Marketing Analytics and the Modern Data Scientist
A large ecommerce company has allocated a hefty budget for marketing activities before its upcoming sale event—its biggest in the year. The marketing team needs to come up with the messaging to be delivered during the marketing campaign to attract maximum customers.
The big question is what should that messaging be?
Should it be, “Get the best discounts,” or should it be, “Get the best international brands,” or will “Get best quality at best rates” work better? Should all three messages be employed?
This is clearly a tricky situation, with millions on the line. The company, being smart, wishes to use data to answer these questions. They naturally reach out to the data scientist in the marketing team.
The data scientist proposes an intuitive and powerful solution:
· First, identify the major types of customers.
· Next, determine what each type cares about.
· Finally, choose a message that would appeal to each customer group.
Fast forward a few weeks. Using this approach, four major customer segments were identified. By studying their characteristics, what they cared about was inferred, and messages were tailored to each group. The company achieved their marketing targets, attracting all types of customers and making the sale event a big success. Such is the impact that a data scientist in marketing can create.
Marketing has been around for centuries in varied forms. In the 20th century, with the advent of radio and television that made reaching huge audiences easier than ever before, brands began to spend millions of dollars to gain customer attention. By the 70s, marketing analytics had evolved as a discipline, and many of the common models we use today (Four Ps and Market Mix Modeling being prominent examples) had been developed. Around 1990, wireless telephones and a more evolved mass media made it necessary for brands to take a scientific approach to marketing. Marketing analytics was flourishing.
With personal computers, the internet, and search engines, the landscape suddenly changed for marketing. Brands were now eager to build their presence and gain a share of voice in the digital space too. Digital channels provide far richer data and can help generate a much better
understanding of the customer. Marketing efforts are now Omnichannel—meaning, online and offline. This digital revolution has caused a paradigm shift in the way marketing is done and gave birth to Modern Marketing and Modern Marketing Analytics.
Not only are the customer touch points and marketing channels far more, adding complexity to measurement and attribution, but the volume of data generated is exponentially larger. Data earlier used to be mainly structured, but now, unstructured data in the form of tweets, blogs, and images are dominant. With huge sums being spent on affiliate marketing and SEO, effectiveness of these channels, the quality of traffic coming from these, and the conversions of the individual campaigns need to be assessed quickly. Customer segmentation needs to be done on enormous data—both structured and unstructured. Clearly, modern marketing analytics is far more complex and needs a modern data scientist.
The data scientist in marketing needs to be updated with not only the latest tools and techniques but should also have the mindset to create business impact in a dynamic and fast-paced environment.
