If we look on the history of Artificial Intelligence over the past year, we witnessed major innovation in business. Facebook dramatically increased its investment in AI research and development to secure its position on business and doesn’t fall behind as a technology innovator. At the NYC Summit in 2018, Amazon announced new competences for its artificial intelligence, computer services and machine learning on the AWS cloud as well as developing new models.
Most major enterprises are looking to become dominant in their growing markets. To accomplish this, they need to offer a solution that uses and incorporates artificial intelligence into the core of their business that will not disrupt business processes and strategies. But what could they offer?
Any AI project requires data. If you’re doing and artificial intelligence project for fraud detection, you’ll need to have data on your customers and fraudulent and non-fraudulent payments. The key is to have enough data; any application is going to require data, but you may not have the right data to train a system to complete tasks correctly. Once the data infrastructure is in place, you must start training models. In this process, you can see if the AI is generating a potentially better recommendations than the ones before.
Issues You May Run into with Training
Algorithms may be one of the most complicated aspects to AI training. You may find that data isn’t even capable of being able to train a particular algorithm. You may spend up to five months trying to train variations of algorithms with collected data to conclude that it’s not better than the status quo.
Majority of AI applications interact with customers, and these applications are the most difficult to master and bring to life for many reasons. The percentage of artificial intelligence talent, data scientists, machine learning engineers, that have built AI software within a company have been deployed in business is rare.
In addition to the talent, measuring the results of AI products and structuring the assessment of measurement is challenging. But everything great takes time.
The Best Ways to Utilize AI Solutions and Machine Learning
Two of the most common uses of artificial intelligence and machine learning are chatbots and image recognition. Chatbots have become prevalent in financial services and becoming more widespread across all industries to enhance consumer experience, and image recognition is generally used in healthcare and medical industries.
Besides these industries, retailers have diverse opportunities to use AI and machine learning to increase the ROI of their business, including:
Natural Language Processing (NLP): The most valuable tool for NLP is measuring customer sentiment. This can be challenging, but negative bias going viral and not being handled as soon as possible can impact your business and need to constantly be improved for accuracy.
Next Best Action: Instead of the “next best product” or “next best offer” optimize the customer experience by choosing the right products, offer, channel and the right creatives to enhance the buyer’s journey. This is also known as personalization but is more complex that adding simple pieces of information into customer communication.
Personalization and Segmentation: Segmentation and classification based on real time available data that supports optimization of the buyer’s journey derived from the specifics of previous buyer’s journeys. Segmentation gives retailers the support they need for the “next best action” in the customer’s journey. Algorithms can also be used to align online and offline treatments.
Marketing Selection: Understanding the marketing attributes that are driving customer conversion are key for retailers. By applying machine learning algorithms, you can identify the most impactful actions for converting customers, purchasing products or services, and ultimately becoming brand advocates. Different attributes fit different customers, and you might find that one ad scaled higher on customer A’s behavior, but email marketing drove customer B to convert. This is why understanding the customer segments and reviewing buyer’s journeys on a regular basis to identify what drives conversion.
If you want artificial intelligence to produce a return on investment for your company, these are some ROI considerations that can be made. The process for defining and measuring AI and machine learning initiatives is a journey. Once you can identify your initial use case, the opportunities for ROI from AI can be limitless.
