3 Benefits of AI for forecasting and operational efficiency
In retail, making the mark — or missing it — leads to the success — or failure — of the business.
Sales predictions, identifying new customers and developing relationships with trusted partners are all a part of that success, and retailers are gaining greater traction with technologies powered by artificial intelligence.
Here are three benefits of AI for forecasting and operational efficiency.
1: Reducing forecasting biases
In earlier times, company executives worked off of hunches that were based on their experience in the industry and with their companies.
One bad guess and the financial numbers for the quarter were sunk.
A second one and the company could be headed for ruin.
Today, artificial intelligence can be employed to augment the success of executives’ expert hunches, and warn against those ideas that are unlikely to work.
When forecasting is treated as a science rather than a guessing game, companies will receive better results.
In general, people are either optimistic or pessimistic, and their forecasting skills are biased as such.
By using AI, a data-driven rationale is used to come to any conclusion.
Not only does this mean more accurate forecasts, but it also provides the “why” behind the numbers.
In times when the predictions are off, it is simple to go back to the data you feed it from POS like Retail Pro and see what went wrong and adjust for the next quarter.
That type of correction is much easier to adjust than a “gut feeling.”
2: Increase inventory accuracy
Understanding what products your shoppers are purchasing, and at what frequency, allows you to more accurately predict inventory needs.
AI will help reduce unwanted inventory, and, according to consulting firm McKinsey, overall reductions of 20% to 50% are possible.
For example, take AppCard’s “Pinky,” an AI loyalty and personalized marketing system comprising artificial neural networks and other machine learning approaches that are orchestrated and optimized via reinforced learning.
The network’s architecture ensures that Pinky takes into account correlations between transaction data in Retail Pro and neighboring days, weekly periodicity, holidays, weather effects and seasonality.
In addition, Pinky learns quickly and therefore doesn’t require huge amounts of data to be a trained rockstar.
Right now, Pinky can reliably predict revenue and target customers that are at-risk, but soon it will be able to predict a customer’s next visit and optimize target customer lists based on a merchant’s estimated lift.
3: Speed up customer acquisition
In addition, to strengthen existing shopper-retailer bonds, AI can also speed up the process of acquiring new customers.
Take a business that depends on cold calls to increase its customer base.
That’s not a time-efficient way of increasing your customer base, and it’s also expensive.
While LinkedIn and other professional networks are helpful to target potential customers, it’s not much use on its own.
Instead, by using an AI-powered software tool in a coordinated effort with social networking you can find prospects more quickly than a human.
You can automatically send them introductory messages, sync calendars and send meeting invitations.
Cold calling will soon be a thing of the past, replaced by a more targeted, efficient method.
AI learning curve
What happens if AI is employed and doesn’t do as well as the former CEO’s hunches?
Tighter inventories, understanding what customers want and broadening the customer base all benefit a retailer’s bottom line, but the effort is wasted if the company can’t deliver on its promises.
Customers who can’t find product on the shelf are unlikely to return. Loyal customers who find that styles have changed will be disappointed.
It takes a good bit of time for an AI solution to learn the business.
It can only learn as fast as data and experiences are being fed to it.
And remember, it does not forget.
Data and coaching increasingly improve the output.
By learning from mistakes, understanding what knowledge gaps there might be and encouraging continuous improvement, a company can employ AI solutions to seize the opportunity to do better for itself, as well as for its customers.
Holistic data fed into an AI system can help retailers gain actionable insights into how they can improve their business and put shoppers first.
Book your NRF 2019 meeting now to start the conversation on how you can unify data in Retail Pro and start making the most of your most important resource.
