What are the Advantages of Business Automation Solutions?
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Keep reading to find out how to use data science for business process automation, the benefits of automating business processes, and where to start.
AI and Data Science in Business
Data science, data analytics, and data engineering. These terms have slightly different meanings but are essentially about turning data into actionable insights. Artificial intelligence (AI) refers to computer systems that perform tasks that normally need human intelligence.
A skilled team of engineers can use these to improve your business processes, efficiency, and strategy.
I‘m not talking about an out-of-the-box product or SaaS solution that use ’data science‘‘ or ’AI‘ to deliver a service. These target a broad audience, and can‘t hope to deliver the benefits of custom software designed for your business.
Every time your customers and your team interact, they are generating huge amounts of data. Depending on your business, this will include things like
- Sales figures
- Conversion rates
- Customer behavior stats
- Website analytics
- Social media activity (shares, likes, page views)
- Customer usage and spending patterns
- Message response times
- Market research
- Survey results
Manually keeping track of all these is difficult. Analyzing how each data set interacts to extract actionable information? Impossible.
Data analytics software can turn your business’s existing data into knowledge that will drive more informed, smarter, and more profitable decisions. Things like improving products, slashing your marketing budget and protecting your business against threats.
AI and Data Engineering in the Mainstream
Every technology starts at the cutting edge and then moves to the mainstream. This has already happened for AI and data analytics, driven by intense competition and the huge complexity of a connected world. Large tech companies like Google, Amazon, IBM and Microsoft have release APIs to allow businesses to use their huge machine learning and AI resources. Smaller companies can now benefit too.
A Gartner study found that 75% of businesses had already invested in big data, or were planning to within 2 years. And that study was done over a year ago. By investing in data science technology, you will be in the top 75%, but we all know this is not enough to thrive. Not even close. To get ahead of the game, when others invest 2x in trends, you should invest 10x more.
Over the last couple of years, marketing costs in many industries have soared and users expect 24/7, fast, accurate, personal customer service.
How can businesses afford this?
Many have unlocked huge potential with data science and Artificial Intelligence, and you should too.
Here I‘ve set out 7 reasons you should build a business automation software solution based on data science to take it to the next level, with some business process automation examples.
1. Cut marketing costs
With more information on how people shop, you can find out
- Which people are more likely to buy from you
- What they want to buy
- The optimum way to market to them
Example: Using the Amazon machine learning API, you could use customer browsing data to determine the moments that customers are most likely to buy and adjust your campaign. Or as Amazon gives a great example:
- “predict whether or not a customer will purchase a particular product based on past behavior, and use this prediction to send a personalized promotional email to that customer”
With this smart targeting of customers, you can generate the same number of leads and conversions with a fraction of the ad spend.
2. Increase Sales
Once you‘ve found your customers, you want to offer them exactly what they need at exactly the right time. Your existing data could improve your search results or help recommend great related products to make your customers feel like their shopping experience is tailored to them.
Example: You‘ve probably noticed that Amazon always recommends great products after you‘ve bought something (“customers also bought”). That‘s not guesswork, that‘s artificial intelligence analyzing Amazon‘s past customer data, and predicting what they will likely buy next.
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You can make use of similar systems in your business. In fact, Amazon has an API for it. You can read more about machine learning APIs from the likes of Amazon in our Artificial Intelligence in Technologies article.
3. Make Better Products
By analyzing how your customers use your app or web-based product, you can determine where problems are occurring or where to improve.
Example: The Google Cloud Prediction API can be used to create a cloud-based business automation software and incorporate some cool features into your applications. Using historical data, you can predict what a user will want to do next before they know themselves. This preempting will create a smoother user experience.
4. Help Your Users
When you have your own data analytics software you can interact with your current customers in intelligent and useful ways.
Example: AI chatbots aren‘t a novelty anymore, they are used in many different industries. You can outsource your customer service to hundreds of skilled AI chatbots.
Microsoft offers some amazing features in their cognitive services. It‘s possible to build chatbots on the Microsoft Bot Framework that can detect levels of emotion in customers responses, to determine where the problems are in your system. You can then adjust your customer service scripts to fix these issues.
5. Stay ahead of your competition
Most industries are now so interconnected even experts can‘t get their head around them. AI took over the stock market long ago, and now it‘s coming for the rest. Using data-driven AI software, you‘ll be able to track trends in your industry and with your customers.
Example: Predict increases in demand based on your core users usage stats and social media activity.
Using this info you could discover gaps in the market, or areas to improve in your own business.
6. Protect against fraud and threats
Online cyber attacks are now so common and sophisticated, they can only be managed with very powerful systems. Analyzing user behavior in real time gives your business the chance to act quickly.
Example: IBM‘s Watson project is “taking on the cyber criminals” with this type of approach. If you detect anomalies in your system, you can send them to Watson to compare against a large corpus of knowledge to identify threats.
If integrated into your system, Watson even offers methods to help you make decisions after your system has been attacked. Check out their video for more info.
7. It‘s not as difficult as you think
Custom data analytics software used to be reserved for companies with million dollar research budgets. Changes in the last few years have slashed costs. Google, Microsoft, IBM and Amazon now offer machine learning APIs, and smaller companies now have access to large computing resources in the cloud.
Who Will Gain the Advantage First?
Many businesses are already adopting data analytics, and these are the ones who will gain a competitive edge for all the reasons above. You need to decide if you will be one of them.
So where do you start?
Out-of-the-box software solutions that use ’data analytics‘ won‘t deliver the tight-fitting, personalized results we know are so crucial for maximizing the return on deep data analysis. Also, I‘m guessing you don‘t have a data scientist on your payroll that could create this kind of software, and with demand for analysts exploding, the price of getting one is insane.
The best way to unlock the potential hidden in your existing business data is finding a team that specializes in this type of custom software.
Frequently Asked Questions
Business process automation involves using computer technology to undertake tasks or processes so that human resources can be used elsewhere.
Not only does it help free up staff for more important tasks, but also helps companies to reduce the number of employees and therefore save money. Computer automation also helps to reduce errors, speed up processes, and improve customer satisfaction.
Computer automation is already being used to help undertake calculations that would otherwise demand teams of humans to do. A good example is the use of process automation to help detect fraudulent claims in the insurance industry. For more information, read this article.