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When testing, look at the big picture

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When testing, look at the big picture

A common martech activity is testing different UI/UX (content, design, flow, etc.) elements in order to optimize some metric like lead form conversion rate.  While the actual responsibility for conducting such a test is usually assigned to a conversion rate optimizer, that doesn’t mean that other practitioners — regardless of whether they’re marketers, makers, modelers, or maestros — are not worth involving, since tests could have broad ramifications.

Indeed, A/B and multivariate testing typically have implications that reverberate throughout a martech stack — maybe beyond it.  In some cases that fact may present itself during the experiment phase, while in others the implications may pop up later on during an implementation phase.  That why it is important for martech practitioners to understand data flows.

Marketing and sales

The interaction between marketing and sales activities, is an example of a factor to consider.  Definitions for marketing qualified leads (MQL) and sales qualified leads (SQL), while related, differ for good reasons.  marketing and sales have different procedures, metrics, and goals.

For instance, a very common way people advocate to boost lead form conversion is to remove friction (in other words, fields).  In many cases, people are more likely to complete a form that requires less input and interaction than a form that requires a lot.  Simplifying a form could increase the quantity of leads while lowering the quality of leads.  What’s good MQL-wise is not necessarily beneficial from the perspective of SQL metrics.

Some of the “expendable” fields are likely for lead scoring.  They cause friction for marketing purposes, but they can help the sales team better understand a potential customer.  This enables salespeople to focus on targets who show strong intent to purchase, as well as tailor their efforts to the lead’s circumstances.

So it’s entirely possible for an experiment variation that has fewer lead scoring fields to boost marketing conversions while inhibiting salespeople from closing deals.  Thus, when conducting such an experiment, it’s important to consider and monitor downstream metrics.

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Multi-departmental considerations

Another factor to consider is how and when to collect required information.  This is important, since removing or moving data collection points in order to boost MQL or SQL metrics could likely have downstream ramifications — even after the sales process is completed.

For example, when selling insurance (health, dental, life, etc.) the final cost a policy will require knowing a lot of detailed and sensitive information about the person covered.  Does it make sense to ask for a lot of that information on an initial lead form, so that when a salesperson follows up they can give the prospect a price estimate with some certainty?  Or does it make sense to gather just some general information and allow the salesperson to provide a ballpark estimate along with how various factors will influence the cost?  There are pros and cons to both approaches, and each could be pursued depending on whether more total leads or more qualified leads are called for.

However, it’s important to know what information is ultimately required.  A prospect will have to provide all of it at some point to turn into a customer.  Thus, toggling between those collection options will require a bigger picture view.  If the decision is to remove a field from a lead form, when and where downstream will that information be collected?

This may involve teams and systems that are outside of the martech stack, and therefore could require multi-departmental orchestration.  Then on top of that, if the decision is made to switch when certain information is collected, what happens to people who are in between the old and new collection points at the time of the switch?

Consider the big picture

It is crucial to consider the ramifications of common martech activities like conversion testing.  That perspective will help better ensure success.  For instance, there’s more to consider than MQL metrics, and at times, improved results with that one factor may come at a great cost down the line or in another crucial area.

As many practitioners can attest, sometimes changes are more complex than they may appear.  It’s really annoying to invest effort into finding a way to boost an upstream metric to only find that it will not work due to downstream issues.  Understanding data flows within and beyond the martech stack is one way to help mitigate against wasting time and effort on UI/UX testing that will ultimately not work out.

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This story first appeared on MarTech Today.

https://martechtoday.com/when-testing-look-at-the-big-picture-248189


Opinions expressed in this article are those of the guest author and not necessarily Marketing Land. Staff authors are listed here.


Author:
Steve Petersen is a marketing technology manager at Western Governors University in Salt Lake City, Utah. He started on WGU’s marketing website team where he helped create and implement several initiatives including site redesign and maintenance, multivariate testing, user testing and mobile app development. 

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We asked ChatGPT what will be Google (GOOG) stock price for 2030

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We asked ChatGPT what will be Google (GOOG) stock price for 2030

Investors who have invested in Alphabet Inc. (NASDAQ: GOOG) stock have reaped significant benefits from the company’s robust financial performance over the last five years. Google’s dominance in the online advertising market has been a key driver of the company’s consistent revenue growth and impressive profit margins.

In addition, Google has expanded its operations into related fields such as cloud computing and artificial intelligence. These areas show great promise as future growth drivers, making them increasingly attractive to investors. Notably, Alphabet’s stock price has been rising due to investor interest in the company’s recent initiatives in the fast-developing field of artificial intelligence (AI), adding generative AI features to Gmail and Google Docs.

However, when it comes to predicting the future pricing of a corporation like Google, there are many factors to consider. With this in mind, Finbold turned to the artificial intelligence tool ChatGPT to suggest a likely pricing range for GOOG stock by 2030. Although the tool was unable to give a definitive price range, it did note the following:

“Over the long term, Google has a track record of strong financial performance and has shown an ability to adapt to changing market conditions. As such, it’s reasonable to expect that Google’s stock price may continue to appreciate over time.”

GOOG stock price prediction

While attempting to estimate the price range of future transactions, it is essential to consider a variety of measures in addition to the AI chat tool, which includes deep learning algorithms and stock market experts.

Finbold collected forecasts provided by CoinPriceForecast, a finance prediction tool that utilizes machine self-learning technology, to anticipate Google stock price by the end of 2030 to compare with ChatGPT’s projection.

