Data Ninja’s Guide: Machine Learning Use Cases in the Retail Industry

Machine learning in retail can be as simple as the self-checkout line, the computer kiosks, or the virtual shopping assistants. It helps facilitate existing customers while pulling in new customers. 

What is Machine Learning?

Machine learning takes large pieces of data and breaks them apart into key insights. It allows producers to take a look into demand, price, and customer behavior. It effectively predicts outcomes for the optimization of profits, creates a better 

understanding of customers’ behaviors and allows you to create new product discoveries. This flourishes your ability to topple other competitors. For more information on Machine Learning visit Machine Learning – A Practical Guide.

ML Use Cases 

  • Pricing strategy, Dynamic pricing and pricing optimization

The way you price an item can pull customers in or out. By using machine learning, it can help improve your pricing strategy. When a price is determined by one person, it is often different from the value held by another. Give a plant, for example, a nature aficionado may value it to be 10 dollars while you price it at 2. This is why this type of pricing can lead to inefficient results.

By using ML, it takes in data that consistently prices an item over and over again. It takes into account historical trends, present trends, and current behaviors to create the optimal price: for example like the brand, the fabric, the condition, etc. It even compares multiple vendors’ pricing against the market price to deliver the best deals. The amount you price your goods determines the profit you will make. ML takes in all economic variables to make a pricing strategy. By looking at how long a customer looks at a product, what products they buy, and what they browse through, ML prices the product efficiently. It even has a randomness function. Say the price has no variability, ML will change the price accordingly to see the profits and inputs of the decision. By analyzing the sales you get at every price point, ML can become smarter and make better price decisions for your company.

  • Product Matching

Product matching is when ML links similar items together. This is especially beneficial when you are comparing prices with competitors or have similar products. Since most customers compare prices before they buy, it is especially important that businesses utilize ML to track their competitor’s prices. When you have a larger catalog of prices, it may be harder to organize all of them and match them. ML can look through the attributes of each item such as its picture, description, and price to match them. If we take Target, for example, the website has many different products of just black jackets. But a typical algorithm will often not match them together. Why is this? Because typical algorithms do not have the strength or data managing capabilities ML has. 

  • Target Market Optimization 

Marketing is a core value in any business. You need to market to get your product out there and known. ML can crack into consumers’ social media and e-commerce platforms to get key metrics: age, gender, interests, persona, state, city, and time on site. Once the ML takes in these metrics it analyzes how they work together and it creates a relationship between the two. With this connection, it will personalize the ad toward the consumer. For example, say you were on social media looking at photos of sushi, ML can see that and connect an ad to that specific interest. This can make sure the target market is reached and the business seeks maximum profits. 

  • Prediction for Inventory Management 

Retailers need to know how much inventory they need to keep, buy or sell. ML will take a deep dive into the current atmosphere: special holidays, seasons, social media, etc. From that it will provide you with ideas for how much inventory you will need. For example, say you sell chocolates and valentine’s day is approaching, well ML will use the event to predict the inventory for that period. Additionally, by looking at past data ML can show when the inventory may be out of stock again. This way a business can let their consumers know without having the problem of insufficient supply.

  • Fraud detection 

In terms of businesses, it is always important to be safe from danger. Just like we humans get insurance, our technology needs fraud detection. ML allows you to take a deeper dive into missing items, suspicious activity, missing patterns, and weird behavior. Since ML looks at these trends when there is a spike in an account or a pattern it can warn you of suspicious activity.

  • Video surveillance and analytics 

Having video surveillance is important to notice any stolen items, broken goods, or suspicious activity. When ML is used in accordance with surveillance, it can analyze the material and make note of suspicious patterns. This highly intricate machine can take into account minute differences that can make or break a business. As humans, we cannot see everything in a video. We may misinterpret something or completely look over it. This isn’t the case with ML. It relies on visual data to make sense of distinct eccentric patterns.

  • Recommendation engines 

ML allows businesses to give a more personalized outlook on their products. It analyzes the type of customer it is dealing with and connects he/she with a product they like. By looking into personal data, patterns, purchases, and browsing history, ML connects the consumer with products they would like. This allows customers to get more recommendations for their interests which can help businesses raise profits.

With all that being said, Data Ninjas can help customers who are looking to implement Artificial Intelligence and Machine Learning solutions in their company. There are many benefits businesses can achieve implementing Machine learning solutions, from pricing strategy to product matching. Data Ninjas can help work through those processes and deliver amazing results. To contact Data Ninjas for your business needs visit https://yourdataninjas.com/contact/

 

Taking a Dive into Responsible Artificial Intelligence

Artificial Intelligence closed gaps in technological mysteries, medical innovations, and efficient production. But it has led to the question of how bias comes into play. Many companies have worked towards creating responsible AI practices: to create a better and safer technical environment. 

 When companies develop AI technology there can often be hidden quirks such as bias. Depending on the technology, much of it can be hidden from us, the consumers. This means there needs to be steps to create transparency and responsibility to create a better future for technology. With our ever-growing world, it is highly important to get the reassurance that our data is safe and secure.

What is Responsible Artificial Intelligence? 

It is a governance framework that points out a company’s plan on how they address the contentions and challenges associated with AI. It is there to make sure it is explainable, understandable, and helpful. This way having proper responsible AI allows the consumer to know the company has anti-discriminatory practices. 

