INBOX INSIGHTS, May 3, 2023: B2B Influencer Marketing, ChatGPT Professional Development - Trust Insights Marketing Analytics Consulting

INBOX INSIGHTS: B2B Influencer Marketing, ChatGPT Professional Development (5/3) :: View in browser

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What Does Everyone Get Wrong About B2B Influencer Marketing?

I wanted to take a break from talking about AI this week and switch gears. Instead, let’s talk about something that AI can’t do – B2B Influencer Marketing.

At its core, B2B Influencer Marketing is about human connection. At the start of a customer journey is a human and at the end of a customer journey is a human. AI can assist with getting a human through the steps of the journey, but at the end of the day, it’s all about a human getting their problem solved.

Where does B2B Influencer Marketing fit it? This is where you find other humans to help the humans that are on the journey get from start to finish.

Since I’m not an expert in Influencer Marketing, I turned to a couple of good friends (thanks Ashley Zeckman and Justin Levy) for some advice. I wanted to know what everyone get wrong about B2B Influencer Marketing. They did not hold back or disappoint. Here is some of what they had to say:

What does everyone get wrong about B2B Influencer Marketing?

It’s boring

One of the misconceptions is that B2B is dull and bland. You’re often selling software or services, so where’s the fun in that? How can you make compelling content or social posts? If you don’t follow Demandbase, you should – purely for the content they share. They very smartly use B2B Influencers that drive really fantastic awareness to their brand. They make it exciting and fun. Their B2B Influencer Marketing program is the opposite of boring.

Not presenting influencers with campaigns or ideas

This is a misstep in any kind of relationship building. Reaching out to someone and immediately making asks of them is going to start you off on the wrong foot. If you have an influencer in mind that you want to work with, you should take the time to thoughtfully put together ideas that you want them to build on. This way you and your influencer can be more aligned with your goals and their comfort level.

Too much focus on quick wins, skipping the relationship building

This is very similar to the point above. Effective B2B Influencer Marketing means relationship building and trust. This means that it will take time. If you want to find someone to endorse you and then move on, that’s fine – but you’ll miss the long term benefits. Which leads us to the next point…

Lack of partnership

A lot of companies approach influencers with pre-existing content. A better approach, and a more authentic approach, is to partner with your influencer and co-create things. You’ll get the benefit of their perspective and expertise (that’s why you hired them, right?) and they will feel more comfortable sharing with their network.

Lack of measurement

Perhaps the biggest area where companies get B2B Influencer Marketing wrong is measurement. This starts with knowing your Purpose (yes, of the 5Ps!) – what is the problem you’re trying to solve? When you make the decision to engage with an Influencer, you should have a solid plan in mind not only for what you want them to do, but how you’re going to measure impact. Is it for awareness? Is it for engagement? It it for sales? These are three different goals with three different outcomes. Take some time to figure out what you need, and how you’ll determine success.

This is by no means an exhaustive list. B2B Influencer Marketing, like any kind of marketing, has it’s own set of skills and education. There is a lot that can go wrong. But with some thought and planning, B2B Influencer Marketing can go very right!

Are you using B2B Influencers?

Reply to this email or come tell me about it in our free Slack Community, Analytics for Marketers.

– Katie Robbert, CEO

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In this week’s In-Ear Insights, Christopher Penn and Katie Robbert discuss B2B influencer marketing, which is becoming more prominent in the B2B marketing space. B2B influencer marketing is an endorsement where a B2B marketer uses their influence to endorse a product or service because of the reputation they have. Unlike B2C, B2B influencer marketing is less transactional and more about building trust. To use influencer marketing in B2B, it’s essential to start with purpose and find the right influencer who orbits around the ecosystem close enough that the work they do is complementary. When identifying influencers, laterals with a similar audience but not doing the same thing can be useful. It’s also essential to look for influencers whose audience is the decision-makers, which is who you are trying to reach. Tune in to learn more!

Watch/listen to this episode of In-Ear Insights here »

Last week on So What? The Marketing Analytics and Insights Livestream, we showed how you can fine-tune a large language model. Catch the episode replay here!

This Thursday at 1 PM Eastern on our weekly livestream, So What?, we’ll be discussing how to measure B2B influencer marketing. Are you following our YouTube channel? If not, click/tap here to follow us!

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In this week’s Data Diaries, not necessarily data per se, but professional development around data. Have you ever been in a situation where you’ve been asked a question and you feel like you should know the answer, and yet you’re totally flummoxed? There’s some gap in your knowledge that just makes getting to the correct answer difficult?

This is quite possibly the greatest untapped secret of large language models and their applications, like ChatGPT. Not to do the work for you, but to help you understand missing concepts in your own brain so that it all makes sense, and you can solve the problem yourself. It’s the other AI – augmented intelligence, where what gets augmented is you.

