Attending Salesforce Connections 2024 at McCormick Place in Chicago was an energizing experience, highlighting the forefront of AI integration in business operations. The event gathered industry leaders to explore the latest advancements in artificial intelligence, data integration, and commerce, with a clear focus on how these technologies are reshaping the business landscape.

One of the most notable innovations introduced was Einstein Personalization, a tool poised to revolutionize customer engagement through real-time, hyper-personalized experiences. This groundbreaking feature, along with the broader integration of AI across the Salesforce ecosystem, underscores the significant shift towards data-driven strategies in the era of the ‘choice economy.’

The Choice Economy and Advanced Personalization

As Robert Marusi, Chief Commercial Officer at Turtle Bay, articulated during the event’s keynote session, we are firmly in the era of the ‘choice economy.’ Marusi emphasized, “It’s about the client, the consumer, being in the middle of this omni-channel spoke. It’s our responsibility as brand people to be delivering choices to that consumer.”

This notion of placing the consumer at the center aligns perfectly with Salesforce’s innovations aimed at providing advanced, hyper-personalized experiences across multiple channels.

Einstein Personalization: Real-Time Customer Profiles

A standout feature introduced at the conference is Einstein Personalization, which promises to elevate customer engagement through real-time, unified profiles that integrate seamlessly across Sales Cloud, Service Cloud, Marketing Cloud, and websites.

The power of Einstein Personalization lies in its foundation within Data Cloud and Hyperforce, ensuring both speed and scalability. By enabling zero-copy data access to Data Lakes, businesses gain a comprehensive, up-to-the-minute view of their customers.

This robust real-time profile functionality allows organizations to convert customer profiles into dynamic data graphs in Data Cloud. These data graphs synthesize information from diverse sources within Salesforce, providing a consolidated view that enhances decision-making and customer insights.

Key Features of Einstein Personalization

• Machine Learning Rule-Based Decisions: Enhances decision-making processes with AI-driven insights.
• Real-Time Targeting Rules: Enables precise and timely engagement with target audiences.
• Experimentation: Facilitates testing and other experimental methods to optimize strategies.
• Analytics and Attributes: Provides deep analytical capabilities to understand customer behavior and preferences

Integration of AI Across the Salesforce Ecosystem

Salesforce’s President and Chief Marketing Officer, Ariel Kelman, highlighted during the keynote that data integration is critical for leveraging AI effectively. Kelman addressed the pervasive issue of “islands of trapped data” within enterprises, advocating for a unified approach to data management.

He introduced Einstein 1, Salesforce’s comprehensive platform that amalgamates data, customer relationship management, and AI capabilities, extending even to external data repositories like Snowflake and Redshift.

As businesses continue to navigate the complexities of the choice economy, tools like Einstein Personalization and the broader Salesforce ecosystem will be pivotal in delivering seamless, personalized experiences across all channels.

Interested in More Connections Content? 

If you weren’t able to attend Connections in person or want to revisit some of the sessions, you can access select sessions on Salesforce+, Salesforce’s free on-demand content platform.  

Perficient + Salesforce 

We are a Salesforce Summit Partner with more than two decades of experience delivering digital solutions in the manufacturing, automotive, healthcare, financial services, and high-tech industries. Our team has deep expertise in all Salesforce Clouds and products, artificial intelligence, DevOps, and specialized domains to help you reap the benefits of implementing Salesforce solutions.  

Perficient was excited to have an opportunity to bring the Flutter Meetup Platform to our Nagpur office for the first time for an interactive, in-person event.

As Flutter Nagpur reached out to Perficient to become a Venue Partner, Perficient is uniquely positioned to develop and accelerate Flutter opportunities.

What is Flutter?

Flutter is a Google framework that allows developers to build mobile, web, desktop, and embedded applications using one shared codebase. Flutter is Google’s UI toolkit for building beautiful, natively compiled applications for mobile, web, and desktop from a single codebase. Flutter uses the programming language Dart and compiles it into machine code. Host devices understand this code, which ensures a fast and effective performance.

Flutter allows developers to write code once and use it across multiple platforms Android, iOS, web, and desktop. This single codebase approach significantly reduces development time and effort. As a result, it helps in saving a significant amount of the cost of app development.

While Flutter does have a learning curve, it can be a good choice for beginners because of its ease of use and comprehensive documentation. Additionally, Flutter’s “hot reload” feature allows for quick iteration and makes.

Flutter is a framework that can be used for both front-end and back-end development. However, most Flutter developers use it for the former. This is because Flutter makes it easy to create beautiful, interactive user interfaces.

Flutter Meetup from Start to Finish

It started with Flutter Nagpur members getting the ball rolling by sharing the meetup agenda and brief introductions to speakers. Prashant Nandanwar, Director, Perficient GDC Nagpur, shared opening ceremony notes that encompassed the platform’s capabilities and mobile solutions alongside Perficient’s broader range of services and capabilities.

Several Guest speakers from Flutter India enlightened 60+ participants with exciting sessions that fostered engaging discussions, including quizzes and Q&A. Some of the session topics were Flutter Webrtc, Computer vision with ML kit, and Hive: Local Storage. Perficient India speakers Suriya Kalidoss and Sunita Vangapandu delivered a step-ahead session on “Flutter-ing into Future with Generative AI,” which took the participants on a fun and excitement-filled ride to the future of Flutter with AI. The session was very well received.

