Themes For Quotes

[Abbie Buesing]

Itseems that internal style sheet options are completely gone. Upon saving a theme they are removed. Is this meant to be so. Second: the preview option offered by View templates is also not there anymore.

The approach under custom themes is easier. When viewing a form (any form, not just sales invoices as with view templates), Switch Theme to your custom theme, then Edit theme. After editing, Update sends you directly to the form you are editing. No need to go back to Settings.

The code MB022RG on the first page corresponds to the image and text on the second page. What I would like to happen ideally is for each product (row?) to fit on a page. Either individually or with multiple products.

Hello there, i have put a lot (and really a lot) of time in putting all kinds of html in my old invoice template. Everything is gone now. Is there a way to get my old invoice back??? It was not only a certain layout but it also had formulas in about when to pay and on what bankaccount and with what reference. Please help!!!

Unfortunately, view templates, which were for sales invoices only, cannot be ported over to custom themes. Themes apply to the entire range of forms and some reports. So a view template would leave you with no code, as an example, for a purchase invoice or a cash receipt. The themes are written in Liquid, but much of the work you did on the template can guide your development of custom themes.

With a CMS quotes theme, you can create a custom quote theme for sales reps to use during the buying process. This guide will walk you through cloning the default CMS quotes theme locally using the CLI, uploading the clone to your account, then making adjustments as needed. You'll then create a quote using the template to view your work.

Please note: while this tutorial uses the HubSpot CLI, you can do all of this in HubSpot using the design manager if preferred. To complete this process in HubSpot, you'll just need to clone the cms-quotes-theme in the @hubspot folder instead of running the fetch command. shown in step 1.

You should now see a folder named my-quotes-theme in your local file system. This folder contains all of the assets needed for the quote theme, including mock data and module defaults within the imports folder.

With the folder downloaded, upload it to HubSpot. While you can use the hs upload command to perform a single upload, you can instead use the watch command to trigger automatic uploads on each file save:

Before a sales rep can use your quote template, it must be customized in HubSpot. This would typically be done by a sales manager so that they can create ready-made quotes for their sales team. However, in this tutorial, you'll walk through this process yourself so that you can understand what the content creation experience is like.

Please note: it's recommended to not edit the JavaScript of the payment, signature, and download modules, as this could lead to breaking the modules. If broken, the end-user might not be able to sign it, download it, or even make a payment.

I'm using a vscode customized theme that I changed to fit my needs, which I based on github dark for scopes. I'm finally in a spot I find it nice looking but I have a problem with fstring quotes, they simply don't get colored.

I've tried some scopes and just the punctuation.definition.string.begin/end worked. When I tried using the meta.fstring.python it changed the whole string color, quotes and text inside. The strange is that the raw (r"") strings work. Also there's this bug with the var inside the raw one.

The order of the scopes in the Visual Studio Code theme file determines the order in which they are applied. To make the punctuation.definition.string.begin.python scope take precedence over the string.quoted.single.python scope, you need to ensure that the former is defined after the latter.

However, even if the scopes are defined in the correct order, there can be a conflict if one of the scopes is more specific than the other. In this case, the string.quoted.single.python scope is more specific than the punctuation.definition.string.begin.python scope, so it will be applied instead. To address this, you can make the punctuation.definition.string.begin.python scope more specific by including string.quoted.single.python as a sub-scope.

Instead of speaking to their customers' needs, they only talk about themselves and offer empty, meaningless platitudes. So, it's easy to say that they're completely out of touch with their customers' motivations. This lack of understanding creates a huge opportunity for a company that knows how to connect with its customers on a deeper level.

As I saw it, we had an opportunity to stand out from them by truly understanding the jobs that its customers were hiring construction companies to do. By using the principles of Jobs-to-be-Done (JTBD) theory, I was able to gain valuable insights into their customers' needs and motivations, that could be used to develop a differentiated messaging and customer service experience.

Recently, I played around with my findings from this research with Chat GPT (a large language model or LLM developed by OpenAI) and wondered if I could achieve a similar or better result by spending less time.

By the end of this article, you'll be able to understand its potential benefits and limitations, the exact prompts that I used and a step by step guide at the end so you can apply it yourself with my data or your own.

Chat GPT claims that it's able to "quickly scan a large number of quotes and identify common patterns." Below is an example output of tasking it to theme the customer quotes that got people started on the journey to build a house.

One benefit of using Chat GPT for organising themes is that you can ask it to refine its output by providing additional input. It's like having a helpful colleague who can iterate on your feedback almost instantly. In my example, I had to ask it for "crisp and actionable" themes, and it was able to generate the above result.

One potential downside of using Chat GPT in this way is that if you input the same prompt and quotes multiple times, it may categorize them differently each time. For example, a quote filed under "personal satisfaction" might be grouped under "desire for ownership" on another occasion. This lack of consistency can make it difficult to use effectively in a team setting.

Synthesizing information is both an art and a science. Quotes from an interview do not include the context or the underlying emotions of what was said, which can lead to different researchers interpreting the same quote in different ways.

To get a desirable output with Chat GPT, I had to do some prompt chaining. When you ask it to generate the job stories using individual quotes, it tries to do it for each user when you need it to create the stories across the group. So, instead, you have to ask it to create the high level themes first.

However, it does make assumptions which can threaten the integrity of the research. In the second point, it writes about not being constrained by the landlord but there was no mention of it in the quotes or interviews as an outcome that they wanted to achieve. It's a plausible outcome in real life but it didn't show up in the research data.

Furthemore, some of the stories don't frame the situation customers are in properly. For example, "When I have savings that I want to invest in a way that will appreciate in value" sounds more like a motivation than a situation. In my research, I framed this as "When my earnings is depreciating in a savings/fixed deposit account" which is a situation that you can design for.

The final step in my research process involves analyzing all the data and identifying challenges that my clients would have to address at the top and middle of the customer journey. This is the point at which customers are discovering the business and deciding whether to engage with it.

I tasked it to take the customer quotes and create some problem statements in the HMW (how might we) format. HMW's are a design thinking tool to reframe problems as opportunities. It's best used right before brainstorming for ideas.

Each of those challenges broadly targets different customer motivations with some healthy overlap. My clients hired me to identify these challenges. I got them in a matter of seconds as opposed to multiple hours worth of work. So now we can go from interviews to insights very quickly.

By this point, I was actually shaken by the output. Mainly because I didn't feed it any quotes around what service attributes customers liked about the company that they choose to go with. It's generating these based only on the customers struggling moments.

From conducting the research interviews, it was clear to me that the segment of customers who had the job of wanting "a space of their own to live with their partner and avoid conflicts with parents" was a viable target segment to go after first.

In Sri Lanka, it's normal for couples to move in with parents after marriage. But as couples mature through time, the need for having their own space, independence and freedom to do things their own way increases. This causes them to switch to a new way of making progress. In our case, this meant building a house.

Either use these customer quotes (the dataset used in the examples above), conduct your own JTBD interviews or look at your own interview transcriptions for quotes specifically around what caused them to switch to your product/service or a competitors if you're doing analysis.

When I started exploring Chat GPT's potential application for streamlining research, I didn't expect a rollercoaster of emotions. It's clear that it's a powerful tool for synthesizing challenges and ideas when it's grounded by customer quotes. Although, it could improve its ability to organise the data, the time savings is clear.

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