How people perceive and process, information is deeply rooted in human psychology. Accumulating data and identifying actionable consumer behavioural patterns is the new normal in this era of modern marketing. This is where data science and data analytics comes into action.
People often interchange the term of data science and data analytics.
Data science is about developing a predictive model using machine learning, regression and other advanced statistical methods. Meanwhile, data analytics refers to the discipline that focuses on extracting useful information from raw data. It involves collecting, organizing and storing data with the help of advanced processes, tools and techniques.Becoming a data scientist continues to grow in popularity due to the huge demand for professionals in this space and our ever growing societal reliance on technology.
Data Scientists uses mathematical modelling and artificial intelligence to uncover new customer insights such as:
Who are the top customers of an organization?
What other products customer uses?
How customers' feels about a brand?
What other products can one sell to specific customers?
And many more.
Having crucial information at hand, marketers can streamline their marketing costs and incorporate personalized methods to promote their products/services.
Acquire new customers through personalization
Modern digital-age customers go through an incredibly complex journey before purchasing a service or product. This process comes with several touchpoints that usually involve an extended period, lasting from days to months and even years.
According to a report by Salesforce, more than 67 percent of market leaders believe that creating a customer journey is exceptionally crucial for the success of a marketing goal. Most marketers also admit that they struggle while aligning teams and strategies due to insufficient or fragmented data sources, limited budget and tracking customer behaviours.
This is where the analytical skills of a data scientist can help organizations in personalized marketing and align with customers' journey. Many marketers are now following journey-driven techniques like customer journey orchestration and journey analytics.
As customers take various journeys to achieve their goals, data science allows organizations to monitor their marketing campaign performance and measure their journey results. As time passes, marketing teams get more equipped with actionable behavioural data from customers. Marketers identify the usable data and discard the rest.
These valuable insights are later used to optimize personalization decisions using journey orchestration tools.
Increases Customer Retention through data modelling
Not all customers think and act the same way. According to research by Esteban Kolsky, only sixty-seven percent of customers report to companies for bad experiences. Besides, the likelihood of a customer complaining is 1 out of 26. Meanwhile, the rest will simply leave without saying.
The point is that marketers cannot rely on customers to approach them for dissatisfactory services or products. With the help of data science and proper analytics, brands or organizations can predict signs of trouble and mend it before it's too late.
The most prominent example of customer data modelling is Netflix. It has leveraged data science technology for tracking customers' content consumption. They have created a behavioural segment for each customer that sends a red flag for customers who fall under minimum usage value. They have introduced a wide range of initiatives to improve their recommendation algorithms and provide personalized content for customers via the app, email and push notifications. They also use analytics to determine what content to license and what genre to promote to a specific customer. This initiative has helped them save $1Billion per year.
Use GPU-Acceleration to measure geospatial data
Most big data analytics used today revolves around some form of geospatial data. For example, telecommunication companies use geospatial data to improve their infrastructure planning and network for maximizing coverage and subscribers. Similarly, road developers use data from autonomous vehicles to analyze driver infrastructure in their infrastructure planning. With such a massive amount of data to flow freely around the clock, industries rely on GPU-accelerate analytics for unrestricted data exploration without time or space limitations.
GPU-based analytics empowers users to handle massive loads of geographic and geometric data types. These are extremely important for running processes like micro-filtering, dynamic real-time geospatial visualizations and backend polygon rendering.
Use machine learning and predictive analysis for customer growth and expansion
Surprisingly, it's not hard for a company to encourage customers for cross-selling or repeat purchases. However, the problem lies with customers' unpredictable behaviour. Sometimes, customers don't know what they exactly want. In some cases, they are unaware of whether the company actually has the product of their desire.
The key here is to give the show the right thing to the right customer at the right time. However, how would a company do it?
The answer lies in the customer behaviour data.
For example, Amazon's recommendation engine almost accounts for sixty percent of their annual revenue.
The company uses customer behaviour data such as their purchase history, shopping cart items, liked and rated items, including items customers have viewed and purchased in the past.
This kind of enterprise business intelligence has become invaluable for a business model such as this. Not just for the vendor, but as well for the customer who expects a level of service akin to a full search engine.
Customer satisfaction is another critical factor to consider while cross-selling, upselling, and repeat selling. For instance, for customers who have recently left a negative review or expressed their non-satisfaction about a product, it may not be the right time to approach them with new products.
In such situations, marketers can identify low-satisfaction customers and move them to a new segment. Then they can target those customers with other retention-based initiatives to win back their trust.
Conclusion
From Netflix to Google, to Amazon, including several enterprises worldwide, are now using data science and consumer behaviour data as a tool for success. The emergence and constant technological improvement in data science have unveiled the key to unlock the behavioural pattern of customers.
About Author
Tech & Finance blogger and digital agency consultant - Dave has worked in digital for 11 years in client-side, agency-side, and freelance consultant capacities.
He now writes engaging content and creates innovative digital strategies for the finance and tech industries. He is the creator of Enviroute - A new travel app to check the Severn bridge status - that is currently seeking investment.
Dave spends more time than he cares to admit watching skateboarding videos and likes to express himself through the medium of internet memes!"
If you want to work with David on your content marketing, or improving your digital agency operations, you can reach out via his LinkedIn profile here.