 

What are the principles of Responsible Artificial Intelligence?

Having this governance is highly important to make sure there is no bias present. Often when AI is programmed there can be bias introduced. This means that the programming developed from AI can also be biased. As such, this is a destructive process that can impact consumers and beneficiaries. That is why responsible AI has 4 principles: fairness, transparency and explainability, privacy and security, and accountability. 

  1. All AI should be fair and equal to all people, races, gender, and sexual orientations. By creating a fair platform, it allows us to wipe away discrimination. 
  2. By being transparent and explainable, it should be easy to understand for consumers and other producers. 
  3. By having good privacy and security, data can be easily secured and unable to be hacked.
  4. Lastly, by being accountable, creators of AI systems can be responsible for their work. 

Now to implementation, how can a company design and implement Responsible AI technology? 

First, develop a strong foot in governance and ethical standards. Once you get a good structure your AI can flourish into a fair and transparent area. Next, Design the technology with the principles listed above. Creating this in the earliest stage allows for a highly open and forthright model. Then, make sure you monitor your performance through the use of metrics. Lastly, you should reskill your AI. You want to see how it affects different types of people and develop plans on how to solve that.  

Through that quick overview of responsible AI, it should be clear that this governance is highly important for the developing AI/ML world. As society is always on their phones, on social media, or googling about anything, it is crucial to put in place a responsible AI system. 

CCPA (California Consumer Privacy Act) Explained

CCPA (California Consumer Privacy Act) Explained

What is CCPA?

The California Consumer Privacy Act is a consumer privacy legislation which passed into California law on June 28th of 2018. The bill, also known as “AB 375,” has been described by some as “GDPR of the US.” This act is one of the strongest privacy legislation enacted in any state now, giving more power to consumers in regard to their private data.

It’s just a matter of time before other states will follow suit in the coming years, companies across the U.S. that take proactive steps today to better protect consumer data will be best equipped to ride the waves of change.

Is your business impacted by CCPA?

These are the three key articles in the law which explains if a business is impacted by CCPA:

  • For-profit entities which do business in California and collect personal information of consumers.
  • Has annual gross revenues in excess of twenty-five million dollars ($25,000,000)
  • Derives 50 percent or more of its annual revenues from selling consumers’ personal information.

What is the scope of ‘Personal Information’?

An important term loosely defined in the bill is “personal information.” According the AB 375, “The bill…would define ‘personal information’ with reference to a broad list of characteristics and behaviors, personal and commercial, as well as inferences drawn from this information.”

Dozens and perhaps hundreds of specific data items are mentioned in the legislation, including:

  • Biometric data
  • Household purchase data
  • Family information (e.g., how many children)
  • Geolocation
  • Financial information
  • Sleep habits

What are the rights of a California Customer?

  • General Disclosure: If a business (as defined by the bill) collects any type of personal information, this should be disclosed in a clear privacy policy available on the website of the business.
  • Information Requests: Should a consumer desire to know what data is being collected, the company is required to provide such information — specifically about the individual. Some of the requests that can be made include:
    • The categories of personal information collected
    • Specific data collected about the individual
    • Methods used to collect the data
    • A business’ purpose for collecting the information
    • Third parties to which personal information may be shared
  • Deletion: If the consumer desires, personal information (with exceptions) will be deleted by the business.
  • Opt Out – The customer has the right to have the business stop disclosing/sharing/selling their personal information to any third party.
  • Same Service: Regardless of a consumer’s request and preferences about how their personal information is handled, businesses are required to provide “equal service and pricing…even if they [consumers] exercise their privacy rights under the Act.”

How to comply with CCPA?

There are many steps a business must perform to comply with all facets of the law.

  • Organized Data Collection: Business first need to know where all their customer information resides and should be able to categorize and classify this information based on personal and sensitive data attributes
  • Clear, Transparent Policies: Consumers can request a report on the types of data collected, data sources, collection methods, and uses for their data. While the data itself needs to be stored in a well-constructed database, many consumer questions can be quickly answered in comprehensive privacy and data collection policies.
  • Knowledge of Specific Provisions: There are clearly outlined requirements within the California Data Privacy Protection Act including things such as:
    • “Provide a clear and conspicuous link on the business’ Internet homepage, titled ‘Do Not Sell My Personal Information,’ to an Internet Web page…”
    • Ensure any individuals who handle consumers’ private data know and understand all pertinent regulations.
  • Ability to honor customer requests: There are many approaches to handle this. The most rudimentary is providing an email address for the customer. This is a very manual process and has the most chance of oversight and failure. A web or application-based form to gather and store this information in a database is the most effective process.
  • Orchestrated workflows – the companies most prepared, have a process to automatically find customer information and deliver it to customers. More importantly be able to delete customer information based on their requests in a timely and effective manner. This is usually the hardest ask for a customer but the most effective way to honor the mandate.

Conclusion

CCPA is the first of probably many steps that companies have to be prepared for with respect to consumer data privacy. It is well worth investing in building processes and automation around finding and categorizing customer data.

Having half-baked solutions or manual solutions will create a lot of churn and manual labor at best and at worst will cause omissions and errors which could cost the company millions of dollars in fines.

It is advisable to work with solution providers who have solved this problem before and have a frameworks and solutions that can be easily reproduced.