According to the most recent long-term estimate, which Finbold obtained on March 20, the price of Google will rise beyond $200 in 2030 and touch $247 by the end of the year, which would indicate a 141% gain from today to the end of the year.

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2030 GOOG price prediction: Source: CoinPriceForecast

Google has been assigned a recommendation of ‘strong buy’ by the majority of analysts working on Wall Street for a more near-term time frame. Significantly, 36 analysts of the 48 have recommended a “strong buy,” while seven people have advocated a “buy.” The remaining five analysts had given a ‘hold’ rating.

1679313229 737 We asked ChatGPT what will be Google GOOG stock price
Wall Street GOOG 12-month price prediction: Source: TradingView

The average price projection for Alphabet stock over the last three months has been $125.32; this objective represents a 22.31% upside from its current price. It’s interesting to note that the maximum price forecast for the next year is $160, representing a gain of 56.16% from the stock’s current price of $102.46.

While the outlook for Google stock may be positive, it’s important to keep in mind that some potential challenges and risks could impact its performance, including competition from ChatGPT itself, which could affect Google’s price.


Disclaimer: The content on this site should not be considered investment advice. Investing is speculative. When investing, your capital is at risk.

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This Apple Watch app brings ChatGPT to your wrist — here’s why you want it

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Apple Watch Series 8

ChatGPT feels like it is everywhere at the moment; the AI-powered tool is rapidly starting to feel like internet connected home devices where you are left wondering if your flower pot really needed Bluetooth. However, after hearing about a new Apple Watch app that brings ChatGPT to your favorite wrist computer, I’m actually convinced this one is worth checking out.

The new app is called watchGPT and as I tipped off already, it gives you access to ChatGPT from your Apple Watch. Now the $10,000 question (or more accurately the $3.99 question, as that is the one-time cost of the app) is why having ChatGPT on your wrist is remotely necessary, so let’s dive into what exactly the app can do.

What can watchGPT do?

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Discord goes all in with AI: chatbots, automods, whiteboards and more

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Discord goes all in with AI: chatbots, automods, whiteboards and more

AI is the future, at least over on Discord.

The messaging application originally made for gamers has become Gen Z’s favorite online hangout destination of choice, and now it’s rolling out a number of features powered by artificial intelligence.

In an announcement(Opens in a new tab) on Thursday, Discord shared what’s coming to the platform soon: an AI chatbot, an automated AI moderator, a conversation summarizer, an avatar remixer, and a whiteboard. Some of these features begin rolling out today, March 9. Others will launch in the coming weeks and months.

While AI has jumped into the mainstream thanks to the popularity of OpenAI’s ChatGPT chatbot, Discord has had an active AI community for quite a while now. According to the company, third-party AI apps already on the platform already have more than 30 million monthly users. Nearly 3 million servers on Discord have some AI element integrated into the community.

In fact, the biggest community on Discord is Midjourney, a text-to-image AI project which allows users to generate art from right within the server. Discord says Midjourney’s server has more than 13 million members.

So, with AI being such an integral part of Discord already, it seemed like only a matter of time before Discord itself started bringing AI directly into the platform.

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AutoMod AI
Credit: Discord

The first feature coming to some Discord servers as soon as today is AutoMod AI. Discord already has an AutoMod feature, which basically automatically moderates rooms for admins based on the rules of the server. Discord has now integrated OpenAI-powered AI into AutoMod, allowing it to search the server and contact moderators when it thinks rules are possibly being broken. According to Discord, AutoMod AI can also consider the context of a conversation so, for example, users don’t get penalized for posts that are misconstrued.

Clyde is a bot that Discord users may already be familiar with, and starting next week, Clyde is getting an AI upgrade. Currently, the Clyde bot provides information, such as server error messages, and also responds to timeout or ban requests from users and mods. However, that’s pretty much all Clyde was able to do. Until now.

Clyde chatbot

Clyde
Credit: Discord

Clyde will now be able to answer all sorts of questions from users, much like OpenAI’s ChatGPT chatbot. Users simply have to type “@Clyde” followed by their prompt. Clyde will be able to pull up information and also help find specific emojis or GIFs based on a user’s description.

Another AI feature coming to Discord next week is Conversation Summaries. Again, the name is fairly descriptive of what it does. With users all over the world, many Discord channels are always moving regardless of time of day. Conversation Summaries will allow users to catch up on what they missed on a Discover Server. The AI-powered feature will “bundle” chats into topics so users can easily read up on what they find most interesting.

Conversation Summaries

Conversation Summaries
Credit: Discord

Starting today, developers can start playing with Avatar Remix, an open-source Discord app that integrates AI art into the messaging app. Avatar Remix allows users to take a fellow user’s avatar and change it up “using the power of generative image models.” What does that mean? In the demo that Discord showed Mashable, a user was able to add a party hat or a mustache to a friend’s avatar by simply mentioning their username and describing what changes they’d like to make.

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Avatar Remix

Avatar Remix
Credit: Discord

The company is also launching an “AI incubator,” offering support for developers creating AI-powered apps on Discord.

Finally, Discord revealed a feature that’s coming soon that has long been requested by the Discord community: a whiteboard. But, of course, this won’t be just any collaborative whiteboard feature. It’s going to be AI-powered, allowing users to collaborate in generating AI art and more.

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