Data Science and Digital Marketing

The 21st century phenomenally paved diverse avenues for Digital Marketing and Data Science in terms of utility and profitability. This upsurge has led to the evolution of a digital ecosystem that facilitates the users to stay connected. It also influences consumer habits and behaviour. Data science has become an absolutely necessary component for several industries right now. As an outcome of its intensifying popularity over the years, businesses are executing data science techniques now more than ever to develop and boost customer satisfaction. 

What is Data Science?

Data Science uses a colossal volume of data by implementing sophisticated tools and techniques to identify unseen trends, harvest meaningful information and make significant business decisions. It implements composite machine learning algorithms to create predictive frameworks that facilitate business decision making. Data science relies upon software that conducts a thorough data search to make these predictions. Scientific tests and experiments authenticate these predictions. The data that helps in the analysis is derived from diverse sources and depicted via different formats for ease of understanding (IBM).

To make the data ready to be processed requires data scrubbing, collecting and exploiting. Data analysis involves developing and implementing algorithms, analytics and models based on Artificial Intelligence. 

Implications of Data Science for digital marketing

Digital marketing helps businesses promote and sell their products online through marketing techniques that motivate their customers and prospects to buy their offerings. There are different offshoots of digital marketing- Search Engine Marketing, Search Engine Optimization and Social Media Marketing.

Data Science significantly contributes to improving the efficiency of Digital Marketing efforts for businesses as it helps tackle challenges like management of data collected from users, the classification and origin of data from datasets and employment of the latest data analysis techniques for knowledge development (Elsevier).  

How does Data Science work?

Data Science Lifecycle

1. Data Accumulation-  Collecting raw data in both structured and unstructured format from various meaningful sources, either manually or through web scrapping from systems to devices in real-time. 
2. Data Preparation and Maintenance- Placing raw data in an organized and consistent format to implement analytics, machine learning and deep learning models. It encompasses data scrubbing, deduplication and reformatting to enable the extraction, transformation and loading along with other data integration technologies to assimilate the data into a data warehouse, data lake or other holistic data stores that facilitate analysis. 
3. Data Processing- Evaluating biases, arrays, patterns and dispersion of values amidst the data to ascertain its feasibility for usage by implementing analytical techniques like predictive analysis, machine learning or several deep learning algorithms. 
4. Data Analysis- Harvesting insights from the prepared data by applying various analytical techniques and algorithms. 
5. Transmission-  Presenting the insights drawn from the analysis and their anticipated impact on the business using visualization tools and languages in the form of reports and charts. 

How big brands harness Data Science to ace their digital marketing game

Netflix

Netflix is thriving at targeted advertising with the help of data science. A Subscription base of a whopping 100 million users provides Netflix with a massive volume of data essential to accomplish the industry status that Netflix boasts. Based on the search history and the content that the user has engaged with, Netflix sends suggestions to the users for the next movie or series they should watch (TechTarget). 

Walmart

Another example is how retailers use predictive modelling to craft strategies. Retail giants like Amazon and Walmart rely on data science, tools and techniques to identify purchasing consumer patterns. For instance, through pattern analytics, Walmart identified that purchases motivated by announcements like a hurricane or a tropical storm generally included the item strawberry pop-tarts among the purchased items. Deriving conclusions from such correlation aids in facets like inventory management and devising effective marketing strategies (TechTarget). 

Zomato

Zomato's personalization game is bang on, thanks to data science. The availability of the sizeable data points factors such as customer affinity towards particular cuisines, locations, nature of establishment and pricing determine restaurant recommendations. Without the data points, users would have no choice but to choose among the most popular or latest opinions at a particular location and time. Learning through experience, Zomato observed that personalizing recommendations enhanced CTR and boosted total order conversions by 15% in contrast to recommending restaurants based on popular opinions. Zomato conducts Natural Language Processing on reviews to determine the most sought-after dishes that an eatery offers as well as the sentiment behind each review. This review is merged with the broader sentiment towards factors like taste, ambience and service quality. It also enables the team to depict the user-generated precisely as review highlights. 

Some key benefits of implementing Data Science in digital marketing

1. Robust campaign planning- Leveraging data science to formulate your campaign plan makes campaign planning lucid and swift. The information collected from the site, ongoing operations and social media platforms give us a clear insight into why, when and how customers engage with your brand. 

2. Streamlining data to customers in real-time- Data science helps businesses amass large volumes of data and track their progress. It familiarized businesses with factors like customer action, buying patterns, the latest trends and the cruciality of the right timing, preparing them to target loyal customers and engaging them with valuable content. 

3. Alignment of marketing strategy with consumer segments- Data science allows businesses to develop a lifetime estimation model that enables customer segmentation based on their presence and activity with the business. It helps businesses segregate high-value clients and offer them privileges like discounts and referral codes. It also helps develop feasible retention policies to attract customers that are likely to leave. 

4. Understanding client sentiment- By paying heed to the authentic insights provided by data science, marketers can analyze and understand customer sentiments like their beliefs, preferences, ideologies, opinions and more. Data science also gives marketers a sneak peek into how well the prospects respond to the campaigns. 

5. Attain leads- All leads do not eventually convert into purchasing customers. A mechanism that helps marketers segregate customers based on their needs can help businesses enhance sales traffic, the performance of each department and ultimately the ROI. Through complex algorithms, data science can help marketers rate their leads. It also helps estimate lead conversion and segment the lead accounts accordingly. 

Digital Marketers cannot afford to overlook the role of data science in order to stay ahead of the competition. Every day, multi-quintillion bytes of data originate globally. Data science gives this data a logical sequence and converts it into meaningful insights. Although almost all industries can benefit from data science, digital marketing holds a brighter potential because of a boom for personalized content that demands delivering well-targeted campaigns. To conclude, data science is imperative for successful digital marketing. As digital marketing continues to advance, the importance of data science will continue to surge.