In the e-commerce industry, the nature of business involves various trackable customer touchpoints such as submitting a product rating, making a purchase, viewing an item, or clicking an ad. The amount of information that we acquire from these practices is massive and it’s quite overwhelming to navigate. But, this data can be highly significant if we can extract valuable insights from it. The term “data science” comes into play here.
Data science involves the scientific methods, systems, algorithms, and processes to extract knowledge or insights from unstructured or structured data. In simple terms, a data scientist is a person who accomplishes data science. Data scientists excel in examining big data to recognize patterns or make predictions about what the future holds for your business.
Consider an example where customers are looking to buy a smartphone. They will usually check the Google and other related websites. They will also happen to share these reviews through Facebook updates or Tweets. These millions of Facebook likes, Tweets, Pinterest and Instagram photos can be organized properly in a way that helps the e-commerce businesses pull out productive and meaningful insights with the help of data science.
ECommerce businesses rely heavily on data and with proper systems in place, these businesses can have a positive impact on their ROI. In this article, the role of a data scientist in the e-commerce and how he/she improves the e-commerce sales have been presented.
How Data Scientists Enhance eCommerce Sales?
Data scientists identify the trends and patterns in the large chunk of eCommerce data by looking at the data sets. These trends and patterns can be interpreted for reaching particular conclusions till you observe the entire picture. Data scientists have innovative techniques for extracting valuable insights from big data. Let’s have an overview of these techniques.
Data Cleansing
Big data consists of a large amount of e-commerce data from various sources. So, it’s often in mismatched formats and is quite messy. Data must be organized and cleaned before trends can be recognized, at which point data scientists begin building models with the data they obtain.
Modeling
Putting in simple words, modeling is a process of forecasting outcomes with the help of data mining and probability. When it comes to this process, we are not talking about pure numbers rather the logical and formulaic nature of representation. Data scientists believe about how something functions and build mathematical representations depending on their beliefs.
Modeling’s most simplified version would be interpreting your belief in a set of conditional statements depending on patterns you identified in the data. Say, for example, you observe when you include a song title in the subject lines of your email, the open rate of your email spikes by 15 percent on the next day.
Based on this, you can create a conditional statement, “if the inclusion of song title, then increase by 15%” – then begin making predictions. Thus, you can predict that there will be a 15% rise in the open rates on the day after tomorrow if you include a song title in the subject line of your email tomorrow. But, variables change by definition, so you should monitor and alter your model when required.
Consider another example where a retailer sells electronic gadgets. Let’s think that the business is doing great due to the on-time deliveries and the quality of the product. As the competition grows and the global trend shifts, there is a requirement for the ecological products. This shift gradually transforms the perfect customers of the company to its competitors – the company will not be able to notice this if it examines the market manually.
The data scientists can identify these small shifts as they write algorithms to monitor the company’s bygone sales continuously by cross-referencing the external sources such as social media updates, news articles with sales. Discussing these trends will locate the correlations with the customers’ interest to buy the products. Thus, data science helps e-commerce businesses retain their customers first rather than simply attracting new customers.
Translating
Analysts look at current and past e-commerce data to understand the interpretation and movement of data; data scientists also do this to a certain extent. But, data scientists take this to the next level by capitalizing on those interpretations in order to forecast the future of an e-commerce business. From all kinds of data, the data scientists select the identifiable patterns and translate the knowledge into terms that are easily understandable.
How Is This Important to eCommerce Marketing?
If you fully understand the role of a data scientist as mentioned above, you can easily grasp what they do is highly significant to e-commerce marketing. The data is interpreted by marketers as well, beginning from open rates to the frequency of clicks. Marketers take up the role of an analyst by identifying trends, observing past data, and coming up with reports and the decisions are made based on these reports.
But, the marketers are observing only a little amount of information that is available when compared with the massive amount of data that gets generated via online commerce. A cyber-footprint is left by an online shopper from every interaction. Thus, data scientists take the e-commerce information overload and break it down into invaluable insights for marketers.
The competition among the e-commerce businesses is getting more fiercer and faster. The habits of customers change every day and the e-commerce businesses should win over that extra nudge in case of fulfilling the demands of a customer. Gut feelings, intuition, and common sense may definitely help but not sufficient for making predictions. The data science algorithms help e-commerce businesses understand customers, processes, services, and products effectively.
Conclusion
In e-commerce, data science helps to offer a greater understanding of the customers by integrating and capturing the information on the online behavior of the customers, how they communicate with various channels, what actually led to the purchase of a service or product, the events occurred in their lives, etc. Data science holds the promise of increasing the customer shopping behavior with an enhanced profitability and improved marketing mix.
Savaram Ravindra was born and raised in Hyderabad, popularly known as the ‘City of Pearls’. He is presently working as a Content Contributor atTekslate.com. His previous professional experience includes Programmer Analyst at Cognizant Technology Solutions. He holds a Masters degree in Nanotechnology from VIT University. He can be contacted at mail . Connect with him also on LinkedIn and Twitter.