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How To Leverage Data And Analytics To Launch Successful Products

How To Leverage Data And Analytics To Launch Successful Products

Ambika Choudhury

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Image Source: Uber

The business world is changing at a fast pace with digital innovation driving growth. Enterprises, even outside the technology domain are now utilising data-driven insights for understanding the customer needs, industry trends as well as competitive landscape. So far, the basic strategy for launching a new product was through due diligence but now data is being leveraged to gain better market insights. The era of data and analytics has created enormous opportunities for developing new products parallel and understand consumer behaviour better.



Companies Are Leveraging Data Analytics For Successful Product Launch

Data and analytics are playing a key role in the enterprises at present. Data analytics is crucial in solving some issues like loss or gain in the customers, loyalty in the market, future probabilities, etc. These insights help an enterprise to gain confidence, optimized resources and flexible enough with the rapid changes in the market.

Market Evaluation: There are various factors why companies are becoming more data-centric. Before a product launch, the foremost task an enterprise should work on is to evaluate the market as well as determining the target audience. The data and analytics help here by undergoing a market and competitor assessment. It helps in understanding the different methods of communication, assets that were previously communicated by similar brands across different regions, etc.

Increased Customer Value: By using data and analytics, an enterprise can develop a new product that provides increased consumer value and minimise the risk of failure during the launch of the product. It helps in deepening the customer brand engagement which ultimately increases the CLV (Customer Lifetime Value). Forecasting the performance of a product can be done using the analytics model during a product launch.

Product Development: A proactive approach can be converted into a new product development when an enterprise uses data and predictive analysis along with the business intelligence tools in the market research techniques. It helps in identifying new opportunities as well as develop new product ideas. Furthermore, data management tools can also be used to optimize the operational aspects of the distribution chain, right from packaging to delivery schedules.

Tracking Reviews And Feedbacks: One of the most important parts after launching a new product is consumer reviews and feedbacks. Data collected of the online surveys, social media analysis can help an enterprise to gain insights into such issues.


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Use Case: Uber

Uber is one of the fastest moving markets that uses data and analytics to reinvent transportation. It uses big data and analytics for surge pricing, better cars, driver ratings, estimating fares, etc. A few years earlier, Uber could only fit into traditional online transaction processing databases but with the help of data and analytics, today it can be done in one click of a button. One more case is of Airbnb, it uses data to not only improve their services but also their hiring practices and customer groups as well.

Recently at MLDS 2019, Ravinder K Sharma, Global Senior Director of Analytics at ABInBev said, “The company is using a framework called category expansion model which relies on market maturity as well as the integration of different  types of commercial levels which deploys a lot of data solutions on pricing, forecasting, media investments, media planning, etc. and fusing all of them into analytics umbrella for zone partners who execute within these recommendation and guardrails in order to optimize every dollar they spend in order to grow market share.” The category expansion strategy is basically about how a product would optimally evolve to address the changing market opportunities.  

In this report, IT minister Ravi Shankar Prasad said that India should become a big centre for data analysis. He further added, “Suppose in a particular area a large number of children get affected (by some diseases) and the government wants to have a policy (to help them). I seek your (start-ups) support. You must have the data, why this particular ailment is happening in this part of the country — geographical, social, economic (data).”

 

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