The Marketing Technologist.

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What you should consider before buying a new tool

When a client asks me: "can you help us implement [tool x]?" or "should we buy [suite a] or [suite b]?", my first reaction is usually a plan to help them implement the tool. What I often forget is the underlying question: why do they think they need this tool? I recently came across two sources that helped me better understand the importance of that question. In this post, I'll combine them to share what I've learned about the topic.

People over products

When clients ask you to help them with a new tool, there are two possible scenarios:

  1. Limitation: They've actively developed themselves to a maturity level where they are limited by their current tool(s). Their research shows that [tool x] might help them overcome those limitations.
  2. Buzz: They've heard that [tool x] is the next big thing and think they need to start using it.

There is a clear difference here. In scenario one, there's a founded business need for the new tool. In scenario two, people just want to use the tool because they 'heard it was good'. Besides that, you'll need people to get value out of the new tool. The tool by itself won't give you any value. Agencies can often offer support with implementation and usage of the new tool. But to get the most out of any tool, you'll need people inside your business that use it. You'll need a team to actively support the tool, use the tool, and get value out of the tool. This brings us to the second source.

The 10/90 rule

Avinash Kaushik, Digital Marketing Evangelist at Google, wrote his post about the 10/90 rule back in 2006(!). Here's a short quote from his story:

If you are paying your web analytics vendor (Omniture, WebTrends, ClickTracks, CoreMetrics, HBX, etc) $25,000 for an annual contract you need to invest $225,000 in people to extract value from that data. If you are actually paying Omniture, WebTrends, HBX etc $225,000 each year then…. well you can do the math.

So let's do the math. For every 1 euro you spend on a tool, you need 9 euros to get value out of it. So buying a suite of tools for €100.000 a year requires you to have €900.000 in resources to get value out of the tool. Let's say your employees' salary averages at €45.000 a year. That's a 20-person team you'll need. Even if you downplay the rule, e.g. to 10/40, you'd still need a small 9-person team. And even when you think I have some people at [team x] who can help me out, you'll just be moving the problem from one team to the other. So before you buy your next tool, ask yourself: do I have the people to support this?

Don't chase a tool, chase value

I'd like to close this post by paraphrasing advice that Michael Helbling gave in the Digital Analytics Power Hour episode about dig data:

Don't chase big data, chase value.

In this episode from 2015, they discuss what an executive needs to know about big data. They share an insight that can be applied to any product: don't go chasing tools or some new buzzword. Invest in a team and prove the value of data with free (or cheap) tools. Stretch the possibilities of each tool. When you reach the limit of those possibilities, see what other tools can help you overcome these limits.

sources:

This post is a collection of information from two data sources. I can highly recommend you to subscribe to both: The Analytics Power Hour Podcast and Avinash Kaushik's The Marketing <> Analytics Intersect newsletter. They offer great value, and it's free!