As a data scientist, there’s rarely a day when I don’t get asked, “What exactly do you do?”
Everyone is looking for information to help them make better-informed decisions about their business strategy. As data scientists, we develop solutions that can automate certain processes to help your staff to focus on more complex problems.
In this post, we’ll be focusing on a few sample use cases for artificial intelligence in business. Before we dive in, it’s important to define artificial intelligence.
What is Artificial Intelligence?
Artificial intelligence (AI) is a combination of computer science, math, and domain knowledge. It is a catch-all term that describes any system that mimics human decision-making, whether it’s a simple if-this-then-that logic or as complex as a recurring neural network.
Machine learning (ML) and deep learning (DL) are the two subfields within the realm of AI. All machine learning and deep learning methods are AI, but not all AI methods can fall into machine learning or deep learning.
If you’re interested in exploring the differences between all of the subfields of AI, definitely check out our blog on the subject.
Why is Artificial Intelligence Useful?
AI seeks to mimic human decisions to automate a particular task. As organizations continue to evolve in their data and technology journeys, AI can provide a level of automation that allows for analysts to focus on the important issues, rather than everything that comes their way.
AI solutions have the opportunity to provide cost savings and additional revenue generation. The best candidates for AI automation are tasks that are repetitive, math-intensive, and have ample data.
Let’s dive into a few examples.
Real Examples of AI in Marketing
Offer Segmentation
Every customer is unique and your marketing should be targeted to them as an individual. With this in mind, it can seem daunting to determine which customers should get what offers. AI can help you determine which offers resonate with your customers.
Marketers are discovering that if they segment their customers by behavior, they can improve the likelihood of that customer completing a purchase if a specific offer is sent to them.
For example, a retailer is considering two of their customer segments: weekly purchasers vs. quarterly purchasers. The retailer knows that individuals in the weekly purchasers segment are likely to purchase a particular product every week. In this segment, they also find that when they send out a coupon for 20 percent off, that group of purchasers tends to purchase double the normal amount.
The quarterly purchasers, however, only purchase 30 percent more when given the 20 percent discount. Having this insight into how certain customer segments respond to discount offers allows the marketers to better optimize their promotional plans around getting the best return. After all, no one wants a coupon for something they’re not likely to buy in the first place.
Website Customization to Increase Purchasing
eCommerce retailers understand that certain customer segments respond differently to features or promotions on a website. To increase the likelihood of a purchase, eCommerce marketers can leverage A/B testing on customer segments to measure how features and promotions impact the likelihood of purchasing.
A/B testing is a process that shows different versions of a website or promotion to customers to determine patterns in the preferred buying experience. The differences between two websites can be as simple as changing the color of a single button or as complex as showing an entirely different landing page.
For example, an eCommerce website might find that the location of a customer influences what they respond best to. An IP from Texas may see a landing page with the title “Lonestar Deals” whereas an IP from Ohio may see the landing page with the title “Buckeye Deals.” This may seem like a subtle difference, but the eCommerce marketers found that playing into a customer’s location increased their likelihood of purchasing a product.
A/B testing is the key to making sure that customizations like this are actually performing well against a control group.
Marketing Campaign Performance Evaluation
Determining the ROI of an advertising campaign can be difficult. How much of the sales can actually be attributed to a certain campaign?
Campaigns are costly to run and it can be difficult to determine if a campaign is failing. Data scientists have developed algorithms on Pay Per Click (PPC) platforms to predict whether or not the campaign will have a positive ROI. This prediction can be used to determine when your ad should be shut off.
It may also provide a recommendation on how to better optimize the campaign. For example, you may be provided a 10 percent discount, but a 20 percent discount might give you the optimal results.
Companies with a heavy eCommerce presence may also be able to determine which types of digital ads or advertising sequences ultimately get a customer to purchase a product. For example, a certain customer segment may see an advertisement for a product on social media.
From there, they click the link to the website but do not purchase the product. Three days later, they see a search advertisement for the product and finally purchase it.
