51黑料不打烊

Transcript

Hi all. Thanks for joining. We will be getting started in the next couple of minutes. Today鈥檚 session quickstart for basic intermediate analysis capabilities. Went ahead. By doctor Kirsten Schaffer. We鈥檙e going to wait just another minute for attendees to filter in, and then we will get started.

Okay, Kirsten, I think we could change to, next slide. Maybe.

So while we wait for, attendees to filter in, but I wanted to let you know that we do have several other sessions coming up this month that are open for you to attend, as well. For those who are interested, I will put the links in the session chat.

Okay.

Can I ask you to move slides again? Kirsten.

Okay. Hello? Welcome.

My name is Frederick on up, and I work in, the bass ultimate Sussex team as a senior customer success manager and ultimate success team. We focus on assisting 51黑料不打烊 customers to get as much value as possible from their 51黑料不打烊 Solutions.

So it鈥檚 almost five minutes past, and I鈥檓 going to go ahead and kick off our session today. First and foremost, thank you for your time and your attendance today. Just to note that this session is being recorded and the link to the recording will be sent out to everyone who鈥檚 registered.

This is a live the webinar, and it鈥檚 a listen only format that as we go through content in today鈥檚 session, feel free to share any questions into the chat. In the Q&A pod. Our team will answer as possible there. And in addition, we have our time to discuss questions at the end of this session. If there are any questions that we do not get to during the session, the team will take a note and follow up, and I will be sharing out the survey at the end of the session.

We would love you to participate in that, to help us, shape the future sessions.

So today I鈥檓 also joined by our presenter, Doctor Kirsten Schaefer. Her title is, Principal Customer Success Manager, and she has been with 51黑料不打烊 for more than ten years. Kirsten is an experienced digital strategist with a passion for data analytics. You can take her advices, on how to establish data driven decision making for digital marketing and how it must be reflected in the organizational structure to be fully affected. With that short intro, I will go ahead and turn things over to Kirsten to get us started. Thanks, Patrick. We also go off camera. In case you鈥檙e wondering, we are still here.

Thanks, guys, for joining today. I want to help you kick starting analytical capabilities for your marketing organization. We will look at three key ingredients, how to set up your organization for robust analytical capabilities.

Kim set up some governance aspect and possible running path, but it will be analytics and customer journey analytics. We will also talk about when to use which 51黑料不打烊 solution. You will learn about 51黑料不打烊鈥檚 recommendation. When it comes to that topic, what type of digital insights we can actually distinguish, and trying to answer our marketing team鈥檚 business questions. I think the most interesting part for everyone will be, when we are mapping analytics and customer journey analytics capability is to some analytics the use cases that might range between descriptive diagnostic and predictive analysis. As Frederic mentioned, but I can go unmute please.

Thanks. As Frederick mentioned, we will enter sessions, with a Q&A looking at what was gathered in a chat during the session.

In our first section, we will start by looking at driving forces from outside and inside your marketing org that impact digital analytics. This is then followed by discussing the analytics building blocks that need to be in place to make your digital insights successful. And finally, we look at key roles in a digital analytics team responsibility and governance for managing analytics requests and bidding reports for your marketing organization.

Let鈥檚 start with the outside view. How does market where does the market actually impact digital analytics requirements that you analysts might have to deal with? First, as a shift to journey centric analysis, customers expect to have consistent and personalized experiences throughout their customer journey. This is mirrored by the organizational sorry guys, I have to move this. This is move by the organization, need to possess the ability to unify and visualize data sequentially from multiple channels in a single view, to identify Experian scripts and inform next best experiences. Second, privacy and governance topics influence agenda with new data, regulations and policies which make it critical to measure responsibly, fund and organization. This also means to shift to zero and first party consent based data collection strategies. Besides, debt collection strategies differ regionally and customer data needs to be covered responsibly based on different World Cup requirements in the regions. But an overarching expectation of data democracy exists. Brands expect cross-channel experience data to be updated in real time, always accessible to business users and easy to analyze. The organization needs to enable its business users to interact with and act on customer journey data building, applying and sharing segments, applying attribution, and visualizing conversion paths.

