The Impact Of AI And Other Innovations On Data Storytelling via @sejournal, @InsightNarrator

This edited extract is from Data Storytelling in Marketing by Caroline Florence ©2024 and is reproduced and adapted with permission from Kogan Page Ltd.

Storytelling is an integral part of the human experience. People have been communicating observations and data to each other for millen­nia using the same principles of persuasion that are being used today.

However, the means by which we can generate data and insights and tell stories has shifted significantly and will continue to do so, as tech­nology plays an ever-greater role in our ability to collect, process, and find meaning from the wealth of information available.

So, what is the future of data storytelling?

I think we’ve all talked about data being the engine that powers business decision-making. And there’s no escaping the role that AI and data are going to play in the future.

So, I think the more data literate and aware you are, the more informed and evidence-led you can be about our decisions, regardless of what field you are in – because that is the future we’re all working towards and going to embrace, right?

It’s about relevance and being at the forefront of cutting-edge technology.

Sanica Menezes, Head of Customer Analytics, Aviva

The Near Future Scenario

Imagine simply applying a generative AI tool to your marketing data dashboards to create audience-ready copy. The tool creates a clear narrative structure, synthesized from the relevant datasets, with actionable and insightful messages relevant to the target audience.

The tool isn’t just producing vague and generic output with question­able accuracy but is sophisticated enough to help you co-author technically robust and compelling content that integrates a level of human insight.

Writing stories from vast and complex datasets will not only drive efficiency and save time, but free up the human co-author to think more creatively about how they deliver the end story to land the message, gain traction with recommendations and influence decisions and actions.

There is still a clear role for the human to play as co-author, including the quality of the prompts given, expert interpretation, nuance of language, and customization for key audiences.

But the human co-author is no longer bogged down by the complex and time-consuming process of gathering different data sources and analysing data for insights. The human co-author can focus on synthesizing findings to make sense of patterns or trends and perfect their insight, judgement, and communication.

In my conversations with expert contributors, the consensus was that AI would have a significant impact on data storytelling but would never replace the need for human intervention.

This vision for the future of storytelling is (almost) here. Tools like this already exist and are being further improved, enhanced, and rolled out to market as I write this book.

But the reality is that the skills involved in leveraging these tools are no different from the skills needed to currently build, create, and deliver great data stories. If anything, the risks involved in not having human co-authors means acquiring the skills covered in this book become even more valuable.

In the AI storytelling exercise WINconducted, the tool came up with “80 per cent of people are healthy” as its key point. Well, it’s just not an interesting fact.

Whereas the humans looking at the same data were able to see a trend of increasing stress, which is far more interesting as a story. AI could analyse the data in seconds, but my feeling is that it needs a lot of really good prompting in order for it to seriously help with the storytelling bit.

I’m much more positive about it being able to create 100 slides for me from the data and that may make it easier for me to pick out what the story is.

Richard Colwell, CEO, Red C Research & Marketing Group

We did a recent experiment with the Inspirient AI platform taking a big, big, big dataset, and in three minutes, it was able to produce 1,000 slides with decent titles and design.

Then you can ask it a question about anything, and it can produce 110 slides, 30 slides, whatever you want. So, there is no reason why people should be wasting time on the data in that way.

AI is going to make a massive difference – and then we bring in the human skill which is contextualization, storytelling, thinking about the impact and the relevance to the strategy and all that stuff the computer is never going to be able to do.

Lucy Davison, Founder And CEO, Keen As Mustard Marketing

Other Innovations Impacting On Data Storytelling

Besides AI, there are a number of other key trends that are likely to have an impact on our approach to data storytelling in the future:

Synthetic Data

Synthetic data is data that has been created artificially through computer simulation to take the place of real-world data. Whilst already used in many data models to supplement real-world data or when real-world data is not available, the incidence of synthetic data is likely to grow in the near future.

According to Gartner (2023), by 2024, 60 per cent of the data used in training AI models will be synthetically generated.

Speaking in Marketing Week (2023), Mark Ritson cites around 90 per cent accuracy for AI-derived consumer data, when triangulated with data generated from primary human sources, in academic studies to date.

This means that it has a huge potential to help create data stories to inform strategies and plans.

Virtual And Augmented Reality

Virtual and augmented reality will enable us to generate more immersive and interactive experiences as part of our data storytelling. Audiences will be able to step into the story world, interact with the data, and influence the narrative outcomes.

This technology is already being used in the world of entertainment to blur the lines between traditional linear television and interactive video games, creating a new form of content consumption.

Within data storytelling we can easily imagine a world with simulated customer conversations, whilst navigating the website or retail environment.

Instead of static visualizations and charts showing data, the audience will be able to overlay data onto their physical environment and embed data from different sources accessed at the touch of a button.

Transmedia Storytelling

Transmedia storytelling will continue to evolve, with narratives spanning multiple platforms and media. Data storytellers will be expected to create interconnected storylines across different media and channels, enabling audiences to engage with the data story in different ways.

We are already seeing these tools being used in data journalism where embedded audio and video, on-the-ground eyewitness content, live-data feeds, data visualization and photography sit alongside more traditional editorial commentary and narrative storytelling.

For a great example of this in practice, look at the Pulitzer Prize-winning “Snow fall: The avalanche at Tunnel Creek (Branch, 2012)” that changed the way The New York Times approached data storytelling.

In the marketing world, some teams are already investing in high-end knowledge share portals or embedding tools alongside their intranet and internet to bring multiple media together in one place to tell the data story.

User-Generated Content

User-generated content will also have a greater influence on data storytelling. With the rise of social media and online communities, audiences will actively participate in creating and sharing stories.

Platforms will emerge that enable collaboration between storytellers and audiences, allowing for the co-creation of narratives and fostering a sense of community around storytelling.

Tailoring narratives to the individual audience member based on their preferences, and even their emotional state, will lead to greater expectations of customization in data storytelling to enhance engagement and impact.

Moving beyond the traditional “You said, so we did” communication with customers to demonstrate how their feedback has been actioned, user-generated content will enable customers to play a more central role in sharing their experiences and expectations

These advanced tools are a complement to, and not a substitution for, the human creativity and critical thinking that great data storytelling requires. If used appropriately, they can enhance your data storytelling, but they cannot do it for you.

Whether you work with Microsoft Excel or access reports from more sophisticated business intelligence tools, such as Microsoft Power BI, Tableau, Looker Studio, or Qlik, you will still need to take those outputs and use your skills as a data storyteller to curate them in ways that are useful for your end audi­ence.

There are some great knowledge-sharing platforms out there that can integrate outputs from existing data storytelling tools and help curate content in one place. Some can be built into existing plat­forms that might be accessible within your business, like Confluence.

Some can be custom-built using external tools for a bespoke need, such as creating a micro-site for your data story using WordPress. And some can be brought in at scale to integrate with existing Microsoft or Google tools.

The list of what is available is extensive but will typically be dependent on what is available IT-wise within your own organization.

