Generative AI has got a huge amount of public attention over the past year, and rightly so. There is so much of talk about how it has changed how people analyze data, develop content and more. But in all the noise of content being put out around Generative AI every day, its true impact in real-world situations becomes hard to figure out. Particularly, for online search, SEO and overall search experience. Let's figure out together…
Evolution of online search
In the early days, online search or Search 1.0 was driven by keyword search. The search engines required you to know what you were looking for and enter the specific keywords. The search results were largely focused on matching the keywords in the search query with showing high ranking website pages that had the highest density of those keywords. The downside of this approach was that it often threw up inaccurate search results. Many people cramped their website content with keywords to rank higher in search results for those keywords. They did it even if the repeated usage of those keywords in the page content did not make sense.
Gradually, and eventually, search engine algorithms got better and they actually started to understand the user intent behind a search query and showed results that were based on semantic understanding of website content. This made a huge improvement in the quality of search results. Search 2.0 had arrived.
And Generative AI has taken that semantic understanding to the next level by generating response to prompts based on the context, intent, and tone of the search. It also made search more personalized by making it conversational. The next step is to move towards hyper-personalization and providing more information related to the search which the user may not have been looking for in the first place.
How is Gen AI likely to change the online search experience?
The thing is, that, Generative AI is still very exploratory and its impact on overall search and discoverability of content is not fully known yet. In its current state, Generative AI is available to two broad categories of users - consumer and enterprise. An average consumer can search using Google Bard chatbot or Bing which provides a better user experience over online search we have been used to. Enterprise users can embed Generative AI into their website either through an API call or building a generative search experience from scratch using the available tools.
Still, the way we discover information on the Internet is pretty traditional. It's funny that I call it 'traditional' while online search has existed for a little over 2 decades only. Anyway, I was saying, that the current search experience is pretty straightforward. You enter keywords related to the information you are looking for, scroll through the search results displayed in a listing format, and you click on the link with a title text that seems most relevant to your query. At present, Generative AI sits as a layer above that usual search experience.
If you look at any business website, and IF they have a search option, it works pretty much the same way. Many of them the Google Search API to quickly implement search facility on their website. Very few business website have a well-functioning search. This has left a wide gap between user expectation and actual website experience. It feels quite intuitive because we have got used to searching for information online this way for a very long time. But, Generative AI might completely change that experience with a conversational chatbot interface.
Driving customer experience and conversion
Whether you use Google Bard or ChatGPT, they both use a pretty similar interface. You simply type in your prompt for the information you are looking for, and the AI-powered bot generates a response for you. Google Bard and Bing take it a bit further by suggesting relevant questions that you may want to ask. Both of them also list out the source links that have been used to generate the response.
This conversational style of information discovery is game-changing. Now you don't have to scroll through top-ranking links, click on the one that might give you the information you are seeking, visit the page, read or glance through the content, and explore the website further, if needed. If you get what you were looking for, great!, otherwise keep repeating the same process.
Generative AI has made online search a lot more intuitive, personalized, and easy. Now imagine, a prospect visits a B2B business website that offers a similar search experience. They are greeted by a minimalistic UI, which starts by asking some basic information about that person like name, company and official e-mail ID.
Then, a personalized welcome message pops up asking them to enter what they are looking for in the text box below. The chatbot generates a response using the language learning model that has been pre-trained only on data relating to the company information; services it offers; its sales collaterals; its USP and differentiators; and the thought leadership materials like blogs, case studies, videos etc.
This will allow the prospect to quickly discover about that B2B company's services and capabilities in a fraction of time. Suggesting relevant follow-up questions after every response generated, and displaying relevant pieces of thought leadership content, will keep the user engaged while accelerating the process of information discovery. Such a search experience will be somewhat similar to an average user's experience on Amazon. As the prospect discovers more information about the B2B company's business in a conversational manner, she gradually moves from the awareness stage to the evaluation stage, leading up to conversion.
Generative AI has the potential to turn any business website into an accelerated lead funnel!
Things to consider before adopting Generative AI
Believe me, this is not a pipe dream. It is a possibility that can be achieved through cultural readiness, clarity of goals, identifying small but achievable use cases, thoughtful planning, and learning from data insights. I'll explain it a bit more for you.
It is no secret that every new technology is looked at with suspicion, and rightly so. For a business to adopt a new technology means taking financial, operational, legal, and data security risks.
So, every new technology initiative needs to go through cost-benefit analysis before getting a go-ahead. But one risk is usually ignored: cultural risk. Having a bit of skepticism is healthy and necessary. But, adopting a new technology requires an open mind to gain awareness about it first, and curiosity to test it out. Define a pilot use case relevant to your business and have KPIs to measure performance, but be ready for failure or limited success.
Activate the experiment, test multiple versions of the same use case to arrive at the optimal version that achieves the KPIs. This is a crucial step because taking a use case a pilot into production requires a lot of expertise. And finally, operationalise it with dedicated teams and infrastructure to oversee development and maintenance. During this entire process, it is important to make sure that the data used for training the language model is not client's data and stored on a secure server. This will also help avoid leakage of your intellectual property data. You could anonymize that data to make sure the language model is trained on realistic data.
In all of this, the human element remains constant. By that I mean, it is the humans who have to decide its adoption strategy, plan for it, test it out, and bring into business as usual state. So, the C-Suite needs to be well-informed about it, and the execution teams need to be continuously trained and up-skilled. Of course, the cost of all these aspects need to be determined, budgeted, and monitored.
3 quick use case ideas to get started with Generative AI
Now, all this sounds all fine and good but Generative AI is quite recent and is not on the radar of many businesses. One of the many reasons is the lack of clarity on the use case. Translating business ideas into a proper pilot use case is not easy. Still, there are small steps that marketers can take today to make sure they are ready to test out a pilot when they have clarity. They can start using Generative AI to generate content like blogs, whitepapers, and video scripts. Many businesses started early on and have made it part of their of their content generation engine.
Optimize that content for SEO with proper keyword density, meta descriptions, titles, alt text etc. Make sure the content is informative, educative, and provides a unique point of view.
Sales teams often struggle with pulling the right thought leadership material from a repository and leverage it for their customer outreach. Having a Generative AI based experience that pulls the content out of the repository, provides a summary of the content, and suggests more related content could be another use case.
You could also use Generative AI to analyze the monthly website traffic data and provide insights that can help improve the user journey, website content, navigation, and user experience.
Look, it's very easy to ignore a new technology that has gained a lot of public attention in a very short span of time. But, Generative AI is an advanced version of AI that we all are used to. Don't agree with me? Here are a few examples - Google Autocomplete, YouTube Search Recommendations, and 'People you may know' on Facebook.
The point I am making is that Generative AI is a new technology that has been in the works long before it reached the general public. Yes, it is not a finished product. But it never will be. It will continue to keep learning and re-learning to remain relevant to an average consumer as well as an enterprise user.
Is Gen AI likely to change the online search? Yes. It IS the future of online search and businesses and marketers cannot afford to ignore it. Because online search is the first touchpoint of information and brand discoverability.
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