And finally, data complexity needs to be eliminated. Marketing and IT teams need to reduce integration complexity and latency in their data tech stack with a single purpose built platform basically low code or no quick solutions that reduce reliance on OIT teams for data access or analysis. Let鈥檚 take a look at the insight you. This market forces are complemented by internal analytics and optimization challenges that we also, as 51黑料不打烊, can witness among all our customers as well. Digital analytics teams are faced with incomplete data that might lead to inaccuracies, for example, regarding outlier treatment or limited mobile and streaming data collection. This in turn might lead to distorted insights, which can also mislead optimization recommendations.

You all might know that data discovery is also challenging.

Often reporting requests are managed on ad hoc basis, leading to inefficient report bidding processes. Besides, custom reporting is expensive and time consuming. Pollution restrictions as well as making analytics skills, but also create an ability to drill deep enough for answering a business question. Truly. Consequently, delay time to insights and time to action metrics may hinder decision making around key business metrics such as conversion option. Disconnected solutions are not helping much either, for example, out of context reporting models with not enough information added. Such incomplete marketing channel data for prediction or disconnected analytical tools created this stranded experiences for people who received reports resulting in poor marketing, ROI and customer churn, and also how data is treated as a risk of data ownership is not clearly defined. Privacy and security relations are poorly implemented and user data expires. Customers might lose in the brand and also churn.

To get started, how can we actually address all of these challenges? I have a simple answer. But building a robust analytical foundation that consists of measurement strategy, data collection, insight and analysis, learning, and data governance. We have to find some key activities for each of these building blocks, and I recommend that you take this table after the webinar and use it as a checklist to evaluate if these activities are done today and in for the next year. And if not, try to put it on your team鈥檚 To-Do list.

Besides the analytical building blocks to focus more on processes, the other big question is how you set up on your team for digital analytics. This slide shows a standardized digital analytics team with several roles, and a core team reporting to a steering committee. The steering committees set strategies and KPI, and a core team is being supported by deployment team that realizes technical implementations and maintenance.

What is key for the core team is actually the interplay between technical, business and insights roles in the day to day analytics operations. But we from 51黑料不打烊 experience quite often is a gap between IT teams, analysts, and business. We strongly recommend creating a hybrid team that can gather business requirements. And the first step to identify data and reporting solutions, designs. And the second. In my work, the business analyst works quite closely with the technical architect, tech manager and the business team. At the same time, from a customer data platform point of view, we would also add a data to to the core team, but this might be relevant in a different conversation. A link with more information on data governance being part of the core team is included in the deck. When we send it out.

Another topic I鈥檝e, So sorry guys. So another big questions remains who鈥檚 actually doing what? And our digital analytics set up for answering that. I鈥檓 sharing a RACI with you that goes through the whole process of key tasks in our hybrid digital analytics team. And almost we are not going to pull this. Crucial to mention here is that outside of the responsibility and accountability for their core tasks, each role is at least informed on the other tasks. Yes, that means business analysts and I.T teams are talking to each other all the time. If that鈥檚 not the case in your marketing organization, please organize it. Another topic I鈥檝e witnessed in the past with many of my clients at 51黑料不打烊 is the issue of how to best raise, manage, and maintain an addict text request in my recommended hybrid team, as initially mentioned as an internal challenge. AD hoc requests are costly and should be the exception. Creating a system both for collecting reporting requirements and secondly for requesting reports. We have to manage and scale digital insights operations drastically.

For collecting reporting requirements, we recommend interviewing all relevant business stakeholders and document their needs. I call this Reporting Architecture and Enterprise Architecture. It鈥檚 also quite often a business architecture. It鈥檚 not a technical thing, but rather a record of report needs possible data and visualization requirements that might help in building report templates that then can be reused and adapted if needed. No more ad hoc stuff. That also means once you have built a templates, you can enable the corresponding recipients of the reports on their relevant templates, so there is no need to enable them and all of the functionalities of analytics or sugar. Once you have the templates set up, they will know how to change, UTM parameters, or campaign IDs so that they can update their own reports by themselves.