The Continuing Role Of The Human In Data Storytelling

In this evolving world, the role of the data storyteller doesn’t disap­pear but becomes ever more critical.

The human data storyteller still has many important roles to still play, and the skills necessary to influence and engage cynical, discerning, and overwhelmed audiences become even more valuable.

Now that white papers, marketing copy, internal presentations, and digital content can all be generated faster than humans could ever manage on their own, the risk of informa­tion overload becomes inevitable without a skilled storyteller to curate the content.

Today, the human data storyteller is crucial for:

  • Ensuring we are not telling “any old story” just because we can and that the story is relevant to the business context and needs.
  • Understanding the inputs being used by the tool, including limitations and potential bias, as well as ensuring data is used ethically and that it is accurate, reliable, and obtained with the appropriate permissions.
  • Framing queries appropriately in the right way to incorporate the relevant context, issues, and target audience needs to inform the knowledge base.
  • Cross-referencing and synthesizing AI-generated insights or synthetic data with human expertise and subject domain knowledge to ensure the relevance and accuracy of recommendations.
  • Leveraging the different VR, AR, and transmedia tools available to ensure the right one for the job.

To read the full book, SEJ readers have an exclusive 25% discount code and free shipping to the US and UK. Use promo code SEJ25 at koganpage.com here.

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Featured Image: PopTika/Shutterstock

Charts: Global Consumer Trends Q1 2024

Nearly half of global consumers (46%) have increased their consumption of climate-sustainable products, while an overwhelming number (85%) have experienced the disruptive effects of climate change. That’s according to PwC’s annual “Voice of the Consumer Survey,” titled this year “Shrinking the consumer trust deficit.”

In January and February 2024, PwC surveyed 20,662 consumers across 31 countries and territories. The respondents were at least 18 years old and were asked about a range of topics relating to shopping behaviors, emerging technology, and social media.

Per the survey, worldwide shoppers have trust concerns with the social media industry, questioning its safety and reliability.

In addition, 83% of respondents state that safeguarding their personal data is critical to a company’s ability to earn their trust.

Moreover, consumers seek personal connections when discovering new brands and products. According to the survey, 55% of respondents prefer visiting physical stores and interacting with salespeople, compared to 49% who rely on recommendations from family and friends and 46% who opt for online browsing.

Charts: U.S. Retail Ecommerce Sales Q1 2024

Every calendar quarter the U.S. Department of Commerce releases total domestic retail sales and ecommerce only. Newly published figures for Q1 2024 (PDF) show total retail sales of $1.820 billion (a slight decrease over Q4 2023) and ecommerce-only retail sales of $289 billion, a growth of 2.1% over the prior quarter.

According to the DoC, ecommerce sales are for “goods and services where the buyer places an order (or the price and terms of the sale are negotiated) over an Internet, mobile device, extranet, electronic data interchange network, electronic mail, or other comparable online system. Payment may or may not be made online.”

Ecommerce accounted for 15.9% of total U.S. retail sales in Q1 2024, up slightly from 15.6% in the prior quarter.

The DoC reports U.S. ecommerce retail sales in Q1 2024 grew by 8.6% compared to Q1 2023, while total quarterly retail sales experienced a 1.5% annual rise over the same period last year.

7 Ways AI Took My Job [To The Next Level] via @sejournal, @CallRail

With AI-powered call attribution, you can gain valuable insights into which channels are driving the most conversions.

How Call Attribution Works

  • Step 1: Assign – Select unique call tracking numbers to assign to each campaign or listing.
  • Step 2: Track – Potential customers see your ad or listing and call the associated phone number.
  • Step 3: Forward –The calls ring directly into your main business phone, regardless of which number they use.
  • Step 4: Analyze – Because they used one of your tracking numbers, you instantly know which ad or campaign inspired them to call.

With AI-powered call tracking, gone are the days of wondering how your digital marketing efforts are tied to high-value inbound calls.

For agencies, this helps prove the real value of your services and extend the life of your client relationships.

2. AI Can Help You Save Time On Manually Reviewing Calls

Listening to and analyzing phone calls manually can be time-consuming and inefficient for agencies.

However, it’s an important part of understanding the customer experience and sales team performance.

With AI-powered call analysis tools, you get quality, keyword-tagged transcriptions with near-human-level accuracy.

Not only can this technology help you save over 50% of the time spent listening to phone calls, but it can also help you deliver actionable recommendations to clients and drive better results.

Conversation Intelligence, for instance, is trained on over 1.1M hours of voice data and enables real-time analysis for instantaneous results.

This advanced tool provides opportunities for you to improve your strategy through the following granular insights:

  • Spotting disparities in the industry-specific lingo your sales team uses, compared to the lingo your prospects are using to describe their business challenges and goals.
  • Identifying trends or gaps in your service offerings based on what your prospects are asking for.
  • Identifying frequently asked questions and other important topics to address through content marketing.
  • Setting goals for lead qualification — not just the quantity of leads generated for your business.

Conversational AI is perfectly suited to summarize the content of long conversations – however, the call summaries still require a human to read them and determine the main takeaways.

But if you work in a bustling small business, it’s unlikely you’d have the bandwidth for tasks such as call transcription, summaries, keyword spotting, or trend analysis.

Rather than displacing human labor, conversational AI is assisting businesses in taking on tasks that may have been overlooked and leveraging data that would otherwise remain untapped.

3. AI Can Help You Lower Cost Per Lead / Save Money On Tools & Ad Spend

Ever wonder why certain campaigns take off while others fall flat? It’s all in the data!

Even failed campaigns can offer invaluable insights into your client’s audience and messaging.

But if you can’t spot the underperformers quickly enough, you risk wasting your ad budget on ineffective tactics.

The quicker you can identify what’s working and what’s not, the quicker you can pivot and adjust your marketing strategy.

With AI-powered tools, agencies can access instant insights that enable them to reduce wasteful spending and improve overall campaign efficiency.

How To Deliver More Value With AI

  • Make a bigger impact in less time: AI-powered technology creates a force multiplier within your agency, allowing you to make more of an impact with the same level of inputs you’re already using.
  • Unlock actionable insights from call data: AI is revolutionizing the way companies leverage call data by enabling them to gain insights at scale. As a result, businesses can increase their ROI and deliver greater value to their clients by analyzing hundreds of calls efficiently.
  • Foster alignment with data-driven strategies: By analyzing customer conversations with AI, businesses can align their marketing strategy with data-driven recommendations, enhancing overall coherence. Additionally, the ability to create triggers based on specific phrases enables automated analysis and reporting, further streamlining the alignment process.
  • Drive effectiveness with rapid insights: Leveraging Conversation Intelligence enables agencies to deliver better insights faster, increase conversion rates, refine keyword strategies, and develop robust reporting capabilities.

With the right AI-powered tools, you can access the insights you need to ensure maximum ROI for your clients.

4. AI Can Help You Improve Overall Agency Efficiency

Are you spending too much valuable time on tasks that produce minimal results?

Many agencies find themselves bogged down by routine, administrative tasks that don’t contribute much to their bottom line.

