Start-ups focused on data science, machine learning, and deep learning have blown up in cities like Seattle, San Francisco, New York, and just about everywhere else. They are hitting every field, finance, health, education, customer service, and beyond. Yet, some companies are still working out the kinks. Some are still trying to find the out how to turn their data and machine learning talent into fiscal gain.
Is machine learning just another fad? Isn’t business intelligence enough? Is there and need to learn languages and tools like R, Tensorflow, and Hadoop?
The truth is, not every problem can be solved with a deep learning algorithm, or automated chat bot. So how do you know if you are facing a problem where a data science solution is necessary?
Whether you are a service based industry or widgets factory, your company will naturally produce data as a byproduct of operations. You may even take the time to track this data in a very organized set of excel sheets or databases. That data is worth looking back at! Here are some projects and techniques that could lead to reduced costs and increased revenue that require basic data science and predictive analytic techniques that don’t require huge changes and investment in new hardware and expensive talent.
Predictive Analytics(A.K.A. Forecasting)
Some basic things that can be done for small companies is setting up a decent excel sheet that predicts future demand and pricing optimization. This isn’t data science per se. Forecasting has been done for business since the beginning of business. Some may call it ‘Predictive Analytics’, to give it a fancier sounding term, but it is just basic forecasting. If you aren’t doing this already, it would be a valuable place to start. Utilizing your day to day data, you can create a basic application that helps spot trends. One such example is what days are the best days to be open for business. One real life example involves a small restaurant that actually found they could save money by closing down on specific days of the year, because they had actually been losing money consistently on the specified days. So, instead, when they discovered this using forecasting they began to give these days off to their staff. This allowed for both the owners to increase their profits, and their staff was generally happier(they would otherwise put in 45+ hour weeks).
If your skill lies outside of this subject matter, it would be best to hire a team specialized in predictive analytics/forecasting. When set-up correctly, the application should be easy enough to maintain with minimal technical skills.
Multi Armed Bandit
For those of you familiar with A/B testing, this concept of the Multi-Arm bandit may sound similar. However, there are distinct differences. The Multi-armed bandit refers to a theoretical problem in which a gambler walks into a casino and has to decide the best method of approaching multiple slot machines (e.g. One armed bandits) in order to get the best outcome (based on the winning probability of each machine). This same idea has been used in website testing, ad-placement optimizing , story recommendations, etc. Basically, any time the computer can test and see which type of content an end-user will most likely click on. This requires you track your end-users actions and set up either a dynamic or time delayed algorithm that figures out what is the most effective piece of content to show an end-user to get them to interact. From here, you could even go as far as testing out some more complex neural networks once there is enough data gathered to see what features in the content itself, make it more attractive.
If you do A/B testing, there are some caveats if you plan to switch to using the Multi-armed Bandit method. Here is a great read about some of the drawbacks from VWO.com. It is good to know the pros and cons!
More complex statistics can be pulled by melding multiple data sources. Not just taking your everyday operations, but melding in customer data from multiple areas like social media, credit card data, etc. Could lead to a treasure trove of new revenue streams. It could help your company find new verticals to expand in, or which new neighborhood you should place your next store.
If you have an online store, you have even more possible data sources. The use cases expand as you increase the types of data. This will require some pruning and feature selection, depending on the style of machine learning/data science you are looking into applying. If you have enough data, with the right formats, you might be able to get into deep learning. However, realistically, if your company is small to medium sized, solid data science techniques will probably work best.
Let’s say you are a charity, and you have a backlog of donors that consistently donate every year. What if you could find more people like them? Some companies try to call thousands of people to find the few hundred that will donate. However, if you have their emails, or better yet, the social media tags. You can utilize social media services that help find these two hundred people more effectively. Companies like Architech Social and Facebook both provide services that help you grow your business by finding ‘look alikes’. Basically, they help you advertise to plausible future customers, that act like your current customers.
How you may ask? they are using a combination of clustering, classification and neural networks. Clustering and classification both utilize complex statistical techniques to help figure out the probabilities that one customer is similar to another. This could be based off of end-user patterns, product preferences, social factors, purchase history, and even comments, and posts on social media. At the end of the day, this requires a combination of most of the techniques mentioned above. This, of course, is only one example of using complex statistical analysis to find value in data.
These services don’t come cheap. Getting a solution in place that is a one time deal can help reduce the monthly bill Facebook and other digital advertisers charge.
These are just a few examples where data science,machine learning and analytics could be useful. If you are not sure about whether or not you have a project worth pursuing, feel free to give us a ping. We would be happy to help you figure out if a project is even worth spending time on.
We are a team of data scientists and network engineers who want to help your functional teams reach their full potential!