Data Analysis in Marketing Trends

Marketing analytics is the study of data garnered through marketing campaigns in order to discern patterns between such things as how a campaign contributed to conversions, consumer behaviour, regional preferences, creative preferences and much more. The goal of marketing analytics as a practice is to use these patterns and findings to optimize future campaigns based on what was successful.

Transcript

1. Name- Rohit Burman Register No.- 2137023
2.  Introduction  Importance  Case Study on Deloitte  Case Study on Mu-Sigma  Real-life Examples
3.  Marketing data analysis is a technique where the business will take all the available information regarding the market and come up with marketing plan.  It also shows you how well you have done in the market using your current marketing techniques.  Marketing data analysis also focuses on external and internal factors. It takes into account the strengths and weakness of the company and how they fare in the market you are going to compete in.  Marketing data analysis gathers information from all marketing channels and consolidates into one common marketing view.
4.  Marketing data can be analyzed using a variety of methods and models, some of them are:  1)Media Mix Models(MMM): Attribution models that look at aggregate data over a long period of time.  2)Multi-touch Attribution(MTA): Attribution models that provide person-level data from across the buyer’s journey.  3)Unified Marketing Measurement(UMM): A form of measurement that integrates MMA and MTA into comprehensive engagement metrics.
5.  When you are an investor or even an entrepreneur you need to know what you are getting yourself into. You need to have all the data to back up your goal or vision for the company. For this reason, you need to do a marketing analysis.  Marketing analysis provides a check for profitability. If the market is showing signs of return on investment , investors will be encouraged to invest heavily.  It helps to understand the customers. It helps to find out what it is they want and helps to provide them with that exact product or services.
6.  It helps to find out who are the real competitors in the market and helps to calculate all the risks that may arise.  For all these reasons, marketing data analysis is so important. It gives us an insight into the market we are about to get involved in.  Marketing data analysis is also important for people who are already working in the market. It gives them an overall report on how their company has done.
7.  Deloitte practitioners recently sat down with data science thought leaders to discuss current and future trends.  Data science and analytics are driving big shifts in marketing. In fact, the possibilities are unfolding so quickly that new applications for data science-led are emerging nearly as fast marketers can imagine them.  Some current applications include:  1)The rise of digital advertising: Especially transformative for small business, data driven, digital advertising gives smaller organizations and agencies, which were once cut out of costly television advertising, new and cost-effective marketing channels in the digital realm.
8.  2)Micro-targeting and micro segmentation: Statistical analysis of semi-structured and unstructured data allows marketers to slice and dice data in ways to inform creative executions against micro targeting strategies. This helps marketers deliver specialized offerings to smaller, highly specific customer groups.  3)Speed and performance: From planning and promotion to execution, analytics-led marketing approaches can increase speed and execution of campaigns.
9.  The Problem One of the largest home improvement retailers sought to move away from a heuristic marketing campaign model to a data- driven statistical model to optimize their marketing spend. As their transformation partner, Mu Sigma helped them turn their whiteboard strategy into a marketplace reality.  The Approach With the increasing complexity upending the legacy marketing models, they partnered with the marketing analytics team to help them develop a more data-driven marketing strategy to: 1)Target customers with more personalized content.
10. 2)Identify behavioural patterns in sales, browsing, demographics, etc. 3)Realize better incremental revenue.  The Impact 1) ~$3M in incremental revenue realized through the support provided for over 20 Direct mail campaigns. 2)60 Million recommendations sent out every week. 3)Global scoring tables-set up with regular update cadence for usage in various ad-hoc campaigns by email & social media teams.
11.  Amazon: Amazon uses big data to drive personalization and customer satisfaction. They have a much wider customer base and different services which require different process.  Kroger: Kroger uses big data to personalize direct mail coupons to customers.  The Economist: The Economist supplemented their big data management with a customer data platform, finding the most precise marketing offers to serve to customers at the critical moments.