About a year and a half ago, I wrote a blog post highlighting 6 practical uses of AI for a digital marketer. Since then, the marketing landscape has shifted (once again). However, AI has yet to hit its stride in the B2B marketing space. With that said, I thought it would be interesting to take a look back at those six original AI use cases and see where they stand today.
Personalization, it seems, is the goal for most marketers these days. At the same time, in the last 18 months, many of them have come across a sobering realization: personalization is hard to achieve. It requires well-thought-out and agreed-upon business goals, a strategy and continuous tuning to reach its potential. It cannot be treated as a turn-key technical solution.
Before you take on personalization, you should first identify a clear business goal(s) you’re trying to work toward. Once you’ve aligned on that, there are three questions to answer before personalizing content to a user.
- WHO - Who are we personalizing for? How do we define and identify them?
- WHAT - What content are you personalizing? What variances of the content do you have? What will it take to create those you don’t yet have?
- WHEN/WHERE – What part of the user journey are you personalizing and when?
Despite all the complexities, personalization seems to be on every marketer’s mind. As brands figure out the infrastructure (People, Process & Tools) required for personalization, it will only become more prevalent. Personalization is here to stay.
A/B testing should be in every marketer’s toolset. It provides invaluable feedback on how your customers interact with your digital properties and your brand. It allows you to test small, incremental changes to your content, design and/or UX, among other things. It can act as the guide to making large changes before it is too late.
A/B testing has only become easier in the last few years, and it is now supported by all leading enterprise CMS and Marketing Automation platforms. If you don’t have an enterprise system, there have been a plethora of new tools that enable A/B testing on your website. Most of these are turn-key solutions that can help you test different variations and pick a winner automatically. However, like personalization, A/B testing is much more effective when it’s backed by a goal and a strategy of what to test and why.
I called voice assistants ‘the new frontier in marketing,’ but so far, it doesn’t seem like that is the case. Though voice-activated systems like Alexa, Google Home and Siri are becoming more prevalent, they haven’t changed much in B2B marketing. Users tend to use them for accomplishing simple tasks like setting reminders, shopping lists and simple searches. Brands have not rushed to make apps for voice, and those who have, haven’t seen much engagement.
One big impact voice has had is in search. Position Zero (P0) in search has become a key player. It is the first Google search result that appears above the organic SEO listing and is what is read out by voice assistants. This has increased the importance of voice SEO, as well as the use of conversational content, as organizations attempt to achieve Position Zero. This is particularly important for local businesses that want to be featured prominently for local searches.
With these latest developments, voice assistance has had a slower start than predicted and may have a longer lead time before it becomes more mainstream for B2B marketers. However, it shouldn’t be completely ignored because of its influence on other areas, such as search.
Chatbots are everywhere in B2C. They have become standard in customer service and have started to show up on news and banking sites as well. The number of frameworks and products that help you build a chatbot for your digital properties has grown exponentially. You can build it for your website or messenger platforms like Facebook. All the major players seem to have come up with a platform – Google being the latest with Meena.
The AI for chatbots is still maturing. It currently requires a good deal of manual maintenance to accommodate the many paths your customers could take as well as a backup system to point to in the event that the chatbot is unable to help your customer. Because of this, it cannot yet completely replace an existing system (e.g.: Search or Customer Service), but it does introduce a new, oftentimes more convenient, way for your customers to engage with your brand.
Search & Recommendation Engines
Enterprise Search engines have gotten smarter in recent years. Their smart suggestions and results ranking based on click analysis have moved them closer to Google than ever before. Search tools have also started to introduce Natural Language Processing, which identifies entities previously tagged within the content and delivers relevant search results for conversational queries. Some enterprise relevance engines also support personalization of search results based on the user or previous searches.
All of this expands what search can do. The ability to identify entities within your content goes a long way in bringing relevant content to the user, but it also relates similar content to each other. One North’s Relevance Engine works on that principle and produces content recommendations that can improve exploration.
CRM Marketing Technology Systems
In my last post, I had called CRM out specifically because the amount of data that is stored in those systems was ripe to be used for AI. But, over time, most marketing automation systems have started to store similar data and are closer to the customer than a CRM. New tools like Customer Data Platforms make it easier to capture data and gain insights from different channels/systems. So, this category requires expansion to encompass all of Marketing Technology systems.
We are seeing a consolidation of features into enterprise systems [LT1] [KV2] that can gather all customer data and help to segment customers, learn their behavior and assist in driving other AI uses like Personalization, Predictive Analysis and A/B testing. Your current Martech stack (and how modern it is) will determine how AI can be used in the future. This also requires that your people and processes are aligned to make use of the tools.
After 18 months, the use of artificial intelligence in marketing is still in a nascent stage. There have been some twists, some turns and some let-downs, but AI is here to stay. Like any other modern technology, AI requires business goals, a strategy and constant measurement and tuning to achieve a decent ROI. Although it may be true that AI has been oversold, it will continue to evolve, and it’s important for digital marketers to educate themselves on the uses and limitations.
Not sure where to begin? I suggest starting with an ecosystem audit of your marketing technology stack to understand the purpose of each tool. This is something our team can help with. If you’re ready to get the process started, give us a call.