Welcome to the Fathom Blog

News & analysis on digital marketing & analytics

B2B Manufacturing and Social Media: How To Begin

By Patricia Thibault | May 16, 2012

While many manufacturing businesses are using social media, many don’t know why they have a Facebook page/Twitter handle or why they are trying to get more followers. Others are contemplating social media and don’t know where to start.  Regardless of where your business falls, you should step back and think strategy.

A strategic approach to social media first involves knowing why you want to be involved and your goals for participation. Then, you need to determine who you want to connect with, where they gather and what topics are important to them.  Knowing the “who,” “where” and “what” starts with in-depth social media research:

Identify conversations: 

• Map the volume of conversations about your brands/products to events (marketing events) and determine which ones generate the most buzz
• Identify the top online channels and their demographics
• Determine the topic of conversations
• Understand the tone of the conversations
• Mark cyclical and seasonal trends and emerging topics

Understand your audience:

• Who are your target audiences online?
• Where are the audiences talking about your brand already?
• Is your company engaging in conversations with your audiences?

Gather intelligence:

• Define your audiences and where they gather
• Identify advocates and influencers
• Listen to the top conversation topics (what matters to your audience)
• Analyze your brand data
• Conduct a competitive analysis

Once you have conducted the research, it’s time to build a social media plan for B2B manufacturing:

• Develop a content strategy
• Determine how you will connect with your audiences
• Define your goals for social media and what/how you want to influence
• Develop a plan based on what makes sense for your brand, your interaction tolerance, and how other channels will be integrated
• Monitor, measure and optimize

Stay tuned for another blog post tomorrow on social media & manufacturing: “If You Build It, They Will Click.”

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Photo courtesy of Voka – Kamer van Koophandel Limburg via Flickr.

Get Real Problem-Solvers for Real Results from Your Big Data

By Chad Luckie | May 15, 2012

It can be very easy for a user to get lost in all the “Big Data” hype, but several things need to be in place to make sure your company finds the gold that lies within large data sets. It should be stated that you could have all the newest tools and experience in the world, but without the proper problem-solvers, your marketing intelligence could be misinterpreted. Big data for marketing is best applied with a combination of the latest software and skilled talent. By taking insights gained from a Big-Data analytics platform, you can make smart marketing adjustments grounded in science.

The ability to understand a problem/opportunity and what data is applicable to that problem/opportunity is what separates a great Big-Data capability from a good one. The ability to mine, process, and collect data has been around for a while, but the ability to analyze that data and quickly make revenue-generating recommendations for digital marketing campaigns is a game-changer. Simply put, with better insights, you can profit from careful segmentation of your target audiences.

Big Data can cost your company rather than benefit it if used incorrectly. The key ingredient to Big Data is finding a service done by a software platform or problem-solvers that has the capacity to make sense of your data. The act of taking the correlations your Big Data reveals and turning them into tactical marketing recommendations is the missing factor that companies need. That missing piece is usually filled by real-life problem-solvers who have experience in the field. If you consider outsourcing this process to a company that has experience applying predictive analytics and the ability to make sense of your large data sets, make sure the results are transparent in the form of increased ROI (in incremental revenue lift).

AdAge 25 Largest U.S. Search-Marketing Agencies (2012)

By Paul Richlovsky | May 11, 2012

Fathom is living large … again. For the 6th year in a row, the company has been named to the Advertising Age list of the 25 largest U.S. search-marketing agencies. Ranked by 2011 U.S. revenue, Fathom ended up at #21.

On behalf of the entire company, I’d like to thank the many clients of Fathom throughout the years for making both this milestone and my career (along with the careers of my colleagues) possible. I’d also like to thank AdAge for this public recognition of our growth and overall success. Though many more results are still to be delivered for current and future clients, making this list is an encouraging sign that we have at least gotten a good start on our quest to be the premier provider of profitable revenue from the digital world.

The “Other” Moneyball: Baseball Business Analytics

By Steve Kessen | May 11, 2012

With the Major League Baseball season only a month old, spring fever has hit the Fathom team as we reminisce about my favorite sport: Baseball. Incomparable to any other sport, statistics rule the game of baseball. Virtually every conversation about the game mentions a player’s batting average, home-run total, ERA (for pitchers) or one of a thousand other measured stats from the national pastime.

After the release of the best-selling book and award-winning movie Moneyball, there is more attention than ever on statistics in baseball. Though with less fanfare than the player side of the game, business analytics in Major League Baseball is what helps teams drive revenue and provide a better experience for their fans.

Analytics has become an area of focus for most major-league teams over the past few years. Most teams have one or maybe two analysts on staff to help the organization make better data-driven decisions on the business side. Having previously worked in analytics for the Cleveland Cavaliers, I understand the many challenges that these analysts face:

“Big Data” Means Knowing Where to Find the Gold

By Janet Aldrich | May 10, 2012

Back during the American Gold Rush, most of the people who went out for gold were enthusiastic amateurs who had to search for it the hard way – they’d sit by a stream where gold had already been found, and run water through a shallow pan, hoping for paydirt.

Today, a lot of people using digital marketing – be it email, SEO, PPC or some other medium – are functioning about the same way those miners did; that is, hit and miss and hope for the best.  They’re missing out on leads and sales because they don’t have a clear idea of when, where or how to direct their efforts.

That’s where “big data” comes in.  To boil it down to one simple image, big data is knowing where to find “gold” in the form of leads and/or finished sales.  It means avoiding guesswork. It means taking your database and extending it with additional data, analyzing it and running it through models. The end result is data on your customers that lets you target them in the right way at the right time – thus achieving the results you need.

One of the biggest mistakes you can make is to be careless about your data. Data that’s not maintained and used correctly can waste money and damage your online reputation, particularly in the area of email marketing.  Bounces, invalid addresses and accounts left inactive (which could be spam traps) can mean that your message won’t get where you want it to go. Even in PPC or SEO efforts, you need to know the message you want to deliver and how to time it for maximum effect.  Big Data can help you make the right choices.

Is your enterprise a candidate for big data? If you want to expend your digital marketing dollar efficiently, you need to look at the data you use and how you use it.  More importantly, you need to know what do with the data once you’ve analyzed it and are ready to implement what you’ve learned.

Big Data is still, to some extent, in its infancy.  Now is the time to evaluate your company’s acquisition and use of data and how you can get ahead of the curve.