Participants engaged in valuable discussions during the networking break at the middle of the meetup and also discovered Flutter opportunities. In addition to insightful group discussions and product demonstrations, the Flutter Meetup also included a trivia contest.

The top 3 winners were awarded. By the end of the meetup, all the speakers were felicitated with a memento and participants indulged into various goodies from Flutter Nagpur.

Perficient Nagpur looks forward to hosting more Flutter Meetups in the future to encourage thoughtful discussion and sharing of knowledge within the Flutter community.

Last week, Google announced Gemini, its groundbreaking multimodal AI model designed to push the boundaries of performance and versatility in AI technology. This means it can generalize and understand; operate across platforms; and is trained across different types of information, including text, code, audio, image, and video.

It’s also the most flexible AI model yet – it can run on everything from data centers to mobile devices, making it ideal for developers and customers to build and scale with AI.

Gemini is available in three versions:

• Gemini Ultra: The largest and most capable for complex tasks
• Gemini Pro: Ideal for scaling across a wide range of tasks
• Gemini Nano: The most efficient for on-device tasks

What Sets Gemini Apart?

Gemini outperforms GPT-4 across multiple benchmarks, including massive multitask language understanding (MMLU), reasoning, math, and code. Gemini Ultra surpasses state-of-the-art results on 30/32 widely used academic benchmarks in large language model (LLM) research.

When to Expect Gemini in Action:

12/6/23: Bard and Pixel 8 Pro can now leverage Gemini Pro and Nano for tasks like Summarize in Recorder and Smart Reply in Gboard

12/13/23: Developers and enterprise customers can access Gemini Pro via the Gemini API in Google AI Studio or Google Cloud Vertex AI

Early 2024: Bard Advanced, launching with Gemini Ultra, will offer a new level of interaction with top-tier models and capabilities

Why Should Salesforce Users Care?

AI enthusiasts have an exciting new benchmark to explore, comparing Gemini’s performance against other models. Businesses seeking AI integration for growth now have a powerful model to assess and potentially adopt.

Salesforce customers can utilize Gemini Pro and Ultra’s generative capabilities through Google Cloud Vertex AI using the “bring your own model” feature of the Einstein Trust Layer.

• Einstein GPT: This integration allows users to leverage Gemini’s generative AI capabilities directly within Salesforce. Generate personalized users, product descriptions, social media posts, and other content based on your customer data.
• Einstein Copilot: This bidirectional integration connects Salesforce with Google Workspace. Seamlessly generate content in Google Workspace, update records in Salesforce, and trigger workflows based on specific actions.
• Custom integrations: To allow for a highly tailored solution, you can build custom integrations using Salesforce APIs to connect Gemini with any specific functionalities.

Benefits of Integrating Gemini with Salesforce

• Increased efficiency and productivity
• Improved customer engagement
• Enhanced decision making
• Personalized customer experiences
• Automated workflows

Use Cases for Gemini and Salesforce Integration

Sales Cloud:

• Personalized email campaigns and product descriptions: Create personalized, targeted email campaigns and product descriptions to drive engagement and conversion
• Automated lead qualification: Automatically analyze data and score leads to allow sales reps to focus on qualified leads and close deals

Service Cloud:

• Personalized customer support responses: Generate personalized responses to customer inquiries for faster resolution times and improved customer satisfaction
• Knowledge base article creation: Create and update knowledge base articles automatically with accurate and informative content
• Automated ticket routing: Streamline and automate tickets to the most qualified customer service agent based on the customer data and issue to ensure quick and efficient resolution

Marketing Cloud:

• Social media content and ad copy: Draft engaging and relevant copy based on audience insights to increase brand awareness and engagement
• Automated email marketing campaigns: Design and automate email campaigns to deliver relevant content to customers for maximum campaign effectiveness

Interested in Learning More?

Contact us to learn more about Perficient’s AI expertise, and multicloud capabilities. Our partnerships with Google and Salesforce make us the partner of choice to help you integrate Salesforce and Gemini.

Last week, Perficient attended the largest AI event in the world, Dreamforce, in San Francisco. During the three-day conference, 40,000 Salesforce partners, clients, and vendors got together to hear from Salesforce leadership, industry experts, clients, and a handful of celebrities, as well as get hands-on experience with the Salesforce platform.  

Fueled by generative AI, IDC reports that Salesforce and its partner ecosystem will create a net gain of more than $2T in business revenue and 11.6M jobs by 2028. This solidifies that Salesforce is leading the charge in AI, and Perficient is leaning into the partnership to bring innovative solutions to our clients.  

We learned about new Salesforce capabilities and technologies, heard new product announcements, and connected with peers and clients from across industries. Read on to learn about my top nine takeaways.  

Data, AI, and Trust 

There were several key themes at this year’s Dreamforce, including Data, AI, and trust. Salesforce announced its Tenets of Trusted, Ethical & Humane AI that guide all of its innovations, which are designed to keep the trust between Salesforce and its customers, and Salesforce partners and their customers.  

These tenets are: 

• Your data isn’t your product 
• You control access to your data 
• We prioritize accurate, verifiable results 
• Our product policies protect human rights 
• We advance responsible AI globally 
• Transparency builds trust  

Launch of Einstein 1 Platform 

The Einstein 1 Platform is a new AI-driven platform that enables businesses to leverage data, develop low-code AI apps, and revolutionize CRM experiences. It also promises to make application building safer and more efficient by using machine learning to identify and mitigate security risks, and provide developers with tools to build more secure applications.  

