“As more and more artificial intelligence is entering into the world, more and more emotional intelligence must enter into leadership.” – Amit Roy
The Natural Language Model Explosion:
It’s still early days, but we thought it might be fun this month to explore what we can learn from some of the leading natural language models. We use the term natural language model vs the leading name in the space “ChatGPT” to help show how fast the category is exploding. In many ways it feels similar the Web Browser explosion that was launched when Netscape was introduced back in December of 1994. Chat GPT has the recognition now, but Microsoft BING and Google has BERT and now BARD being introduced into the marketplace. Each has a slightly different focus right now, but all represent an evolution in AI and information retrieval. Siri, Alexa, and Cortana all seem almost outdated in comparison as these AI models are shifting from task execution to conversation. The watch out of course is that just because the new models make information retrieval and story telling easier, it does not mean they are always correct or bound by societal appropriateness. As users, it is imperative that everything is taken with a grain of salt and validated for accuracy through other credible sources. If the entire internet is the foundation of the information these tools draw from, we are all aware that that foundation is filled with almost as much inaccuracy, biased perspectives, and falsehoods as it is truth.
So with our warnings out there, on with our test.
The first question we posed was to list the biggest challenges marketers are facing today. The answer seems on the money:
- Increased competition and standing out from the crowd
- Changing consumer behavior and preferences
- Fragmented media landscape and the ability to be consistent across touchpoints
- Data privacy and security concerns while being transparent and ethical in the use of data
- ROI measurement and attribution especially in the era of multi-channel marketing
- Technology and skills gap required to connect and effectively use the ever-advancing technologies
The good news for us here at Adstra is that apart from the challenge of market competition, our identity graph platform – Conexa – was built to help marketers take on and succeed against the other 6 challenges.
The second question we asked was “What is a marketing identity graph”. We certainly believe that brands must have a clear customer identity strategy to succeed but wanted to see what ChatGPT had to say. Here’s what we got back:
“A marketing identity graph is a tool used by marketers to unify and organize data about their customers or target audience. It is essentially a database or a framework that connects various data points related to a person’s identity, behavior, and preferences across multiple devices, channels, and platforms.
The goal of a marketing identity graph is to create a complete and accurate picture of an individual customer or prospect, enabling marketers to deliver personalized, relevant, and consistent messages and experiences across all touchpoints.
The data points that make up a marketing identity graph may include demographic information, such as name, age, gender, and location, as well as behavioral data, such as browsing and purchase history, social media activity, and email interactions. This data can be collected from various sources, such as CRM systems, website analytics, social media platforms, and third-party data providers.
By building a marketing identity graph, marketers can create a single customer view, which helps them better understand their customers’ needs and preferences, anticipate their behavior, and tailor their marketing efforts accordingly. This can lead to improved customer engagement, loyalty, and ultimately, business growth.” Not sure we could have said it better. Thanks Chat GPT.
With the rise of AI tools, marketing and sales departments are able to streamline processes, automate tasks and make data-driven decisions. From lead generation to customer engagement, AI has the potential to transform the way businesses approach marketing and sales. But like any new technology or capability, success often comes from the investment in the people using the tools and the strategies being employed.
To maximize the value of big data and drive results, brands must start by recognizing the key components of business data analysis and decision-making. Action IQ VP Tamara Gruzbarg points out that there are five primary stages of organizational analytical maturity:
- Predictive analytics at scale
We might add that it all starts with a strategy around data connection and organization in preparing the data being run through these 5 steps.
In today’s world of ever-changing conditions—economic uncertainty, geopolitical instability, and a lingering pandemic, leaders are being challenged to provide confidence, direction and empathy that motivates an organization. Simultaneously, the same leaders are working to optimize business efficiency. Certainly a tricky balance to tackle.
Other articles to consider:
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Welcome to the new ideal.