Retail is moving at alarming speeds today, and the ecommerce market is moving alongside it at the same speed. In addition, shoppers are now a whole lot more demanding and tech-savvy since their experience is much more personalized. They are even able to utilize various different online tools, allowing them to compare all of the different offers with ease. On top of that, big data retail teams now have to go through so much data just so that they can establish the most optimal price, making it impossible to do so on their own now. As a result, the retail market is now taking advantage of artificial intelligence, with machine learning at the heart of it, in order to optimize prices. Take, for instance, Amazon; in 2018, they had captured almost half of the American ecommerce market. A large portion of the $248.33 billion made was acquired by outsourcing its pricing process to AI. Indeed, its recommendations engine makes 35% of their revenue simply by using customer purchasing history in order to establish optimal prices. If you were to calculate it, you’d realize just how much AI helps out Amazon.
So, why are the algorithms so helpful?
Based on a report by Gartner, price management that’s powered by artificial intelligence helps boost revenue anywhere between 1% and 5%, raises profit margins by anywhere between 2% and 10%, gets rid of 80% of discount approvals, as well as increases customer LTV by 20%. This is accomplished by the algorithm which takes into account patterns, handles copious amounts of data, and gains a sixth sense by using information from both the successful and the failed experiments that firms had paid for. In addition, the machine knows whenever the market changes and it never has any disastrous days or difficult weeks, which can’t be said for people. On top of that, the algorithm is always able to provide optimal solutions for each situation, understands what the results will be from each pricing and promotional decision, and it is able to forecast the short-term future. Since the machines handle all of the routine jobs, retail managers are able to focus on making a balanced strategy, instead, that way their customers have positive experiences.
Why, though, aren’t solutions powered by AI universally recognized yet?
Deloitte’s recent study shows that even though a lot of money has gone into it and there have been positive projected forecasts, machine learning hasn’t been able to take off yet. Indeed, based on the 2017 survey of American executives, the business shows that, at most, 62% of companies that either utilize cognitive technologies or that understand what they are “had five or fewer implementations or the same number of pilots underway”.
It isn’t easy to quickly put out AI-powered solutions due to the fact that machine learning needs very quality data, a polished infrastructure, as well as a well trained and insightful team in order to solve real-life business problems. That being said, that same Deloitte report states that the amount of AI applications will increase four-fold by the year 2020.
So, what kind of data do retailers require if they want to reap the advantages that come from the algorithms?
The machine learning solution gets its knowledge from historical, competitive, as well as customer data which has to go back at least a year. This is due to the fact that it needs to go through each transaction that way we can truly understand what the customer journey is for each product. If the data is of poor quality or all of the information isn’t there, the system will not work the way it should as the algorithm won’t be able to either figure out the right price nor will it be able to provide the correct price prediction of both sales and margins.
That being said, it doesn’t need all of the information that retailers have. What the retailer may consider useful, the algorithm could think otherwise. Therefore, in order to figure out just what the system requires and which factors impact sales, it is crucial to gather all of the information in the proper format.
What’s the most effective way of adopting algorithms?
Over half of the success that comes from adopting an AI-based solution stems from data preparation. That being said, for companies, the data is only the beginning. As soon as the information has been gathered and structured, retailers need to do the following three steps if they want to successfully use machine-learning.
1. Select an approach and then create a model.
Retail is quite a complex industry that is based on a number of different variables like promotional and marketing activities, assortment and pricing, along with so many others. On top of that, each one of these domains comes with so many distinctions that the system will need to take into consideration in order to predict both prices and sales. As a result, retailers must understand both the business objectives as well as the steps that will be the most helpful to them. By understanding the business objectives, firms are able to pinpoint the data algorithms that retailers will have to work alongside.
2. Bring out the infrastructure
The algorithm on its own isn’t the answer to everything. In order to process copious amounts of data each day, the system has to have a solid infrastructure that is based on multiple levels of both checking and notifications, keeping errors from coming about during crucial decisions. Each in-house solution looks to be the best option as it is able to protect information that is confidential. That being said, not that many firms are able to put out, maintain, and then update such a complex system. As a result, since machine learning algorithms are starting to become a whole lot more affordable, external providers may be the way to go.
3. Work on the solution and put it in motion.
In order for this to occur, the retailer’s entire team needs to be engaged. Often times, managers are worried about losing their positions to AI or they won’t listen to the recommendations that come from AI since they don’t understand the logic behind it. However, retailers can get over this confusion through a pilot as it can show how efficient the solution truly is, gaining the team’s trust.
Summary
If a firm wants to be on top in today’s retail market, they have to be agile since this is the best way to create a rewarding customer experience, allowing them to watch their revenue grows, too. Machine learning is only becoming more and more capable of AI-powered solutions becoming more affordable to a wider range of retailers as well. Indeed, these solutions let companies establish optimal prices as well as make their managers better since they can focus on more strategic jobs instead of routine ones. This is accomplished by utilizing objective historical and competitive information while taking into consideration seasonality, customer behavior, and various other factors such as business goals. Retailers won’t be able to reach as big of a market share as those who utilize this technology as it allows them to cater to the needs of their customers in actual time.