Always Curious

I have been growing closer to stringing strategy from research, through to consumer touchpoint and post-purchase. I am familiar with many of the Google tools like Ad Manager and GA4. Recently I completed a shopping ads course, using AI to optimize advertising with Google.

Starting with understanding retailer algorithms like A9 and how search terms are used, I got my first taste of small variances in copy making an impact on discoverability. Now I have evolved this thinking into considering all copy with terms relative to how consumers are searching. One more step towards consumer-oriented thinking, backed by data. See below for more examples.

In a global CPG corporate setting where brands are being acquired and divested regularly, things get messy. Really messy. Regional versions of product content, translations, mid-stream updates, legal requirements, retailer requirements, and the list goes on. With other stakeholder input, I developed a naming convention automation involving SAP, Salsify, and WorkFront to increase content consistency across all regions, not just my own. Reducing the repeat workload on like-assets performed by our creative team.

This process was aided by creative directors, data scientists, IT architects, and integration consultants from multiple vendors, however, it was this naming convention that connected them all. Click to see the whole PDF!

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The tool is now out of date as cafes tend to change their equipment once every 5-10 years. However, this method can be easily recreated when given a set of parameters and with some industry consultation. With the evolution of AI, web scraping is becoming more savvy by the day. I could now theoretically complete this work in half the time it took me to complete this one with more web scraping budget.

While working as a coffee technician, our market was always changing due to our service potential being linked to a specific group of brands. Identifying the potential cafes in the city that could become service clients due to the machinery they owned, or the age of the current machines allowed me to begin to segment these leads.

To do this, I used a free web scrape Chrome plug-in and Google Maps. I exported our list of invoices from our accounting software and isolated addresses and business names. From that list, I was able to get a sense of why each client had a higher or lower CLV by examining past invoices. Different cafe equipment carries various trade discounts, so by identifying the cafe equipment with the highest margin, I was able to rank these leads. This is a zero investment leads generation method designed to bolster a small business client list with 0 capital.