With the change in the decade, fields like artificial intelligence, machine learning have grown into an area of application than just being the area of research. And their applications have seeped into our daily lives. Today, we embrace these advancements rather than fearing them.
Have you wondered how Amazon has recommended the products that were on your mind? Personalized playlists on Spotify are another example of AI application. And the most widely used application is Netflix. Netflix uses AI and algorithms to find a pattern in the content you watch & your streaming timing. These observations help them deliver high-value recommendations. In the B2B context, imagine if you have advertised your product at the exact time when the user is most likely to browse or purchase your product or service.
Now, marketers are listening and learning more about their customers for better engagement with them. AI and ML create deep engagement with personalized content that is inch-perfect. The knowledge of the customer helps them in boosting their sales effectively using customized emails, web, and social media.
Some of the most common AI & machine learning applications are:
- Chatbots that provide 24/7 customer service.
- Generating substantial content which saves resources and time. The generated templates help in producing unique content that gives the impression of human work.
- Product recommendation
- Image recognition, Visual search, & Voice search that has exalted the shopping experience.
- Sentiment Analysis and social listening
- Sales forecasting and predictive analysis
- Digital Advertising & Ad targeting
- Dynamic pricing
Roadmap to apply AI in your business
The first step is to learn about AI and its application in marketing to solve your problems. What is the difference between an inferential AI model and a deterministic marketing model? AI is capable of solving specific problems but when applied in the right way. AI can provide insights about how to communicate with customers, how to create the right content.
Let’s look into the simple steps required to implement AI into your business.
1. Know the difference between AI and ML
Terms AI and machine learning is used interchangeably. AI refers to the ability of machines to think and imitate human behavior.
While ML is a part of AI which believes machines can learn from data and can take decisions without human intervention.
2. State your business requirements
Clearly define the objectives that you wish to achieve using these two technologies. It may be an outcome you want to achieve, a problem you desire to overcome, or a business expansion motive. Look out if you have the required data and what additional data you need.
3. Prioritise the main motive
Watch the value drivers such as increased value for customers or business. Identify the potential business and financial benefits of the AI project.
4. Evaluate your potential
Decide on the strategy for implementing your AI project. You can build it from scratch, buy something readymade, collaborate with someone to implement, and outsource the development.
5. Consult an AI expert
If you don’t have AI expertise with you, consider consulting one outside your business.
6. Prepare your data
Learning abilities of AI algorithm depends on the data you provide them. Therefore, it is crucial to have high-quality clean data ready.
7. Start with a small implementation
Start with a small dataset and check if AI fulfils the objectives of its implementation. AI integration will play a significant role in staying ahead of the competitors.
Let us know how you have leveraged the benefit of AI at email@example.com