But with AI automation, agencies can streamline their operations and redirect their energy towards more strategic endeavors.

From email scheduling and social media posting to data entry and report generation, AI can handle a wide array of tasks with precision and efficiency – giving you time to focus on high-impact activities that drive growth and deliver tangible results.

Ways Your Business Can Benefit From Automation

  1. Automatically transcribe your calls to boost close rates: See how your team is handling difficult objections and ensure that they’re delivering your businessʼ value proposition in an effective manner.
  2. Score calls based on quality and opportunity: Take the time-consuming work out of scoring your calls and determine which campaigns drive the best calls to your business.
  3. Classify calls by your set criteria: Qualify, score, tag, or assign a value to the leads that meet your criteria, automatically.
  4. Automatically redact sensitive information: Protect your customers by removing billing or personal information. Keep your data safe and secure through complete HIPAA compliance.
  5. Monitor your teamsʼ performance: Use Conversation Intelligence as a valuable sales training tool to ensure your team doesn’t miss any key messaging marks.
  6. Know your customersʼ needs: Identify conversation trends in your phone calls and stay privy to evolving customer needs.
  7. Improve your digital marketing strategy: Use AI-powered insights to inform your digital marketing strategy and boost your online presence.

By automating mundane tasks, agencies can optimize workflows, increase productivity, and improve efficiency across the board.

Looking for 5 – 7? Download The Full Guide

Rather than fearing AI, the future belongs to those who embrace it.

By strategically combining human creativity with artificial intelligence, you can unlock capabilities that transcend what either could achieve alone.

Want to discover even more ways to level up your agency with AI?

Get the full guide here.

What Is Conversion Rate & How Do You Calculate It? via @sejournal, @coreydmorris

Conversion rate is one of the most common metrics used by marketers, sales folks, and business professionals.

It is discussed often and taken on the surface as an important metric or key performance indicator (KPI) for most businesses.

However, it can also be misapplied, misunderstood, or improperly established for use as a key metric.

It is important to revisit conversions, conversion rates, and the use of the metric periodically.

It is even more important for any new initiative to have the metric well defined and understood before positioning it as a key KPI.

In this guide, I’m going to dive deeply into what conversion rate is, how to calculate it, why that’s important, and ways to improve it.

What Is Conversion Rate?

Google provides one of the more concise definitions of conversion rate:

“Conversion rates are calculated by simply taking the number of conversions and dividing that by the number of total ad interactions that can be tracked to a conversion during the same time period.”

Now, let’s get into what it all means.

Conversions

Unlike some business and marketing metrics, understanding conversion rates require some self-definition.

It starts with defining what a conversion is – which can mean different things for varying types of brands and organizations.

You can have more than one type of conversion. As a goal, you can have it factored into a marketing funnel or customer journey. Or, it could be a firm financial metric your business hinges on.

Step one is to clearly define what a conversion is for you.

One of the most common definitions I see relates to someone becoming a lead for a business that is focused on driving leads via its website.

Another applies to ecommerce businesses, where the conversion is the completed sale transaction.

Other common definitions include certain engagement metrics for businesses that rely on ad revenue generated by page views.

Secondary types of conversions get into events, engagement, and other things like email signups that help support funnels, customer journeys, and overall sales processes.

Conversion Rate

Conversion rate is a %.

In high-level terms, it tells you the % of how many people came to your site who took the conversion goal action you defined.

Some sources provide benchmarks for specific industries or areas to help you understand a good conversion rate and offer some objectivity.

I’m not telling you to copy your competitors, but I think if you want to value conversion rate, you need internal and external research to validate where you stand and where you want to be.

Match this up with your persona research, target audiences, marketing funnels, and customer journeys.

You likely know what you want your site visitors and audience to do.

How many of them do you want to do it? How big is the universe of your target audience? What is realistic regarding the number of total visitors you think you can get?

Find answers to these questions along with mapping out your conversion goals and conversion rate goals.

How Do You Calculate Conversion Rate?

Conversion Rate Formula

The formula to calculate the conversion rate is straightforward:

Conversions / Visits* = Conversion Rate

*I have to include an asterisk, though, as some definitions might not be as straightforward.

You could also call these “clicks” or “sessions” or look at them more granularly.

My definition here can be adapted based on the language and definitions used by your analytics platform and your other KPIs.

An example in calculating conversion rate for my site (a marketing agency providing services to clients) with the inputs and calculation:

  • August 2022 website visits: 1,122.
  • August 2022 contact form submissions (my conversions): 61.
  • 61 conversions/1,122 visits = 5.4% conversion rate.

Getting It Right

Again, conversions are custom-defined by you.

It can be a common conversion goal like a lead form submission, something more secondary, or something more obscure.

That part can be somewhat custom or variable for you as well.

You can look at it as clicks to a website from a specific channel or ad campaign.

You can get really granular with the segmentation of your data, source and channel filtering, and even with the definitions themselves.

That becomes especially variable or custom if you’re tracking specific actions that lead up to a conversion goal and how granular you want to be with it.

Make sure the definition of what you’re counting as a conversion and what you’re counting as the total audience (clicks, visits, or some other “total” metric) is mapped out in a meaningful way.

Why Do I Need To Be Able To Calculate Conversion Rate?

First, where do you measure and track conversion rate? You can use Google Analytics, other analytics suites, or any data you must manually calculate.

Google Analytics

If you’re relying on Google Analytics (GA), you’ll want to ensure you have your “Goals” set up properly and test them. Conversions are reported based on the goals you configure.

Out of the box, Google has no context as to what a conversion is for you and no ability to calculate a conversion rate off of it.

If you use GA, dive into conversion goal configuration and testing to ensure things are in a good place before you trust the metrics you see (if you inherited the setup) or move forward with any measurement and improvement plan.

And, speaking of mapping out – tracking and measurement are critical.

You want to ensure that your tech stack and tools can help you properly track visits, conversions, and the overall conversion rate in alignment with your definitions and goals.

Getting this right is critical, whether it is Google Analytics or third-party reporting tools.

Segmentation & Filtering

Plus, you’re able to then segment at the levels you want to with examples, including:

  • By conversion type (if you have more than one).
  • All website traffic.
  • By source or channel.
  • By pages/actions/events in the session.
  • By campaign or initiative.

There are many more segments and ways to filter and slice up conversions and conversion rate reporting.

You want to be able to calculate the conversion rate and get into the details with segments of traffic and your audience to help understand where you can improve.

What Is a Good Conversion Rate?

Calculating conversion rates and having the data is one thing; using it to make improvements is where the real work starts.

Improving Conversion Rates

You can look for improvement in two broad areas, and I strongly recommend evaluating both.

One is sources of traffic and the influences that drive visitors to your site.

That includes advertising, referrals, and any awareness activities and campaigns you have that generate traffic.

The other area consists of what influences the traffic that has already arrived at the site – things like UX/UI evaluation, review of messaging, calls to action, and ways that users navigate through and engage with the site.

