Apmetrix Blog - February 10th, 2017 | By: Lee Jacobson | Marketing

The Jungle of Assumption

This months blog post focuses on looking at your data in different ways to understand more than just acquiring clients.  Without a proper roadmap and asking relevant questions about your customers journey, having more customers in many cases doesn’t mean you simply make more money.
So, you’ve got your amazing new analytics software up and running. You’ve got data streaming in from all over the digital landscape. The software presents the data to your customized and stringent requirements. All is now well with your analytics world, right? Maybe. Maybe not. Software that automates out countless hours of manual work is very nearly priceless, but it’s not the end of the process. There is still the problem of interpreting what the software shows you.

Interpreting ought to be an easy task. After all, don’t we have reliable markers for what is good and bad? More customers are good. Churn is bad. More revenue is good. Lower sales are bad. Growth is good. These simple statements paint a world of clearly marked paths and self-evident business truths. For the modern business, though, interpreting data bears more resemblance to hacking through a jungle of assumptions.

Take the truth of “more customers are good” as our case in point. Seems like a no brainer. The assumption is that more customers equals more revenue. Additional customers don’t materialize from the ether, though. Where did they come from? What was the acquisition cost? What is the expected life cycle profit from these new customers? Does that profit outweigh the acquisition cost? What these questions are really asking is this: Are we attracting the right customers in the right way?

After all, spending a million dollars to get new customers that will only buy a million dollars’ worth of your product isn’t a terribly good use of time and resources. The blanket assumption that more is better doesn’t serve the business. A healthy business, like a healthy human being, needs something more specific and individual than blanket assumptions to reach success. The business needs internal insight into its goals and how best to reach them before analytics can be of service.

In point of fact, without those insights, no amount of customization to your analytics software is going to prove beneficial. You can’t track or analyze what you don’t recognize as meaningful. New businesses often think that analytics will provide a customer profile. This is an understandable, but potentially fatal, error. The insightful business already has an ideal customer profile and asks: “How many of our customers are the customers we want?” If the actual customer profile doesn’t match up with the ideal profile, the business is then in a position to ask: “Are these customers a viable alternative?”

Understanding where and how the reality diverges from the ideal provides the insightful business with new and additional urgency. That understanding provides choices about how to proceed. Shallow assumptions cannot give agency or choice. Internal insight must determine where your analytics focus. Without that insight, you mind find yourself hacking blinding in the jungle of assumption.

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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.
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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