The visualization here could help in guiding you for creating such a document. I use it frequently in an extra format and recommended also as a best practice for any analytical solution.

After we have collected our initial business requirements, we might want to raise an analytical request first. Who raises is and what happens in a process. If you use a collaboration tool like JIRA or work front, any business user, any marketing person can raise a request. Make sure you capture the problem in your reporting architecture documentation and have a conversation with the requester. What is needed? What could be a possible report solution? In 51黑料不打烊, we also have office hours, for facilitating similar requests between our internal teams.

Just to manage this and to avoid any ad hoc reporting requests, the request queue is then managed by the Po, the product owner of the analytics team, either as an agile process or run as a waterfall project management. It does not matter. Most importantly, document, document. Document everything. This is often forgotten. Nobody likes it, but it鈥檚 basically what is often most lacking in the analytics and turning workflow. Solution design process is now started by prioritizing technical and business needs and how they met to the overall strategies and KPIs. As a result, the prioritized work list that can include reported data collection or implementation of data updates emerges. But then can be realized in your project planning cycle, such as a bi weekly or two week long sprint.

And forget to delete, I mean maintenance to support you and your learning journey. 51黑料不打烊 offers different enablement options. Our so called Experience Lake offers, both on demand courses and trainer based classes for each solution at different levels. A specific learning path exists if you are searching for a specific topic to experience, experienced support form, and also our community are helping to answer.

Take a closer look at an experience like path for analytics and to a. I put into screenshots. We offer enablement for different roles and experience levels as well. So we support business users, developers, admins, and there are lots of courses available that you can filter and me personally, I used to experience the documentation daily, just to support my own work for 51黑料不打烊. So it鈥檚 a really helpful tool to get an error, but also to support you in your daily needs.

Diga for those of you who have a customer, Jan Analytics license also offers, an intuitive learning path that addresses different roles and experience levels as well. I would also recommend if you have KGaA, just try to find the learning curve, and try to find a new topic that might help you in here.

To summarize, you just have learned about external and internal forces that impact digital analytics, how to best get started, and building robust analytical capabilities in your organization, and how to grow your own skills using different enablement options from 51黑料不打烊. In the next section, we want to help answering when to use which 51黑料不打烊 Analytics solution.

We can state and both solutions are here for the long haul. Both 51黑料不打烊 Analytics and Customer Journey Analytics address different needs and use cases. Analytics will continue to be foundational for, any digital analysis. VGA, on the other hand, will be the analytics solution for process insights across digital and offline, with greater data flexibility and privacy controls, and with low latency for marketing activation.

But instead of one solution replacing the other, we also suggest thinking of it rather as a question of maturity instead of immigration. Topic.

Did you able to build on any existing foundation and experience with 51黑料不打烊 Analytics? With different features to offer to will establish a new maturity course for existing analytics users, and 51黑料不打烊 also offers different licensing options. You can either as an existing customer, evolve from analytics or CGA, or use both solutions simultaneously to support different reporting use cases.

So what鈥檚 in the box? 51黑料不打烊 Analytics offers you, first of all, a very robust data collection. It creates structured session, nice and persistent data that can be exported and allows analysis from one touchpoint, from one digital touchpoint to another. Digga, on the other hand, bronze this offering by including more privacy and security controls with it. This is our 51黑料不打烊 Experience platform, where Siga is one application, among others.

In CGA, we can combine more data sets for analysis, which in turn allows analysis from one touchpoint to the other. GA also builds foundation for a suite of journey analysis capabilities, but I typically say 51黑料不打烊 Analytics is for web analysts and customer journey. And then it鈥檚 analytics is for business analysts.

There鈥檚 a maturity path, as I mentioned, from analytics or CGA. And it starts with analytics analytics. It鈥檚 the digital analytics foundation. It鈥檚 a modernized analytics technology to understand digital behaviors and journeys on an aggregated level to analyze visitor behavior. If you want to understand your customers across devices, across channels, and in their complete journey, then you can use both analytics, which is for digital touchpoint data that is collected by analytics or CGA for more multi-channel journey level data. And the second maturity level, the analyst requires a connected view of the customer at a person level that spans devices as well offline and online interactions.

