Episode 294 / Victoria Lorenzana / Tala / Senior Product Marketing Manager
Why Marketers Should Switch to AI Tools for Customer Feedback Analysis
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Customer feedback is a gold mine of data for marketers who want to truly understand their audiences, says Victoria Lorenzana, Senior Product Marketing Manager. And, on the Shiny New Object podcast, she explains a way to increase speed, effectiveness, and accuracy in getting this information.
While many may be concerned about the impact of AI tools on creativity, there is a place to use them that makes perfect sense and can significantly improve your understanding of your target audience. Switching to ChatGPT and Google Gemini for her customer feedback analysis has enabled Victoria to:
eliminate significant human error
speed up the data retrieval process massively
overcome limitations from traditional tools like SQL
work on complex data analysis without specific technical training.
Tune into the podcast to listen to how she does this in detail and get her top data driven marketing tips, too.
Transcript
The following gives you a good idea of what was said, but it’s not 100% accurate.
Victoria Lorenzana 0:00
It's okay to make mistakes, ask questions. You don't need to be perfect the first time, we all here are learning.
Speaker 0:13
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Tom Ollerton 0:46
Hello, and welcome to the shiny new object podcast. My name is Tom Ollerton. I'm the founder of automated creative, the creative effectiveness ad tech platform, and this is a podcast about the future of data driven marketing. Every week or so I have the pleasure and the privilege of interviewing one of our industry's leaders about where they think the future of data driven marketing is going. So if that's your kind of show, stick around, because I am on a call with Victoria Lorenzana, who is in Mexico, and she works with Tala, and she is a Senior Product Marketing Manager. So Victoria, for anyone who doesn't know who you are and what you do. Could you give us a bit of background?
Victoria Lorenzana 1:23
Sure. Thank you so much. Tom for having me on this podcast. And yes, let me introduce myself. I studied social psychology in college, and something that has defined my professional career is the change. So I start working as market researcher, then I jump into the UX research world, then into the UX design world, and then into product manager. So currently, as you mentioned, I'm a product marketing manager. I have a lot of experience on analysing user behaviours, understanding market needs and marketing positioning, and I will say that one of my strengths is this ability to effectively switch into different careers, because my main interest is to make sure that I'm making the most impact into the organisation that I am collaborating. So that's all. That's me. I live in Mexico City. I'm Mexican. I'm huge fan of cats and horror movies, and that's me.
Tom Ollerton 2:25
Is there any horror movies that features cats of note?
Victoria Lorenzana 2:31
actually, but not any, any horror movie that involve involves cats? Comes to my mind only one about this kind of elf, which is like, uh, evil creature, and the cat is the hero, because he saves the little girl from this elf, or gnome or something like that...
Tom Ollerton 2:52
Right. I don't work, yeah, we'll put that in the show notes. Uh, but maybe there's a gap in the market for cat based horror films, you know, maybe that, maybe that could be another career switch for you anyway, back to back to marketing. So if you were going to advise a student who was doing all the right things, like they were motivated, building their network, making content, all that stuff, what would you advise them to get started in this industry and make a career in data driven marketing?
Victoria Lorenzana 3:17
I will say that this is a great question, actually, because I have been mentoring young students across the globe, and my biggest advice is to be fearless. I know that it may sound challenging, but when we are recently graduate and when we are looking to introduce ourselves in an industry or area, we have these huge expectations on ourselves. So I will say that the first advice, or the most important thing, is that we need to understand that we all, no matter the expertise level, we all are continuously learning, and it's okay to make mistakes, ask questions. You don't need to be perfect the first time. You don't need to execute perfectly everything. So it's okay to make mistakes. We all here are learning, and the other important thing that I want to mention is, as we keep learning, it's important to have guidance, right? So maybe find these figures that represents for you, like the ideal professional profile that you want to become, and ask, do these profiles to be your mentor or ask for advice, because you don't need to be alone on this career path. You can be also, you can have also the follow up and guidance from more experienced profiles, so feel free to ask for help and guidance. That's all my advices.
Tom Ollerton 4:59
So that's fantastic advice for someone looking to make their first steps in the industry. But I'm also keen to know what is your advice? What's your best top bit of advice to become a data driven marketer, so when you're talking to your colleagues and people on your team, what is the silver bullet, golden rule, bit of advice that you find yourself sharing most often?
Victoria Lorenzana 5:16
Okay, so I will say that make sure that you understand your audiences. Because in marketing, something that happens a lot is that the area the industry require us to be creatives, right? And it's fantastic. This is one of the things that I really appreciate about marketing that sometimes you need to rethink, or you need to think about how to tackle different challenge from a creative perspective, however you need to always keep in mind who is your customers, who are your audiences. By having this deeper understanding of them, of your audiences, their needs, and also by taking a look to the competitive set, you will find out the right answers. You will find what you need to communicate, and you will find the most effective ways to communicate these messages or position your product and things like that. So having this understanding is critical, and that means that you need to have these research skills at least high level to understand who are your customers.
