INBOX INSIGHTS, August 9, 2023: Auditing Marketing Tactics, Generative AI for Data

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Are Your Marketing Tactics Out of Date?

Trying to keep up with all the changes in technology is daunting, am I right? I know that at least some of you feel this way right now because we’ve talked. You’ve shared your concerns. You’ve acknowledged that you feel like you’re falling behind.

I cannot speak to all your fears, but I can share with you what helps me when things get overwhelming. Go back to what you know best. For me, it’s the 5P Framework. That probably comes as no surprise to you. It’s not just the 5Ps but what I can do with it. I start to bend and stretch it, see what else I can do with it. I know what the 5Ps are, what they can do backwards and forwards. So how do I use what I know to keep moving forward?

I’d be a fool if I didn’t think that AI wouldn’t touch my company, my reporting, my services. Yes, we’ve built our company processes on machine learning but the outputs have remained fairly consistent. We knew that the methods for analyzing digital channel data would change, but the data itself, the channels themselves, were reliable.

Then AI came along. No, it’s not new technology, but the implementations of it are finally picking up speed and becoming mainstream in the marketing industry. Search, Social, Paid, Email, you name it, AI is changing it. So how we do assess if our marketing tactics are out of date?

Let’s start by running them through the 5P framework and see where we land. Why? Because we all want to hit the panic button right now. We all want to shift budgets, cut corners, and get ahead of the pack. None of these things can happen without a plan and we need to slow our breathing. So let’s make a plan, shall we?

As a refresher, the 5Ps are: Purpose, People, Process, Platform, and Performance. Each P is reliant on the others to create a full story. For this exercise, we’ll take a look at our Competitive Analysis report. We designed the report to show you where your strengths, weaknesses, opportunities, and threats are with your digital marketing. You can read more about it here.

How does the report look if we run it through the framework?

Purpose: The purpose of the report is to understand the digital footprint of your company compared to your competitors.

People: The people that care about this report are you, and any other decision makers.

Process: The process to generate this report is manual. There is a spreadsheet with formulas that someone needs to fill in to generate the report. Now my wheels are turning…let’s keep going.

Platform: The platforms used are numerous. There are a lot of systems needed to extract data. This is likely where you would start the audit. More on that in a second.

Performance: The performance of the report is whether you, the decision maker, has enough information to start making decisions about your tactics.

The 5P Framework told me a lot. I can see the process of generating the report is manual. Perhaps I could look into introducing some automation. But I’m getting ahead of myself. We want to pick apart the platforms that provide the data before automating the data collection.

When I look at the platforms used to create this report, I can see that we need to update them, especially around Search and Social. My next tasks is to figure out what data to remove, and what to replace it with to keep the competitive analysis valuable.

Once I have the platforms updated, then I can look into the process opportunities. From there, I will run the report back through the 5P Framework and see if it still aligns with the purpose.

The point is that when the industry is changing rapidly and things feel like they are spiraling, focus on what you can control. We’ll be digging into the competitive analysis report deeper on the Livestream this week. Join us on Thursday at 1pm EST

Are you taking a step back and auditing your marketing tactics? Reply to this email to tell me about it, or come join the conversation in our Free Slack Group, Analytics for Marketers.

– Katie Robbert, CEO

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Binge Watch and Listen

In this episode of In-Ear Insights, Katie and Chris discuss how to pick the right AI tools. We explain starting with the problem, not the tool. Katie recommends the 5 P’s to evaluate AI readiness. We emphasize choosing AI intentionally, testing tools against requirements.

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

Last time on So What? The Marketing Analytics and Insights Livestream, we prerequisites for fine tuning large language models. Catch the episode replay here!

This Thursday at 1 PM Eastern on our weekly livestream, So What?, we’ll be talking about how to audit your marketing tactics with the 5P framework. Are you following our YouTube channel? If not, click/tap here to follow us!

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Data Diaries: Interesting Data We Found

This week, let’s talk about generating data using generative AI. In the past, we’ve often had to struggle to obtain even publicly-available data in formats we’re able to work with; there are even cottage industries dedicated to selling us public data in our desired formats. The rise of generative AI makes that much easier for us.

Let’s look at a simple example. Suppose we wanted to work with the Fortune 500 list. This is publicly available data, to be sure. It’s published on the Fortune website without restriction. Yet getting the data into an obvious, workable format is challenging. You can, and people have, rigged elaborate systems and code to scrape that information.

However, there’s now an easier way: ask a large language model. The same data that would take minutes or even hours to generate, we can now simply ask for. Here’s an example using Google Bard:

Fortune 500

Because large language models have response limits, we might have to ask for datasets in pieces, but even in manual retrieval, this is often faster and cleaner than what we would have done previously.

You’ll also note you can ask for data blends; the official Fortune 500 listings do not contain the geographic locations of the companies, but I added that into my query.

I requested this specific format (pipe-delimited) because it’s very easy to work with in other data analysis software. You can also request in a variety of formats.

Each language model will have different capabilities based on the types of data we are asking from them; some models are better at certain topics than others.

And of course, be sure you have a subject matter expert or at least a QA person inspecting the outputs of the models; as with all forms of generative AI, extraction of data can still be hallucinated, providing very official-looking but completely incorrect data.

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