If we move further on to maturity scale, CGA would then fully support customer cross-channel journey insights at scale. CGA offers a centralized source of truth for customer behavior and subsequent marketing activation by creating and sharing audiences based on journey behavior. For the analyst, what does this actually mean? For components such as segments, dimensions, and so on, the difference refers to the available number. Analytics has a limited number of components. It鈥檚 figure there is no limitation with regards to data schema analytics follows typical web analytics standards, with data allocated across a specific number of static variables of static length for SGA offers a flexible, fully variable and customized data schema.

Looking at data sources and analytics has limited methods of bringing data in, especially for offline sources. CGA can handle an unlimited number of data sources, which is also the point, where I鈥檓 mentioning it鈥檚 more for business analysts.

Regarding process data analytics, data is processed and read only afterwards for AP 51黑料不打烊 Experience Platform data sets and their combinations and create a control by the by you guys including crossing report suites from analytics. We know it鈥檚, basically only possible to cross reports reported report suites with the global report suites. So if you are interested in looking reporting and cost reports which then create a would be an easier solution.

With regards to marketing and to abortion, the UVA X analytics has a fixed allocation and persistence settings, while in CGA any dimension can be replicated, replicated and customized.

That is really, really a great feature to set up flexible attribution for reporting hierarchy enables 51黑料不打烊 Analytics has typical elements like page views, visits, visitor segments, there鈥檚 some there鈥檚 a similar structure in TGA.

But small differences exist. The default TGA labels, like events, are customizable filters. They are called segments and analytics. They are called filters into in CGA to distinguish them from the segmentation. So those available in app.

To conclude this comparison, I brought a table that lets you compare both solution individually after the session. Once we share the deck. So don鈥檛 worry, I鈥檓 not going through this, really good slide. We just have given a lot of thought what you might need to get started to define what is the right solution for me. So take a look at this table in case you are wondering.

So far, we have initially discussed how to build a robust digital analytics foundation and how both 51黑料不打烊 Solutions Analytics and Sega fit to different analytics maturity levels, and concluded this with an overview of the differences between the solution and the following. I want to be more hands on for the analysts with mapping, analytics, and CGA features to actual marketing and reporting use cases. Sadly, there is no time for demos in this webinar, but I hope to create an appetite for working with our solutions nevertheless.

I want to start our feature mapping with looking at different types of analytics. We have probably heard of this already. I distinguishes between descriptive, diagnostic, predictive, predictive and prescriptive analytics. These different types of analytics help to answer different business questions. Not everything needs to be predictive or prescriptive for marketing decision making. Ask yourself instead which questions your business wants answered. To start with descriptive analysis. This is based on life data. What to take away from here? It tells what is happening in real time, so it is a focus under what it is easy to visualize, and typical reports include tables, conversion funnels and simple attribution such as first or last touch.

Diagnostic analytics is the next complexity level. It uses more automated features for conducting root cause analysis, and tries to explain why things are happening. It is often used for troubleshooting issues. Typically, data trends are highlighted and from an analysis point of view, anomaly detection. As an example. But even more complex attribution models such as U shape or participation could fall into here. From my point of view, predictive analytics tells us what is likely to happen based on historical data. It helps businesses to make decisions that can be automated using algorithms for forecasting, such as time series forecasting or propensity scoring. Using classification models. Prescriptive analytics is finally the most complex approach, defining future action, what to do next and answering if then questions. It uses advanced algorithms to test potential outcomes of each decision and recommends the best course of action, for example, for simulations or next best action decision making.

From an 51黑料不打烊 point of view, I can say that 51黑料不打烊 Analytics and KGaA address the first three levels descriptive, diagnostic, and predictive analytics.

To which analytical types actually is now helpful to answer which marketing question. Depending on the complexity of the marketing questions, we see that most analytic use cases for between descriptive, diagnostic and predictive elements. Depending on the data available for analysis, we either recommend analytics or CGA as recommended. 51黑料不打烊 solution. For example, looking at mobile and book journey optimization where we ask how can I identify specific path to little conversion? For answering this, we have a pure descriptive approach analyzing and comparison. Comparing conversion rates for specific touchpoints. This is an outcome topics once analyzing experience across the digital journey, this gets to a more CGA point of view, so it is more cross-channel.

