Tag: AWS

In the ever-evolving marketing landscape, one technology emerges as the potential linchpin – Gen AI. Revered for helping businesses move further, faster and more efficiently, does it also hold the key to a new way of marketing?

I recently discussed this topic and more with Rio Longacre, Managing Director at Slalom, and Jon Williams, Global Head of Agency Business Development at AWS.

Gen AI: Making sense of the mania

During our conversation, Longacre describes a paradigm shift in the Gen AI landscape. Moving beyond experimentation, companies are now forging strategic pathways, identifying areas where it can genuinely make a transformative difference.

“When ChatGPT launched, it quickly became the thing every client we worked with was suddenly dedicating significant resources to. They were building out Gen AI demos, investigating it. People were asking us to help them create a Gen AI strategy. And there was a lot of experimentation. There was a lot of wheel spinning too. It was like this Gen AI-mania – everyone just went all in,” he says.

“Within the last few months, there’s been a big shift, which is very positive. Instead of ‘Let’s just try different things’, it’s now, ‘let’s have a Gen AI strategy’. They are looking to identify areas where Generative AI could make a big difference and move the needle. They want to invest in those, whether it’s eCommerce, operations or creative; they want to come up with ideas that could work and test them. If they work, great. They’ll look to start to commercialise them. If they don’t, that’s OK too, then they can pivot and try something else.”

AI as a marketing assistant

Williams shares where Gen AI is shining as a marketing assistant, of sorts. “Amazon Q is a new type of generative AI-powered assistant that can be used specifically for work to be tailored to your business to have conversations, solve problems or generate content. It uses the data and expertise found in your company’s information repositories, such as codebases and enterprise systems.

“You could use Q, for example, to:

● Learn a brand style guide, then
● Use that information to turn a press release into a blog post that adheres to those standards, then
● Analyse how a brand has shown up on social media, then
● Create new posts around those releases that will make sense to followers, then
● Analyse the results of those posts, and finally
● Summarise them for review for teams

“It’s almost like this self-fulfilling circle of incremental productivity that’s happening as a result of leveraging some of the generative AI capabilities that come to use as a result of a bot but are plugged into the systems and data that your enterprise organisation owns. We’re only in the very early stages of that, which is pretty exciting.”

At Amperity, we work with many customers that want to be more data driven and customer-centric. We know that comes down to an understanding of the customer – even figuring out what the key segments are that you should be going after and what types of campaigns you should be launching.

If you think about the analysis you have to do to answer those questions today, there are so many barriers you must understand, including:

● Your underlying data and the schema
● SQL
● How to build visualisations

There are many barriers to a user from the customer data to actually figuring out what strategy should be launched. We’re seeing a lot of folks throw Amperity into the mix here too, developing tools where that data can be more democratised. You can ask questions of the data with natural language as opposed to needing to write SQL.

This will lead to a lot more data-driven decision making as folks are able to more easily access their data relative to the mini barriers that were in place previously.

3 Ways marketers are leveraging Gen AI for greater efficiencies and cost- savings, according to Longacre:

1. eCommerce company: This company has written descriptions for 10,000 product SKUs using Gen AI in a couple of weeks, saving them months of time and about a million dollars.
2. Paid media: As it relates to paid media tools, such as those designed for Amazon Marketing Cloud, there’s a background image generator specifically tailored for crafting lifestyle images. Findings indicate a remarkable 20 to 25% increase in conversion rates for products showcased with lifestyle images compared to those with a plain white background. Swiftly deploying these features, testing their efficacy, obtaining results and subsequently, optimising based on these insights is a gamechanger.
3. Banks and finance: The bank’s creative briefs are now being generated by artificial intelligence, reducing the time spent on back-and-forth communication with agencies by approximately one week. Even with segmented strategies, brands often face resource challenges.

Accelerating the creation of creative briefs, creative imagery and product descriptions allows for a faster customisation of on-site experiences. This progression toward personalisation doesn’t require them to go in a ‘hands-off’ mode where Gen AI is really running the show. Instead, it’s truly like a genuine one-to-one chatbot interaction or conversational AI.

Keeping the human in the loop with AI

Longacre points out that every use case he shared has a human in the loop. Since we’re in the early days of AI, that’s not surprising as most brands are starting with ‘human in the loop’ use cases. This is where AI generates outputs that a person then approves and potentially refines. ‘Human in the loop’ use cases enable productivity gains while minimising risks arising from hallucinations or unexpected outputs.

“Maybe the copy is being written by Gen AI, but a human reviews it,” Longacre says.
“The image might be generated, but it’s not being pushed out into the wild.

“We’re starting to see a little bit of that, but generally, there’s human oversight. Even with chatbots. I mean chatbots have been around forever. Most of them were machine learning based. You need that knowing of, ‘OK, when do you have the escalation? Where do you pass from the chatbot to a live person for certain use cases?’ Identifying that is still super critical.”

Setting your brand up for success with Gen AI

In the journey of crafting a Generative AI strategy, Williams points out five key elements that play a pivotal role in ensuring success. They are:

1. Tech stack: Your tech stack is vital. You should have the ability to explore models, test use cases and choose the right ones.
2. A solid, mature first-party data foundation: Generative AI relies on the data to function properly, which means you must have robust data ingestion storage and management capabilities to make sure that the first-party data is accurate and as close to real time as possible to provide accuracy in the model outputs.
3. Human oversight: You still need that human in the loop intervention to make sure that what you thought was going to happen is happening and there are no anomalies.
4. AI-specific analysis skills: Leveraging AI requires the ability to interpret and accurately apply AI model outputs. You must ensure that your teams have the expertise to understand how the tools work and how best to put the data to work.
5. Process redesign: Consider existing processes or workflows that need to be resigned to take advantage of General AI.

Start small with AI for big results

My advice to brands and organisations when rolling out AI: start small. I would start with a small use case that’s highly measurable and one that doesn’t require major change. One place where clients we work with have seen a lot of success is just with subject line optimisation or optimising the body of emails or paid media ads. Since you can have a human in the loop here, it’s a great opportunity to experiment with creating different segmentation strategies and different messages.

This article is written by Joyce Gordon, Head of Generative AI, Amperity.