Improvement in this arena is often called Conversion Rate Optimization or CRO.

Traffic Sources Optimization

In the case of the traffic you’re sending to the site, you can look at targeting, ad creative, and keywords you’re organically ranking for – the ways that ad targeting and creative provide the first impression or directly funnel traffic into the site.

There are a variety of optimization and refinement tactics to shift your focus to higher quality traffic and aim to increase conversion rate by getting more qualified visitors from external sources that you influence.

Beware, though, that you need to have a good idea of your customer journey and not knock out traffic that is awareness focused or at the top of the funnel (e.g., traffic tied to thought leadership).

Increasing the conversion rate is important, but make sure you segment well enough to not inadvertently stop targeting the top of the funnel, awareness-level visitors, and sources.

Conversion Rate Optimization

Now, onto looking inward at the traffic you already have.

This is where most people start digging into CRO tactics. Web analytics can help you see where people exit, bounce, and stop short of getting to your conversion actions.

Beyond that, great heat mapping and CRO tools will give you insights into UX and UI issues and how people truly engage with your site versus how you intended in your design.

By focusing on CRO and putting a strategy into place, you can evaluate everything from site speed to content, messaging, and UI.

I strongly encourage you to do so.

Conclusion

Conversion rate continues to be a valuable marketing metric.

Understanding it, defining it for your organization, measuring it, and improving it are all important.

Whether you have a small business or enterprise-level website, you likely care about specific conversion goals.

In short – for conversions and conversion rate – understand, define, measure, and improve it.

Yes, we all want more traffic. And maybe a static conversion rate is fine if you add more traffic.

However, wouldn’t you like more traffic and a higher conversion rate?

It is possible to have both, and crucial to understanding what levers to pull to influence it.

More resources: 


Featured Image: eamesBot/Shutterstock

FAQ

What is the significance of conversion rate in online marketing?

Conversion rate is a crucial metric for assessing the effectiveness of online marketing strategies. It represents the percentage of visitors who complete a desired action, such as making a purchase or signing up for a newsletter. Understanding this rate helps businesses evaluate the success of their marketing efforts and identify areas for improvement. Accurate measurement and analysis of conversion rates can lead to better targeting of marketing campaigns, improved user experiences, and increased return on investment (ROI).

What are some effective strategies for improving conversion rates?

Improving conversion rates involves optimizing both the sources of traffic and the user experience on your site. Key strategies include:

  • Refining ad targeting and creative to attract more qualified traffic.
  • Enhancing site usability and navigation to make it easier for visitors to complete desired actions.
  • Testing and updating calls to action (CTAs) to ensure they are compelling and clear.
  • Employing A/B testing to compare different versions of landing pages and identify the most effective design and messaging.
  • Using analytics and heat mapping tools to gain insights into user behavior and address any barriers to conversion.

Why is it important to periodically revisit and redefine conversion metrics?

Periodically revisiting and redefining conversion metrics is essential to ensure they remain aligned with evolving business objectives and market conditions. As your business grows and changes, the definitions of conversions and the goals associated with them may need adjustments. Regularly updating these metrics helps maintain their relevance and ensures that your marketing strategies continue to drive meaningful results. This practice also allows for the incorporation of new insights and technologies, keeping your approach current and effective.

Google Universal Analytics 360 Sunsetting Soon: Migration Tips & Top Alternative Inside via @sejournal, @PiwikPro

This post was sponsored by Piwik PRO. The opinions expressed in this article are the sponsor’s own.

This year, Google will finally phase out Universal Analytics 360, requiring paid users to switch to Google Analytics 360.

This is not something you can skip or postpone, and the clock is ticking.

The new analytics differ significantly from the previous version, and you can’t migrate data between them, so the transition can be challenging for organizations.

Since you’ll be starting from scratch, now is a good time to explore other options and determine if there are better solutions for your needs.

The three main areas to consider when deciding if you want to stay with Google or move to another platform are: the migration process, privacy and compliance, and ease of use.

When Is Google Universal Analytics 360 Sunsetting?

July 1, 2024 is when Google will phase out Universal Analytics 360.

What Should I Do Next?

Google encourages you to migrate to Google Analytics 360 as quickly as possible.

If you don’t, you could:

  • Lose critical advertising capabilities.
  • Lose the ability to export historical data.
  • Face delays in setting up Google Analytics 360.

    How To Migrate To Your Next Analytics Platform

    Moving to a new platform is much more than just implementation; it is vital to plan your migration properly. Below are five steps to help you through the entire process.

    Step 1. Evaluate Your Stack & Resources

    Before you switch analytics tools, take the time to evaluate your entire stack, not just the tool you’re changing. Ensure that your stack is up-to-date and meets your current business needs. Migrating to a new analytics vendor almost always requires more people and more time than originally estimated. It’s a good occasion to remove redundant tools from your stack; it might also allow you to integrate with new ones that can help you run your analytics and collect data more comprehensively.

    Step 2. Tidy Your Data

    Over time, data collection may get messy, and you find yourself tracking data that isn’t relevant to your business. A migration gives you a chance to clean up your data taxonomy. Ensure that your new tool allows you to use the same categories of data as the previous one. Pay close attention to any data that needs to be collected automatically, like location data (country, region, city), and device details (device type, browser). Finally, make sure the SDKs you need are supported by your new tool.

    Step 3. Implement A New Platform

    This step involves setting up the tracking code that collects data about visitors to your website or app and making any necessary modifications. Remember to set up tags to gather more detailed data through events or connect third-party tools.

    Speed Up The Transition: If you switch to Piwik PRO, you can use a migration tool to easily transfer your settings from Universal Analytics (GA3) and Google Tag Manager.

    Step 4. Evaluate Tour New Data

    Once you’re done implementing your new platform, you should run it parallel to your existing tool for a few months before finalizing the migration. During this time, you can audit your new data and correct any errors. In this manner, you can retain your historical data while simultaneously generating new data segments on the new platform.

    Step 5. Provide Training For Your Team

    All end users need training to comprehend the platform’s operations, retrieve necessary data, and generate reports. This step is frequently missed as it falls at the end of the project.

    Upon finishing this step, you will be set to switch to your new platform fully. If you find the migration process challenging, consider getting help from outside sources. Some analytics vendors offer hands-on onboarding and user training, which accelerates product adoption.

    Is Switching To Google Analytics 360 Worth The Hassle?

    You might be thinking, “Migrating to the successor of UA 360 won’t be a walk in the park,” especially if you work for a large organization.

    In addition to subscription and data migration costs, you may also need to train your staff or increase fees for external marketing agencies that will face new challenges.

    While Analytics 360 has incredible use cases, there may be other tools that better suit your needs.

    Switching to alternative solutions may be a good option for you.