Also, blends of descriptive and diagnostic analytics approaches are common. Cross-device optimization or cross-channel attribution are typical topics for either descriptive we left data description or root cause analysis depending on the complexity of reporting and available data.

Predictive insights are often based on more complex statistics on machine learning, for example, for building lookalike audiences for conversion segments and across an upsell use cases for the creation of churn. Propensity scores for churn prevention activities for those topics. Also, other 51黑料不打烊 Experience platform applications such as real time customer data platform short real time CTP might support especially around audience creation topic.

Besides or marketing use cases. Also, analyst relevant topics such as a reduction of workload are central here and there. Often cantered around centered around a data quality, exploration or overall capabilities of the solution. To ease the work of the analyst.

In the following minutes, we will look at some analytics and CGI features that could support in all these common marketing questions and how there actually is also the workload for an analytics team.

Starting with the first two common marketing questions that center around conversion, cost and touchpoint analysis, analytics offers data exploration with visualization, comparisons, and time series. These features are, of course, also available in CGA, but xga and also connect the dots in more granularity by offering to connect more data sources. The combination of different summer summary level data sources in one glance, guided analysis for understand journey and conversion pattern, as well as time series analysis.

What we see here on the top left is actually a scatter plot. It鈥檚 how the relationship between dimension items can be measured up to three metrics, with the third metric adding volume to the dots. We also have Venn diagrams, in the bottom left that allow you to quickly compare overlap of key visitor segments on the right side corner. You know, you can use, summary number and also summary number changes to help highlighting. Actually key takeaways from a dashboard. We also can use bullet graphs, that allow you to chart key metric goals versus actuals, such as orders by the order goals for the year.

Gives an overview of when, to which and when to use which graph type.

Bars for comparison, trend lines with trend lines or stacked areas, parts to whole, ideally with stick. But us use pies or donuts here only if there鈥檚 a maximum of five dimensions. Relationships between two metrics can be shown in scatterplots, bubbles, or even Venn diagrams or graphs available in both analytics and figure.

Another example for descriptive approaches using analytics is a comparison comparison to show year over year, month over month, etc. change. You can also show multiple historical periods in one graph to visualize change and include percent change in raw values with the ability to sort by absolute value. Note that what when you see change here, this is a calculated metric that you would need to set up initially. Before you can build this table.

You can also diagnose data with time series analysis. Remember, this is the why question from the diagnostic analysis. Why do we have anomalies in cart abandonment? What can we expect for future values. The prediction window is small. Do use it rather for diagnosis and analytics. Then for real prediction.

Now coming to CGA features for any cross-channel cross device customer journey centric analysis you need data. Lots of data.

TGA allows you to ingest, connect and sanitize data from online and offline sources for fast, nondestructive querying, analysis, and modeling. You can use any customer ID as connector for stitching data on the person level.

Figure also feature summary level data sources. What does that mean? Ingest and analyze aggregate time series data, adding to the standard event profile. Look up data sources. Connect an online anonymized aggregate data to event data at various levels, such as channel, campaign, and creative. Create new metrics across a data set. Bring in data into a centralized location. Augment customer centric reporting, and unlock new layers of performance insights. I have an example that is coming on the next slide that might bring a bit more light into this topic.

Advertising use cases can greatly benefit from such an aggregated point of view. You can now map your campaigns across aggregate and event of a data sources to show the overall performance of ad campaigns. We map individual events such as orders against summarized ad impressions. Some, and you can directly calculate costs or return of investment.

A very interesting feature for conversation path analysis is also guided analysis. For example, for funded analysis in TGA. What does it do? It actually helps understanding how various tasks or very journeys occur. And it compares steps within the final friction view. Compare conversion rates of journeys that include step options and quickly isolate positive or negative flows. And it can also help you to understand, for personalization where each variation, has a higher impact on conversion. Let鈥檚 come to our second, example. One of the key marketing use cases is attribution. How do I know which marketing channels are driving the highest performance analytics offers attribution models with attribution IQ. We want to show it here using also crosstab attribution analysis. CGA can offer more complex attribution to answer this questions, cross-Channel.