Tom Ollerton 6:22
And what are those research skills that you suggest that people have? So I totally buy into got to understand your audiences. Know who your customers are, but what do you find are the most successful ways? Are you heavy quant, or are you in person interviews? So what is your preferred approach to really understand the person that you're trying to speak to?
Victoria Lorenzana 6:39
Yeah, this is a great question. I would like to think in terms of internal sources or current sources of information that in the organisation you may find like, for example, take a look to your users' demographics. A lot of companies that their user demographics for different purposes, and this is a nice starting point. By taking a look to the internal data that you may have around your customers, in order to have a nice picture about who are them. Also internally, you can take a look to previous research pieces, because a lot of companies have these user research areas, insights areas or marketing intelligence areas in which you can find these different information sources about your customers, their needs, their preferred channels, consumption behaviours and things like that. So just by having the ability to take a look and find out internal data sources or internal inside sources around your customers, you will be able to keep in mind or effectively understand what are your customers. But if you want to go ahead and develop by your own research, I will say that it's worth for people to have this basic knowledge about how to conduct a successful research project, and this is completely linked with the research objective. So for example, let's say that you don't have these internal data sources, and you want to understand, for first time, who are your customers, you will need to be able to design a survey and effectively reach out these customers, customers, to ask them about their demographics, behaviours, preferences, usage behaviours and things like that. But if you want to have a deeper understanding on a specific behaviours or needs, you need to think on qualitative terms. And maybe for these purposes, you may be it's more it's more beneficial for you to conduct this kind of qualitative methodologies, like, for example, in deep interviews, sit in front of your customer and ask this customer about their behaviours, daily life needs around the the product, the service that you are offering, and things like that. So it's all relate with the research objective, how you can apply different methodologies. But I think that there is a lot of information sources and a lot of guidance from the different organisations that you can take a look and use them as references. Because for external you want to to develop by your own research projects, if you want to execute you will find a lot of guidance on internet from from very, very accurate sources.
Tom Ollerton 9:47
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So now we're going to move on to your shiny new object, which is the use of AI to analyse customer feedback. So this feels incredibly cutting edge and futuristic. I think I know what you mean by that, so but for someone who's listened to this, and I'm not sure what that means, could you explain what using AI to analyse customer feedback is, and how do you do it, and why it's a good thing?
Victoria Lorenzana 10:51
Well, sure, first of all, I want to start by sharing why it's important to listen the customer feedback, and it's because we design products to effectively meet customer needs, right? This is our main objective from all companies, from all services. But how we can effectively meet these consumer needs, we need to understand that we are in a continuously change environment, and consumer needs may change quickly according to different lifestyles or adoption or for according to different competitor softwares and things like that. So this is why it's important for us to keep an eye on the customer feedback, but also in order to be able to optimise our product service or offer to effectively meet customer needs. So listen our customers. Is the key for a successful business. It's also completely related with how we can strategically meet our business objectives. So having said that, that customer feedback matters and it's important for us to keep an eye on that. I will say that why this is new AI. We have listened a lot of AI, and I think that from different perspectives, at the beginning, it sounds like scary, right? Like AI has the capability to effectively design product experiences, or also to design copy and made some straightening in terms of specific roles or tasks that we are continuously developing, particularly for example, in marketing. So in marketing, one of the most critical elements is to be able to design a right marketing copy for different purposes, a tight line value propositions or things like that, but leaving aside the scary part of the AI and this idea of replacement for different professional profiles, I think that AI, it's super beneficial if we understand how to take advantage of it and how we can how identity and it's critical in terms of identifying what are the most challenging tasks that we are continuously developing on our daily basis, and how we can take advantage of the AI capabilities. So by merging these two ideas of how relevant is to listen the customer feedback and how effective is the AI to simplify tasks. This is why I am bringing this proposal to effectively using these tools to analyse customer feedback. In the past, for example, to analyse customer feedback, and this is coming from my experience as researcher, we need to review each of each one of the customers' comments. For example, let's think on Google Play Store reviews, or let's think on social media comments. Or, for example, when we have this kind of customer surveys, and we have open end questions. We needed to go and review each one of the customer responses. And I'm a little old school in terms of research, what we used to do is that we codify each response based on the answer based on this topic, but it was a manual process, so you need to go through each one of these answers, each one of these comments, codify them, and after that, being able to effectively categorise them, and also to present a kind of quantitative landscape in order to highlight what are the most frequent comments or topics across customer answers. It was a very hard task. And for example, let's say that when you had a sample of 1000 responses, that means that you needed to manually review 1000 comments. And usually what happens. Is that customer responses, customer comments, not only refer to one single topic when a customer is providing feedback, customer may address one, two or three or more topics within one answer. So the complexity of analysing qualitative information is huge, but currently, with AI, we can simplify this, this task, and I will explain how a Tom. I just want to make a pause to make sure that we are good.
Tom Ollerton 15:32
Yeah, this is fantastic. I love that you're going into a very practical demonstration of how to do this. Yes, I'm all ears. Thank you.
