How to best verify the accuracy of self-reported data?

shutterstock 252668938 1280x720 1

How to best verify the accuracy of self-reported data?

shutterstock 252668938 1280x720 1



What is Data Collection?

Data collection is the procedure of collecting, measuring, and analyzing accurate insights for research using standard validated techniques.

Put simply, data collection is the process of gathering information for a specific purpose. It can be used to answer research questions, make informed business decisions, or improve products and services.

To collect data, we must first identify what information we need and how we will collect it. We can also evaluate a hypothesis based on collected data. In most cases, data collection is the primary and most important step for research. The approach to data collection is different for different fields of study, depending on the required information.

collect data


A significant number of scientific investigations denote a lack of rigor,  and this is largely due to the non-validation of the instruments used. This is much more evident in the behavioral sciences, where the most frequent methodology is qualitative,  a type of research where an indiscriminate use of instruments is observed, which are not typical of this methodology. This responds to an interest in the search for contextualization and homogeneity.

In an analysis carried out on 102 doctoral theses developed in the last 10 years, it was detected that the most used instrument is the survey; that each investigation designed its own instrument; and that, in the best of cases, responded to the objectives set (Conference in postdoctoral course: Analysis of the use of instruments in doctoral research, presented in 2014 by Tomás Crespo Borges, at the Pedagogical University of Villa Clara).

Due to its importance and complexity of application, instrument validation is considered a type of study within intervention studies, that is, at the same level as experimental, quasi-experimental, among others.

The questionnaire is an instrument for collecting information, designed to quantify and universalize it. For this reason, the moment of validation is of great importance, since the results obtained from its application can falsify the research, and thus, lead to fatal consequences in robust studies, in the social, constructive, and life of a patient. , among others.

In this work, dividing sections will be used; in practice it is a process that is presented as a system, where all its elements have an important function.

A first conception that has two phases is described below:

Phase 1: Generalities of validation

An instrument must meet two fundamental elements: validity and reliability, to match the gold standard instrument. If it does not exist, then it must meet a series of requirements to be reliable enough to accept the results in scientific research.

First: Validation involves two fundamental concepts, what has been applied up to this point? Is it good, surely? Second: How accurate is the new instrument to compare it with the one accepted by the scientific community, as correct in its measurements?

Phase 2: Internal validity

Validity is the degree to which an instrument measures what it is intended to measure. To obtain it, the instrument to be used must be compared with the ideal, gold standard or  Gold Standard .

Reaffirmed as a process, five sources of evidence have been postulated for it: according to the content, the internal structure, in relation to other variables, in the consequences of the instrument and in the response processes.

Reliability is the degree of congruence with which an instrument measures the variable. It is obtained by evaluating reproducibility, which is when there is a good correlation in the measurements at different times; and on the other hand, reliability, which is the accuracy of the measurements at different times. The application of both concepts is revealed in a recent article, where an instrument is validated with the purpose of being used in a study on tourist destinations in the province of El Oro, Ecuador.

When exploring the state of the art, the first thing to do is verify the existence of instruments applied in previous research, used for the same purpose, that have been validated at the time, as part of the investigative process. The most used tests, depending on the measurements of the variables, can be Student’s t  or Anova, if the data follow a normal distribution; otherwise, their non-parametric counterparts; Wilcoxon or Kruskal Wallis, in the case of two or three measurements, respectively, in both situations.

When there is no instrument that fits the objectives of the research, then it must be formed and contrasted with the ideal or gold standard.

In the second option, validity is very difficult to prove, since it has been decided to use an instrument different from those existing in the literature consulted.

Next, reliability is verified. For this, reproducibility is measured .  The instrument is applied several times (two or more) in samples that belong to the same universe or population where the research is carried out. To obtain a correlation considered good in the results (according to the Pearson, Spearman coefficients or the CCC coefficient of agreement) between the measurements, a value greater than 0.7 is accepted, although the ideal is 0.9.

Generalities of validation

For reliability, it is proven that in the different measurements, taken in the same universe or population, the responses of the subjects do not differ significantly, that is, there is accuracy in the instrument measurements at different times. The most used statistical tests are Aiken’s V and Dahlberg’s error. Therefore, validity is measured with another instrument, and reliability with the same one.