The key to Einstein 1 is Einstein Copilot, a new conversational AI assistant that is built into the user interface of Salesforce applications to help users with their Salesforce tasks. Users can interact with the assistant in natural language, and it can offer additional actions beyond user queries, such as automatically completing tasks, providing recommendations, and answering questions.  

Free Data Cloud and Tableau Licenses 

Perhaps the biggest announcement to come out of Dreamforce, Salesforce is providing free Data Cloud and Tableau licenses for existing Sales and Service Cloud customers – Enterprise or Unlimited edition – and includes two free Tableau Creator licenses and Data Cloud licenses for up to 10,000 profiles.  

Enhanced Data Cloud Features 

With Data cloud embedded in the Einstein 1 platform, dynamic, real-time data from different systems can be automated, called Data Cloud-Triggered Flow. This allows users to create flows that are triggered by changes in data. Another new feature is Data Graphs, which are visual representations of data relationships that can help users better understand their data.  

Increased Capabilities for Marketing Cloud and Commerce Cloud 

Salesforce introduced 26 generative AI capabilities between Marketing Cloud and Commerce Cloud, 14 of which are in GA now, with an additional 14 to arrive by the end of 2024. These features are built on Data Cloud, and help create more personalized and engaging customer experiences. Those currently available are: 

• Marketing Cloud: Marketing in Starter, DC + Account Engagement, Personalization Insights in Core, Data Spaces in Data Cloud, Advertising Audiences in Data Cloud, Einstein Lookalikes in Data Cloud 
• Commerce Cloud: Salesforce Payments, Order Management, Omnichannel Inventory, Reorder Portal, Pay Now, Generative Product Descriptions 

New Slack Innovations 

Slack now has AI features to enhance productivity and save time. These features include thread summaries, channel recaps, and search answers; automation; and Slack Lists to track projects, product launches, and handle approvals.  

Most Sustainable Dreamforce Ever 

• 74% of structures were recycled or reused 
• 5,500 square feet of biodegradable signage 
• 10M gallons of water conserved 
• 100% compostable meal packaging 
• Multiple Net Zero Cloud sessions 
• New Net Zero Cloud capabilities, including simplified ESG reporting for companies amid changing regulations; Einstein for Net Zero Cloud is expected in Spring 2024 

New Life Sciences Cloud 

The newest industry cloud, Life Sciences Cloud, is tailored to expedite onboarding on the Salesforce platform. More information will be available in the coming weeks.  

Expanded Partnerships 

Google 

Salesforce and Google are expanding their partnership to further integrate their products. Salesforce is the first partner to integrate with the Google Workspace Duet AI extensions framework, and Google Workspace is the first extensibility partner for Salesforce Eistein Copilot. These integrations will generate tailored content in Google Workspace and Salesforce, ensuring Salesforce records stay current with Google Workspace insights.  

AWS 

In 2024, Salesforce and AWS will pilot a series of integrations to enable the sharing of data lakes and large language models (LLMs). Amazon Redshift, EMR, Athena, Bedrock, and SageMaker users can leverage foundation models within Salesforce with security – the process has no copying and moving of data.  

An existing integration between Service Cloud Voice with Amazon Connect allows users to launch contact center services with Salesforce’s agent desktop with various Amazon services to improve agent productivity.  

Databricks 

Salesforce and Databricks are expanding their partnership to allow for seamless merging of data from Data Cloud with external Databricks Lakehouse Platform data. Ultimately, this will reduce the cost and complexity of moving and copying data while keeping security, governance, and trust standards. Users can access Databricks data in Data Cloud and vice versa. 

Interested in More Dreamforce Content? 

If you weren’t able to attend Dreamforce in person or want to revisit some of the sessions, you can access select sessions on Salesforce+, Salesforce’s free on-demand content platform.  

Perficient + Salesforce 

We are a Salesforce Summit Partner with more than two decades of experience delivering digital solutions in the manufacturing, automotive, healthcare, financial services, and high-tech industries. Our team has deep expertise in all Salesforce Clouds and products, artificial intelligence, DevOps, and specialized domains to help you reap the benefits of implementing Salesforce solutions.  

Google’s announcement of the upcoming retirement of their Optimize A/B testing product took me by surprise. In my mind’s eye I still see experimentation as shiny and exciting technology. I’ll blame this on the finesse of leading software providers, who have adeptly added sophisticated testing and personalization capabilities over the years, right in line with the mounting complexity faced by digital marketers.

The most recent optimization innovations like hyper-personalization, audience patterns and insights, and experimentation automation – all powered by machine learning – have kept geeks like me enamored and sparked the interest of less-nerdy marketers and even IT teams. Setting AI aside, though, these solutions have been around long enough to see two generations of Google testing products enter and exit the stage. They’re downright mature.

Once the demise of Google Optimize began trending everywhere I noticed a peculiarity. Usage seemed to be far lower than I’d have expected given the maturity of the technology. I’ve seen rough guesstimates that a whopping 25,000 Google Optimize accounts have been created during the product’s lifespan, yet only about 5,000 have ever run an experiment. More than 6,000 accounts are estimated to have installed the tool but never used it, with thousands of others conducting just 1 test before shelving it.