Let me give you an example. A friend of mine was stuck on what was a psychology stats problem. She had four Markov chain transition matrices, each from a test group, and she wasn’t able to easily do a comparison of the four different matrices. In marketing terms, imagine having four different attribution models and you want to know what the statistical difference is among the models. So she asked me for some help… and I had no idea what to tell her. You can do analysis of variance (ANOVA) on sets or vectors of numbers, but it’s really hard to do that on matrices. Imagine trying to understand the difference between entire tabs of data in Excel, comparing one tab to the next. That would be an insanely difficult problem to solve, row by row, cell by cell.

So what did I do? I asked GPT-4, via ChatGPT, how it would solve that problem. And it came back with a straightforward answer: build steady-state probabilities from the Markov chain matrix and then do analysis of variance on that.

Umm… okay. Fun fact: I failed statistics in college. My grade on the final exam was 37/100, and I would have outright failed the entire class if they hadn’t graded on a curve (which is basically ranked grading, but that’s another story for another time). I know what a Markov chain matrix is, and I can code that pretty easily. But I had no idea how to build a stationary distribution of steady-state probabilities – or even what the heck that meant.

This is where people go wrong with tools like ChatGPT. They tell it to do the work for them, take the item off their to do list, and move on with their day – but they don’t get any smarter. I could have told it to just write the code for me so I could hand it off to my friend and be done with it, and she would have benefitted – but I wouldn’t have. So I asked ChatGPT to explain it to me.

It said:

To find the stationary distribution of a Markov chain in R using tidyverse methods, we can use the eigen() function from the base package to calculate the eigenvectors of the transpose of the transition matrix. Then, we can use the tidyverse package to create a data frame with the probabilities of being in each state in the stationary distribution.

This was unhelpful. So this is the simple trick to make it more useful. Ask it to explain in terms of something you personally really understand. I asked it to explain steady-state probabilities in terms of… pizza.

It said:

Let’s imagine that we have a pizza that is divided into several slices of different flavors. We can represent the different flavors as states in a Markov chain, and the probabilities of moving from one flavor to another as transition probabilities in a transition matrix.

Now, let’s say that we have a friend who randomly takes a slice of pizza, eats it, and then randomly takes another slice, and so on. The flavors that they choose to eat will depend only on the flavor of the previous slice they ate and not on any previous slices.

If we run this experiment for a very long time, we will reach a point where the probabilities of eating each flavor of pizza will stabilize and will not change over time. This is similar to reaching a stationary distribution in a Markov chain.

The stationary distribution of the pizza flavors represents the long-term probabilities of eating each flavor of pizza. It tells us the percentage of time that our friend will spend eating each flavor of pizza in the long run.

For example, if the stationary distribution of pizza flavors is 40% pepperoni, 30% mushroom, and 30% cheese, this means that if our friend continues to randomly choose slices of pizza for a very long time, they will eat pepperoni pizza 40% of the time, mushroom pizza 30% of the time, and cheese pizza 30% of the time on average.

I don’t know about you, but I get it now. I understand what steady-state probabilities are in a Markov chain matrix. It’s the probability you’ll get a given flavor of pizza if you randomly pick up a slice.

Now think about that in terms of attribution modeling. If you have a stationary distribution of your attribution model for this month, you can compare it last month, or the month before, and the difference would tell you how your marketing effectiveness is changing over time. Rather than try to compare channel by channel, you could tell which month your marketing was most impactful and then understand the holistic makeup of your marketing for that month.

But you won’t get there if you let AI do the work for you without understanding the work it’s doing, in the same way a manager won’t improve their skills if they let their team do the work and never ask how the work is done. There’s no special secret here except to know that you can ask ChatGPT to explain things in the format that works best for your brain. I asked it to explain in terms of music theory appropriate for an 8th grader:

For example, let’s say we have a song that goes from a C major chord to an F major chord and then back to a C major chord. We can think of these chords as states in a Markov chain, and the probabilities of going from one chord to another as transition probabilities in a transition matrix.

Now, if we keep playing this song for a very long time, we will notice that certain chords are played more often than others. These chords are the ones that create a sense of stability and familiarity in the song. The stationary distribution of the chords represents the most common chords that are used in the song.

In other words, you can get these large language models to take any concept you don’t understand and explain it in terms that you do. That’s the powerhouse secret that will level YOU up as a professional – but it relies on you being curious enough to ask the question in the first place, and asking for what works best for you, what domains of knowledge you understand best.

If you’ve ever sat at a conference, a webinar, or even a meeting and had a question, and thought to yourself, “I really don’t want to ask this question aloud because people will think it’s a dumb question”, this is one of the most powerful applications of a tool like ChatGPT. You can ask it all the “dumb questions” until you’re caught up, with no one judging you, until you’re well above average in your knowledge.

Large language models can be the best confidential career coach and professional development tool you’ve been looking for. Take advantage of them!

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