Other authors include the term optimization. It is associated with minimizing the error when providing a criterion, at the time of decision-making, based on the results obtained from the instrument.

In general sense, in the studies discussed it can be seen that there are several ways to carry out the validation of measurement instruments. The one that the researcher considers most appropriate can be used, but keeping in mind that the one selected meets all the necessary scientific rigor.

Below, a methodology will be shown to validate a measurement instrument, which is a hybrid between the conception of two different groups of authors, who are essentially similar.

Qualitative, which coincides with content analysis, is part of internal validity. To this are added the reliability and the construct, which belong to the quantitative, as well as the criterion, stability and performance. These last three correspond to external validity.

A second conception, which has six phases, in correspondence with  Supo ‘s idea , is described below:

Phase 1 : qualitative or content validation. It is part of internal validity. It is the creation of the instrument. It is divided into three moments, which do not have to follow an order, but are mandatory. It coincides with a type of diagnostic investigation.

  • Approach to the population: its purpose is to investigate the problem being addressed, approach the units of analysis or variables that should be used in the research. To do this, interviews, population survey studies and others can be carried out to provide this information.
  • Expert judgment: the selected experts are responsible for assessing whether the items in the instrument are clear, precise, relevant, coherent and exhaustive.
  • Rational validity (knowledge): they must be concepts that have been searched in the literature. It is assumed that the researcher is knowledgeable about the topic being studied.
qualitative or content validation

Phase 2 : quantitative or reliability. It is within the internal validity of the instrument.

This phase was detailed previously. According to  Aiken : “…strictly speaking, rather than being a characteristic of a test, reliability is a property of the scores obtained when the test is administered to a particular group of people, on a particular occasion, and under specific conditions.”



What role does technology play in your best data collection process?


What role does technology play in your best data collection process?

data collection

Data Collection

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem.

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

  • The aim of the research
  • The type of data that you will collect
  • The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Data collection is the process of gathering data for use in business decision-making, strategic planning, research and other purposes. It’s a crucial part of data analytics applications and research projects: Effective data collection provides the information that’s needed to answer questions, analyze business performance or other outcomes, and predict future trends, actions and scenarios.

In businesses, data collection happens on multiple levels. IT systems regularly collect data on customers, employees, sales and other aspects of business operations when transactions are processed and data is entered. Companies also conduct surveys and track social media to get feedback from customers. Data scientists, other analysts and business users then collect relevant data to analyze from internal systems, plus external data sources if needed. The latter task is the first step in data preparation, which involves gathering data and preparing it for use in business intelligence (BI) and analytics applications.

For research in science, medicine, higher education and other fields, data collection is often a more specialized process, in which researchers create and implement measures to collect specific sets of data. In both the business and research contexts, though, the collected data must be accurate to ensure that analytics findings and research results are valid.

What are information technologies?

Information technology is   a process that uses a combination of means and methods of collecting, processing and transmitting data to obtain new quality information about the state of an object, process or phenomenon. The purpose of information technology is the  production of information  for analysis by people and making decisions based on it to perform an action.

Information technologies (IT)

The introduction of a personal computer in the information sphere and the application of telecommunications media have determined a new stage in the development of  information technology . Modern IT is an information technology with a “friendly” user interface using personal computers and telecommunication facilities. The new information technology is based on the following basic principles.

Information technologies

  1. Interactive (dialogue) mode of working with a computer.
  2. Integration with other software products.
  3. Flexibility in the process of changing data and task definitions.

As a set of  information technology tools , many types of computer programs are used: word processors, publishing systems, spreadsheets, database management systems, electronic calendars, functional purpose information systems.

Characteristics of information technologies:

  • User operation in  data manipulation mode  (without programming). The user must not know and remember, but must see (output devices) and act (input devices).
  • Transversal information support  at all stages of information transmission is supported by an integrated database, which provides a unique way to enter, search, display, update and  protect information .
  • Paperless document processing  during which only the final version of the paper document is recorded, intermediate versions and necessary data recorded on the media are delivered to the user through the PC display screen.
  •  Interactive (dialogue) task solution mode  with a wide range of possibilities for the user.
  • Collective production of a document  on the basis of a group of computers linked by means of communication.
  • Adaptive processing  of the form and modes of presentation of information in the problem-solving process.

