The First Three Steps in the Analytics Journey

When ancient Biblical warrior David fought Goliath, he refused to wear heavy armor and a helmet or to wield a sword and a shield. David knew that the battle garb and weaponry would significantly limit his mobility. Instead, he chose to confront Goliath with a sling and some stones. He was nimble and precise – and ultimately victorious.

For b-to-b marketers, there are many analytics tools from which to choose – and most are feature-rich and laden with options. Marketers who have the time to learn how to use a new software platform can derive plenty of value.

Alternatively, most people already have a basic understanding of how to use Excel, a tool more powerful than most realize. It’s a great way to begin your analytics journey. Of course, when it comes to heavy-duty statistical analysis, I typically use R. However, if I want to quickly visualize some data, check correlations or run some regressions, I often use the data analysis package within Excel.

In most cases, the data is already in Excel, and that’s the way in which it was delivered by the client. Within minutes, I can do the following:

• Review dozens of charts

• Spot-check hundreds of correlations

• Generate dozens of multiple linear regression synopses

Here are the first three steps you can take in your analytics journey:

• Step one: Visualize the data using simple line charts. These charts will reveal trends, volatility and outliers in each data series and highlight relationships among several data series.

• Step two: Using correlation analysis, find out how the data is related to one another. You can line up dozens or hundreds of data series in rows or columns and have Excel provide a matrix of bivariate correlation coefficients. With another step, you can have a heat map to highlight the strongest correlations. For example, your pipeline opportunities are probably related to the number of sessions on your Web site, the number of content downloads, the number of emails opened and clicked through, and the number of teleprospecting calls. However, it may be that the highest correlations between pipeline opportunities this month and downloads (to select one) is not with downloads this month, but with downloads that occurred three to six months ago. Voila! Now you know where downloads fit into the buyer’s journey.

• Step three: Estimate the impacts of key business drivers using multiple linear regression. You can include internal data and external data to explain changes in your leads, pipeline, bookings or revenue to generate a comprehensive analysis. Voila! Now you know the return on marketing investment and how it contributes to the major business drivers.

Excel can’t do everything, but it’s a good place to begin the analytics battle. It’s familiar, it’s capable , and it’s already on your computer! Just install the data analysis add-in and before you know it, you’ll be generating useful insights, and want to eventually move on to a more powerful analytics platform. I can guarantee that after a while, like me, you’ll find that there will always be a place for Excel in your analytics journey.