Google Optimize’s staying power tells us some organizations found it to be a good fit for their experimentation needs, especially if they wanted to keep things basic and low volume. But others weren’t shy about their frustrations, like an unintuitive user interface, cumbersome workflow, performance issues, and the complexity of trying to leverage GA integration (e.g., for audience targeting).

A Broader View

Taking a broader view, Gartner reported that slightly more than half of large enterprises have implemented some form of A/B testing, whether Google Optimize or one of many other experimentation products. This is strikingly low adoption among a segment that has prioritized investments in improving the customer experience and in martech overall. Contrast this with how marketers were, as a whole, embraced a slew of marketing technologies over the past decade to help improve customer experience and power marketing performance: CRM, CMS, marketing automation, google ads, ABM, video marketing…and so many more. At one point these were each new, but we adopted, adapted, and made them central to how we work.

So…why not experimentation? Why hasn’t every organization made it core to how they shape and refine impactful digital experiences for their customers? And why have many tried but abandoned it, while adopting other martech with relative ease?

The Complexity Factor

The plain fact is many optimization tools “for marketing” were not truly designed for ease of use by marketers. They are of great value and easily usable by data scientists and analytics experts. The problem is (most) marketers are not data scientists.

I’ll share a personal example: I have configured and run experiments using a couple of different legacy platforms. With a master’s degree in tech marketing, stats courses, and even some SPSS under my belt, I still found myself referring back to textbooks and Google to try to decipher a cryptic UI and data labels. I am by no means a data scientist, and I’m fairly certain the slow adoption of testing among marketers overall correlates with the level of frustration we’ve experienced over the years.

I’m also certain of this: We have to figure this out because we need data science to solve the complex marketing problems of 2023 and beyond.

I’m thinking about questions and challenges like:

• Which deals in the pipeline are most likely to close and should be getting most of our sales team’s focus?
• What changes to our commerce site would drive the biggest reduction in cart abandonment?
• Which of these experience design options is more likely to engage our target user on their journey – and should be implemented in our app?

Whether we’re talking about lead scoring, conversion optimization, user testing, or any number of other questions, experimentation powered by data science is the right hammer for the nail.

Next Steps: Harnessing the Power of Experimentation

Thankfully, marketing leaders seem to realize this, with nearly 80 percent reporting plans to increase investment in experimentation over the next 12 months in a recent Econsultancy study. Each of those leaders now faces important questions about how to get set up for success and make the most of their investment to reap measurable KPI improvements.

I’d love to be able to offer all of those marketers – and you – a standard guidebook that will fit all of your organizations but there’s truly no silver bullet. I can at least share a few suggestions that come to mind for any marketing team looking to start or improve an experimentation program:

1. Assess experimentation platforms carefully and choose one that will work for you, rather than just making more work for you. Look for those that are more intuitive, better performing, and yield more reliable results than legacy options (like Google Optimize). They exist – Optimizely immediately comes to mind – and they work.

2. Invest in data literacy development for the entire marketing team – not to become data scientists, but to become comfortable in the language of data, to understand the meaning of being a data-driven marketing organization, and to be able to effectively and collaborate engage with the data and analytics teams across your organization to assess problems and challenges.

3. Call on experts to help you build an experimentation program that fits your organization and will be sustainable once the technology vendor and consultants are gone. That includes training and enablement for your team on everything from building hypotheses and prioritizing tests, to configuring and executing experiments, through assessing results and deciding what adjustments to make. Look to your vendor partners for help with defining what a culture of experimentation could look like in your unique organization, honing processes, and roles, and outlining a governance model to prevent friction and costly missteps.

Seizing the Moment

Thanks to Google, experimentation is in the air. I encourage you to seize the moment and strike up a conversation about its potential and your path forward – with your team, your leaders, or just me .

Over the past several months I start receiving messages from Google announcing its deprecation of Universal Analytics in favor of Analytics 4 from July 1, 2023 (and Analytics 360 got an additional 3 months beyond that date until October 1, 2023). The new tool, the company claims, has more power to measure many different types of data, providing new analytics capabilities, in particular through machine learning. So let’s find out…

What is Google Analytics 4

This is a new resource looking a little different than Universal Analytics (UA) which is much easier and faster to set up. Google repeatedly calls GA4 the future of analytics, justifying it as being:

1. Event-centric scalable cross-platform analytics.
2. The new resource featuring ML and NLP functions available to all GA users.
3. Privacy-driven platform, eliminating the need of setting cookies to maintain confidentiality
4. Seamlessly integrated with all Google products. Yes, YouTube is inclusive!
5. Capable of cross-platform identification, exposing the entire user journey as all their actions from various devices.

Being even-centric GA4 processes analytics in the same standardized way across all devices and platforms. This definitely improves the quality of data and provides a consolidated report across the entire user journey. Sounds sweet, so let’s look at these features in more detail.

Machine Learning

One of the main advantages of Google Analytics 4 is machine learning combined with NLP features used for:

• Predicting conversions’ likelihood and creating Google Ads audiences based on these predictions.
• Alerting you on important data trends, such as products with growing demand as the result of increased demand.
• Predict customer churns so you can effectively invest in customer retention well in advance
• Find some other anomalies in reports.

Developers plan to even further enhance features by adding new predictions, such as ARPU marketers could adjust their strategies for increasing ROI by using ML insights.

Privacy becomes the top priority

• GA4 is privacy-driven. For example, gtag.js library is designed to work without setting cookies. There is an expectation from Google to abandon using ClientID in favor of the internal CRM-generated identifier such as User ID which stays shared cross-platform between devices and browsers.
• An IP address is anonymous by default and that cannot be changed.

