Data-Driven Marketing is the approach of optimizing marketing mix based on data obtained on consumer behaviors. Data-driven marketing uses consumer & customer data to predict their ongoing and future needs, their expectation from a product & services and can predict possible future behaviors. Such insight helps organizations and marketers to develop personalized strategies to meet consumers’ specific and generic needs and can contribute to getting a better return on investment for the business.
Marketing, which has traditionally been an approach towards identifying consumer needs& desires, once it is defined we plan and meet such expressions with products or services at a given quality, price, packaging and make it available across channels, though a lot of successful brands have used data to define target audience, customer delight, benchmarking and channel efficiency.
Challenges of Data-Driven Marketing
The present and near-future need provides better insights in making marketing interventions more functional & contribute to better return on investment. Currently the challenges for marketers are at its peak and it is only going to get complex in future. For instance the new consumers entering to most of the specific product or service category has seen many-fold increase in last 10 years and such consumers are entering the market with diverse options of products sold through very competitive points of sales and service. On the other hand new technologies, digitalization and violently emerging e-commerce have made existing consumers smarter with extended options to make their choice. Both Intra-Market and Inter-Market competitiveness has increased.
How to Conquer Challenges in Data-Driven Marketing?
The only way to solve such puzzle is to assimilate data from customers &consumers of your existing or intended product category, which can be collected at multiple points where consumer interacts with the product in related categories or through structured research to answers behavioral questions: for example changing consumers behaviour towards media viewing habit specially between the traditional media like TV & Print with emerging media like social networking, blogging and social influencers etc. A data-based finding on average time spent in each media type can help marketers to efficiently plan the communication mix.
The use of data goes beyond just improving communication, in fact, it gives a clear direction to personalize customer & consumer experience, target well-defined market segments and improve trails or repeats for the product. The continuous interaction with data improves response time in improving strategies. While the concept of using data to make decisions looks simple but when marketers working on multiple paradigms like getting clarity on target audience, influence potential consumers, ensure continuous omnipresence of the product at widespread point of sales, promotion mix for noticeable impact, trade marketing, customer and consumer delight, personalization, consumer retention, etc. The kind of data needs to be looked-into for meaningful trade-off in a continuously changing market is just not big but super big. Hence data-driven marketing requires the right mechanism to deal with such big amount of data for conclusive inferences.
While we looked at the volume of data in data-driven marketing, but data as an ingredient for data-driven decision making comes with velocity, variety, variability & complexity. To have meaningful impact organizations are taking help of machines in the form of computer programs to deal with such big data. More and more organizations are implementing machine learning, the branch of artificial intelligence to make sense from big data to hold their competitiveness.