Types of information technologies

The main  types of information technology  include the following.

  • Information technology for data processing is   designed to solve well-structured problems, whose solution algorithms are well known and for which all necessary input data exist. This technology is applied to the performance level of low-skilled personnel in order to automate some routine and constantly repeated operations of administrative work.
  • Management information technology is   intended for the information service of all company employees, related to the acceptance of administrative decisions. In this case, the information is usually in the form of ordinary or special management reports and contains information about the past, present and possible future of the company.
  • Automated office information technology is   designed to complement the company’s existing staff communication system. Office automation assumes the organization and support of communication processes both within the company and with the external environment on the basis of computer networks and other modern means of transferring and working with information.
  • Information technology for decision support is   designed to develop a management decision that occurs as a result of an iterative process involving a decision support system (a computer link and the object of management) and a person (the management link, which sets input data and evaluates the result).
  • Expert systems information technology is   based on the use of  artificial intelligence . Expert systems allow managers to receive expert advice on any problem about which knowledge has been accumulated in these systems.

Types of information technologies


The use of modern technology is more economical than ever and electronic tools now offer a cost-effective alternative to paper questionnaires for collecting high-quality data. In order to help you decide if using computer-assisted personal interviewing (CAPI) is for you, this blog reviews the potential benefits and challenges of using CAPI and shares a recent survey experience. carried out in Guyana in which free software developed by Survey Solutions was used.

Paper questionnaires: the traditional way to collect data

Conducting surveys of this magnitude with paper questionnaires can be costly in terms of economic, administrative and logistical efforts while presenting a series of challenges: printing and transporting questionnaires to and from the field is often associated with with a high cost; Corrections to questions can represent a significant challenge in terms of cost and time. There is also the real risk that questionnaires will be lost in the field or damaged by weather or transportation before the data is systematized.

collect data

Even when all interviews have been conducted, responses must be manually entered into a digital file before the data can be analyzed. This process represents a lot of time and manual work and increases the margin of error. Data quality checks are limited, and errors are sometimes only recognized after the survey has ended, making them more difficult to correct.

However, there is an alternative to paper questionnaires: computer-assisted personal interview (CAPI). In recent years, CAPI has attracted more attention as it presents a more economical way to collect high-quality data.


CAPI: an increasingly popular tool

With the new processing speeds of today’s computers, the increasing global availability of Internet service and the falling prices of mobile devices, CAPI has become increasingly attractive. The CAPI tool creates the questionnaire using special software that can be downloaded directly to a mobile device (usually a smartphone or tablet), which the interviewer uses to administer and fill out the questionnaire. Information from these questionnaires is uploaded to a central server where it can be accessed and reviewed remotely.

Depending on the size of the survey sample, purchasing tablets to complete electronic surveys becomes increasingly more affordable than printing paper questionnaires. The technical requirements for such devices are relatively low and a large number of questionnaires can usually be saved on the device without danger of running out of storage. Additionally, once a questionnaire has been entered on a mobile device, it can be modified: If an error is detected in the early stages of the survey, it can be easily corrected without incurring additional printing costs.

Machine Learning and examples for Best Datasets

Machine Learning Datasets

Introduction to Machine Learning Datasets The accompanying article gives a layout to machine learning Datasets. AI dataset is characterized as the assortment of information that is expected to prepare the model and make forecasts. These datasets are named organized and unstructured datasets, where the organized datasets are in plain organization in which the line of the … Read more

Importance of Datasets in Machine Learning and AI Research

Machine learning

The majority of us these days are centered around building machine learning models and taking care of issues with the current datasets. However, we want to initially comprehend what a dataset is, its significance, and its part in building powerful AI arrangements. Today we have an overflow of open-source datasets to do explore on or … Read more

What are the best five methods of data collection?

image dataset in machine learning

What are the best five methods of data collection?

data collection

Data collection

Data collection is very important. The process of collecting and analyzing information on relevant variables in a predetermined, methodical way so that one can respond to specific research questions, test hypotheses, and assess results. Data collection can be either qualitative or quantitative

In the era in which “information is power”, how we collect that information should be one of our main concerns, right? Also, which of the many data collection methods is the best for your particular data needs? Whoever is the answer to the two questions before and now, one thing is certain: whether it is a company, an organization, an agency, an entrepreneur, a investigador , or implement study and a careful and bold person, la recompilation of data should be one of his principal es prior data.

collect data

Why collect data

Data collection is defined as the “process of collecting and measuring information about variables of interest, in a manner If this ematicis established, it allows one to respond to queries, ask research questions, test hypotheses, and evaluate results.”
There are numerous reasons to encourage data collection, but here I will focus primarily on those related to business and marketing:

It helps you learn more about your customers.