Victoria Lorenzana 15:39
Okay, perfect. So. But also, let me, let me also mention another way in which we used to analyse qualitative comments, and it was by programming on SQL or even using Excel capabilities. But what happens? What used to happen with this kind of analysis is that it was hard for the different softwares, even for Excel or for SQL to effectively find out multiple responses within a single answer from a customer. So at the end of the day, you miss part of the information. And also with this manual coding that I was mentioning, the risk was that, since was a purely human process, it was, for sure, sensitive to human mistakes. So what happens if you are very exhaust and you are not properly doing the coding or identifying the different topics? It was a challenge, right? So now what we can do is by having the right prompt to any AI tool to well, particularly, I have experience by using two AI tools, ChatGPT, and the other one is Google Gemini. I have a successful experience on implementing this tool and using these tools to effectively coding customer qualitative customer responses by prompting correctly. So what you need to do is to select a significant sample of these qualitative responses. And for these, I need to refer to how to effectively identify a significant sample. There are some online calculators that help you to estimate the right sample. So for example, for 1000 of comments, the right sample or the accurate sample, you don't need to analyse 1000 comments. So a statistical level, you need to find the right number of answers. And by finding this right number, you will find the right representativeness. Cross the World sample. So for example, for 1000 I will say that with a nice statistical level, is like 200 responses, just one example. So on satisfying this sample, this significant sample, and after entering the right prompt to ChatGPT or Google Gemini, you will find these qualitative analysis within seconds. And you can ask to these AI tools to provide you not only the list of the most recurring topics, but also you can ask to these tools to give you also the percentage the number of time that this topic was mentioned across the customer comments, for sure that my understanding is that This kind of AI tools are still in progress to keep optimising their models, their analysis models, and sometimes the tool can crash out a little bit. So what you need to do is just retry, re ask, and see and see if the tool is working fine and providing you the right data, but in my experience, I will say that I have run at least 20 analyses by asking these tools to analyse Google Play Store comments or open end responses from very complex questionnaires, and it has been very successful, and it has been super efficient for me, because in the past, for example, with the manual coding, it took like one week of purely analysis of pure analysis of the open end responses. But now, with these tools, ChatGPT and Google Gemini, you can have this analysis of open end customer feedback responses within minutes in one day. So it's super efficient. And I think that in the past, for example, several organisations were scared to analyse their customer feedback sources, right? Because the complexity of the analysis, but now, but using these tools, you can have a recurrent reporting on the customer feedback, on the open end comments, without requesting too much energy on the analysis. And you will be aware and keep keep yourself up to date on what customers are saying. So I will say that just a quick recap, you can use this methodology for social media listening purposes, in order to analyse customer feedback, sources like CSAT surveys, NPS, the open ended responses, or to analyse when you launch a questionnaire and you have open ended responses, and you can also use this methodology to analyse Google Play Store or App Store reviews.
Tom Ollerton 20:56
So what I'm hearing is that it was a very laborious process, even with SQL or with Excel skills, and this has fundamentally saved you a lot of time. So you've got open ended responses. It's all messy. One of the AI providers, GPT or Gemini, can just instantly go through that and work out percentages. So is it purely a time saving thing here, or have you used it to help spot things that you wouldn't have spot before?
Victoria Lorenzana 21:24
Yeah, I will say that the advantages, let me list some of the advantages that I have seen so far. For sure that time efficiency is the most relevant. The other one is, for example, for SQL or for Excel, you need specific skills, but now with GPT and Gemini, everyone can prompt these tools and run the analysis. So I will say that is accessible for everybody, without the need to having a special training. And the other advantage, for example, in comparison with the analysis that you can run with SQL, is that with SQL, you have certain limitations, and maybe you can skip certain topics. So in my experience, with these AI tools, you have more accurate analysis, and even over, for example, the manual coding, the manual review, because on this methodology, with the manual reviewing one by one. You are sensitive to make mistakes, but with GPT and Gemini, the accuracy is impressive. So this is a nice way for you to have a very, very accurate landscape about customer feedback topics without losing information.
Tom Ollerton 22:43
So unfortunately, we've come to the end of the podcast, and I really appreciate you helping me understand and the listeners understand more about your approach. It's very rare that someone lays out exactly how they do what they do, so I'm very grateful for that. So if someone wanted to continue this conversation with you, how would you like them to reach out to you? Where do you want them to reach out to you? And what makes a message that you will respond to?
Victoria Lorenzana 23:06
Oh, awesome, sure. So you can find me on Ali B list as mentor or on LinkedIn, Victoria Lorenzana, and the only thing that you need to do is reach me out and let me know if you want to learn more about the usage of AI for analysed customer feedback, or if you want to learn a little more about my experience switching careers, I'm super happy and excited to provide mentoring to everybody or support.
Tom Ollerton 23:36
So yeah, fantastic, Victoria. Thank you so much for your time.
Victoria Lorenzana 23:40
Sure, thank you so much for having me.
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