How AI is Demystifying B2B Customer Loyalty

In this special guest feature, Dan Zimmerman, Chief Product and Information Officer at MSTS, discusses how AI can help crack the code of brand loyalty. Dan is responsible for strategy, innovation, and engineering for the company’s software product lines. He has over 20 years of experience leading technology, product, and software engineering teams concentrated in the card and payment processing industry including senior roles at TSYS, Western Union and vice president of technology at Nordstrom Bank. Over his career, Dan has led the launch of multiple SaaS applications, led three successful agile transformations, and won two CIO 100 awards. Dan holds a bachelors of Computer Information Systems from Strayer University and an MBA from the University of Colorado.

Businesses in the B2C world have invested heavily in the use of artificial intelligence and machine learning in order to influence buyer loyalty. In fact, Amazon’s recommendation algorithms drive 35% of the retail giant’s sales. 

The success of Amazon and other major B2C players has inspired a legion of other tech platforms to bring the power of AI to organizations at a smaller scale. The outcome of which is machine learning as a service (MLaaS). With these technologies and AI software more readily available, the time has come for smaller B2B companies to catch up with their B2C counterparts, commissioning AI initiatives to start influencing their own buyers’ loyalty. 

How can AI help B2B companies with buyer loyalty?

Marketers have been moving toward a one-to-one marketing strategy for years, expanding the definition of what’s considered customer loyalty to more of a spectrum, rather than a “yes” or “no” question. With the help of AI tools like MLaaS, B2B companies can inspire deeper loyalty by continuing to market to the individual customer. 

At the highest level, AI prediction works like this:

1. Identify a goal
2. Source and prepare data
3. Create models using historical data 
4. Implement and test the models
5. Continuously train the models using results 

Predictive models like these can determine which customers are more likely to respond to certain marketing efforts (the goal). Models can reveal answers to questions like, what communication is most effective for certain customers? What time of day is most profitable? What are customers’ browsing behaviors?

With historical data on which marketing efforts have the most impact on satisfaction, organizations can create models using accessible MLaaS products. Once they thoroughly test the model, they can continuously run new customer data through, which both trains the model and produces useful predictions. Ideally, predictions that reveal which customers will respond most positively to specific marketing efforts and become loyal customers. 

Data is the key to valuable loyalty insights 

The entire AI insights process is less about determining which customers are already loyal, and more about how to predict what will make every customer loyal. Because at the end of the day, marketers want to provide tailored offers for products buyers actually want, not just what they, the business, want to sell  — and to do so requires data. 

Every piece of data is potentially valuable when building machine learning models. This holds especially true considering that the most important indicator of behavioral loyalty is the frequency and variety of products customers purchased. In addition to these critical data points, things like purchase time of day, geography, communication methods, and product relations are all relevant and helpful data elements for businesses to gather. 

With valuable data in-hand, companies can find the model and software that best fits both their goals and those data elements. There are many benefits to partnering with one of the many AI research firms or platforms that can help execute an AI model setup. The aforementioned MLaaS products should all have tutorials and tools that make launching machine learning within reach for most technical teams. Each of the leading four cloud MLaaS platforms (Amazon Machine Learning Services, Azure, Google Cloud AI, and IBM Watson) allow for fast model training that can greatly accelerate the time it takes to get to actual results. 

Machine learning and AI can’t reveal everything there is to know about customer loyalty, but they can demystify what makes one customer loyal and another seemingly-identical customer not. With greater understanding and knowledge of what makes their customers tick, B2B businesses can join the train already taking advantage of AI resources to inspire unprecedented levels of loyalty. 

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