    How To Pick A Replacement For Universal Analytics 360

    To decide whether to choose a new platform or stick with Google, consider a few important factors:

    1. Because GA 360 is a different software, your marketing and analytics departments will need to allocate extra resources to learn the new platform. You will also need the support of analysts, developers, and data architects to help you reconstruct reports based on the data architecture of the chosen platform. Choosing a solution with similar features and user experience to UA 360 can be a good option, because it saves resources, making onboarding faster and easier.
    2. You will also need to redesign your entire customer journey, because the data model in GA360 has changed from sessions to events. This process can be more challenging and costly than choosing a session-based platform or one that offers you freedom of choice.
    3. Another important consideration is the level of support offered by the vendor. This can greatly affect the quality of the migration and onboarding to a new platform. Although Google Analytics is currently the most popular tool for analyzing web traffic, the level of support it provides is limited. Other companies like Piwik PRO can offer more in this area, including personalized onboarding, product implementation, training, and dedicated customer support at every step.

    Consideration 1: Think About Privacy & Compliance

    Organizations around the world are increasingly concerned with data privacy and compliance. A 2023 Thomson survey found that 80% of business professionals acknowledge the importance of compliance as a crucial advisory function for their organizations. Gartner, on the other hand, predicts that, by 2025, 60% of large enterprises will use at least one privacy-enhancing computing (PEC) technique in analytics, business intelligence, and/or cloud computing.

    This is due to a growing number of new regulations that place greater control over personal data at the forefront. The EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) are two of the most prominent examples. The landscape has been further complicated by events such as the Schrems II case, Brexit, and China’s Personal Data Protection Law. Data protection is also increasingly important in some sectors, such as healthcare, where regulations like HIPAA are mandatory.

    If your company operates globally or has ambitions to do so, the first thing to consider is who has full ownership of the data, where the servers hosting the data are located, and who owns them. Google Analytics 360 only offers cloud deployment in an unknown location, which means that data might be transferred between data centers in the Americas, Europe, and Asia. This makes it difficult to know exactly where the data is stored and ownership is unclear. For now, the issue of data transfers between the US and the EU has been resolved by the EU-US Privacy Shield framework agreement, but the future stays unclear. Last year, NOYB, led by Max Schrems, announced that it would soon appeal this decision to the Court of Justice of the European Union (CJEU).

    To meet privacy and compliance requirements in different countries and industries, choose a platform that allows you to customize your hosting plan and set specific parameters for data collection and analysis. Platforms like Piwik PRO Analytics Suite enable you to store your data on servers in Europe, the US, and Asia, based on your preferences. This translates into flexibility and security of your data.

    Consideration 2: Ease Of Use & Integration

    This may sound counterintuitive, but the new GA 360 might be too complex for many. While it offers numerous advanced functions for data analysts, it lacks features specifically designed for marketers. As a result, marketers may need help in configuring the system to efficiently use the data.

    On the other hand, in GA 360, the data model shifts from session-based to event-based. This is especially important if your teams depend on UA 360 behavioral reporting, benchmarking, and e-commerce flow reports, as these features are unavailable in the new release. You also need to revise all the reports for all the stakeholders.

    Conversely, Piwik PRO strongly emphasizes simplicity and enables marketers to quickly access the necessary data. Additionally, the data model combines both session-based and event-based structures. This approach ensures that you can start working with the data faster and deliver the reports that stakeholders are used to. Another big advantage of Piwik PRO is its model for working with raw data, which is a valuable source of knowledge about users and provides richer reporting in more contexts. Google Analytics does not provide raw data exports, so you have to use various services and tools to accomplish this. To be fair, however, exporting large raw data packets with Piwik PRO software may take longer than with Google solutions.

    The new GA 360 is most effective when used mainly with products from the Google ecosystem. When considering data activation, Google Ads is the most suitable option. When it comes to Piwik PRO, you still have this option, but integrating with other solutions is much easier. The platform offers four modules: Analytics, Tag Manager, Consent Manager and Customer Data Platform (CDP). The CDP module, available in the paid plan, lets you create detailed customer profiles and categorize your data into various audience segments. You can activate them to provide a personalized experience and run effective campaigns across multiple channels.

    The landscape of modern analytics is constantly changing. On the one hand, there are ongoing discussions about privacy and compliance regulations, while on the other, companies are trying out various methods to collect and analyze data. In the end, your choice of analytics platform will impact the performance of your marketing and sales efforts. So take the time to explore, and you may find other solutions that better suit your organization’s needs.

    Piwik PRO is a solid choice to explore for your next primary analytics solution. Book a personalized demo of the Enterprise version and see the benefits of introducing Piwik PRO Analytics Suite in your organization.


    Image Credits

    Featured Image: Image by Piwik PRO Used with permission.

    Charts: U.S. Manufacturing Trends Q1 2024

    The Institute for Supply Management is a leading not-for-profit global procurement organization. Founded in 1915, ISM’s 50,000 members from 100 countries manage about $1 trillion annually in corporate and government supply chain procurement.

    The monthly “ISM Report On Manufacturing” is among the most reliable economic indicators for supply management professionals, economists, analysts, and government and business leaders.

    The report includes the closely watched purchasing managers index — “Manufacturing PMI” — based on data compiled from purchasing and supply executives. Participants report activity in multiple categories, such as new orders, inventories, and production. Those indices are then combined to create the PMI. A PMI above 50 designates an overall expansion of the manufacturing economy, whereas below 50 is a contraction.

    In March 2024, U.S. manufacturing activity expanded for the first time in 16 months, with ISM’s Manufacturing PMI at 50.30, up from 47.80 in February and 46.30 one year ago.

    ISM’s data also shows new U.S. manufacturing orders in February 2024 were $576.76 billion, up from $568.57 billion in January and $571.26 billion year over year — an increase of 1.44% and 0.96%, respectively.

    ISM’s March 2024 U.S. Employment Index for the manufacturing sector is 47.40, indicating a slower decline from 45.90 in February and 46.90 one year ago.

    Act Now: Your Ads Measurement & Privacy Readiness Plan For 2024 & Beyond via @sejournal, @adsliaison

    The PPC ecosystem is about to undergo significant changes driven by regulation.

    With regulation updates such as regional consent requirements and Chrome’s deprecation of third-party cookies later this year (see the timeline from Chrome), as well as other shifts such as Apple’s App Tracking Transparency (ATT) policy and cross-device customer journeys, the amount of visible data available to marketers is on the decline.

    With that, Google’s ad measurement products and the ecosystem as a whole must evolve to meet this moment and be positioned for the next era.

    So much change can feel overwhelming, but with a solid plan, you’ll be ready.

    When I joined Google in early 2021, it was clear that regulatory and privacy changes and AI advancements would be key focus areas for marketers over the next several years. Fast-forward three years, and we’re now at the inflection point.

    In this article, we’ll walk through the big pieces to put in place to ensure your measurement capabilities continue now and in the years ahead.

    Preparation Is Key

    AI has been playing a critical role by enabling predictive and analytical capabilities and filling measurement gaps where data is not available.

    AI-powered conversion modeling, for example, is essential for maintaining measurement, campaign optimization, and improved bidding capabilities.

