AI models incorporating constant machine learning updates from real-time (or close to real time) data can process the information quickly enough to render it actionable and difference making. JL
Zac Johnson reports in business.com:
Even with the advent of machine learning, building a new data model for every new prediction is far too resource-intensive. The prediction engine at Amazon anticipates not only what products are going to be popular within the next while, but where those customers are located. The inventory can then be allocated among distribution centers proactively. When an order comes in, the item will be in stock at a fulfillment center closest to the customer. The price per prediction is reduced, making AI predictions accessible for even smaller retail businesses, actionable insights that can then be integrated into workflow and timeline.
Artificial intelligence and machine learning are increasingly at play in just about everything we do, both personally and professionally. It is because of these technologies, for example, that an online store can automatically determine what products should be featured on the homepage. These main pages and lists of product recommendations are oftentimes custom-tailored to the individual customer, boosting the likelihood of sales and growing the bottom line.
As you might imagine, similar advancements in technology can be seen clear across the world of retail business, spanning nearly all industries, verticals and niches. You have to think about your market, your customers and their actions as continuously moving targets. It's not enough for you to know where they've been or even where they are right now; you need to accurately, quickly and reliably predict where they will be tomorrow.
Be ready for tomorrow today
You need to anticipate their movement so that you'll already be where the opportunity will arise tomorrow. If you're already there, you'll be ahead of the competition and poised to capitalize. That's the power of automation and artificial intelligence, because too many manual tasks take up far too much time and way too many resources.
You need tomorrow's information today, and it needs to be fast and accurate.
Perhaps one of the most incredible examples of this in action today is Endor, a protocol that allows for automated predictions on encrypted data. In the past, the only way that you could gain access to this level of business intelligence is by hiring a team of data scientists who could then sift through the mounds of available data. With Endor, the process is automated with artificial intelligence.
This is accomplished by leveraging proprietary social physics technology and massive machine power, making accurate predictive analytics available at a scale to just about anyone. Whereas the old prediction model required a team of data scientists, utilized limited data, and took upward of two months per prediction, the AI-powered predictions of Endor are vastly more affordable and can be produced in under a day.
The price per prediction is dramatically reduced, making these sorts of AI predictions accessible for even smaller retail businesses. This empowers them to stand up against behemoth corporations with their big data science departments. That's huge!
Accurate predictions now
Say, for example, that your company is developing a new product. You're in the final stages, and while you're certain you'll make the product available in English, you'd also like to approach the international market. Because you have limited resources, it would be impossible to offer your product in dozens in languages, so you'd like to zero in on about three. But how can you decide?
It's not just a matter of what languages are most common around the world; it's a matter of where your product has the best shot at success based on a myriad of factors. When you upload the customer data that you have available, the AI protocol can work its way through it and provide you with its best possible prediction. And you'll get that prediction within a day.
We have to realize that these types of forward-thinking predictions, getting ahead of the curve before it even gets there, is really how you can take your business to the next level. By gaining the ability to be proactive with your decisions, rather than simply reacting to crises, you can improve engagement, increase sales and bolster customer loyalty.
Getting ahead of the curve
As possibly the world's largest online store, Amazon is oftentimes the elephant in the room. This is particularly true in conversations revolving around the modern retail environment, even if Amazon is primarily online and not brick-and-mortar in the traditional sense. You'll find that Amazon utilizes automation and artificial intelligence extensively.
One example of this really drives this point home: With the promise of two-day delivery with Prime and even same-day delivery in some situations, Amazon cannot afford to simply react to customer orders as they come in. An incredible level of automation is necessary if it wants to avoid sending express parcels clear across the country as the majority of its shipments.
The prediction engine at Amazon anticipates not only what products are going to be popular within the next while, but also where those customers are located. The inventory can then be allocated among the distribution centers proactively. That way, when an order comes in, chances are that the item will be in stock at a fulfillment center closest to the customer. At least, that's the ideal.
These predictions are based on an incredible amount of customer data collected over the years, looking not only at categories of products, but specific items, down to color and size. When you consider just how many products are available on Amazon at any given time, this is no small task. It is certainly an unsuitable task for a human being. Artificial intelligence is necessary to work quickly and accurately enough at this kind of scale.
More AI prediction applications
A gargantuan corporation like Amazon can take advantage of machine learning and effective artificial intelligence for effective inventory management across its vast network of fulfillment centers. But how can predictions and AI work for retail for businesses of all sizes? Predictive questions, based vast organizational data, can help you to determine the following:
The resulting predictions lend themselves to actionable insights that you can then integrate into your workflow and timeline. In addition to Endor, some other companies that offer AI data management and predictions are IBM Business Intelligence, Pega, Deloitte Omnia AI and Qlik.
- Who is most likely to upgrade to a premium product?
- What subset of customer will respond best to a new promotion?
- Which inactive customers are most likely to reactivate?
- What product color will lead to the greatest conversions?
- Who is likely to spend the most money on products in the next three months?
- When should the email newsletter be sent for maximum click-through rate?
A brand-new paradigm
Conventionally, data scientists would need to construct a new data model for every prediction they'd like to form. The circumstances are different and thus call for a different approach to arrive at an accurate prediction. But this is time-consuming and prohibitively expensive. Even with the advent of machine learning, building a new data model for every new prediction is far too resource-intensive.
To be quick and nimble, a new framework needs to be developed to handle the growing demands of today's retail environment. And it needs to do so rapidly and accurately. Machine learning and AI predictions represent the future face of modern business. If you want to get ahead, you'll need to get there even before your customer realizes where they're headed.
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