How Big Data Improves Customer Relationship Management
In this information era, data is king. A significant volume of data is produced every minute from various sources. And so, the capacity to identify and examine vital information from these data sources is essential for businesses to gain a competitive edge and enhance their operations.
Similarly, companies use big data in customer relationship management (CRM). Big data CRM is the act of integrating big data into a company’s CRM processes. Its purpose is to enhance customer service, predict the client’s behavior, adjust products or service offers to generate more profits, and calculate the return on investment in different enterprises.
Moreover, most of the data linked to CRM is structured data associated with products bought, contact information, latest contacts, etc. The arrival of big data techniques has introduced more improvements to this system as companies can now also process, store, and analyze unstructured data that isn’t provided by customers and use it to gain additional insights. So, if you’re looking for tools to streamline your CRM processes, you should take the time to look for the best CRM software.
If you want to dig deeper into this topic, read on to learn more about how big data improves customer relationship management.
Better Customer Understanding
Big data can fruitfully facilitate and transform customer and company relations if used responsibly. By using big data to analyze your business efforts from the customer’s perspective, your company can reduce the marketing and sales strategies gaps. It also allows your company to understand your customers more, such as knowing the customers’ perception of your products or brand. This can lead to more personalized campaigns and communication that can lead to better engagement.
Likewise, by using CRM analytics, you can integrate all customer data points such as social media, the internet, email, and hotline to group customers based on their behaviors. This will help you know the most profitable customers whom you can offer special offers or provide preferential treatment to foster their lifetime patronage. All in all, the effective use of big data awareness can lead to a better customer experience and satisfaction.
The preferences of digitally empowered customers are continuously changing. With these dynamic needs in mind, the use of big data and predictive analytics is vital to enable companies to foresee how customers will respond in the future based on their segmented demographics and history of behavior. This will allow you to give your customers suitable recommendations that can lead to higher conversions.
Likewise, predictive analytics is beneficial for your company for other reasons. For instance, it can lower the effort, time, and costs of foreseeing business results. You can factor things like the environment, regulation changes, competitive intelligence, and market conditions into your mathematical models to gain a complete view of your business outlook without spending too much. Another reason predictive analytics is beneficial for your business is that it can provide forecasts like headcount planning, competitive analysis, churn analysis, and demand forecasting.
Similarly, you can use the following different types of predictive models to foretell the customer’s behavior in the future:
- Forecast model – A prevalent model that works on anything with a numerical value based on learning from past data.
- Classification model – Known to be the simplest model that classifies data for direct inquiry response.
- Time series model – This model works to assess the sequence of data points based on time.
- Outliers model – A predictive model that examines abnormal or outlying data points.
- Clustering model – A model that groups customers’ data based on shared behaviors and characteristics.
Getting Personal With Customers
Whenever a customer contacts a business, even if it’s for a complaint, they start a conversation. This communication is an essential point of interaction that defines your brand. Situations where a customer communicates directly with a business are opportunities for you to leverage personal data to engage them in a meaningful and productive conversation.
One example of using big data to get personal with customers is to tailor the message of one’s advertising campaigns based on the customer’s unstructured data, such as data from social media or those that came from marketing research. You can make your targeted advertisement more youthful if the personal data that your company collected shows that your particular audience consists primarily of young adults.
A detailed level of customer personalization in brand communication, mainly as an outcome of social media, is expected. However, if businesses use the abundance of online and offline media to integrate information in the most important and engaging ways, brand reputation and customer relationship will thrive across all communications channels.
Your company can track and compare its performance to other competitors in the same industry with benchmarking. This comparison includes the competitor’s business strategies regarding marketing competition in your particular niche. Likewise, your company can carry out comprehensive benchmarking over time with big data. Big data in benchmarking allows businesses to determine important indicators like retention, customer sentiment, and cost vs. revenue per service call.
Big data in relation to benchmarking can also transform backward-looking descriptive analytics into forward-looking prescriptive analytics. Prescriptive analytics helps companies process data in real-time to gain actionable insights. Similarly, big data in benchmarking can enable companies to expose operational areas that are falling behind and those above the company or industry standards. This knowledge of the specific regions of operation can allow the business to address its weaknesses and maintain strategies that are performing above expectations.
Identifying and studying helpful information from various data sources is the key to having a competitive edge and boosting business operations. Moreover, companies usually use big data in customer relationship management (CRM). Its purpose is to enhance customer service, adjust products and service offers to generate more income, predict client behavior, and calculate the return on investment in various enterprises.
Most of the data connected to CRM is structured data consisting of things like customer purchases, contact information, and many others. Moreover, the development of big data techniques has transformed this scenario as companies can now use unstructured data as a tool to gain deeper insights. Altogether, big data can improve customer relationship management because it leads to better customer understanding, helps in benchmarking, allows businesses to get personal with customers, and assists in predictive modeling.