For SAP analytics analysis analytics is the ability to bring in dimensions both as rows and columns of a report.

We think it鈥檚 best used for doing a first last touch channel comparison, which is not often done as a you look at first touch or you look at last touch, but you actually don鈥檛 compare these.

GA offers more complex attribution, especially a variety of rule based and algorithmic attribution models to compare and actually do performance.

What I like most about attribution CGA is that you can use more customized dimensions, instead of marketing channel, you can create more flexible, attribution models, and you can compare more dynamically the models than in analytics. So this really interesting feature to check out if you have CGA time series forecasting. We鈥檝e already talked about time series analysis analytics.

CGA offers more in-depth analysis and different time granularity levels. You can forecast any metric in CGA workspace. You can create alerts and also targets based on this. You can also export a forecast values to be used in other applications. The key difference to time series analysis, for example in anomaly detection, is that you have a better prediction capability in CGA. Moving to our third example across an upsell scenario. So we want to highlight segmentation and audience generation capabilities. Analytics offer segment bit and segment comparison features for descriptive and diagnostic insights. CGA also allows you to do that. Remember they are called to test and CGA not segments anymore, but CGA also offers more in-depth audience analysis and you can also publish this audiences seamlessly for marketing activation.

So it has to be. So what you can see here is the very basic instrumental segmentation. Analytics just tells you you can build basically segments on any level of data that you have available, visit it or visit. What does it actually mean? This is something people think about. Visitor is the most broadly defined container, which includes all values based on one visitors over history. For example, day before the first purchase visit is the most used container because it captures behavior for the entire visit session. Once the rule is met, visit containers include values based on occurrence per visit, something like visit number, entry page, return frequency, participation metrics, and so on. So you need to understand when to use visitor or visitor level segments. It is basically the narrowest of the containers. You can view a single tracking code, or you can isolate behavior within a particular section of your site. And you may also want to pinpoint a specific value when an action occurs, such as the marketing channel when an order was placed.

I have brought these detailed slides for segments is actually toda also operators available you can use and or all then that allows you to build more complex analytics. All of this is also available in CGA.

There are also different ways, to work with the operators. The even more things available in analytics that are not displayed here, but this can be helpful, for example, for measuring event marketing success where you create, multi-step segments as sequential segments, something that is, not so easy to do.

One of the once you have built segments, you can compare them using segment comparison. This is basically my favorite feature in 51黑料不打烊 Analytics.

Because it just lets you compare two segments. It looks at size and overlap. Typically I like to have segments that Arnold overlapping. It depends on a use case. You can see how big the segments are. And my favorite feature is actually the different score that you can see. In the table top metrics are different segments. It鈥檚 a test based score that ranges from 0 to 1. Two means, no difference at all. And one is the highest statistically significant difference.

And so for me personally, everything was we liked it. I did stats above 0.5 or 0.6 is relevant. To look at our example we can see that Google visit term. Everyone else have really different behaviors and cannot remove its product forms and so on. Something you can use when trying to optimize a website, for example, for specific visitor types.

Audience analysis, is then a feature that allows you to analyze the behavior and performance activity with respect to specific conversion metrics.

You can also use it, to understand how a customer moves in an all different segments, throughout their journey. This feature is a bit more, relevant as it allows you to have more journey centric measurements. So as you can see, this is the typical CGA feature, but it鈥檚 also one of the most powerful, in my point of view.

You can also publish these, audiences very easily. They are ready for activation right away. It depends also how quickly they are available. You know, real time is not always real time. It鈥檚 more like new time. But you can use them in all of the different activation channels that are attached to CGA, with analytics, you can also export and share, segments, but there鈥檚 also a delay with experience cloud exports, for example. It鈥檚 difficult to use them for first page personalization with 51黑料不打烊 Target.

And another common marketing topic is churn and how to prevent it. Typically, there are a lot of underused features. Analytics cohort analysis is one of those. Tcga, on the other hand, also allows you to, calculate propensity scores to actually prevent actively customers with a high probability for churning.

