Quantitative data refers to numerical, measurable, and objective information that you can collect, analyze, and compare. Examples of quantitative data include website traffic, conversion rates, sales figures, and customer satisfaction scores. Quantitative data helps you track your performance, identify trends, and optimize your campaigns.
Qualitative data refers to descriptive, subjective, and contextual information that you can gather, interpret, and understand. Examples of qualitative data include customer feedback, reviews, testimonials, interviews, and surveys. Qualitative data helps you explore your customers' needs, preferences, motivations, and emotions.
Both data types are valuable because they provide different insights and perspectives on your marketing situation. Quantitative data tells you what is happening, while qualitative data tells you why it is happening. By combining them, you can get a more holistic and accurate picture of your marketing problems and opportunities.
In order to make the best marketing decisions, qualitative and quantitative data must be combined. While quantitative data provides statistical patterns, qualitative data reveals the "why" underlying customer behaviours and emotions. Together, they offer comprehensive understandings of the target audience, assisting in the creation of strategies. Quantitative data exposes market trends and ad effectiveness, while qualitative data delves into client opinions to inform personalised message. Combining these three elements reduces risks, assures a customer-centric strategy, and improves the overall customer experience.
It is also a matter of context and meaning; qualitative data can contextualize your quantitative data and give meaning to the numbers. Statistical analysis is a great way to identify trends and patterns, but qualitative data can explain why the numbers are what they are. For example, while studying customer experience, you may find that customers rate their overall experiences as 8 out of 10. The quantitative data gives you an average rating, but the qualitative data can give insights into why customers gave this score - what particular aspects of the experience they liked or disliked. Qualitative data can also be used to identify new opportunities for improvement.
Yes, if we look at these from office or work it from home, the strategy looks fine. If we work effectively with the customers, face to face, things are a little bit more different. Let's see quantitative data, is that real? If we have a certain traffic and a tens of thousands of followers and likes, it doesn't mean that the sales are growing and the company makes billions. People addicted to Internet and socials are not mandatory customers, they are not buying, just entertaining. Smart people don't waste their time. If I need a perfume I will buy it from the airport, if I need shoes I chose a shoe store, in order to get goods that suits me, represents me and meets my expectations. I will always prioritise the quality over quantity.
When it comes to collecting both quantitative and qualitative data for marketing purposes, there are many options to choose from. Web analytics tools allow you to measure website performance and conduct A/B testing, segmentation, and attribution analysis. With CRM systems, you can manage and store customer data, create customer profiles and personas, and analyze purchase history. Social media platforms enable engagement with your audience, content sharing, brand reputation monitoring, and metric analysis. Surveys and polls are useful for asking customers or prospects specific questions about their opinions, preferences, satisfaction or expectations. Interviews and focus groups provide an opportunity for in-depth conversations about experiences, challenges, goals, and emotions - allowing for the collection of rich qualitative data that can uncover hidden insights.
When doing user interviews and focus groups for qualitative data its critical that you don't introduce bias. Here are some key things our organization does that you should consider as well prior to hosting any user interviews. - Use neutral language - Avoid hypothetical scenarios and focus on real-life situations - Don't reveal your own opinions or preferences and don't provide personal anecdotes - Don't introduce any non-verbal cues
The only valuable data comes from customers. Web tools gives us web figures and that's all. If a patient will go throw a questionnaire about the level of his pain he will give a smaller value but meeting a doctor will change the perspective drastically, as the specialist can see and feel and touch the patient. Is a massive difference even when we talk about data. Traffic, content, audience, when we sell a hammer or a bulb, not health services or hospitality, or clothes, or food. Experiences, taste, emotions, fragrance, are personal and must be treated as they are. Marketing is that science defined by "packing customers money full of value, quality and satisfaction". Everything else is how to manipulate for our advantage. Is it worth it?