Seamless integration with Google tools

As I mentioned above, YouTube integration is a killing feature of GA4 and developers are actively working on improving the quality of scoring YouTube campaigns (such as view-through conversions). This enables one to address the questions:

• How does YouTube campaign influence engagement metrics?
• How does it affect the bounce rate?
• Any other (than conversions) site events?

With deeper Google Ads integration, you can set the audiences and run campaigns for bringing more relevant customers, regardless of the device they use.

Previously with Universal Analytics, BigQuery Export was only available to paid users, while GA4 features if free of charge for everyone.

Cross-Platform User Identification

Google Analytics 4 counts actual users interacting with your business, not the session-based devices or browsers, as it was previously. It relies on 3 levels of identification:

• user_id
• device_id
• Google Signals

With event-based analytics, you can more accurately track user journeys, starting from the first touch to the repeating conversions. What is important here, when the user performs the same event on different devices, this data merges into a single event. For example, putting an item in the cart on both a laptop and a smartphone would count as a single “Add to Cart” event.

Google Analytics 4 vs Universal Analytics

Let’s start by comparing the key tracking concepts of GA4:

• Analytics gets built around events rather than sessions. Sessions are artificial concepts, therefore Google moves away from them. Nevertheless, would you have the actual need for session data, you can generate it from the raw data in BigQuery.
• There are end-to-end data collection settings for the entire site and those that change with each event.
• UserID is the first-class citizen in GA4 so you don’t need to create an end-to-end view, as these are OOB.

There are 3 types of events in GA4:

• Automatically collected: such as pageview, sessionstart, viewsearchresults, scroll, file_download.
• Recommended: grouped by some business area, like Retail & Ecommerce, Travel, or Gaming.
• Custom: all other events that you would like to implement and track – are subject to the event collection limits.

You have to implement recommended and custom events yourself. Each event can have Custom Dimensions parameters which are end-to-end for most reports.

Google Analytics 4 does not feature concepts of category, action, and event labels. Custom dimensions and metrics must be explicitly set up.

Page views become a pageview event. As I said above, it is collected automatically as soon as you implemented config function of gtag.js. You may read more about it at this link.

Pageview event operates following pre-defined parameters:

• page_location
• page_path
• page_title
• page_referrer

Counting session in Google Analytics 4

GA4 still has sessions in reports, but they are counted differently than it was in Universal Analytics:

1. The session is automatically initiated by session_start event.
2. The session duration is the interval between the first and last events.
3. Interactions get automatically recognized (meaning you don’t need to send an interaction event).
4. Late hit events are processed up to 72 hours late (compared to 4 hours in UA Properties).
5. Currently, GA4 does not allow configuring session duration.

If you count and compare sessions in a GA4 report and Universal Analytics, you may find fewer sessions in GA 4. That occurs because the hits sent after the session end can be later assigned to the correct session within 72 hours. That also means that session reports will take longer to generate.

New Feature: User Properties

GA4 brings a new feature – User Properties, the attributes describing groups of your user base, such as their language preferences or geolocation, gender, city, new or returning customer attribute, etc. You can define any custom properties for segmenting your audience for personalizing the ads.

Should I hurry up switching to GA4?

If you actively use YouTube Ads and User-ID based remarketing, consider switching to GA4 straight away. The same applies to active Firebase users and those familiar with its data collection logic, as well as exporting tables in BigQuery.

The sooner you move to GA 4, the sooner you start collecting historical data, the more information you have for decision-making, and the sooner you benefit from ML insights. As I mentioned above, the data structure and logic of collections in GA 4 and Universal Analytics significantly differ. Therefore, it will be problematic to combine the data from both resources.

Concerns

There is no reporting API, so one has to use BigQuery. If your team has not yet worked with raw data in BigQuery, and is not familiar with the principles of Firebase it may take some time. Also, export conversions and integration with other Google products are not yet fully functional.

Upgrading to Google Analytics 4

I’d suggest using both versions of GA assets for a while. In order to do so:

1. Create and configure a GA 4 resource.
2. Add the tracking code (manually or through GTM which is faster and easier).
3. Consider what events and parameters you want to collect in the new resource type.
4. Use two resource types at the same time to compare how data is collected.

Summary

1. This is the most dramatic update of the GA logic: now everything is built around events, event parameters, and users rather than sessions as before.
2. Cross-platform analytics between the site and applications OOB is one of the key features of GA4.
3. To set up a new GA4 resource, you can use configured GA via gtag.js or using GTM.
4. Google recommends launching the new Google Analytics 4 in parallel to collect data into it, using standard GA as the source of historical data.
5. There are shortcomings in the new GA4  with not all features available so far, but developers are rolling them out gradually.
6. You can configure free data batching from GA4 to BigQuery.
7. The earlier you set up GA4, the more historical data will get collected.

Hope you find this helpful!

If you are a Google Optimize customer, then September 30^th is an important date for your organization. Google  Optimize and Optimize 360 will no longer be available after this date. Businesses that rely on Google Optimize will have to find a new solution and should start researching alternatives.

Optimizely, the leader in experimentation, is the ideal choice for Google Optimize users seeking a change. Not only is the platform endorsed by Google, but it is also the premier experimentation platform that evolves alongside your company’s growth.  Optimizely Experimentation provides a unified platform for expanding experimentation, driving innovation, and optimizing customer experiences. It allows teams to quickly comprehend data, uncover meaningful insights, and engage with your customers.