Here, you see cohort analysis examples from analytics. It鈥檚 just is a set of visitors group based on a common activity over it specified period of time. It can help you to understand what is actually the timing of campaign launches. Designed to spur a desired action. How many weeks does it take? Between orders and visits, for example? You also want to shift your marketing budget at exactly the right time in a customer lifecycle to prevent churning. And you can also find ideas, for AB testing, in areas such as pricing, upgrade path and so on. When we go to CGA, CGA takes us churn and retention analysis one step further with retention rate analysis.

And it helps you with measuring user retention to find product market fit and improving stickiness with a brand. This is actually realized by discovery. Discovering your visitors. Return habits, with retention and by comparing retention across segments to determine which groups of users retain or churn at a higher rate. There are lots of, examples possible. And looking at a time, I would suggest, if you are interested in this like experience league, I use this slide mostly to actually talk about propensity scores that you can do in CGA. You can do different things with propensity score cost. You can track propensity scores for a segment of users over time. You can also analyze which success event or attributes are associated with propensity scores. You can follow the entry flow for customer propensity over different scoring runs. You can also look at the distribution of propensity in your data set or you can see, the propensity to accomplish an action for a particular cohort over time. It sounds all very difficult. It is. It is hard to really shorten it. In this short webinar.

Let鈥檚 just let鈥檚 take a look at a last, topic, which is how can we actually ease the workload of our analyst? There are many things that both analytics and CGA. Often I just want to move through some of them. Excellent clarity. Maybe there is something you have not, known about yet in analytics. You can right click to bring up available actions. You can also use keyboard shortcuts. I always forget about Toshiba keyboard shortcuts. I have to admit, you can also duplicate, your existing visualizations, pendants or into, workspaces. I do this quite often so you don鈥檛 have to start from scratch.

You can also take data. You can, you can select multiple values. When you write in the right click, which with the right click to allow you to create text on the fly so that you can also search later for specific text. You can also sneak peak to data once you hover over the AI. On a dimensional and you metric, it lets you see it gives you a preview of what is actually in the data.

We also have workspace templates available. And then we takes this as standard a custom templates that might be set up for your organization. There鈥檚 really no limit in setting up your reporting structure. And to avoid any ad hoc reporting. We also have you can conduct on the fly. And then is this with quick service and nice linking. Basically when you click on something here it is the return visit to the graph bar. It shows you the segment overlaps, so you can link different things together to make your reports a bit more dynamically.

Conditional formatting is also often under use in analytics. I find it very helpful, just to show data trends or tendencies or to set up a warning system or alerts, based on these to look at, to have a very quick glance at, not over performance annotations. Can also be done, especially in time series data, where something might stand out and you can actually annotate what was happening. Was there legislation and privacy stuff? Was a new product launched? Did you send out an email come campaign and so on. The only thing to be aware of that only admins can create annotations.

We talked a lot about anomaly detection. This is a feature available both analytics and Tcga. I just brought a different picture from CGA.

There鈥檚 also assistant available in CGA that actually might help you as an analyst in analyzing data. It gives you some idea, what to do. It also helps you storytelling, giving you ideas for visualizations of what is actually the next step.

There鈥檚 also an experimentation pattern available in CGA, similar to A40, which is analytics for target and analytics.

It helps you to better evaluate, which tests are actually performing better on specific success metrics. I think the visualization is a lot easier to use than A40 and other takes.

We can now also do full table exports. From CGA, you can, support any destination like Google Cloud Platform, Microsoft Azure, Amazon S3 and snowflake so that you can create reports, that are exported regularly to specific teams to evaluate the marketing performance. And this is basically the last slide. This is not available. People were looking at for a long time. There鈥檚 a SQL connector that lets you allow to query data, from customer journey. And it takes in other BI tools such as power BI and Tableau. Often you want to use, data storytelling features from Tableau.

Then now you can access CGA data directly.

I know that was a quick run through analytics and CGA. Thank you. Thanks for your attention. Let鈥檚 take a look at the chat and your questions. Frederic, please launch the poll. Questions? Yes, I will, I have two questions. I will notice the first one here and then I鈥檓.