Once you have collected both quantitative and qualitative data, it's important to analyze them and extract meaningful insights that can inform your marketing decisions. Data cleaning and validation is a necessary step for checking and correcting errors, inconsistencies, outliers, and missing values. Data visualization and reporting is also key for presenting and communicating the data in a clear way. This can be done through charts, graphs, tables, dashboards, and reports which can highlight key findings and trends. Additionally, data interpretation and inference involves understanding and explaining the data to meet marketing objectives and strategies; SWOT analysis, SMART goals, and ROI calculation are some methods that can be used. Lastly, data triangulation and validation is important for comparing and cross-checking data from different sources to verify and enhance insights. Quantitative data should be used to support or challenge qualitative data so that the data is consistent and valid.
Over time I have learned it's really important to bridge the gap between the what and the why as much as possible. (Or, as some say, "quant to qual") There are many ways to do this, depending on what kind of data you are looking at. One of the fastest ways to do this is by leveraging AI. (And I'm not referring to ChatGPT) Training a machine learning (ML) model to classify text data can be incredibly valuable if it's a big data set or will be an important ongoing analysis. If there isn't any in-house expertise to support an ML project, some incredible self-service, no-code tools might do the trick. Another option is to invest in small projects with experts on Upwork or Arc to get the job done.
In '90s I met a guy who owned a restaurant (he past recently). I tried to sell him advertising, profitable back then but he said "I will never need or buy advertising. As last as I have the best chef, best food, best services, people will come. With the money that advertisers are asking, I prefer to pay the chefs and my employees to stick with me and do the best job". His restaurant was the best, known in many countries, until the day he past away. People just told everyone what great experience they had. Even today I still believe that the quality, respect and loyalty will beat all the analyses. I know I am probably out of context here but let's stick to the values; people, quality over quantity.
Balancing quantitative and qualitative data is a dynamic and flexible process that should be tailored to your marketing context and goals. To start, you should have a clear and specific research question or hypothesis that guides your data collection and analysis. Both types of data should be used to answer different aspects of your research question or hypothesis, with quantitative data measuring results and outcomes, and qualitative data understanding customers and processes. Additionally, both types of data should complement each other, with qualitative data exploring new ideas and opportunities, and quantitative data testing and validating them. Furthermore, you should use both data types to address your strengths and weaknesses, leveraging quantitative data to improve efficiency and effectiveness, and qualitative data to improve creativity and empathy. Finally, both types of data can be used to optimize resources and capabilities, with quantitative data optimizing budget and time, and qualitative data optimizing content and value proposition.
In my experience, it is very important to first understand the meaning/results of qualitative data. Leverage that data along with the specific brand/consumer/customer (what is most relevant) insights to determine the implications for your qualitative information to identify the ultimate mix for the optimization that is needed to meet your specific objectives and budget.
Quantitative data analysis gives you concrete direction. Qualitative data can give you insights. Interpretation of qualitative data requires more skill than quantitative ones.
Ultimately, you need to combine quantitative and qualitative data when making marketing decisions and taking action. Quantitative data can be used to set SMART goals and KPIs, while qualitative data can be used to create customer personas and journeys. Additionally, quantitative data can help plan and allocate resources and channels, while qualitative data can create and deliver messages and offers. It is also important to measure and analyze results with quantitative data, as well as collect feedback and testimonials with qualitative data. By balancing quantitative and qualitative data in your marketing decisions, you can increase your marketing effectiveness, gain a better understanding of customers, competitors, and the market, as well as create more value for your customers.
In my personal experience, it is easy to understand the quantitative data. A business starts a campaign and you can measure the output. However, the success of the campaign lies within the qualitative data. If a campaign is not performing to the expectations then a relook at the marketing material in the ads. Are the images not right for the target audience? Is the ad copy not explaining the product or service correctly? Combining both quantitative and qualitative data will provide a well-rounded story about a marketing campaign to share with the stakeholders.
Please don't forget that cutting the costs implies cutting the quality. We cannot produce the same quality for a lower price, just if we pay people less, or we use inferior materials. Would you fly over Atlantic with a cheap airline? Starting from here, we can creat an image of how the real successfully business looks like. That doesn't mean that other businesses are bad, on paper.