Google Optimize and Optimizely Experimentation are both popular experimentation and optimization platforms for digital experiences. However, there are key differences between the two:

• Feature Rich: Optimizely Web Experimentation offers all the features you need for web experimentation. It enables you to run every type of experiment, create customized experiences, target specific messages, and make recommendations.
• Integration: Optimizely Experimentation integrates with a wide range of tools, including Google products, while Google Optimize is primarily designed to work with Google Analytics.
• User-friendly: Optimizely Experimentation has a more user-friendly interface with a visual editor that makes it easier to create and run experiments.
• Accuracy: Optimizely Web Experimentation boasts a highly reliable and precise statistical model, surpassing the accuracy of other solutions in the market. Its ability to consider changes over time and avoid false positives in real time makes it stand out. The stats engine provides immediate, accurate results at a rapid pace.
• Customization: Optimizely Experimentation provides more customization options. This allows users to experiment with web and server-side changes to perform more complex testing.
• Easy to Implement: Eliminate the need for Google Optimize with a simple update. This solution is website agnostic and enables marketers and developers to experiment and test at every customer touchpoint.
• Experimentation Length: Optimizely does not impose restrictions on test duration, avoiding the issue of limited exposure and insufficient traffic faced by 90-day tests. This allows for focused tests to be executed without constraints.

How We Can Help

Optimizely Experimentation is often considered a more comprehensive and versatile platform for web experimentation. Conversion optimization is about consistent, data-driven improvement to drive success.

If you’re considering how experimentation with Optimizely could help you improve the customer experience and increase conversion rates, Perficient can help. As an Optimizely Premier Platinum Partner, we have the resources and readiness to assist Google Optimize users with their transition to Optimizely. Our team of Optimizely MVPs is prepared to support you in achieving greater ROI by enabling your team, helping to develop your optimization program, and sharing best practices to shorten your learning curve — and your path to positive KPI impacts.

For those interested in learning more about our Optimizely Experimentation capabilities, reach out to me on LinkedIn or fill out our contact form.

The battle to use artificial intelligence is heating up as companies worldwide are investing heavily in the technology. Companies are investing in AI to gain a competitive edge in the market, as well as to automate processes and increase efficiency.

Google on Monday released its own chatbot similar to ChatGPT, called Google Bard, as the battle to use artificial intelligence heats up.

Bard is an experimental conversational AI service powered by LaMDA. Built using our large language models and drawing on information from the web, it’s a launchpad for curiosity and can help simplify complex topics → http://goo.gle/3HBZQtu.

Bard aims to “combine the breadth of the world’s knowledge with the power, intelligence, and creativity of [Google’s] large language models” by drawing from information around the web and presenting it in fresh, easy-to-understand ways.

Bard is a new voice-based search technology that allows users to perform web searches using their voice. This technology allows users to simply speak their search queries into their devices, and Google will return relevant results.

One of the key benefits of Google Bard is that it makes searching the web much faster and more convenient. Rather than having to type out long search queries, users can simply speak their query and get instant results. Additionally, Google Bard can help users save time by providing more relevant results. For example, if a user says, “show me the latest news on Elon Musk,” Google Bard will present the most recent articles about Elon Musk rather than having to sift through multiple pages of irrelevant results.

Another benefit of Google Bard is that it is designed to be highly accurate. Google’s speech recognition technology has been fine-tuned over many years, and the company has invested heavily in ensuring that the technology works well for various accents and languages. This means that no matter where you are in the world, you can use Google Bard to quickly and easily find the information you’re looking for.

In conclusion, Google Bard is a powerful tool that makes it easier and faster for users to find information on the web. Whether you’re looking for news, product reviews, or simply answers to your questions, Google Bard makes it easier to get the information you need quickly and accurately.

Picture this: You’re sitting at your desk, anxiously refreshing your website’s rankings, eagerly awaiting the moment you see your target keywords sitting pretty at the top of the search engine results. But then, the moment of triumph turns into one of disappointment and frustration as you realize that the page Google has chosen to rank for those keywords is completely off-topic or irrelevant. Sound familiar? You’re not alone in this frustrating situation. Unfortunately, when the wrong page ranks for your keywords, it can affect your user experience and lead to a higher bounce rate. But before you throw your hands up in despair, know there is a solution. In this blog post, we’ll explore why Google might be ranking the wrong pages for your keywords, and more importantly, we’ll share the steps you can take to fix the issue and reclaim your website’s search engine visibility.  

Why is Google Ranking the Wrong Page? 

You’ve probably heard of the Search Engine Optimization (SEO) process, but have you heard of Search Engine De-Optimization? 

It is the flip side of what we all do, and the main goal is to remove a website from SERPs that is driving the wrong type of traffic. If the wrong page appears in Google (or any other search engine), it will lower your CTR (click-through rate) and organic traffic due to its irrelevant title or description. Now that you know a wrong page is ranking, what if I say there is a solution to fix this? Look out for these points. If you found more than two, there you are! You must improve your keyword rankings. 

• Your pages earn some traffic but no conversions. 
• You are getting queries from users that are not relevant to the business. 
• Your pages are falling short of expectations to reach the goal. 
• Your content ranking for conversion keywords rather than revenue-generating pages. 

