Use case: Product launch

Quick feedback after launch

When  planning a new product launch, organisations need to understand their audience. Leadership teams rely on marketing departments to conduct thorough market research to gain valuable insights into who the target customers are and what they want. This research will often form the starting point for all the other steps in the product launch plan, and is often the basis of the marketing strategy.

 

And yet, all too often companies develop, build and launch new products based on the wrong assumptions. Even with all the research done beforehand, the voice of the ‘real’ customer and what they need is difficult to hear especially for products not yet launched. Launching a new product can be difficult and risky project with lots of unknowns and assumptions. The result of a failed  launch is the loss of income, loss of development costs, as well as damage to the overall product portfolio, brand reputation and strategy.

Why should you read this case study?

Are you dealing with product launches as marketer or product developer for FMCG? Do you want to find ways to fully test your assumptions about the market or customer groups? Do you want to have the tools that help you minimize the risks involved in a product launch and convince fiance and senior leadership that the overall plan is the right one? Please read this great example of a use case where a major FMCG organisation used Wonderflow customer feedback analysis to learn new aspects of their own product launch process that they would have never learned otherwise.

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80

of product launches fail after one year

3

is the average cost of a product launch in consumer goods

75

of young parents check their baby every night

Your customers tell you what the problem is

In this case, Wonderflow helped a large consumer care company to find out why their highly innovative baby product did not meet the buyers’ expectations. 

 

The product in this case is an adhesive band-aid/plaster with a sensor which measures the body temperature of the baby. The sensor is connected to a phone through a dedicated application. Parents are notified through the app if and when the temperature of their baby is going up or down. It was a major launch and one which excited many of the company’s marketing team and senior leadership.

 

The product was launched with a big marketing campaign to support it. As expected sales took off and increased but very soon after sales began to fall flat. The company called Wonderflow in to find out why, given that overall feedback seemed positive and all of the market assumptions seemed to be correct. The assumption was that some feature of the launch process had gone wrong or had been poorly communicated.

The challenges we faced:

  • Key question: find out what went ‘wrong’ with the product launch
  • Find out what data to collect and to analyze
  • The assumption was that it was technical aspects of the product that were causing the problem, rather than the usage

Your most unhappy customers are your greatest source of learning

Many products were sold online, giving Wonderflow the opportunity to collect valuable feedback from the e-commerce platform. Furthermore, feedback, reviews and reportage on several independent blogs for young parents were also analysed. All this data was collected, cleaned  and all structured in one database.

By analyzing the customer feedback data by using the AI-based NLP (Natural Language Processing) engine of Wonderflow, it came to light that the product had one big omission, which had nothing to do with technology failures. The product would only notify parents in case of the temperature of their baby changed, yet it did not tell them if nothing had changed. The assumption with product developers was that no news was good news and parents only wanted to be notified when something was not normal. 

 

In fact, worried parents wanted constant updates even if nothing had happened so many reported getting out of bed in the middle of the night to check on the baby themselves in case the product was not functioning and double-check the baby’s temperature themselves anyway. A significant portion of customers assumed the app wasn’t working effectively as they had been told nothing new.

 

Cause: It turned out that none of the product developers were parents themselves and therefore didn’t fully understand the customer group on an emotional level- they assumed that parents would only want to be notified when there was a problem with the child’s condition. However, the lack of feedback was perceived as bad news by the parents. Customers consequently felt the product was either broken or nonfunctional in some way.

 

After this issue came to light through the analysis done with Wonderflow, the following actions were taken:

 

  • A modification on the app: continuous feedback about the baby was added
  • The communication and packaging information were changed
  • The company ran an influencer campaign for damage control

 

Result: In a timespan of only 3 weeks, Wonderflow was able to provide in-depth analysis for over 500 customer reviews gathered on different platforms. After the modifications were made, sales of the product improved again.

Conclusion

Launching a new product can be a huge, risky project requiring commitments of time and money of large numbers of people within an organisation. Even with all this energy and attention it can be very difficult to understand why things don’t go to plan. 

In just a very short time-span after the launch, Wonderflow was able to identify the problem and support the development of solutions. Through gathering and analysing hundreds of records of customer feedback our work had an immediate impact on the client’s bottom line, preventing the company from spending more on advertising or even canceling this highly innovative product.

More information

If you want to know more about Customer Feedback Analysis and building a feedback loop, check out this YouTube channel.

The information in this case study is confidential. Brands and company names are anonymized.

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