The recent release of ChatGPT, a generative AI chatbot with the ability to compose and understand complex language streams, caused much consternation at tech behemoths, like Google and Facebook. That seems slightly counterintuitive, as both enterprises rely on internet advertising for their revenue as opposed to content creation. However, the generative AI revolution also includes innovations that directly influence the sales/advertising process, including in the B2B space.
Vector embeddings allow the large language models used in ChatGPT to also target customers more effectively – even those from the business world. Simply explained, vector embeddings allow a word and its context to be defined in a three-dimensional space. While keyword matching remains how most internet searches happen today, adding contextual information to a word with vector embeddings provides the means to predict customer intent beyond their “likes” or purchase history.
So let’s take a closer look at how to leverage vector embeddings to improve your organization’s B2B sales as well as it customer relationship management processes. This AI technology provides the means to truly drill-down into a client’s current and future needs as opposed to relying only on their purchase history.
As noted above, vector embeddings encapsulate a word and its context into something able to be mathematically represented in a three-dimensional space. This embedding ultimately allows equations, like cosine similarity, to be used to determine the closeness of the relationship between two words. This relative proximity is known as semantic distance. As an example, a tiger and a lion have a small semantic distance, while a hamburger and a pencil boast a large one.
When used together, both concepts have the potential to revolutionize internet search. Again, the extra context provided by this approach goes far beyond using keyword matching and purchase history to determine a customer’s needs. In fact, Facebook is known for regularly showing ads to users for products they recently purchased and no longer need. It also illustrates the reason both Meta and Google/Alphabet fear a significant reduction in their ad-based revenue.
Any business serving the B2B space typically maintains a decent amount of information about their clients within the company database or CRM suite. While much of this data is likely stored in a relational format, expect some of it to be in the form of unstructured data. This includes loan application information, the results of a credit check, and other information where data privacy is a priority. After all, many B2B sales transactions depend on loans and credit, so each client – current and potential – needs to be scored in a similar matter as a credit rating service.
Leveraging vector embeddings for credit scoring loan applications for B2B commerce makes perfect sense. As enterprises and larger business increasingly use data scientists in this role, AI definitely helps make this effort easier for them. This process effectively determines the semantic distance between companies with good credit and those without. It streamlines this effort for businesses, helping them approve credit more quickly than before, helping to enhance sales.
Over time, a business builds a model containing the semantic comparisons from previous credit scoring processes. This repository can also leverage the AI and vector embeddings’ ability for self-improvement as new data gets fed into the database. It helps improve the efficacy of the scoring process over time without retraining the underlying models.
Additionally, vector embeddings help provide predictive insights on businesses with the highest potential of failure. This plays a crucial role in any business’s decision-making process when determining the credit terms to offer a client. A data scientist simply uses the text-based reasoning for a business failure to make semantic comparisons to other businesses on their client list (or potential new customers.)
The same benefits gained from using vector embeddings when advertising directly to customers also apply when trying to attract new business clients. Capturing company press releases and other publicly posted information in a large language model helps your sales reps identify potential new clients with a higher rate of efficiency. They simply look for embeddings from that unstructured data with a close semantic distance with your business’s top clients.
In addition to enhancing a business’s processes for finding new clients, vector embeddings also provide the means from generating new sales from existing B2B customers. Once again, the large language model for each client gets updated with new data from the news, press releases, and more, with no retraining of models. Use the semantic analysis from this effort to offer a loyalty program or other discounts to your top clients, helping drive sales while retaining a large client base.
If your company wants to ride the leading edge of the generative AI wave, reach out to the team at NineTwoThree. We boast significant expertise in machine learning projects, including ChatGPT and large language model training. Our strong level of business wisdom makes a great partner for you. Connect with us to discuss the possibilities.