The Top Two Reasons Why Google Ranks the Wrong Page for a Targeted Query

1. Search Intent Mismatch 

If the keywords used on a website do not match the user’s search intent, that page may rank incorrectly. To avoid this, ensure your content is tailored to the user’s search intent. 

• If your goal is to let users buy your product, choose keywords with transactional intent. 
• If your goal is to find helpful information or answers to their questions, choose the informational intent keywords.  
• Or, if your goal is to find a page, choose terms with navigational intent. 

So, tailoring your content as per the search intent is crucial.  

2. Keyword Cannibalization 

This keyword cannibalization occurs when multiple pages on your website target the same focus keyword and can confuse Google and result in both pages having a lower position in SERPs. However, it can be resolved by following specific strategies. Let’s dive in! 

How To Improve Keyword Rankings? 

How can you de-optimize or remove a page from SERP and put the desired one in place? Here’s a simple and practical 6-step guide to improving your keyword rankings and indexing the relevant page.  

Step 1: Identify the page you want to rank for a specific keyword. Ensure that the page is not blocked by the robots.txt file, as this will prevent Google from crawling and indexing the page. 

Step 2: Analyze the intent behind the focus keyword and assign related keywords to relevant pages. Use a tool such as Semrush to find related keywords. 

Step 3: Optimize the content on the page by reviewing the meta information, such as the title, description, and keywords. Ensure that the content is relevant, unique, and high-quality. 

Step 4: Check the internal and external links to the page. The desired page will not rank for the targeted keyword if the links point to an irrelevant page. Use Screaming frog or Google Search Console to audit your links. 

Step 5: Analyze user data from analytics tools such as Google Analytics. Identify issues such as low click-through rates (CTR), high bounce rates, and poor conversion rates and work to improve them. 

Step 6: Once the above steps have been completed, submit the page to Google Search Console and wait for Google to index the page. Depending on your site and relevancy, it could take a couple of days or weeks to take effect.  

Note: Improving keyword rankings is an ongoing process and requires consistent effort to maintain and improve. Additionally, it’s always a good idea to keep an eye on Google algorithm updates and adjust your strategy accordingly. And It’s also important to note that manipulating or attempting to manipulate the SERP can result in penalties from Google. Hence, it’s best to focus on creating high-quality, relevant content and building a solid backlink profile. 

Further Considerations for Improving Keyword Rankings 

Even after implementing the above steps, if you are getting the wrong page ranking for your focus keyword, follow the below, which could help you fix the issue.  

• Use a 301 redirect if you want to remove your original page during de-optimization. 
• Analyze the Backlinks profile and compare it to competitors. This will help you fill the gaps and earn high-quality links to your desired page.  
• Page Speed Optimizations – Focus on improving page speed by following the opportunities listed in the Google PageSpeed Insights tool.  
• Monitor engagement metrics such as page scroll, time spent on the page, and conversions and make adjustments to improve user experience if necessary.  

By implementing these additional strategies, you can resolve the issue of the wrong page ranking and drive targeted traffic to your intended page. 

We have all encountered those annoying bots called reCAPTCHA which appear just when we are about to hit that Submit button. They are needed to keep fraudulent activity at bay and promoting legitimate customers get in and attackers out. In this blog post, we will explore some options we have to enable reCAPTCHA Enterprise on a Sitecore Website. For ease of reference, I have explained the topic in parts. Before we start, a good starting point would be to follow the official documentation for reCAPTCHA.

https://cloud.google.com/recaptcha-enterprise/docs

The flowchart on this document https://cloud.google.com/recaptcha-enterprise/docs/getting-started is very useful in determining the correct installation path. In this Demo, we will explore the paths checked.

In this post we will be following the path to Setup Enterprise reCAPTCHA on non-google cloud environments. The steps are

1. Create the reCAPTCHA SiteKey and ApiKey. Since this is Enterprise, our friendly IT people did the job for me. You would need the following.
   • SiteKey – going with score-based since they have minimal friction / no user interaction.
   • ApiKey
   • ProjectId
2. Install the score-based SiteKey . Recommended to be installed in all the following places.
   • Forms
   • Actions ( user interactions)
   • In the background of all webpages.
3. Create an assessment / get the score for the user interaction.
4. Interpretation of the score.

Since Step 1 is done, moving on to Step 2 – Enterprise reCAPTCHA on WebPages

Healthcare organizations play a key role in offering access to care, motivating skilled workers, and acting as social safety nets in their communities. They, along with life sciences organizations, serve on the front lines of addressing health equity.

With a decade of experience in data content and knowledge, specializing in document processing, AI solutions, and natural language solutions, I strive to apply my technical and industry expertise to the top-of-mind issue of diversity, equity, and inclusion in healthcare.

Here are five questions that I hear commonly in my line of work:

1. What is the digital divide, and how does it impact healthcare consumers?

There are still too many people in this country who don’t have reliable access to computing devices and the internet in their homes. If we think back to the beginning of the pandemic, we can see this in sharp relief. The number one impediment to the shift to virtual school was that kids didn’t have devices or reliable internet at home.

We also saw quite clearly that the divide is disproportionately impacting low income people in disadvantaged neighborhoods.

The problem is both affordability and access.

The result, through a healthcare lens, is that people without reliable access to the internet have less access to information they can use to manage their health.

They are less able to find a doctor who’s a good fit for them. Their access to information about their insurance policy and what is covered is more restricted. They are less able to access telehealth services and see a provider from home.

