Bad Data

Apmetrix Blog - September 15th, 2016 | By: Lee Jacobson | Marketing

Bad Data

This months blog post focuses on the importance of understanding what data is useful and what is not.  Single channel metrics are no longer enough to understand your customers, and if you’re not careful and using the proper tools you may be making decisions with bad data.
bad-data
Data-driven is one of those flashy terms that corporations like to throw around during press conferences. You hear statements such as, “Our new data-driven culture will lead to a 12% increase in net profit.” When sentences like that get thrown around, one has to wonder if the speaker is the victim of bad data. While using big data to support business decisions is the new norm, and a survival requirement for doing business online, the way the term data-driven is used misses the boat in many ways.

For one, it implies that all decisions made before the age of big data were not driven by quantitative or qualitative information. This is obviously and patently untrue. Good businesses have always used data as a tool. More importantly, the term imbues data itself with mystical powers to transform a mediocre or failing business into a success. This misunderstands the nature of data, the limits of data, the reasons businesses succeed and the inevitability of bad data.

Even if you’re doing everything right, asking the important questions, focusing in on the relevant areas, none of this guarantees that the analytics are going to point in the right direction. The results are only as sound as the data you analyze. While most businesses assume that there will outlier data that needs to be ignored, fewer ever stop to wonder if there might be a more systemic problem with their data. Is there a flaw in how the data is collected? Is there information that we don’t have that might make this result look more or less cheery?

Take this as a hypothetical. A small software company gets a sudden influx of orders from New Zealand, where they’ve never sold anything before. The payments all clear and the software company executives think they’ve somehow opened up a new market. They begin marketing their software widely in Auckland, where most of the orders came from. Seems straightforward, right?

What the software company doesn’t realize is that all those orders came from employees of Company Q. For internal reasons, Company Q had employees purchase the software and reimbursed them for the costs afterward. What looked like dozens of discrete customers was really nothing more than a bulk license order from one customer. It was also an order with little chance of being repeated. The software company is likely wasting its marketing budget due to bad data, or at least incomplete data.

If the software company had been aware of the context, or asked the right questions during the purchasing process, it would have viewed the sales opportunities in New Zealand much less optimistically. Data-driven decisions can be good decisions, but they depend on the quality and completeness of the data itself. Assuming that becoming “data-driven” cures all problems is shortsighted and unrealistic.

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Apmetrix Blog - September 15th, 2016 | By: Lee Jacobson | Marketing

Bad Data

This months blog post focuses on the importance of understanding what data is useful and what is not.  Single channel metrics are no longer enough to understand your customers, and if you’re not careful and using the proper tools you may be making decisions with bad data.
bad-data
Data-driven is one of those flashy terms that corporations like to throw around during press conferences. You hear statements such as, “Our new data-driven culture will lead to a 12% increase in net profit.” When sentences like that get thrown around, one has to wonder if the speaker is the victim of bad data. While using big data to support business decisions is the new norm, and a survival requirement for doing business online, the way the term data-driven is used misses the boat in many ways.

For one, it implies that all decisions made before the age of big data were not driven by quantitative or qualitative information. This is obviously and patently untrue. Good businesses have always used data as a tool. More importantly, the term imbues data itself with mystical powers to transform a mediocre or failing business into a success. This misunderstands the nature of data, the limits of data, the reasons businesses succeed and the inevitability of bad data.

Even if you’re doing everything right, asking the important questions, focusing in on the relevant areas, none of this guarantees that the analytics are going to point in the right direction. The results are only as sound as the data you analyze. While most businesses assume that there will outlier data that needs to be ignored, fewer ever stop to wonder if there might be a more systemic problem with their data. Is there a flaw in how the data is collected? Is there information that we don’t have that might make this result look more or less cheery?

Take this as a hypothetical. A small software company gets a sudden influx of orders from New Zealand, where they’ve never sold anything before. The payments all clear and the software company executives think they’ve somehow opened up a new market. They begin marketing their software widely in Auckland, where most of the orders came from. Seems straightforward, right?

What the software company doesn’t realize is that all those orders came from employees of Company Q. For internal reasons, Company Q had employees purchase the software and reimbursed them for the costs afterward. What looked like dozens of discrete customers was really nothing more than a bulk license order from one customer. It was also an order with little chance of being repeated. The software company is likely wasting its marketing budget due to bad data, or at least incomplete data.

If the software company had been aware of the context, or asked the right questions during the purchasing process, it would have viewed the sales opportunities in New Zealand much less optimistically. Data-driven decisions can be good decisions, but they depend on the quality and completeness of the data itself. Assuming that becoming “data-driven” cures all problems is shortsighted and unrealistic.

Subscribe to our monthly newsletter!

Stay up to date on the latest best practices and trends in entertainment analytics.



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By | September 15th, 2016|Marketing|Comments Off on Bad Data

Hyperlocal, Proximity Beacons and You

Are you taking proximity beacons seriously? If not, you should be, because Apple is taking them seriously and so is Google, via the Android OS. Proximity beacons, or what they can do, provide businesses the chance to interact with customers not just on the local level, but the hyperlocal level. In other words, you can communicate with customers inside the building, department or even aisle.

The profitability in achieving that kind of personalization is, frankly, mind-boggling. A substantial portion of the smartphone-owning public, a constantly increasing number, is willing to tell you where they are in exchange for more relevant marketing or deals. Although not proximity sensor driven, this is the same conceptual model Groupon employs by providing city specific deals in its emails.

Take a grocery store, for example. Few retail environments are so plagued by the problem of choice paralysis. There are countless brands competing or trying to compete for consumer attention and dollars. Without some compelling reason to do otherwise — such as conscious cost-cutting or a reason to believe another product will perform better — most consumers opt for their normal brands. This decision or non-decision lets them avoid the choice paralysis of trying to figure out, on the spot, which of the dozen or so choices might best serve their needs.

Enter proximity beacons and hyperlocal marketing. If you want to move a particular product or set of products, proximity beacons can help provide your customers with a reason to choose a specific product. Instead of waiting for customers to “discover” a product on the shelf, every customer with the appropriate software on their phone can receive a coupon for a product on entering the store. If you want to help move secondary products, messages or coupons can be delivered in the aisles. The previous tendency to grab the familiar is now offset by an opportunity for novelty at a reduced cost; two things that are likely to drive sales.

The trick, of course, is to keep track of the information. It’s not enough to simply send the messages. How many people are receiving the messages? How many people are using them? This is where a robust analytics 2.0 package, such as Apmetrix, becomes invaluable. Not only can it import the data and give you a real-time view of what’s happening in the store, but it can also help you track social sentiment about the deals on social media.

By | September 1st, 2015|Blog, Marketing|Comments Off on Hyperlocal, Proximity Beacons and You