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Erickson Research Blog

All posts in Technology

What is “Buyer Speak” and Why Should You Care?

LanguageAre you confident that your company’s in-house terminology or creative jargon is the same as your buyers’?

If not, it’s time to rethink the language you’re using.

Using the right words in your communication is crucial. It not only increases prospective buyer engagement but also enhances your credibility as a provider.
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Are You Up to the Challenge? An Invaluable Exercise for all B2B Sellers.

stock photo agencyHow quickly (and accurately) can you finish this sentence?

Our product (or service) can help your company…

If you found yourself struggling for a definitive answer, you need to find one…ASAP.  Otherwise, a lot of potential customers might pass you by.

Here’s why…

Put yourself in your buyer’s shoes for a moment. Imagine you are a baked goods manufacturer looking to purchase a new oven. You’re exploring the various options, hoping to find something that will improve efficiency, save energy, and decrease downtime.

You do some research for potential suppliers and come across Mark’s Fabulous Bakery Equipment. When you ask about their offerings, they are very enthusiastic about their latest oven.

This is how they describe it to you…

“Our state-of-the-art Pro Baker Oven is a best-in-class product that’s a smarter choice for your production line. Its sleek design includes diamond-shaped controls that have a superior appearance to traditional round ones. And it comes in exciting colors like red, orange or navy.”

Sounds impressive, doesn’t it?  But as a buyer, how do you know what they’re actually trying to sell you? In other words…

  • What do they mean by best-in-class product? By what measure?
  • Why is it a smarter choice? Smarter than…what/who?
  • What makes these controls any better than traditional (besides their ‘look’)? Why do you need to change this?
  • Is there something important or useful about the variety of colors? (note: every other production line oven you’ve ever seen is a standard silver)

Bottom line: why should any of this matter to you?

From talking extensively with B2B purchase decision-makers, we’ve learned that many products and services are dismissed because the seller is unable to

  • Clearly demonstrate what it actually does
  • Credibly establish how it’s different or better than competitors’

The thing is, B2B buyers aren’t impressed with product descriptions that are loaded with fluffy language.  It doesn’t help them with their purchase decision. Instead, it mostly just causes confusion and makes them question the knowledge and integrity of the seller.

On the other hand, simple, focused product descriptions speak volumes.  Like this…

Our product catalog can help your company attract buyers by creating listings that are more visible and appealing. (EBay)

or this…

Our products can help your company provide accurate and objective health and medical information that’s credible, relevant and timely. (Health Day News)

Remember the ‘finish the sentence’ exercise you tried earlier?  Try it again…with your team (especially sales reps).  Then share your results with each other. This provides a great opportunity to:

  • Address and clarify areas of misinformation about your offering
  • Discover alternate ways of talking about your product/service that could be more effective
  • Establish a consistent value proposition throughout your organization
  • Create enthusiasm for your offering and in turn, better motivate your sales team

This is just one of several key (often game-changing) findings from our research in the B2B marketplace.  For more tips, download our e-book, “What B2B Buyers Really Care About.”

Why Big Data, By Itself, Doesn’t Create Big Insights

bigdataIt seems like you can’t go a day without hearing something about big data. Much digital ink has been spilled on all sides of how big data could affect the research industry, from the breathlessly optimistic to the foretelling of the end of the profession.

I am very optimistic.

Here’s why.

It’s easy to see why people get so excited about the prospects for big data in marketing.  If you believe all of the hype, it is very easy to convince yourself that if you only had enough data, the answers to all possible marketing problems are within your reach. The problem is having enough data is a necessary but not a sufficient condition to generating insight.

What else do you need?

Judgment and strategic thinking.

These happen to be things that people are far better at than computers.

Dr. Michael Wu wrote a great piece in TechCrunch about the big data fallacy.  In it, he systematically destroys the notion that a lot of data means a lot of insight. The point he is making is that there is far more data than there is information, and there is far more information than there is insight. Further, he argues that more data and more information don’t necessarily result in more insight.

I think the reason boils down to this:

It is the analyst — not the analysis — that assigns meaning.

That’s because there can’t be any meaning without context.  Context requires a broad understanding of how the data fits into the issue and how the issue fits into the world.

The skills needed to make those connections?

Judgment and strategic thinking.

To me, this means marketers need skilled analysts to work with big data to generate the insights that will benefit the business.  This critical link in the chain isn’t going to be automated anytime soon.  In fact, one of the big worries marketers have is where to find all the data scientists to actually do the work.

It seems to me that there are plenty of people working in research today with all the requisite skills. The research industry just needs to change its mindset from strictly thinking about primary research to thinking about using data, wherever it comes from.

As Tom Anderson has mentioned, and I agree, the act of sifting through volumes of data to understand patterns and identify potentially lucrative groups in the market sounds a lot like segmentation.

Researchers certainly have the skills and the experience to be key players in big data.  Whether it is in the data manipulation and analytics or in the ability to connect the dots to create insights from the data, there is opportunity there for anyone who chooses to seize it.

So…which side do you fall on…optimist or pessimist?  Would love to hear your thoughts.

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