It allows me to discover ir sentences in which people change their opinions and behaviors aldol ar go of time or in different circumstances.
It allows us to segment ar su audiences into different groups of clients and di rive different marketing strategies in each of these groups  in function of their individual needs.

Facilitates decision making and improves the quality of decisions made Helps solve problems and improve the quality of your product or service in function from there the comments obtained. Before delving into the different technical cases and data collection methods, let’s make a brief distinction between the two main types es de dat os: quantitative and

Quantitative versus qualitative data

Quantitative data

This type of data deals with things that are measurable and can be expressed in numbers or figures, or using other values ​​that express quantity. That said, generally quantitative data
are expressed in numerical form and can represent size, length, duration, amount, value. , etc.

Quantitative research is more likely to provide answers to questions such as who? When? where? That? And how many?
In most cases, quantitative survey questions are closed-ended and created in accordance with the research objectives, which makes the answers can be easily transformed into numbers, charts, graphs and tables.

The data obtained through quantitative data collection methods can be used to test existing ideas or predictions, know your customers If so, measure general trends and do important things. For example, you can use it to measure the success of your product and what aspects may need improvement, the level of satisfaction of your customers, for to find out if your competitors are selling you more than you and why, etc.

Qualitative data

Different from the data that you have, which deal with numbers and ciphers, the data that you have is of a descriptive nature and goes further that number and approx. For January, the data that you have can’t be measured right away and how much you can get by observing a vacancy or a survey to ask questions . It is more likely that the investment that was made and proposed is answered by asking the question “why?” and how?” How?

As mentioned, it is more likely that the methods of data collection that are qualitied and vos consist in question as abiders  or no value or
number and co. Data such as this is an excellent way to obtain information about the thoughts and behavior of your audience. 

Data collection methods

Some common data collection methods include surveys, interviews, observations, focus groups, experiments, and secondary data analysis. The data collected through these methods can then be analyzed and used to support or refute research hypotheses and draw conclusions about the study’s subject matter.

Data collection methods

Quantitative data collection methods
1. Closed-ended surveys and online questionnaires Closed-ended surveys and online questionnaires are based on questions that provide

Respondents were given predefined response options to choose from. There are two main types of closed surveys: those that are based on categorical questions and those that are based on interval/ratio questions.
Categorical survey questions can further be classified into dichotomous questions (‘yes/no’), multiple choice questions, and questions with almost 100 questions. They require
verification and can be responded to with a simple “yes” or “no” or with specific predefined information.

Interval/ratio questions, on the other hand, can consist of rating scale, Liker scale, or mat rise questions that amplify a set. A point of value is predefined to choose from on an affixed scale. To get more information, we have prepared a guide on different types of closed-ended survey questions.

Qualitative data collection methods
2. Surveys and open-ended questionnaires
In opposition to closed answers, there are open-ended questionnaire surveys. The main difference between the two relations is the fact that closed surveys of r receive predefined response options between relations that the respondent owe the ergo , I believe that open-ended surveys allow respondents much more freedom and flexibility to proportion their answers.

When creating an open survey, keep in mind the length of your survey and the number and complexity of the questions. You should carefully determine the optimal number of questions , since answering open-ended questions can be time-consuming and demanding, and you don’t want to overwhelm your survey respondents. ados.
Compared to closed surveys, one of the methods of collecting quantitative data , the results of open surveys are more difficult to compile and analyze due to the fact that there are no uniform response options to choose from.

3. 1 on 1 Interviews
Personal (or face to face) interviews are one of the most common types of data collection methods in research. which it at i goes. Here, the interviewer collects data directly from the interviewee. Because it is a very personal approach, this data collection technique is perfect when you need to collect highly personalized data.

