INBOX INSIGHTS, January 3, 2024: Process Over Outcome, Data as an AI Advantage

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Focus on the Process, Not the Outcomes

Ah, the new year. A fresh start for everyone, whether you asked for it or not. Metrics roll over, expectations are reset, and resolutions are made.

We often pressure ourselves to not only set big goals but also to achieve them. Dreaming big and being ambitious are admirable, but it’s equally important to inject some reality into our aspirations.

Focus on the process, not just the outcomes.

It’s easy to throw out numbers and claim them as your goals. Lose 20 pounds. Visit 20 countries. Adopt 20 dogs. However, these are merely numbers; they represent your desired outcomes. While it’s crucial to understand your direction and aspirations, it’s more important to know how you’ll achieve them. So, focus on the process, not just the outcomes.

Take, for example, the goal of losing 20 pounds. That’s a a good start, but it’s vital to build a plan around achieving this goal. What if you don’t reach it? You might feel defeated and less inclined to try again. That’s why focusing on the process is key.

You might wonder, “Isn’t this just a KPI map?” Not exactly. A KPI (Key Performance Indicator) map breaks down the metrics that indicate whether you’re on track to meet your goal. While a KPI map keeps you aligned with your goals, it often lacks context on how to improve those metrics. Let’s say your goal is to lose 20 pounds by eating 1500 calories a day and working out four times a week. These metrics would be part of your KPI map (note: this is just an example, as I’m not a fitness expert).

However, focusing solely on numbers doesn’t set you up for success. You still need to focus on the process. Start with the KPI of eating 1500 calories a day. What does that entail for you? How will you ensure adequate protein and nutrient intake? What adjustments are needed for days with more intense workouts? Establishing a detailed process will guide you toward your goal. Similarly, for working out four times a week, consider the type of exercise, necessary equipment, duration, and scheduling.

Now, think about your team and company. What ambitious goals have been set for 2024? Likely, they revolve around revenue. But how will you achieve this? Put the revenue target aside for a moment and concentrate on the process.

You might say, “We need to increase revenue by acquiring three new clients and upselling five existing ones.” Excellent. Now, break it down into repeatable processes. What steps are needed to create awareness, attract prospects, and qualify them? Focusing on each step of your process will simplify reaching your goals.

Remember, it’s only January. You have time to thoughtfully set up processes that will aid in achieving your goals. By documenting these processes and measuring progress, you can make necessary adjustments along the way.

What’s next? Set your goals, translate them into KPIs, and then prioritize the process.

Are you setting goals for 2024? I’d love to hear about them. Reply to this email to tell me 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, the Trust Insights podcast, Katie and Chris discuss generative AI in 2024 and the rapid advancements in generative AI over the past year, including significant developments in various AI models.

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Data Diaries: Interesting Data We Found

In this week’s Data Diaries, let’s talk about one of the major factors that will determine success or failure when it comes to generative AI: the quality of your data.

Right now, we’re still in the early days, the infancy of using tools like ChatGPT or Stable Diffusion. The use cases people are applying these tools to are mostly generic – “help me write a blog post about marketing automation”, for example. That requires no external data, relying on the data that’s already inside the models.

However, what’s inside the models is an aggregation of publicly available data. It’s good, certainly, but one thing it isn’t is unique. Everyone’s using the same base model and the same base data. What would differentiate you? Your data, the data you have behind closed doors that was never published on the Internet for language model makers to scrape.

Back in November of last year, OpenAI rolled out one of the first mass customization capabilities for non-technical users with their Custom GPTs. This facility lets the non-technical user customize ChatGPT to use their own data, function calls, and custom prompts. What makes a Custom GPT work really well? You guessed it – the data you upload.

How would you use your own data in an example like this? Say I was going to build a Custom GPT for email marketing. A while back, I wrote a book that is no longer publicly available on 52 tips for email marketing. I still have a digital copy on my hard drive, but the public version is gone and has been gone for a decade now. Much of the advice is still practical and useful, so I would take that PDF and load it into a Custom GPT. Now, my Custom GPT has knowledge that is unique, that is different than what others would be using if they were to build a Custom GPT for email marketing.

Here’s another example. Suppose I wanted to make a Custom GPT that could talk with a specific perspective about marketing analytics. As the administrator and owner of the Analytics for Marketers Slack group, I could export the entire archive of conversations from the last 5 years, anonymize and de-identify it to protect user privacy, and then load that conversation data into a Custom GPT. Those conversations aren’t publicly available; no search engine or crawler can get to it and use it. But my Custom GPT could leverage that knowledge to be a better conversationalist about marketing analytics by giving it more specific conversations just about marketing analytics.

A third example that many companies can probably relate to are conference calls. You’ve been on so many calls, and some of those calls have been super valuable. Maybe you have a call center with call recordings. Maybe you have a sales CRM that logs calls from your sales team. Whatever the case is, you have a treasure trove of audio data that you could use AI to transcribe, de-identify, then add to a generative AI system. That custom data would give an incredible amount of context to any AI system, and it’s uniquely yours.

Python script to deidentify transcripts

So your first task is to examine the data you have access to that others don’t. What data do you have laying around, on laptops and servers, that might be useful to help a language model increase its context and understanding about your specific way of doing things?

As generative AI continues to evolve, your specific data will become more and more valuable – if you can find it and use it well. Now is the time to start inventorying it and preparing it for use with AI.

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