Benefits and challenges in data-driven marketing
As consumer behavior has been more diverse, nowadays it has become difficult to turn consumers around by providing information centrally through mass marketing. In order to fulfill individual needs, it is quite hard to succeed in marketing by only experience and intuition even if which was determined by great experienced marketers. Today, marketing has undergone a fundamental shift to data-driven personalization approach.
Data-driven marketing is the approach of optimizing brand communications based on customer data to predict their needs, desires and future behaviors when marketing teams build their strategies, futhermore data-driven personalization makes it possible to fulfill customer's needs individully. As not only consumer behavior has changed to a more diverse one in recent years, but also the cutting-edge technologies make it possible to collect and analyze massive amounts of data about customers and markets, the analysis based on vast amounts of data from various sources will offer insights into customer's preferences and broader trends for the highest possible return on investment (ROI).
Benefits
Adopting data-driven marketing benefits companies by allowing them to craft more effective marketing campaigns, build brand recognition and enhance customer loyalty. Rather than guessing what people want, marketers can tap into information that they collect from diverse sources for each one of customers to base their marketing decisions. Since the decisions are made based on data analysis, it is become easier to retrospect for the future's marketing and reproduce them if succeeded. Not only could data sometimes reveal target customer's preferences but also uncover the better channels for promotion. Most importantly, you can provide better experience for your customer by recommending what the customers really want instead of showing just popular items.
Challenges
Any marketing strategy worth its salt involves data from multiple sources such as Google Analytics, CRM software, etc. The data you collect from many sources could be inconsistent. In order to analyze the data more effectively, the data need to be normalized and up-to-date. Finding and maintaining high quality data is one of the essential keys for successful data-driven business, which is why not only data science but data engineering is also getting considered important in the big data era. When data exists across different isolated locations, data silos happen often. Breaking down silos and having all output in one place are very powerful, which enables to use all data you have thoroughly. Otherwise, data-driven marketing strategies will be less effective due to redundancy and contradictions.
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