    As I wrote last year about GA4, for example, these shifts were a major driver for developing a measurement platform that can account for less observable data via third-party cookies and more data being aggregated to protect user anonymity.

    Many marketers are still deeply reliant on third-party cookies.

    As our products have evolved, there are important actions you should take now to ensure you’re taking advantage of new capabilities designed to help you maintain ad measurement in 2024 and beyond.

    Let’s dive in.

    Sitewide Tagging

    This may sound basic, but the very first step you should take is to implement sitewide tagging with either the Google Tag or Google Tag Manager.

    And if you have tagging set up, do a double-check to ensure it’s implemented correctly and collecting the data you need to measure conversions. Here’s how to get started with conversion tracking.

    To check that you are tracking conversions correctly, check the “Status” column for each of your conversion actions in the summary table (Goals > Conversions > Summary). You can then troubleshoot if you think there may be problems – Tag Assistant is also a helpful tool for this.

    Once your tagging is implemented fully, you have your measurement foundation in place and can start building on top of it. Which leads me to…

    First-party Data

    For years, discussions have been going on about the growing importance of first-party data – consented information you have collected directly from your visitors and customers – as a key part of building a durable measurement plan.

    The need to focus on building your first-party data strategy may still have felt abstract, but with the deprecation of third-party cookies and less observable data, first-party data is what will power your advertising strategy in this new landscape.

    Of course, better ads measurement is just one reason to have a first-party data strategy. When thinking about your first-party data plan, it’s important to start with a customer-centric point of view.

    What’s the value exchange you’ll be able to deliver for your customers?

    It could be early access to new products or services, special discounts, bonus content, loyalty rewards, or other offers that can help you build stronger customer relationships, improve customer lifetime value, and grow your customer base.

    We’ve discussed how enhanced conversions for leads uses first-party data.

    Additionally, you can connect CRM and customer data platform (CDP) platforms with Google Ads, Google Analytics 4, Campaign Manager 360, and Search Ads 360.

    First-party audience lists like Customer Match can help improve audience modeling, expansion, and remarketing. Working with a Customer Match partner can make this process simpler.

    Additionally, we introduced Google Ads Data Manager last year to make it much easier to connect and use your first-party data, including Customer Match lists, offline conversions and leads, store sales, and app data.

    It’s continuing to roll out and will reach general availability this quarter. You’ll be able to access it in a new “Data manager” section within “Tools” when it becomes available in your account.

    When you connect your customer and product data to Google’s advertising and measurement tools, you’ll have a more holistic view of the impact of your advertising.

    This is also where AI comes in to enable conversion modeling, predictive targeting, and analytics solutions, even when user-level data isn’t available.

    Enhanced Conversions

    Enhanced conversions is an increasingly important feature as the privacy landscape evolves.

    It can help provide a more accurate, aggregated view of how people convert after engaging with your ads, including post-view and cross-device conversions than is possible with site tagging alone.

    Enhanced conversions work by sending hashed, user-provided data from your website to Google, which is then matched to signed-in Google accounts. Sales originating from Google Search and YouTube can then be attributed to ads in a privacy-safe way.

    Supplementing your existing conversion tags with more observable data also strengthens conversion modeling and provides more comprehensive data to be able to measure conversion lift from your advertising, understand the incremental impact of your advertising, and help better inform Smart Bidding.

    There are two flavors of enhanced conversions:

    Enhanced Conversions For Web In Google Ads And GA4

    Already available in Google Ads, we recently rolled out support for enhanced conversions for web in GA4 as well.

    An advantage of implementing enhanced conversions in GA4 rather than only in Google Ads is that user-provided data can be used for additional purposes (such as demographics and interests, as well as paid and organic measurement).

    Wondering if you should set up enhanced conversions in one or both? Here’s some guidance:

    • If you are using Google Ads conversion actions, you should use Google Ads enhanced conversions.
    • If you’re using GA4 for cross-channel conversion measurement, you should use Google Analytics-enhanced conversions.
    • If you’re doing both, you can opt to set them both up on the same property. However, you need to be aware of which one you are bidding to and including in the Conversion counts to avoid double counting conversions. Be sure your Google Ads conversion tracking setup only includes the appropriate conversions in the Conversions column. In other words, be sure you’re not including the same action from both Ads and GA4.

    You’ll find details on setting up enhanced conversions in GA4 and/or Google Ads here.

    Enhanced Conversions For Leads

    If you’re tracking offline conversions, enhanced conversions for leads in Google Ads enable you to upload or import conversion data into Google Ads using first-party customer data from your website lead forms.

    If you’re using offline conversion imports to measure offline leads (i.e., Lead-gen), we recommend upgrading to Enhanced conversions for leads.

    Unlike OCI, with enhanced conversions for leads, you don’t need to modify your lead forms or CRM to receive a Google Click ID (GCLID).

    Instead, enhanced conversions for leads uses information already captured about your leads – like email addresses – to measure conversions in a way that protects user privacy.

    It’s also easy to set up with Google Tag, Google Tag Manager, or via that API if you want additional flexibility. It can then be configured right from within your Google Ads account.

    Learn more about enhanced conversions for leads here. Note there are policy requirements and restrictions for using enhanced conversions.

    Consent Mode

    The accuracy of conversion measurement can also be improved with consent mode.

    Consent choice requirements are part of regulatory changes and evolving privacy expectations (your legal and/or privacy teams can provide further guidance). Consent mode is the mechanism for passing your users’ consent choices to Google.

    Consent mode has become especially relevant for advertisers with end-users in the European Economic Area (EEA) and the UK as Google strengthens enforcement of its EU user consent policy in March.

    As part of this, consent mode (v2) now includes two new parameters – ad_user_data and ad_personalization – to send consent signals for ad personalization and remarketing purposes to Google.

    You can find more details on consent mode v2 here. The simplest way to implement consent mode is to work with a Google CMP Partner.

    If you have consent mode implemented but don’t update to v2, you will not have the option to remarket/personalize ads to these audiences in the future. To retain measurement for these audiences, you should implement consent mode by the end of 2024.

    Consent mode also enables conversion tracking when consent is provided and conversion modeling when users don’t consent to ads or analytics cookies.

    In Google Ads, when conversion modeling becomes available after you’ve met the thresholds, you’ll be able to view your conversion modeling uplift on “domain x country level” in the conversion Diagnostics tab.

    You may have seen a notification in Google Ads asking you to check your consent settings. This message will appear to all customers globally to alert you to the new Google Services selection in your account and to check your settings.

    We recommend all relevant Google services be configured to receive data labeled with consent to maintain campaign performance.

    Conversion Modeling

    Conversion modeling has long been used in Google’s measurement solutions and is increasingly important with the deprecation of individual identifiers like cookies on the web and device IDs in apps.

    Additionally, Google privacy policies prohibit the use of fingerprinting and other tactics that use heuristics to identify and track individual users.

    How it works:

    Google’s conversion modeling uses AI/machine learning trained on a set of observable data sources – including first-party data; data from platform APIs like Apple’s SKAdNetwork and Chrome’s Privacy Sandbox Attribution Reporting API; and data sets of users similar to those interacting with your ads – to help fill in the gaps when those signals are missing.