Depending on your specific needs, the interview can be informal, unstructured, conversational, or even spontaneous (as if you were talking to your friend) . go), in which case it is more difficult and requires more time to process the data obtained, or it can be semi-structured and standardized to a certain point or (if, for example, you ask the same series of open-ended questions).

4. Discussion Groups
The focus group data collection method is essentially an interview method , but instead of doing the one one, here we have a group discussion.
If the resources for individual interviews are limited (either in terms of people, money or time) or you need to create a If the individual’s social situation is needed to collect data on people’s attitudes and behaviors, focus groups can be very useful practical.
Ideally, a focus group should have 3 to 10 people, plus a moderator. Of course, depending on the objective of the research and what the data obtained will be used for , there must be some denominators common to all members. of the focus group.

Discussion Groups


Regardless of the field of study or preference for defining data (quantitative or qualitative), accurate data collection is essential to maintaining research integrity. Selection of appropriate data collection instruments (existing, modified, or newly developed) and clearly outlined instructions for their correct use reduce the likelihood of errors.

A formal data collection process is necessary as it ensures that the data collected is defined and accurate. In this way, subsequent decisions based on arguments embedded in the findings are made using valid data.3​ The process provides a baseline from which to measure and, in certain cases, an indication of what to improve. There are 5 common data collection methods; closed surveys and questionnaires, open surveys and questionnaires, 1 on 1 interviews, focus groups and direct observation.

The main reason for maintaining data integrity is to support the observation of errors in the data collection process. These errors may be made intentionally (deliberate falsification) or unintentionally (random or systematic errors).

There are two approaches that can protect the integrity of the data and ensure the scientific validity of the study results:

Quality assurance: all actions taken before data collection
Quality control: all actions taken during and after data collection

What is the best purpose of your data collection?

Untitled 1 1

What is the best purpose of your data collection?

data collection

Data Collection.

Data collection is the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes.

Are you going to do market research and don’t know what data collection technique you are going to use? I remind you that the design of your research will depend on this, so think carefully before saying whether you will do interviews, use the observation method or perhaps online surveys.

Before deciding which method you will choose to collect data, it is important to know what you want to obtain through this research, to be clear about the objectives to know which data collection technique will give us the best results.

What is data collection?

Data collection refers to the systematic approach of gathering and measuring information from various sources in order to obtain a complete and accurate picture of an area of ​​interest.

Collecting data allows an individual or company to answer relevant questions, evaluate results, and better anticipate future probabilities and trends.

Accuracy in data collection is essential to ensure the integrity of a study, sound business decisions, and quality assurance. For example, you can collect data through mobile apps, website visits, loyalty programs, and  online surveys  to learn more about customers.

collect data

How to collect data correctly?

There are different  data collection methods  that can be useful to you. The choice of method depends on the strategy, type of variable, desired precision, collection point, and interviewer skills.

The research interview

Interviews are one of the most common methods. If you decide to do it, pay special attention to the questions you will ask, which also depend on whether you will do a face-to-face interview, over the phone, or even if it is by email.  Know the  types of interviews  and select the appropriate one for your research.

Take into account that more resources, both financial and personnel, are usually needed to carry out interviews. Especially if you decide to conduct interviews in the field, or by telephone. Use all the information you have at your disposal. There may be archives of interviews from previous years that can serve as a reference for your research. 

Knowing the past behavior of your consumers is of great importance when analyzing how consumer habits have changed.

Telephone interviews 

Telephone interviews allow   researchers to collect more information in a shorter amount of time, saving on expenses such as travel and survey materials. An advantage of this tool is that participants feel more confident when answering because they are not being observed. 

Among the advantages of this tool is its great scope and the easy management of the data obtained. However, in many cases, the researcher does not have control of the interview; in addition, he or she must ensure that it is a short process so that it does not cause the participant to abandon it.

The questionnaire for data collection

Questionnaires  are  useful tool for data collection. To obtain the expected results, they need to be done carefully. That is why before writing it, it is important that the researcher defines the objectives of his research. 

There are two formats of questionnaires: open questionnaires, which are applied when you want to know people’s opinions, experiences and feelings on a specific topic.

data collection

On the other hand, in the closed questionnaire the researchers have control of what they ask and want to know, which can cause the participants’ responses to be forced and limited. 

Observation method

If what you prefer is to do on-site observation to know the behavior of your clients, I remind you that you can do it using other methodologies.