Customers who see the search ad 14 days later, however, do not purchase the product. Having this visibility can help you determine how to best target your customers to optimize your marketing efforts.
Real Examples of AI in Sales
Lead Scoring
Lead scoring is one of the most commonly used AI methods in sales. Lead scoring is a method where inbound leads are given a numeric value to indicate how qualified they are to buy your product, how interested they are in learning more, and how likely they are to follow through to the final sale. Lead scores are a useful metric for increasing ROI on sales efforts because your team can spend more time on the leads that are most likely to convert.
Lead scoring engines consider factors like a lead’s engagement with ads, number of meeting requests, number of times they opened an email, and the number of materials they have downloaded from your website.
After generating a lead score, you can use this information to find patterns in customer purchasing behavior. Building a propensity to convert model on top of your lead score can help your sales team determine what the next best action may be to convert that lead into a sale.
Cross-Selling Recommendations
Think about when you log into Netflix or browse on Amazon. On both platforms, you see recommendations about what to watch or purchase next based on your previous behaviors. Both platforms developed their own recommendation engines to show you other movies or products to potentially buy because it will increase your overall cart value or usage of their platform.
A similar approach can be done to determine what products will be best for cross-selling.
Cross-selling is easy when you find the right product fit for a specific customer. On the flip side, it can cause friction with customers if you are trying to cross-sell a product that they are completely uninterested in. By developing an internal recommendation engine, you can determine what types of products certain customer groups are purchasing. From there, you can find trends within those groups of products purchased and recommend them within that customer group.
If you are leveraging eCommerce, you can build a recommendation engine based on what products a particular customer viewed. For example, if customers who buy dresses from your site also buy a pair of sandals, you can recommend sandals for customers who are also viewing dresses.
Real Examples of AI in Supply Chain
Demand Planning
Have you ever ran to the store for one particular item and they were out of stock at the most inopportune time? The COVID-19 pandemic has made everyone hyper-aware of the critical role that supply chain plays in our daily lives. Whether you’re a hospital or a grocery store, having the right materials available at the right times is crucial to operating your business.
By leveraging AI, you can create a model that will forecast what demand will be for a particular product or location at a given time. This model will learn from historical sales and incorporate seasonality to reflect patterns throughout the year. You can also incorporate a variety of other external factors, like weather, traffic, and econometric data, to further improve your forecast.
Starting with a better demand forecast has implications throughout the entire supply chain. When inventory levels are below demand levels, you can trigger a production plan to fill the gap.
Understanding which locations will be requesting orders can help you better plan your transportation routes. Having a data-driven, AI solution to determine what you will sell will trickle throughout your supply chain, helping you save on costs while improving your execution.
Inventory Management
A great partner to demand planning models is inventory management models. While understanding what demand will be for a given location or product is a starting point, it doesn’t do much good if there is not enough inventory. An AI model in conjunction with RFIDs or barcodes can help you know where inventory levels stand at any point in the process.
For situations where the demand forecast exceeds, the AI model can send a recommendation for how much inventory needs to be produced or where to pull inventory from another location to fill the gap.
Augmenting your inventory management with AI can help you reduce out-of-stock situations and improve your customer execution, resulting in both cost savings and additional revenue generation.
Network Optimization
Customers are demanding their products in extremely tight timeframes and are consistently expecting high levels of service. Leveraging AI can help you determine where to place manufacturing sites and distribution centers to better distribute your network to optimize the number of orders that make it to your customers on time and in full.
Network optimization models consider factors such as historical orders, growth areas, and access to transportation to determine where to place infrastructure. These models take into consideration business constraints, like the number of locations, cost per location, and size of the employment pool to put parameters on where the location should be put. You can customize these models to match your business needs.
Leveraging a network optimization model can help you realize greater revenue based on satisfied customers as well as reduce costs associated with late orders and transportation.
Conclusion
Artificial Intelligence can be leveraged to work directly with your business strategy. All of these algorithms can be modified to work within any industry if the data is available.
If you’re interested in getting started with AI, definitely check out our free guide that helps businesses of all sizes and industries build a successful AI strategy.