    Conversions are categorized as “observable” (conversions that can be tied directly to an ad interaction) and “unobservable” (conversions that can’t be directly linked to specific ad interactions).

    We then identify an observable group of conversions with similar behaviors and characteristics  (again, based on a diverse set of observable data sources noted above) and train the campaign model to arrive at a total number of conversions made by all users who interacted with your ad.

    To validate model accuracy, we apply the conversion models to a portion of traffic that’s held back.

    We then compare modeled and actual observed conversions from this traffic to check that there are no significant discrepancies and ensure our models can correctly quantify the number of conversions that took place on each campaign channel.

    This information is also used to tune the models. You can read more about how conversion modeling works here.

    You’ll find modeled data in your conversions and cross-device conversions reporting columns.

    How To Improve Your Conversion Modeling

    This is where everything we’ve discussed so far comes together! 

    The following steps will ensure you’re capturing as many “observable” conversions as possible. This will provide a more solid foundation for your conversion modeling.

    The first step to improving your conversion modeling, no surprise, is to be sure your conversion tracking is set up properly with Google Tag or Google Tag Manager.

    Next, implement enhanced conversions for web. For conversions affected by Apple’s ITP, enhanced conversions help advertisers recover up to 15% additional conversions compared to advertisers who haven’t implemented enhanced conversions.

    Advertisers who implement enhanced conversions also see a conversion uplift of 17% on YouTube and a 3.5% impact on Search bidding.

    Then, consider using consent mode. Again, this is particularly relevant for advertisers in the EEA, UK, and CH regions whose measurement is affected by the ePrivacy Directive.

    Additionally, for app developers, on-device conversion measurement helps increase the number of observable app install or in-app conversions from your iOS App campaigns in a privacy-centric manner.

    Data-driven attribution looks at all of your ad interaction account-wide and compares the paths of customers who convert to those of users who don’t convert to identify conversion patterns. It identifies the steps in the journey that have a higher predictability of leading to a conversion. The model then gives more credit to those ad interactions.

    Each data-driven model is specific to each advertiser. Those who switch to a data-driven attribution model from a non-data-driven one typically see a 6% average increase in conversions.

    That additional conversion data also helps inform Smart Bidding.

    GA4 properties began including paid and organic channel-modeled conversions around the end of July 2021.

    Reports such as the Event, Conversions, and Attribution reports and Explorations will include modeled data and automatically attribute conversion events across channels based on a mix of observed data where possible and modeled data where necessary.

    Marketing Mix Modeling

    With the loss of visible event-level data, many CMOs are also taking a fresh look at aggregated measurement methods such as marketing mix modeling (MMM).

    While MMMs aren’t new, they are privacy-friendly and have become increasingly accessible for companies with robust first-party data strategies.

    This month, we introduced an open-source MMM called Meridian to help advertisers get a more holistic picture across channels.

    By open-sourcing the model, advertisers can choose to use the MMM solution as it is, build on top of it, or use whichever pieces they find most useful.

    It’s launching with three primary methodologies to help marketers:

    • Get better video measurement by modeling reach and frequency in MMMs.
    • Improve lower funnel measurement by accounting for organic search volume; and
    • Calibrate MMMs for accuracy by integrating incrementality experiments across channels.

    Meridian is currently in closed beta, but all eligible non-Meridian MMM users can now review and use any of these three methodologies in their own models.

    Take Action Now

    Now is the time to ensure you have an action plan for durable, future-proof, privacy-first measurement.

    I know these may sound like a bunch of buzzwords, but the aim is to have a plan that will prepare you for third-party cookie deprecation and can evolve with future changes.

    More resources:


    Featured Image: Photon photo/Shutterstock

    21 AI Use Cases For Turning Inbound Calls Into Marketing Data [+Prompts] via @sejournal, @calltrac

    This post was sponsored by CallTrackingMetrics. The opinions expressed in this article are the sponsor’s own.

    If you’ve been enjoying having random conversations with ChatGPT, or trying your hand at tricking a car dealership chatbot into giving you a new car for $1, just wait until you start using safe AI professionally.

    Marketers are finding lots of ways to use generative AI for things like SEO research, copywriting, and summarizing survey results.

    But one of the most natural and safe fits for AI is marketing data discovery during conversational call tracking.

    Don’t believe us?

    Here are a ton of AI marketing use cases that make perfect sense for your teams to start using.

    A Quick Call Tracking Definition

    Call tracking is the act of using unique phone numbers to tie a conversation to its marketing source, and collect other caller data, such as:

    • Location of caller.
    • New or returning caller.
    • Website activity associated with the caller.

    It can help attribute sales to:

    • Best performing marketing materials.
    • Best performing local website landing pages.
    • Best performing PPC campaigns.

    Manually tracking and analyzing each conversation can take hours, and often, important nuances are missed.

    This is where AI can help speed up marketing insight discovery and automatically update contact and sales pipelines.

    All you need is a prompt.

    What Prompt Or Quick Recipe Can I Use To Get AI Insights From Call Tracking?

    Your automatically logged call transcriptions + an AI prompt = automated conversation intelligence.

    Once you have this setup configured, you can drastically speed up your first-party data collection.

    To get more specific, prompts have two main parts. The question you want answered, and how you want AI to answer it. As an example:

    The question: What prompted the Caller to reach out?

    The prompt [how should AI answer]: You are a helpful Sales agent responsible for identifying what marketing channel prompted the contact to call. If the contact did not identify what prompted their call please only respond with “None”.

    Below are some example responses on what a contact might say:

    • Podcast ad.
    • Social post.
    • Friend or family recommendation.
    • Stopped by event booth.
    • Read reviews online.

    1 – 18. How To Use AI To Update Customer Contact Fields

    Starting off boring, but powerful: Generative AI can take your customer conversations and automate data entry tasks, such as updating caller profiles to keep them relevant and qualified.

    21 AI Use Cases For Turning Inbound Calls Into Marketing Data [+Prompts]Image created by CallTrackingMetrics, March 2024

    Impressive? No.

    But the time savings add up quickly, and let your team work on the things they like (that make the company money) instead of manually filling out wrap-up panels after a call.

    What Contact Information Can AI Automatically Update?

    1. Name – You’re going to get a name from caller ID which is a great start, but is it the name your caller prefers? Is it up to date or is it still the name of a former customer who left their company to chase their dreams? With a quick AI prompt, you can make sure you’re greeting the right person when they call back.
    2. Email Address – It might be a default value for form submissions, but getting an email address from a caller can take a lot of back and forth. AI isn’t going to ask for that last part again, or require you to read it back to them to verify. It’s just going to do it.
    3. Company Name – You might be using a sales intelligence tool like ZoomInfo to pull this kind of thing from a database. Still, you might also enjoy the accuracy of extracting directly from the words of your prospect.
    4. Buyer Role – Maybe not a basic field, but one AI can fill out nonetheless (much like other custom fields below!). Give your AI a list to choose from like a researcher, influencer, or decision maker. Sure would be nice to know how much influence they actually have without having to ask directly.