All this compounds because we’re using digital and internet-connected tools to improve healthcare and outcomes for patients. But ultimately, the digital divide means we’re achieving marginal gains for the populations with the best outcomes already and not getting significant gains from the populations that need support the most.

2. How can organizations maintain an ethical stance while using AI/ML in healthcare?

Focus on intrinsic bias, the subconscious stereotypes that affect the way individuals make decisions. People have intrinsic biases picked up from their environment that require conscious acknowledgement and attention. Machine learning models also pick up these biases. This happens because models are trained on data about historical human decisions, so the human biases come through (and can even be amplified). It’s critical to understand where a model comes from, how it was trained, and why it was created before using it.

Ethical use of AI/ML in healthcare requires careful attention to detail and, often, human review of machine decisions in order to build trust.

3. How can HCOs manage inherent bias in data? Is it possible to eliminate it?

At this point, we’re working to manage bias, not eliminate it. This is most critical for training machine learning models and correctly interpreting the results. We generally recommend using appropriate tools to help detect bias in model predictions and to use those detections to drive retraining and repredicting.

Here are some of the simplest tools in our arsenal:

• Flip the offending parameter and try again.
• Determine if the model would have made a different prediction if the person was white and male.
• Use that additional data point to advise a human on their decision.

For healthcare in particular, the human in the loop is critically important. There are some cases where membership in a protected class changes a prediction because it acts as a proxy for key genetic factor (man or woman, white or Black). The computer can easily correct for bias when reviewing a loan application. However, when evaluating heart attack risk, there are specific health factors that can be predicted by race or gender.

4. Why is it important to educate data scientists in this area?

Data scientists need to be aware of potential issues and omit protected class information from model training sets whenever possible. This is very difficult to do in healthcare, because that information can be used to predict outcomes.

The data scientist needs to understand the likelihood that there will be a problem and be trained to recognize problematic patterns. This is also why it’s very important for data scientists to have some understanding of the medical or scientific domain about which they’re building a model.

They need to understand the context of the data they’re using and the predictions they’re making to understand if protected classes driving outcomes is expected or unexpected.

5: What tools are available to identify bias in AI/ML models and how can an organization choose the right tool?

Tools like IBM OpenScale, Amazon Sagemaker Clarify, Google What-if and Microsoft Fairlearn are a great starting point in terms of detecting bias in models during training, and some can do so at runtime (including the ability to make corrections or identify changes in model behavior over time). These tools that enable both bias detection and model explainability and observability are critical to bringing AI/ML into live clinical and non-clinical healthcare settings.

EXPLORE NOW: Diversity, Equity & Inclusion (DE&I) in Healthcare

Healthcare Leaders Turn to Us

Perficient is dedicated to enabling organizations to elevate diversity, equity, and inclusion within their companies. Our healthcare practice is comprised of experts who understand the unique challenges facing the industry. The 10 largest health systems and 10 largest health insurers in the U.S. have counted on us to support their end-to-end digital success. Modern Healthcare has also recognized us as the fourth largest healthcare IT consulting firm.

We bring pragmatic, strategically-grounded know-how to our clients’ initiatives. And our work gets attention – not only by industry groups that recognize and award our work but also by top technology partners that know our teams will reliably deliver complex, game-changing implementations. Most importantly, our clients demonstrate their trust in us by partnering with us again and again. We are incredibly proud of our 90% repeat business rate because it represents the trust and collaborative culture that we work so hard to build every day within our teams and with every client.

With more than 20 years of experience in the healthcare industry, Perficient is a trusted, end-to-end, global digital consultancy. Contact us to learn how we can help you plan and implement a successful DE&I initiative for your organization.

In this episode, Jim and Kim look back on season three of the podcast and some of the exceptional guests they had the opportunity to interview. They dive into the topic of innovation including why it’s important and why getting back to basics is fundamental. 

You’ll hear clips from: 

• Entrepreneur and star of TV’s “Shark Tank,” Mark Cuban, on why getting the fundamentals of innovation right is so important. What If Mark Cuban Joined The Podcast?
• Google’s Chief Measurement Strategist, Neil Hoyne, on changing the objectives you’re tracking with the data you already have. What If You Could Measure Everything That Matters? An Interview with Google’s Chief Measurement Strategist
• Microsoft’s Senior Director of Employee Experience, Chris Echelmeier, on how better employee experience leads to better customer experience. What If You Could Make Employee Experience Part Of Your Customer Experience? An Interview With Microsoft’s Senior Director of Employee Experience, Chris Echelmeier.
• Alan Hart, the creator, and host of “Marketing Today with Alan Hart,” on the death of the brief and what fuels innovation today. What If The Key To Digital Success Is Getting Back To Basics? An Interview With Alan Hart, Creator and Host of “Marketing Today With Alan Hart”

We’d love to get your feedback on a “white noise” word or topic you’d like for us to cover. Send your suggestions to podcast@perficient.com. 

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Meet the Hosts

Jim Hertzfeld is Principal and Chief Digital Strategist for Perficient. He works with clients to convert market insights into real-world digital products and customer experiences that actually grow their business.

LinkedIn | Perficient

Kim Williams-Czopek is a Director of Digital Strategy at Perficient. She works with clients to devise digital experience strategies and how to translate strategies to tactics. She specializes in digital commerce, digital product development, user research and testing strategies, and digital responsibility.

LinkedIn | Perficient