Simplify Decision-Making with Holistic AI Synthesized Data
In the world of business, we’re always making decisions. While some are small and won’t affect the overall trajectory of the company too much, others will directly impact where the business goes and how quickly it will get there. In order to help us with the more important decisions, we’ve seen the introduction of data in recent years. With more data points, we can make informed decisions that are backed by numbers. Even if something goes wrong, at least we can be confident in the decision we made and justify it strongly.
With generative insights, we can even get help with real-time decision-making. Suddenly, we’re looking to move faster than the competition and make decisions that have the potential to get the brand in front of consumers. As you’ve probably seen, artificial intelligence and machine learning have helped tremendously with this.
What’s Synthesized Data?
Whenever we think of the word ‘data’, we imagine Excel sheets filled with percentages, pie charts, diagrams, and old-school business reports. These days, the dashboards of our favorite marketing tools allow us to see lots of different statistics and data points. With synthetic data, the only difference is where this data originates. Rather than a report on what’s already gone, synthesized data has been created artificially.
Created with the help of algorithms, it can be used for a whole manner of tasks. You might ask why we need synthetic data, but there are some very important uses not only in business but in the healthcare sector and many other locations. For example, we can meet conditions that aren’t available in real data. If privacy restrictions prevent us from accessing important data, we can generate synthetic data to help with decision-making.
Despite the growth of synthetic data in recent times, it actually goes right back to the 1990s; it just so happens that we have more computer power now and can better utilize what this data offers. Here are two uses of synthetic data that actually benefit us all every day:
- Healthcare — Firstly, synthetic data has been critical for medical professionals because it allows record data to be made public without directly interrupting the patient confidentiality that is so important in this industry.
- Finance — In the finance industry, one of the biggest problems they’ve encountered over the years has been fraudulent activity. Although they want to test new fraud detection methods, they can’t play games with real data which is where synthetic data has found a home.
When privacy acts are preventing access, or the information simply isn’t available, synthetic data allows us to make decisions and innovate when it wouldn’t normally be possible.
Synthesized Data in Business
Bringing it back to business, marketers will know the idea of ‘next best offer’. After buying a set of pencils, we might send a customer a discount voucher for a set of pens or erasers. Ultimately, we want to offer consumers the next best offer that will appeal to them based on their history. For a modern example, we only need to look at Netflix which suggests a show we might like after finishing another.
As marketers, it’s our job to send the right offers at the right time based on the information we know. For new mothers, you will have almost certainly seen this after signing up for a baby registry. All of a sudden, your inbox has emails with offers right through the birthing process. You’ll get a coupon before the baby is born, just after for newborn clothing, a few weeks later for larger clothing, accessories, and other related products.
With the help of synthesized data and algorithms, we’re starting to learn the effectiveness of personalized offers. In years gone by, we would create a special offer, send an email to a huge group of people, and hope that it caught the eye of a certain percentage. Now, we can target specific people with personalized offers based on either what they will or might enjoy. With automated platforms and machine learning, all this hard work is also being taken out of our hands. With very little input from ourselves, these tools send targeted offers to people who need exactly what we’re offering, and this means that we’re wasting less of our budget each year.
How will people respond to an offer? This is an important question, and it’s one that we can answer with machine learning and synthesized data. At first, it started with learning how small segments would respond to an offer. Then, we were able to refine it to a micro-segment. Now, we can see how individuals will respond (impressive, right?). By using historical data and synthetic data, we can assess how one person reacted and therefore estimate how a similar person from our target market will respond. As long as the two have similar characteristics, we get an accurate prediction.
With the right learning model, this goes into the rule-based decision management technology. For example, we want to exclude those who aren’t eligible for a specific product. From here, customers get the next best offer across all channels. To keep the process clean and avoid mistakes, we maintain a contact history; this shows how a customer has responded to a similar offer and whether it would be worth sending another.
As more time goes on, we’re able to track responses and shape all future offers based on this information. As you’ve probably guessed, most of this happens automatically which means we aren’t wasting time trying to get it right.
Benefits of Using Synthetic Data
When using synthetic data, we can create generative insights, and this leads to a number of benefits:
1. Real-Time Decision-Making — In order to be competitive, we need to react to changes in consumer demand as soon as possible. When marketing, we also need to offer the right information and offer it at the right times. With synthetic data, real-time decision-making is possible, and it helps a business to become more efficient. Rather than sending offers to people who won’t respond, we can accurately predict responses and get a better return on all marketing efforts.
2. Fewer Restrictions — Next, privacy and other regulations can restrict real data. Rather than settling for something less accurate, we can replicate important data and make those strong decisions without violating privacy rules and running into all kinds of legal issues. We don’t need to expose real data; we can replicate it effectively and get similar results.
3. Step into New Territory — When it’s not privacy rules causing problems, it’s the fact that the real data simply isn’t available or doesn’t exist. Previously, we would have to take large risks and potentially fail. Now, we can take measured steps with synthetic data. Again, this simplifies decision-making and puts us in a stronger position.
4. Prevent Potential Issues — When using real data, there’s a risk of statistical problems and this includes skip patterns, item nonresponse, and more. With synthetic data, this risk is eliminated, and we can be more confident in the data.
There’s never been a better time to start taking advantage of AI and machine learning for your business decisions.
Bonus! Marketing Tools:
2. Bilbi AI: Daily Marketing Campaign Insights
