers really need is the ability to reach consumers wherever they are, including the floor level in a multi-story mall, and entice them to enter the store. To achieve this, high-accuracy 3D location is needed. Fortunately, new capabilities are in place to help retailers design more effective campaigns, which will drive better results and raise consumers’ expectations to new heights (pun intended!).
Heard on the Street, Episode 33: Building Network Effect for Location Intelligence
Responsible location intelligence involves practices like “stop data,” to measure users’ location dwell times, and the scale Foursquare achieves in its network of app publishers. Placed is one of the first location data players and a leader in attribution since 2011.
Now that the two companies have come together via acquisition, how does that position Foursquare for interstellar domination of the location intelligence market? It’s about greater capability and scale, say Foursquare’s Josh Cohen and David Shim, our guests on the latest episode of Heard on the Street.
Report: Location Targeting Ecosystem Suffers from Inaccuracy
Location Sciences analyzed 500 million digital location-targeted impressions in the US and UK in the first half of 2019. It concluded that for every $100,000 spent on location targeting, $29,000 fuels targeting outside the desired geographic range, and $36,000 in targeting does not produce strong enough signals to ensure accuracy.
5 Questions Brands Must Ask Their Agency About Location Data
Advertisers are unknowingly wasting 30 to 50%, and as much as 80%, of their location-based targeting spend on inaccurate, poor-quality data, some of which is fraudulent. They are being told by their partners that “everything is fine,” but the answers to a few questions could reveal a very different story.
Here are five questions brand managers should be asking their agency partners about location data. The answers will help vet the quality of the data you are purchasing.
Why Marketers Still Struggle With One-to-One Personalization
Personalization has long been touted as the future-proof way for businesses to connect with and retain customers. With Gartner predicting enterprises will win or lose due to customer experience in 2019 and beyond, offering customers meaningful, personalized experiences takes on even greater importance.
To uncover the truth about how personalization efforts are affecting the bottom line of the Global 2000 and just how much one-to-one personalization is taking place, we conducted a survey with Forbes that asked 200 marketing leaders just that.
Good Data vs Bad: How to Decide What to Keep and What to Discard
Unfortunately, there’s no “silver bullet” for separating good data from bad. Instead, organizations should think of data quality as a habit, with “good” data clearly defined and concrete processes in place to harvest what’s valuable and discard what isn’t.
With that in mind, here are three steps to taking unfiltered data and deciding what to keep — and what to throw out — to achieve optimal data accuracy.
Four Targeting Myths That Devalue the Real Power of Location Data
Jake Moskowitz, head of the Emodo Institute, debunks some myths about location data. Here’s the first shocking one: Location data can’t find you 60 million devices that visited a Hyundai dealership within the last month or two… or three, because that’s impossible. Throughout all of 2017, across the entire US, there were only about 17 million cars sold in total. That includes Hyundai, Honda, Ford—indeed, all brands. In data stores, users run across super-sized segments all the time. It’s not uncommon for vendors to claim that their single-brand auto dealership visitor segments include tens of millions of consumers. Location data is powerful, but it can’t make up shoppers.
Poor Data Quality Is Hurting Location-Based Campaigns—Here’s How to Fix It
“Location data offers the ability to turn universal ads into local ads. Same as local TV. The issue is how location targeting is being executed,” says location-based ad veteran Warren Zenna. “People don’t look at ads on their phones when they are out doing things like shopping and driving around. They look at them, sometimes, when they are inactive. Mobile ad creative needs to be better — more engaging and more contextual — and presented when someone is in a contextually relevant mindset.”
Don’t Panic: Location-Powered Mobile Marketing is (Still) the Future of Advertising
Why all the recent talk of going data-only when data shows that customers are amenable to providing location data if it leads to relevant advertising? Let’s take a closer look at what a long-term commitment to our media partners really requires when it comes to location, minus all the triopoly panic.
How to Fix Costly Subpar Data at the Source
As marketers increasingly rely on their own first-party data, the elephant in the room needs to be addressed: subpar data will yield subpar outcomes. In fact, according to Gartner, data of poor quality costs organizations an average of $12.9 million every year.