    Can AI Automatically Tag Conversations In My CRM?

    Of course!

    In CRMs and sales enablement tools, tags are used to categorize and segment your conversations for further analysis or follow-up.

    Some popular tags for call tracking are marking someone a new or returning caller.

    You can set a tag manually. You can set a tag using an if/then trigger. And because of what this whole thing is about, you can update tags using AI.

    21 AI Use Cases For Turning Inbound Calls Into Marketing Data [+Prompts]Image created by CallTrackingMetrics, March 2024

    Use AI to automatically add tags to your prospect’s profile, based on their actual calls.

    1. Spam – Sure, you can mark something spam yourself, but why not let AI do it for you so you can move on to real work?
    2. Product Tags – What was the caller asking about? Add product tags to calls for further analysis, or to jump right into the sales pitch when they call back.
    3. Lifecycle Tags – Have AI examine what kinds of questions your prospect is asking and qualify them along a scale of just learning to ready to buy. Or even, mark them as an existing customer.
    4. Target Account – Did the caller mention their company size? Maybe you asked them about revenue or tech stack. If you let AI know what your ideal customer looks like, it’ll help you quickly identify them when you’re talking to one.

    Can Generative AI Score Leads In My CRM?

    Yes! However, if 100% of your calls end in sales, skip this part.

    For the rest of us, phone, text, and chat leads range from “never going to buy anything” to “ready to give you my credit card info.”

    You need a way to gauge which leads are closer to “ready.” This is where lead scoring comes in.

    21 AI Use Cases For Turning Inbound Calls Into Marketing Data [+Prompts]Image created by CallTrackingMetrics, March 2024

    While there are lots of ways to score your conversations, you can use AI to sift through the transcription and qualify a lead for you.

    For call scoring, this often looks like a score of 1 to 5.

    So, here are a few examples of how AI can automatically score your leads from transcripts and chat logs.

    1. Readiness to Buy – The most classic approach to scoring is asking, “How likely is this lead to buy?” A score of 1 is unqualified, and a score of 5 is they’re already paying us.
    2. Ideal Customer Fit – Just like adding a target account tag above, train your AI on what a good customer looks like, and it can also give you a score. How closely does this caller fit your ideal profile?
    3. Coaching – Not everything has to be about the lead. Sometimes we want to grade our own team. How well did your sales team stick to the script? Were they friendly? Let AI roll it up into a score for you.
    4. Follow-up Priority – Aggregate readiness to buy, customer fit, and other inputs to decide on how aggressively to follow up with your leads.

    Can Generative AI Capture & Update Custom Fields From Phone Calls & Chat Logs?

    Your company is likely not the same as every other company using call tracking to get customer insights.

    You’ll want some flexibility to determine what’s important to you, not what your call-tracking provider has determined to be important.

    With custom fields, you get to put your creativity and strategy together with AI’s scalability to automate pretty much anything.

    21 AI Use Cases For Turning Inbound Calls Into Marketing Data [+Prompts]Image created by CallTrackingMetrics, March 2024

    AI can accurately assess and notate:

    1. Product Familiarity – You’ve tagged a call with a product name, but how much time do you need to spend educating the prospect vs. selling them?
    2. Related Products – What else could you be selling this person?
    3. Appointments – If your team runs on appointments or demos, having an AI add a calendar date to a custom field opens up a world of automated possibilities.
    4. Next Steps – Follow up with an email, a call, or an appointment confirmation text. Have AI pull the best next step from your conversation.

    19 – 21. How To Use Generative AI To Take Action On Automatically Updated Sales Contacts

    Ok, so there are some time-savings when you use call tracking and AI to update fields.

    If that’s not quite exciting enough, let’s see what you can actually do with those automated fields.

    21 AI Use Cases For Turning Inbound Calls Into Marketing Data [+Prompts]Image created by CallTrackingMetrics, March 2024

    19. Automate Advertising Optimization

    Use conversion data to inform your decisions.

    Throw AI into the mix, and you go from A to optimized without lifting a finger.

    How?

    The tags and fields your AI just updated become qualifiers to send only the signals that matter to your business over to platforms like Google Ads where their machine learning will go wild to find more of the same. Where you might have been stuck sending a simple conversion (like any call with talk time over 90 seconds) now you can send those conversions with a three or better score for readiness to buy, and a product tag.

    20. Better Personalization In Your CRM

    To kick things off, your AI automatically scraped the conversation for an email address, so now you can add a new contact to an email-centric tool like HubSpot immediately at the end of the conversation. H

    ave you updated product tags? Use that as a great trigger to enroll them in a highly relevant email drip.

    Feed your call scores and product tags into your CRM’s lead scoring system and add complexity to a usually surface-level approach. Or do something as easy as sync their company name to their record so you can personalize outreach.

    21. Following Up & Closing Deals

    You’re not having AI fill out custom fields for fun, you’re doing it to make your job easier.

    And one of your primary jobs is following up after a conversation to get someone closer to purchasing.

    Agreed on a time for your next meeting? Send that date field to your favorite scheduling tool and get a calendar invite in their inbox. Or maybe you had a softer “call me next week” agreement? Use that to send the caller to an outbound dialer that’s set to call as soon as you log in the next week.

    How To Use AI For Analyzing Calls

    Moving beyond data entry, when you give AI a call transcription to work with, it can pull out insights to help your team get better.

    In the time it would take you to read through one eight-minute phone conversation, AI has analyzed your whole day’s worth of calls and is off taking whatever the robot equivalent of a coffee break is.

    What can AI do to upgrade your conversation intelligence? Unfortunately, after 16 use cases, we’re bumping up against our word count and we’ll have to save that for part two: Another Ton of AI Use Cases for Call Tracking.


    Image Credits

    Featured Image: Image by CallTrackingMetrics Used with permission.

    Charts: Fastest Growing Ecommerce Companies 2023

    The first challenge in ranking fast-growing ecommerce companies is the definition. Should “ecommerce” include only companies that sell their own inventory? Or does it also include platforms and tech providers that serve those sellers?

    Yahoo Finance adopted the latter last month when it ranked the fastest-growing, publicly traded ecommerce companies. The list includes retailers and platforms with at least $100 million in annual revenue in fiscal 2023.

    According to Yahoo, the Chinese firm PDD Holdings Inc. (owners of Temu, the consumer marketplace) experienced the greatest annual percentage revenue increase in 2023 at 51.91%. Turkey-based D-Market Elektronik Hizmetler ve Ticaret A.S. (an electronics marketplace) was second with a growth of 36.25%.

    In terms of market capitalization (stock price times the number of outstanding shares), Amazon holds the lead by far with $1.85 trillion in March 2024.

    According to Insider Intelligence, in 2024 global retail ecommerce sales will surpass $6 trillion, accounting for approximately 20.1% of all retail sales.