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.

 

 

 

 

 

 

Human engagement through Artificial Intelligence

 

 

It is a question that every retailer ponders: How are customers engaged with my brand and how can I keep them engaged?

In the past, surveys were a common tool for gauging customer interest, but today's shoppers are inundated with requests for their opinions and most often those get lost in the shuffle.

Since humans are often too busy or forgetful to be reliable sources of feedback, some retailers are relying on machine learning — artificial intelligence (AI) — to learn if they are succeeding with customers.

Shoppers provide retailers with an abundance of data, simply by voting with their dollars at the POS: What styles or products are popular, what is not trending, what colors are in vogue, etc.

Size, gender and age are among many characteristics that are vital in creating personas, but also, shoppers' actions are quantified.

The backbone of AI is machine learning; Programs take all that customer purchase history data in POS like Retail Pro, and unify it with other data sources like browsing behaviors, to analyze exactly how customers view a brand, a store, or a style.

Interestingly, shoppers tend to follow some repeated patterns.

For example, a person often buys the same things, behaves in a predictable way and follows similar intuitions.

By learning one buyer’s pattern, another's might be revealed as well.

 

Human intuition

A recent survey by retail management firm BRP Consulting reported that 45 percent of retailers were planning to increase the use of AI to improve customer experience over the next three years.

For many years, retailers relied on sales associates' perceptions to determine how engaged customers were.

But humans can be biased, and that information is therefore inaccurate.

Today, AI can combine the intuitiveness of human employees with a machine’s ability to analyze massive amounts of data in seconds.

AI helps retailers understand consumers, improve worker productivity, boost efficiency as well as raise sales.

 

AI-generated recommendations

AI is increasingly being integrated into commerce and retail experiences.

A great example of using AI to recognize customers' preferences is Amazon's Recommendation Engine.

Shoppers see items similar to ones they have viewed, as well as others that are commonly purchased displayed prominently alongside the item currently being viewed.

Different recommendation “entry points” are integrated into Amazon's tool as well, which maximizes cart value.

Recommendation engines help retailers forge a relationship with their customers.

Users can click on the “Your Recommendation” link to display a page that contains categorized products that might be of interest, or they can refer to the section containing similar items with previously viewed products.

McKinsey has reported that up-sell technique is responsible for some 35 percent of Amazon's revenue.

Small wonder why Amazon has decided to make the underlying technology available through AWS with the "Personalize" tool.

Another instance of a successful personalized user experience is Netflix’s extensive "smart" list of movie and TV show suggestions.

Roughly 70 percent of all content watched by subscribers is a personalized recommendation, according to Netflix.

Shoppers enjoy personalization because it makes transactions feel customized, which leads to a feeling of "specialness."

And predictive technology lets retailers promote products in a targeted way, which provides customers with a curated experience.
 

Commit to understanding their preferences

By continuously revising the ad content across various marketing mediums, the likelihood of a purchase increases, although retailers must guard against ad fatigue that can easily evolve into ad blindness, ultimately leading to ad blocking.

Customer engagement has always been a top concern for retailers; the cost of cultivating new customers is steep.

Enticing customers to return by demonstrating a commitment to understanding their preferences pays off not just at the cash register, but also in the word-of-mouth advertising that is gained by being intuitive and responsive, which AI solutions are expert at providing.