Emerging Need Of Marketing Analytics

Posted By: editor
Posted On: 12 Feb, 2021
Last Updated On: 17 Jun, 2021

Big data has brought a wave of many changes to the way organizations operate. It has brought in a new challenge as understanding customers has become difficult more than ever now. With ever-changing trends and demands coping up with them and offering time-sensitive promotions has become more complex. Let us understand why is need for marketing analytics is emerging these days.

What is Marketing Analytics?

 By definition, marketing analytics is not a branch of such, but it covers the operations taken up to gain more ROI, enhance marketing effectiveness, etc. Ut us a process where data is used to drive better marketing strategies. Very similar to all other analytics, even marketing analytics has three categories. Descriptive where by looking at historical data, some knowledge is acquired and alerts are created. Predictive is a way of saying what might happen in different scenarios, and Prescriptive, which tells you the best outcome based on the available knowledge. So basically begin learning from the past, understand and suggest what can be done, and the best option to achieve what is expected.

For example, consider a global cosmetics brand. With the growing competition, they have to stay ahead in the race of technology, and they have to move to digital platforms. This company has its Website where based on the user preference, the product recommendations are given. This recommendation mainly considers all the past purchases as well as the history of browsing. The new launches are released with special discounts for frequent buyers. It even recommends sizes to buy based on how frequently the user is buying or just browsing the product. This eventually drives two main objectives of marketing analytics. Achieving customer satisfaction as well as focused and targeted branding. It has proven to be a great success over time.

The Customer Lifetime Value is one such measure of estimated profit gained for an average customer. This measure is generally calculated as avg sales * count of recurring customer transactions * their retention period * profit margin.

To get to this calculation business has to make many assumptions that might change over even a small period; hence, the value is subjected to changes.

Brand Value, Marketing, and Customer Loyalty play a crucial role and contribute to any business’s maximum number in profit margins.

Ways to improve customer lifetime value:

  1. Offering great product/service.
  2. Providing complete attention to customers and their needs.
  3. Adding the customer requested features.
  4. Marketing focused on retargeting the same customers.
  5. Ensuring retention with some added measures like consistent service and quality.

Marketing analytics sits right at the center of below business functions.

marketing analytics and business functions

Objectives of Marketing Analytics:

Here are some key objectives that help you diagnose the root problems and achieve business goals more effectively.

  • Gain ROI
  • Increase profit margins
  • Improve customer experience
  • Increase market reach.

Key functions in setting up market analytics:

  • End users or Customers
  • Service provider and service offerings
  • Market demand and competition
  • Providers and suppliers
  • Transactions exchanged

Summary of data gathering exercise may include below performance measurements:

  1. Customer loyalty rank
  2. If it was an organic search
  3. Site traffic to Actual Sales ratio
  4. Sales revenue
  5. Promotion cost
  6. New customer count
  7. Average profit over time
  8. Employee performance where applicable
  9. Marketing ROI
  10. Growth of sales and revenue etc
  11. Brand Value and Equity

Data Science – Machine Learning Approach:

The first step to resolve any objective with marketing analytics involves building a model. Putting together the entity and relationships together helps gain clarity. The main pillars of getting attention from users need to be identified in this process. This answers the question as to what data is needed for this model. This helps to create a base for predictive models.

The next step is converting relevant identifiers that can be utilized in a mathematical model since it can be a string, a text, a log from a sensor, or just social media feeds. These mathematical numbers are then run through the predictive model to predict expected outcomes such as whether the product will be sold or predict revenue growth.

Tackling the problem of getting customers to come back – Is achieved generally by a great product or service offering that satisfies customers. Understanding what customers want and adding those features add significant value to profits.

Yes, marketing analytics will definitely help you in achieving better sales revenue and more satisfied customers. Also, reduce the cost of acquiring a customer & improve conversion rates. Get started with marketing analytics with Daton now!

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