As artificial intelligence is becoming more and more important across enterprises and industries, we’ve entered an era of intelligent automation. When we think of recommendation engines, we might think of Amazon and Netflix and considering that 35% of what consumers purchase on Amazon and 75% of what they watch on Netflix come from product recommendation engines we have a good reason to.
Sooner or later, most companies inevitably face the following questions;
- Who should I target with this product?
- What offers should I make to new customers?
- Who’s the target audience for this new product line?
- How should I bundle my products?
Those questions could be answered by an AI-powered recommender engine. This type of engine can make recommendations based on tens of thousands of data points – producing highly relevant results for the end user.
Not All Recommendation Engines Are “Intelligent”
Many current recommendation engines and “out of the box solutions” essentially try fitting people into boxes based on past behaviour.
We do it a bit differently at MindTitan. Our NBO engines use not only historical data on user interactions, but also factor in two additional variables;
- The parameters of the items being recommended
- and the information on the person a recommendation is made to.
This means that even if you add a new product to your inventory you can still offer it to the people most likely to buy it because you know what the item is like and the people most likely to be interested in it.
It also allows you to provide people with novel content. It’s a common issue for recommendation engines to reinforce their own recommendations, nudging people further and further into an echo chamber. Correlating relevant background information and item properties allows the recommendation engine to look beyond the stuff a person has already seen and present them with new experiences.
But let’s have a look at how “hard-coded” traditional models differ from “intelligent” model.
Hard-Coded Traditional Model
In a traditional model the system works on a trigger and rule basis – a user performs a predefined action and the system sends them an offer. This action could be anything from a product view, a click or adding something to cart – anything that could signify interest.
Intelligent Sales Recommendation Model
An intelligent sales recommendation model is a step forward in both its capabilities and also the data that it can use for its benefit. Some of its benefits compared to traditional would be:
- matching the right product, with the right customer, at a specific moment
- correlations across the entire customer database impact a single sales attempt
- understands features & benefits, matches with complex customer profiles
This allows companies to improve their sales offers and make recommendations based on location or past or current activity.
But to really call a recommendation engine intelligent, it should be able to:
- Know your customer: customer’s are people and people are complex – a useful recommendation engine is able to take advantage of this personalization opportunity. It should understand what the customer needs at any given moment.
- Adapt to the changing world: as consumer demands and product selection are in constant flux the recommendation engine should be smart enough to adapt and ensure high performance as trends and preferences change.
- Enable scenario testing: the ability to run simulations against your customer profiles can be immensely beneficial as it allows companies to minimize risks and deliver products that customers really need.
- Serve many uses: a recommendation engine is not just a device for pushing inventory – it’s a useful internal tool and should enable your business to make better decisions, measure goals and understand your audience.
Wish to learn more how MindTitan recommendation engine could benefit your business? Book a free meeting here: https://www.mindtitan.com/contact/