Data Science in Marketing
In today’s world, data has never been more essential or accessible when it comes to running a business as there are increasing sources of data ranging from web databases to social networks. This has highlighted the importance of data scientists who process and translate data for business owners to make informed marketing strategies. Present in all fields, there is not a single industry that will not benefit from data science for marketing. The combination of digital marketing with data science is sure to give your company an advantage as it enables us to learn, improve, and measure the success of our efforts.
What is Data Science?
Data science is an interdisciplinary field that studies big data using modern tools and offers a holistic and refined look into raw data. With data science, you can focus on insights that will help you make important business and marketing decisions for the future.
Data science requires a strong foundation in statistics and math. Once familiar with mathematical concepts, data scientists can move on to programming skills such as Python and R, two of the most popular programming languages used in data science. Data scientists will also need to know how to work with databases such as structured query language (SQL), understand data analysis methods, learn how to use data science tools, and work on data science projects.
Implementing Data Science in Marketing
With data science, you gain insight on various marketing aspects such as customer experience, behavior, and intent that can help optimize marketing efforts and achieve maximum revenue. Here are some ways your marketing can improve with the application of data science:
Optimizing The Marketing Budget
There is always a strict marketing budget and the aim of data science is to derive maximum return of investment (ROI) from the allocated funds. Data science allows you to analyze the spend and acquisition data to build a model where the budget is optimized for your key metrics. With data science, you can put together a comprehensive and automated digital marketing plan that covers all the different variables that might affect your business. Tracking campaign URLS and parameters are extremely important to be able to track back your spend to what’s driving leads and customers, otherwise known as data-driven marketing.
Since marketing campaigns tend to be broadly distributed, data science can help by analyzing data to help marketers understand the demographics of potential customers that is giving them the highest ROI, allowing them to achieve their goals and revenue targets. Simply said, marketing data science paints a more accurate picture of the type of individual the business should be targeting.
Combined with a detailed analysis of their social media interaction, it forms a pathway that highlights your missed opportunities such as on Instagram, Youtube, or any other channel that is popular among your target audience.
Having a productive marketing plan means to recognize the different needs of your customers as no two customers are the same. To improve your marketing strategies and conversion rates, you can segment customers based on different attributes such as purchase patterns, location, and how they navigate through your website. Through machine learning algorithms, you can determine which products appeal most to them, allowing you to optimize your channels and targeting strategies.
Lead Targeting & Lead Scoring
Data science in marketing can help you reach out to the right customers at the right time through extensive analysis that allows you to predict which products or offers are most attractive to which customers at different times.
This means the value of each lead can be scored depending on factors such as their word choice during interaction, characteristics of the customer segment, and behaviors of other customers in the past. The process of each predictive algorithm will save you guesswork and go on to inform future business and marketing decisions.
Content Strategy Creation
As always, having a good content marketing strategy where you produce relevant and valuable content is key to attracting customers. Without data, it can be difficult to know what content your customers enjoy. With data science, you can pull audience data that helps create the best content for every customer such as by basing it on keyword searches.
That said, testing is still required to understand your content quality. Serial testing is a great method that allows you to determine the details such as word choice while techniques such as time-series forecasting helps you to predict when it will be most effective across platforms, enabling you to put out optimized content to the right people at the right time.
Sentiment analysis is where marketers use data science to determine the target audience’s opinions, beliefs, and reaction to marketing campaigns. This can go a long way to shaping how customers view your business or brand.
This can be done by assigning specific values to words to give each social media post a score based on the comments section, Google reviews, email correspondence, and speech-to-text phone conversations. This helps you single out desired reactions and to determine if there are any breakdowns in customer service.
Building brand loyalty is crucial as it translates to a better customer lifetime value for your customer which means it could be potentially more profitable to your marketing budget compared to acquiring new customers. Appealing to a customer base that already has had a good experience is easier and more cost effective than marketing to a cold lead.
With data science and machine learning, you can determine the next best action or offer for each customer as they interact with your brand, how they might react in a specific circumstance, and identification of the issue if there is poor client retention.
Predictive Analytics & Recommendation Engines
With predictive analytics, you can target customers with a higher lifetime value, determine the effectiveness of digital advertising campaigns, view data in real time, and make better business decisions.
Based on successful predictive analytics, collaborative filtering recommendation engines can suggest products to customers based on purchasing habits while content based filtering recommendation engines are more aware of the product and description, therefore recommending only similar products.
Data Science vs Data Analytics
Both data science and data analytics are hot commodities that are complementary fields in the marketing realm as data-based decision making is quickly becoming the fold standard for successful marketing operations. For companies that are unfamiliar with both, understanding how to distinguish between data science and data analytics can help you recruit the right talent for your business. Generally speaking, data science is more strategic while marketing analytics is more tactical.
Data science is more responsible for understanding accumulated data and the actionable insights it can have for your company. It helps you to anticipate trends or predict the impact of a business decision. More classically trained in business management, data science marketing teams translate data into actionable advice.
Meanwhile, data analytics oversees the process of collecting, sorting, and analyzing data. It helps you to develop and implement the systems your business needs to handle and read data. Those in marketing analytics are excellent at identifying past trends and showing you results of previous campaigns. Currently, although there is a huge gap in marketing analytics that needs to be filled, there are more data analysts compared to data scientists.
At DDM Generation, our team of experts are experienced in both data science and data analytics. Named after “Data Driven Marketing”, we truly believe in the value of data as numbers simply do not lie. That being said, we are confident in our ability to provide the services you need for your business. Learn more about our services and how we can help your company use data to gain an advantage in your industry’s landscape.