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Surveys have become a very helpful, though at times tedious, tool for marketing and sales teams.
They fulfill two objectives: (1) Surveys allow us to better understand our audience; and (2) Surveys facilitate improvement of services based on valuable information that is not collected elsewhere and cannot be purchased. For example, in the pharmaceutical field, surveys can aid in determining the number of patients being treated with a certain medication, or perhaps provide insight into a team of doctors’ knowledge regarding a new product launch.
In an environment where there is an increasing amount of data, being able to draw conclusions that lead to better management is key so that all the work involved in managing these surveys has a real return. However, this is not always easy and requires a lot of time from different teams performing manual analyses and managing information. As a result, many companies undergoing digital transformation are beginning to automate the processing of surveys to allow agile analysis. This approach does have limitations, however, as surveys can only contain closed answers and this does not always allow us to obtain our desired level of insight.
Thanks to the recent emergence of generative AI, led by ChatGPT and a host of other services, we may now have the opportunity to analyze more than just closed and structured survey responses. These technologies can allow for the automated analysis of more valuable fields such as free text, image, and even audio formats.
Today, we are able to get much more out of surveys using natural language processing (NLP) techniques, topic modeling, and generative AI engines. These technologies present three main advantages:
- Agility in Survey Management: By not always having to define closed answers for subsequent analysis, teams are given more flexibility when they are defining content and questions to ask.
- Automation of the Data Collection Process: Manual processing of responses is avoided and human work focuses on the data quality process and the interpretation of the results, elements that add significant value.
- Greater Capacity for Analysis: We go from having a purely descriptive capacity to being able to generate actionable insights. For example, the system could propose concrete actions to be carried out with doctors based on their responses, or redefine messages to achieve a higher level of commitment to the brand by different audiences.
In short, new technologies could make tools like surveys, which typically require a lot of manual management, more accessible. Surveys have the potential to help both sales forces and marketing teams get to know their interlocutors better, as well as understand the perceptions that they have about their products and services.
The rapid adoption of generative AI is accelerating the extraction of value from enterprise data. In a recent Gartner survey of more than 1,400 executive leaders, 45% reported that they are in pilot mode with generative AI, and another 10% have already put generative AI solutions into production. It is important to remember that a good data-driven strategy involves integrating new technologies and ensuring that they maximize the adoption of analytics in organizations.
In recent years, analysts such as Gartner have revived the concept of augmented intelligence, which Douglas Engelbart already anticipated in the 1960s. This concept details how AI, when put at the service of people, can be a catalyst to expand cognitive performance and specifically speed up decision-making.
Original article in Spanish available here.