As surprising as it may sound, marketing is an industry that can benefit from artificial intelligence. However, training an artificial intelligence with machine learning is a complex process that involves many important steps, including scaled data labeling.
Scaled Data Labeling Defined
So, what is data labeling? Data labeling is when the training data for an AI is the process of organizing and tagging data samples that will be used for AI training. This helps to regulate both the inputs and outputs as well as scalability.
Unsurprisingly, data labeling can be done using multiple approaches. It can be done in-house or by using an AI that’s already been trained. Alternately, you could use crowdsourcing through a third party which can be useful when preparing for large-scale deployment.
When it comes to using scaled data labeling for marketing, outsourcing is one of the most popular methods and can be more cost-effective than creating and training your own AI. With that in mind, let’s go over some of the ways that scaled data labeling can help you optimize current marketing campaigns and build better campaigns in the future.
Optimizing Prices
Determining the best price is a big part of marketing. This is because higher prices don’t necessarily mean you’ll generate more revenue, and lower prices don’t necessarily guarantee greater sales. Finding the perfect price for your products and services is important for making your business as profitable as possible.
The good news is that scaled data labeling can help you to optimize your prices. Generally speaking, this is done by using AI to analyze the pricing structures that competitors are using to see what works best. That said, this is a long-term strategy since the more data you gather, the more accurate conclusions your AI will draw from it. This is especially true because seasonal events and special offers can skew the data if your sample size is too small.
Determine Product Availability
A large part of market research is determining what to sell and when. Scaled data labeling can analyze information scarped from competitors to see what they’re up to and use that data to identify gaps in your own line of products and/or services. This, in turn, will allow you to better allocate your resources and cut down on “dead stock” in your inventory.
Once again, this is a long-term project since low amounts of data can be skewed by a variety of factors. That said, scaled data labeling can help an AI to make accurate predictions even when it has a small amount of data to work with.
Personalization Optimization
Personalization is one of those concepts in marketing that is a very fine line to walk. If overdone, it can feel creepy or intrusive. On the other hand, personalization can make prospective customers feel more valued if done right and can dramatically increase conversions by matching offers with the people most likely to purchase them.
Fortunately, scaled data labeling can help you to engage in just the right amount of personalization for the best results. This is typically done by identifying characteristics that your audience identifies with and working those characteristics into various steps of your marketing campaign, such as ad copy, social media posts, and your website. You can also use this data-driven personalization for research and development so that future products and services better align with the needs and wants of your demographic.
Analyzing Customer Reviews
Customer reviews have become an important part of the marketing process online. In fact, studies have shown that over 90% of consumers say that reviews play a major role in influencing their purchasing decisions. This means that getting good reviews is a must, but you can take things a step further with scaled data labeling.
By having your AI go through the reviews left for you and your competitors, it can compile data on the general sentiment of the customer base. Not only does this allow you to quickly respond to negative reviews, but you can also gain a better understanding of the benefits and features that customers like the most.
The Advantage of Data Labeling
Due to the complexity of marketing and the sheer volume of information available, getting the most out of your data is practically impossible without scaled data labeling. By using a properly trained AI to sort through your data, you can optimize existing marketing campaigns and plan future campaigns that will have a much greater chance of success.
