Why You Don't Need to Be a Data Scientist to Reap the Benefits of Big Data

Business users don’t have to be data scientists to benefit from big data. By using AI-powered customer analytics platforms that present data from a wide variety of channels in easily understood visuals they can gain actionable insights, says,  Derek Wang, co-founder & CEO, Stratifyd.

CUSTOMER DATA PLATFORM (CDP) BUYERS’ GUIDE 2019

Welcome to the 2019 edition of CDP Buyers’ Guide. As customer data platforms are becoming increasingly necessary for enterprise marketers, it is also becoming more complex to choose the best fit CDP platform amongst the pool of new and old vendors.

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Data scientists are sought after by companies around the world to extract meaning from and interpret data using tools and methods ranging from statistics and machine learning, to human reasoning. As data is never in a ready state for analysis, data scientists spend a lot of time collecting, cleaning, and munging data, essentially transforming and mapping data from one raw data format into another, with the intent of making it more appropriate and valuable for a variety of downstream purposes, such as analytics.

Of course, the proliferation of data from innumerable sources can be overwhelming. For example, companies collect customer data via surveys and by mining the various interactions they have with customers through online orders and chats, as well as inbound and outbound conversations with call centers. Public sources of data such as reviews on Amazon, Walmart, and other online shopping sites also offer a wealth of data, including feedback on competitive products.

Also Read:  6 Ingredients for a Successful Customer Analytics Implementation

What Happens When Data Scientists Are in Short Supply?

According to an August 2018 LinkedIn Workforce Report, there were more than 151,000 unfilled openings for data scientists. With qualified data scientists being in short supply a little more than a year ago, and even more so today, how can a marketing professional take advantage of all this buyer data and not be overwhelmed by the enormity of it all?

Thankfully, technology has advanced to the point where there are now tools and platforms that incorporate the same algorithms, models, and analysis solutions generated by data scientists. These tools use taxonomies and natural language processing, along with artificial intelligence (AI) techniques and machine learning, to do the data processing and analysis. They handle the “heavy lifting” of data analytics so that marketers and product managers can apply context and understanding of their markets and customers to interpret the data.

Finding a solution that a marketer can actually use to get meaningful insights means searching for one that uses AI to not only interpret data but also to train itself to “understand” what answers the user is seeking. The system needs to be able to learn by acquiring information and rules for using the information. It must then be given rules to reach conclusions and self-correction. Of course, the larger the data pool with which to train the system, the better the conclusions and analysis. Large in this instance refers to millions of records or more. Of course, this is not a realistic amount of information for the human brain to correlate.

One such way customer data can be interpreted is with a solution that uses natural language processing (NLP), a technique that analyzes text using linguistic and statistical algorithms to extract meaning. NLP relies on machine learning and artificial intelligence to understand human languages. Some customer analytics platforms allow for tuning of analyses by providing feedback to the initial unsupervised machine learning process. By altering taxonomies, stopwords, and sentiment lexicons during analysis it is possible to fine tune for more meaningful results.

However, to achieve the same kind of results as a data scientist, it’s not only about the technology; customer and public data must be compiled with as much information from as many sources as possible using an omni-channel approach. This includes capturing and analyzing unstructured as well as aural data to achieve deeper insights. For example, in addition to speech to text tools, biometrics can be a source of data. How fast or loud a customer speaks during a call, along with pauses or talk-over, can also provide information about the interaction.

Also Read: Why Marketers Need to Work with Partners Proficient in Tech & Marketing

How Can Business Users Interpret Customer Data Analysis?

Once the data is compiled, correlated, and analyzed, the customer analytics platform should be able to present findings in a format that makes it easy for marketing, product management, and other teams that are not trained as data scientists to easily interpret the information. Visualizations such as graphs, charts, and word clouds can point to trends, while color-coding distinctions can also be immediately delineated. The right customer analytic platform will provide the ability to “slice and dice” the data by cohort, geography, time interval, and more; and also enable users to drill down to specific data points such as comments provided by a specific customer or set of customers.

Ultimately, a customer analytics platform can only deliver value if it can deliver actionable insights that can be easily interpreted by business users such as marketers. A solution that uses powerful AI and NLP to interpret data from every available data source and present the findings in a clear, visual format is key to providing these actionable insights. The teams close to the customer can then bring their understanding of the context, as well as their creativity and intuition to the results, making it easier to deliver better service, build more advanced products, or find new approaches that put the customer at the heart of everything a company does.

In the case of a “burning smell” reported by a major auto manufacturer’s customers, a connection was made between the reported issue and the timing of a national college football game. When the manufacturer studied customer feedback to find out why their J.D. Power customer satisfaction scores had declined, the customer analytics platform they used enabled them to drill down deep enough into the data to find that all similar customer complaints related to the same car model manufactured at one particular factory on the weekend when a national college football championship game was being played – in which the local team was competing. Being able to pinpoint the specific cars needing attention prevented a major recall and saved millions of dollars. A business user – not a data scientist – had access to an AI-driven customer analytics platform that enabled them to extract the depth of data required to determine the cause of the problem, the location of the specific manufacturing facility, and the particular vehicle model it impacted. This is one of many examples of why you don’t need to be a data scientist to reap the benefits of big data.