Machine learning is a scary word for some retailers. Maybe they fear super-powered robots bent on taking over the world, but more likely they are nervous to place so much trust in artificial intelligence or new technology. While change can be frightening, machine learning is the best way for businesses to gain powerful marketing insights and engage with customers.
What marketers need to understand is that machine learning doesn’t fundamentally change operations. It only makes it easier, faster and more accurate.
Here are a few things you’re already doing that can only get better with machine learning:
Tailored communication with customers: Retailers might be tempted to target consumers based solely on general demographic information such as age and gender, or high-level purchase history. However, relying on this oversimplified data doesn’t tell the whole story, and often targets customers with promotions for products they are not interested in. Machine learning can determine why each individual makes a purchase and what will motivate them to buy again, resulting in highly personalized content catered to individual customers.
For example, a shopper recently purchased perfume from a retailer selling personal care products. This retailer might take this information, combined with the customer’s age and gender, as a trigger to continue targeting her with promotions for more perfumes. There are two things that could go wrong with this scenario: first, the consumer may have purchased the product as a gift. Second, the customer likely does not need more perfume at this point in time. Machine learning, however, can rely on additional shopper behavior to better predict why this customer made the perfume purchase and what types of products she is likely to buy in the future.
Customized pricing and promotions: Retailers also determine pricing and promotions on a regular basis. But rather than making a gut decision on how to promote your products, your decisions should be driven by data that supports specific goals. With AI-based analytics, you can offer the exact price to the right customer at the ideal time to inspire purchases, without playing a guessing game. This isn’t scary – it’s smart.
Let’s look at the perfume example again. Perhaps you’ve determined (based on purchase history) that this customer did buy this product for herself. But, rather than promoting more perfume at a similar price point, machine learning can determine what price point is likely to get this customer to buy again. If she purchased the item on sale or with a discount code, it’s likely that the original price was out of her budget. AI-driven technology can determine which items are within her budget, or what discount would get her to buy another product within that price range.
Prepared product launching: When launching a new product category or a private label product, your team analyzes data to determine the best pricing and promotions. This can be a tedious process that, if done incorrectly, will miss the mark with consumers or leave money on the table. Machine learning, however, uses predictive models to identify the best possible options prior to launch. This solid foundation of data ensures you’re well-prepared.
Perhaps the personal care retailer is launching a new line of skin care. The retailer might be tempted to determine pricing based entirely on competitors that are also selling skin care. However, the price point at which a customer will buy varies from retailer to retailer. If the price is set too high or too low for the typical buyer, the retailer will miss out on potential sales. Machine learning will consider the retailer’s customer base, the products they’ve purchased in the past, the amount they’re most likely to spend combined with competition data to determine the best possible price point. Human analytics alone cannot dictate pricing for new products with this degree of accuracy.
Aside from all the internal benefits that machine learning provides, remember that consumers aren’t afraid of it either – in fact, they expect it. Every day, consumers watch AI-curated Netflix feeds, browse social media platforms powered by machine learning and ask Siri questions through voice recognition. Shoppers won’t tolerate marketing efforts that aren’t personalized to fit their needs, and this degree of personalization is only possible through machine learning platforms.
CFO’s may hesitate when they see the upfront costs for machine learning software, which can be significant. But by reducing time invested in man-powered analytics, machine learning technology quickly pays for itself. Quality platforms can reduce headcounts, replace outdated technology and eliminate disparate data systems. It’s best to shoulder the cost rather than putting off the benefits gained by machine learning.
Ultimately, you should be scared if you aren’t using machine learning technology, because your competitors already are. Without the right insights to power vital business decisions, you’ll be shooting in the dark.