Author: Michael Stanisz

There are hundreds of vendors, technologies and services that use analytics, but deciding on what the right solution is for your business can be difficult. In this article, the author presents 6 practical tips for selecting the right analytics solution. This article is specifically relevant to pricing with the continued growth of Big Data applications and the importance of consumer analytics in pricing decisions. Michael Stanisz is a Principal at Revenue Management Labs, where they help companies develop and execute practical solutions to maximize long-term revenue and profitability. Connect with Michael at

The Pricing Advisor, September 2017

The first piece of advice that I always give people looking to expand their analytical capability is this: “Don’t trust anyone using the term analytics.” Now watch me be a hypocrite as I use the term ten times throughout this article.

The term “analytics” is ambiguous. Analogous to the claim: “I can help you do math.” A third grader doing fractions has very different needs compared to a university student solving partial differential equations. Both activities constitute math. Specificity matters. The term has been used to describe a wide array of topics from aggregating databases, data visualization, and reporting to cutting edge advanced statistical modeling.

The world can be a confusing place for those of you out there looking for help to solve business problems using analytics. There are hundreds of vendors, technologies and services that use analytics, but deciding on what the right solution is for your business can be difficult. Over several years, I have seen companies implement hundreds of analytical solutions with varying degrees of success and frustration. To reduce the risk and enhance success, I have developed 6 practical tips when hunting for the right solution.

1. Don’t Be Fooled by Attractive Words

Terminology in the analytics space is constantly evolving. It’s hard to read through a business magazine without hearing about machine learning, deep learning, AI, VR or analytics.

These concepts may be at the forefront of solution providers marketing, but most of these concepts have existed and been utilized for years without the preponderance of fancy lingo. People often find themselves enamored with “buzz words” and miss focusing on the business problems they are trying to solve.

Algorithms and back-end technologies are simply tools that are comparable to a hammer or saw. Do not lose sight of the overall goal or business problem you have to deliver on.

2. You Don’t Need a Ferrari

A client once described to me a situation that occurred when implementing an analytical solution: “Picture a cave man who is trying to light a fire with two sticks. I drive up to him in a brand-new Ferrari, throw him the keys and expect him to drive”. This is all but too common within many organizations.

They finance large, complex, expensive solutions to drive an analytical culture without truly understanding the infrastructure (e.g. people, structure, targets, etc.) that needs to be in place for success. This often leads to a brand-new Ferrari (tool) collecting dust in the garage. Before buying a Ferrari, test drive cheaper alternatives and train the organization to drive slowly and approach methodically.

Pilot programs or home-grown solutions are often a good alternative to test an organization’s capability of successfully transitioning to a data lead organization.

3. Don’t Get Too Hung Up on the Data Acquisition

This is often a point of contention with many people spouting the adage “garbage in, garbage out.” Let me preface by saying that I agree with this notion whole-heartedly, but there is a vast difference between perfect data and incomplete data.

I have seen managers and directors so focused on gathering the perfect data set that they go months without being able to make a business decision. A good strategy is to utilize the information available, even if it only gets you eighty percent of the way there. This not only allows businesses to make actionable decisions but also creates an environment to learn about quirks in the data that require future adjustment. That being said, the incomplete data needs to be verified for quality and should constantly be updated.

4. The Plain English Rule

Put simply, make sure you understand the process and are able to describe what you are doing in plain English (one to three sentences). For example, a tool that segments customers using hierarchical Bayesian k-means clustering methods can be simplified to “grouping likewise things based on attributes.”

Technical jargon is endless and is often used to portray the illusion of complexity and sophistication. I often hear vendors respond with “it’s complicated” followed by a slew of technical jargon that I can barely comprehend. Swallowing pride and admitting a lack of understanding of the technical jargon can force the conversation back to plain language. Make sure never to commit to a tool or project which you do not fully understand in plain language!

5. Use the Sniff Test

The sniff test, a valuable tool to use in everyday business life, is just as germane and relevant to analytics. Most people would be familiar with the concept of doing a smell check or back of the napkin calculation to ensure the outcome is reasonable.

What is surprising is that I see professionals everyday trusting the output of analytical tools blindly, without the sniff test.  Recently, I worked with a company that was using elasticity outputs to quantify increasing prices.  They were shocked to see a much larger volume impact when the price was adjusted. There was an obvious underlying error in the model not accounting for distribution gains, artificially lowering the elasticity on certain SKUs and costing the company millions. A simple sniff test would have identified the problem and saved millions.

6. Show me the Money

Possibly the most important thing to remember when embarking on new analytics initiatives is to remember the wise words of Jerry Maguire: “Show me the money.”

It is easy to get lost in metrics like clicks, conversions, market share etc. Without understanding the financial value of the metrics, they are useless. If a click on an advertisement does not lead to increased revenue or profitability, it does not make sense to invest in a solution to drive and monitor clicks. A narrow, one dimensional, blinded viewpoint can be costly so, before investing have a clear vision of the objectives and financial return.

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