Can  online surveys be combined with other methodologies ?

What would it be like to be doing observation and have a platform like  at hand, for example, on a mobile device, where you have access to the questionnaire that you have created with the points to investigate, and fill it in instantly with the information obtained during your observation? . Remember that you can access our tool online and offline.

Keep in mind that the way you record the information will be of great help when analyzing it. Being able to measure and present reports with accurate and real data is very important for correct decision making.

Use online surveys to collect data

Collecting data through online surveys has great advantages. If you use platforms like QuestionPro, you have various types of questions at your disposal, the use of personalized and logical variables that allow you to obtain better results and help you understand your clients in depth. 

Through our platform you have the results instantly, you can see them in real time to follow up on your research; In addition to generating reports in various formats.

Also consider that collecting data through online surveys has a lower cost than, for example, doing it through in-person interviews, without forgetting that you can have your results in less time, instead of days, weeks, or even months, which is the time it could take to collect data through interviews or the observation method.

Conduct a focus group

focus group  is a form of qualitative study that consists of holding a meeting where people can discuss or resolve an established topic. This type of debate helps generate ideas, opinions, attitudes that cannot be observed with another method of data collection. 

With this method, large amounts of information can be obtained, since participants feel confident to give their opinion and offer honest and accurate answers. 

Group sessions are the ideal tool to obtain feedback from participants. However, they do have some disadvantages. Among the most important is the lack of control during the debate, which causes time to waste on irrelevant topics and complicates the analysis of the information. This can be solved with a moderator who is an expert in the area. 

Online panels for data collection

Online panels are a tool that allows data to be collected through highly professional and qualified people. One of the advantages of this method is that participants will give specific and clear answers. 

Some of the advantages of using online panels are its ease of accessing channels and obtaining direct information from the target audience. In addition, it is a very economical research method that allows obtaining quality information.

Make correct decisions based on the data obtained

Regardless of the method you decide to use to collect data, it is important that there is direct communication with decision makers. That they understand and commit to acting according to the results. 

For this reason, we must pay special attention to the analysis and presentation of the information obtained. Remember that this data must be useful and functional to us, so the data collection method used has a lot to do with it.

The conclusion you obtain from your research will set the course for the company’s decision-making, so present your report clearly, listing the steps you followed to obtain those results. Make sure that whoever is going to take the corresponding actions understands the importance of the information collected and that it provides the solutions they expect.

Purpose of data collection

Don’t just collect data for the sake of it. Do it to help make a decision or to answer a specific question.

The importance of data collection comes only when the data is used for something. It might seem obvious, but many of us end up collecting data that is never used and serves no purpose.

If you come up with a question you want to answer ahead of time, you can be laser focused about collecting your data instead of wasting time and energy collecting data that is unimportant.

A couple of years ago, I was planning a trip to Berlin. I was super excited. Traveling internationally is such a special opportunity. I was determined to make the most of it. So I went about collecting data: the sights, the foods to try, things to be careful about, helpful tips and suggestions, etc.

I didn’t set a limit on my research. When it came to booking hotels, I pored over comments on and TripAdvisor for hours and hours. I wanted to make the best possible decision. But I didn’t realize that I was wasting time collecting data that didn’t inform my decision to book a hotel room.

Now I know: if I don’t prioritize what data to collect, then I’ll likely head down the wrong path.

Now I define the question I’m answering before collecting any data.

For example, if I’m researching a hotel in Berlin I might ask: Can I find a hotel in the Mitte neighborhood with a good work desk for less than 100 euros per night?

Types of data: quantitative and qualitative

  • Both quantitative and qualitative data are useful to make decisions.
  • Quantitative data is expressed in numbers. It tells you what is happening.
  • Qualitative data is expressed in words. It often tells you why it’s happening.
  • Some time ago I ran a product line that allowed small businesses to accept credit card payments from customers.
  • A funny thing happened one month. Once we enrolled a new business, their credit card payments would start off strong then suddenly stop after a few days.
  • We had the quantitative data that there was something wrong, and it rang the alarm bells to take action.
  • But the data we had wasn’t pointing us in any direction, so we didn’t know what action to take.
  • We set about calling customers and collecting qualitative data. We asked them in a friendly yet direct way why they had suddenly stopped using our product.