How to Ensure LLMs Understand Your Brand and Product

How to Ensure LLMs Understand Your Brand and Product

Every marketer fears negative brand reviews. With the development of AI-powered chat programs such as ChatGPT, Meta’s Llama 2, and Microsoft’s Copilot, marketers must be ready for the worst statements about their brand to be repeated as search query responses.

Large language models (LLMs) will eventually answer billions of search queries every day, thus every marketer must understand what these models believe about their brand.

More advanced marketers are beginning to track a new metric: every competent CMO knows their market share, but do they know their “share of model”? Measuring how each model sees your brand, compares it to rivals, and why recommends your items to buyers will become a critical job for any marketing team.

Here are three things you can do to guarantee that LLMs realize your brand’s uniqueness and why your product is worth considering.

First impressions last

Every AI researcher will advise you about expecting a model to act like a person. However, they are not required to sell vehicles, coffee, or credit cards to make a living.

The simplest way to understand the effects of an AI-everywhere future is to observe how customers act now. You frequently visit its website when you want to know if a brand is trustworthy or relevant. And, when every major language model begins to link to the open web to answer requests, they will do the same.

Even the most well-known brands will struggle to change (or even recognize) the historical training sets for the basic models that will eventually run most of the internet, from search queries to content playlists, product suggestions, and customer support. So, any piece of information you manage outside of that training set becomes an important opportunity to promote your business and products.

This is why evaluating creative assets is the first step for companies looking to track and optimize their model share. As search engine crawlers hunt for keywords and cues in your metadata, any item you’ve ever released online might inform a model’s reasoning for the next evaluation.

The only way to grasp what the model will think is to look at each piece of content from its perspective. Humans may like your Instagram Reels, but do they teach models that your vehicles are dependable? Those excellent long-form recipes on your website may boost your page rank, but do they appear too luxurious for an LLM’s taste?

Each model has its thinking process. Once you know how each model perceives those assets, you may optimize them to focus on the preferred perceptions. Consider it the most important focus group possible, with only four attendees: Gemini, Copilot, GPT4, and Llama.

When in doubt, Ask

If you want to go further into what the models have previously learned, all you have to do is ask them.

Prompting and comparing multiple models is similar to tracking brand sentiment across different audience segments. The advantage of LLMs is that they do not become bored in a focus group; you can encourage them often to build a baseline and then ask increasingly more complicated questions on audiences, marketplaces, and proof points.

The results may be stunning, as one popular airline discovered when our team showed that a model responsible for over 1 billion unique chats to date was sure they were best suited for retirees.

This technique, along with what you already know from examining how each model sees your brand, allows you to match the most successful content to each model, gradually optimizing your share of the model.

Optimise and observe

Good news for marketers: these models are ready to learn. The bad news is that kids never stop learning.

Nothing in an AI-enabled world remains static, and marketers must realize that a new set of metrics monitoring model perception will be just as crucial as the net promoter score (a market research indicator based on a single survey question). It is critical to develop a share of model as a comparable indicator to the share of market or share of voice.

As the handful of models that will dominate become more distinctive over time, we need to constantly track their perception and optimize accordingly. Regularly analyzing their outputs will aid in the adjustment of creative briefs to increase model share and pinpoint the assets causing perception changes.

Regardless of how often you monitor your model share, it is obvious that models will determine your market share shortly. While many of the emerging best practices for optimizing generative AI performance begin with search, social will have the largest influence.

With Meta investing more in the Llama family of models and deploying AI-enabled experiences across millions of discussions in groups, marketers must understand the principles of an AI-everywhere future now, while the conversation is being dominated by a few LLMs.

If you don’t accept these models as new members of your target audience now, you might not have an audience at all tomorrow.

Source- adweek

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