Recently there’s been a lot of chatter around location data and its benefits for marketers. While location data certainly has its place, it’s important to recognize where it provides value versus transactional data.
Transactional data fuels marketing intelligence, which helps you truly know what consumers buy, how often they purchase and where they spend. This detail helps you know who to target and how to enhance the customer experience with relevant communications.
From our research, we learned that 80% of consumers are more likely to do business with a company if it offers personalized experiences. Transactional data intelligently enhances these experiences to create personalized messages for consumers and ultimately drive business results.
Why is there so much interest in location data?
It certainly holds much promise. From a targeting perspective, it encourages consumers to walk into your brick-and-mortar location with a timely offer when they are near or in store.
It can give valuable insights on frequency of store visits and path to purchase that help with messaging, targeting and conversion. It’s also a great tool to understand if marketing efforts have had an effect on driving a consumer actually go to a location with the potential to purchase.
But, does this tell the whole story?
How many consumers can actually be reached? And how accurate is it? No data set is perfect, and location data is no different. In its infancy, there were issues with accuracy when it was primarily based on bid-stream data – it was estimated at 10% - 40% accuracy.
Apps have made improvements, but location data is still recognized as 60% - 70% accurate. App usage has definitely increased the reach and accuracy of location data, but it is still limited and tied to the number of consumers who both have the app and location services turned on. This decreases the reach potential and possible impact of any marketing campaigns.
So why is transactional data beneficial?
It tells the true story of consumer purchase behavior and it is the final measure of conversion. It’s actual and factual. I would never say it's perfect, as there is no data set with 100% of all transactions. But it surely should not be forgotten because it delivers results.
Why transactional data?
Provides intelligence: Knowing where consumers spend outside of your brand is just as important as knowing what they buy with you. Transactional data can help you understand a consumer’s total spend, timing and frequency of purchases. That’s critical information when it comes to targeting the right people and developing personalize messages.
A restaurant, for example, should know that I rarely go to fast food outlets (even when my kids were little), I frequently buy my lunch at healthy casual restaurants and go to a nice sit-down restaurant on Friday nights. Those interactions paint a complete picture of how often I eat out and my preferences. The holistic view changes how to message me and signals whether I would be a good target.
Not to mention all of the other habits I have. I prefer to go to Starbucks versus Tim Horton’s, spend too much at Lululemon, travel a lot, purchase groceries every week from an organic store, and occasionally make a purchase at Marshall’s or HomeGoods. Amazingly, I spend very little online or on Amazon, but I do use Uber, Spotify and Netflix. Outside of sounding like a total cliché, imagine how understanding my total purchases across multiple categories tells a brand a lot more about me along with how to talk to me in a relevant and compelling way.
Highly predictive for acquisition and prospecting: Past purchase behavior is still one of the best predictors of future buying behavior. Recency and real-time are talked about a lot, but when you look at purchase patterns for individuals across two years, we are in fact very consistent for where we like to shop, how much we spend and how often.
Transactional data helps you find highly qualified prospects whose purchase behavior resembles your existing customers – in your category and beyond. Predictive analytics combined with transactional data can make it even more effective when looking across multiple categories of purchases.
For many years, catalogs have been highly effective at acquiring new customers based on a consumer’s total spend behavior. With data science and artificial intelligence, top prospects can be found across billions of dollars of spend and millions of transactions. Even in the digital age, the catalog still drives a lot of acquisition and conversion.
Available at scale with precision: Transactional data is available for a significant percentage of the population – even beyond a customer’s first-party data. We have transactional data on 130 million consumers, 60B transactions and $3.5T in spend. This level of scale and accuracy is hard to beat and drives results.
The ultimate measure of success: Knowing a consumer took a test drive or went to your store are great insights into how far a consumer progressed along the path to purchase. Some would argue that it shows the marketing tactic delivered on its objective, if that was the KPI. And if the consumer did not convert in-store, then something needs to happen to improve the in-store experience.
There is some truth to that, but let’s be real: If not enough consumers ultimately purchase and purchase enough, did you really reach the right target? Or is the campaign ultimately delivering results and ROI?
Transactional data in action
Let’s look at this from the consumer perspective. Transactional data can impact how people are reached. For instance, have you ever received information on a furniture item sale when you just made multiple furniture purchases for your new home?
This common occurrence hurts consumers’ relationships with brands. You may feel like that brand is totally out of touch with your needs. But, it can easily be solved by using transactional data to send more relevant communications before, during and after a purchase. Instead, the furniture retailer could send deals on throw pillows to match your brand new couch.
Transactional data also impacts who gets reached. For example, a travel and hospitality company we partner with was looking to identify consumers who would fit their desired target audience of their new cruise ship (55+ years old with high net worth who spend on travel, especially cruises).
Our transactional data set let us look at the spend behavior of both their current top customers and heavy travelers to learn where, when, how much and how often they’re spending. This powerful targeting solution helps that brand understand individual consumers’ likes and lifestyles and identify high-value prospects.
As a result, they’ve gained meaningful insights and achieved a 2X lift over their modeled demographic, lifestyle and spending trend data. They are now able to reach the right customers and convert them.
So, transactional data vs location data – is one better or can 1 + 1 = 3?
Next time, we’ll look at how transactional and location data can complement each other, and where each has its strengths.