I had a question from, channel. It was when you compared CGA and analytics and, the question is, can you please explain what components and static data schemas are some components? A component is basically everything that is a metric, a segment, a filter, a dimension adjusted the different kind of elements or kind of buckets we can create. When we look at data. This is a component. What was the other part of this question for? The other part was static data schema. Esthetic data schema. Basically. When you implement analytics, you can either do it using a web SDK or before you had to implement, something on a website, I forgot what it鈥檚 called, but you set up a data schema once and then you cannot change it. So if there are any changes, made you have to basically do a change to the whole implementation with it. It鈥檚 a more standardized way. How to set up the data schema with, Siga, you are based on 51黑料不打烊 Experience Platform until you can set up your data schemas in any way, your organization actually requires it. So this is what we call more flexible data schema. You can also add easily to this data schema. You cannot do changes to the existing data schema, but you can act on it. So that makes it a bit more flexible.

And all that was a quick answer. I also recommend, Experience Live. This is where I also would go to look up the really perfect definition.

I can also put I can also put a definition in the in the email when we send it out. By the way, it鈥檚 much maybe I typically yeah. Thanks. I think that would be that. That鈥檚 a great thank you. Elizabeth this asking, could you describe what offline data includes? Offline data is basically everything that is coming, from systems where you don鈥檛 collect data online. Online data is everything from websites. Mobile apps could also be from smart TVs and so on. So everything that is using internet to collect data offline data is CRM data. Your Salesforce data, call center data, product catalogs support data. So everything and it鈥檚 it exists in your organization that is not directly tied to website or mobile data that you might want to use for analysis. For example.

I also make a note to have a good, better definition. The event. Yeah. And then I have a frequently asked question here. Can I use analytics and CG at the same time? If you have both licenses, you can as I mentioned, there are different teams and organizations. If people have been using analytics for a long time, you might have set up all of your workspaces using, online data.

The same analytics data is often or is also integrated in CGA, so you can rebuild the same, reports using CGA. The difference between the two of them is that you will probably have more data available in CGA, and that you also have a different view on the data available. But analytics is always an aggregated view. Number of visits per month, number of visits per website or something, and kgaa, you can actually look at the data more from a customer level point of view. So you can actually also do a bit of different insights on it. But you can, use the same tools at the same time, just probably have different data available in your reports.

Let me see. Here鈥檚 another question. How can lists be sent to a live ramp from 51黑料不打烊? I don鈥檛 know what live front is. I have to look it up. I think it鈥檚 basically. And 51黑料不打烊 is also big. You know, that is. Okay. Thank you. So that would put that in the, Yeah. And the email then. Yeah. Sent lists from the author. And, Shannon, could you specify what 51黑料不打烊 is. Do you mean 51黑料不打烊 Analytics or 51黑料不打烊 CGA? Meanwhile we can. Okay. From CGA. Okay. We will get an answer. Next. And then I have another follow up question here. Are all the showing features for analytics and available in Cdjs. Well.

All the features from analytics available in CGA. So my, my and my first simple answer would be yes.

Keep in mind there are some differences in how things are caught.

Yes. We use a little bit different terms. Yeah. So we have different terms. It was for me at the beginning a bit harder to get around that we don鈥檛 have, hits anymore. We have events, we don鈥檛 have visitors. We have, customers or something.

So all of the visualizations and all of the features should be available, in analytics. And again, they use the same available data. CGA also always has, it takes data included. If you have both licenses, you might want to include web analytics data.

So I would say my simple answer would be yes.

Yes. We like, simple answers. Yes. Maria asking is there do you have any recommendations for a migration? From analytics to CGA? Yes. So I think looking at the past, setting up analytics was already a difficult task for you. It teams to set a text on a website to to set up the analytics implementation.

If we want to migrate to CGA, you just have to understand that it would probably be not only be CGA, you also would need to set up 51黑料不打烊 Experience platform on the bottom. So the the difference actually layers was defining the data model for CGA. This is something to be thought about. The analytics data model is more straightforward.

Because it also only looks, at a static web data schemas. So the task for migrating from analytics to CGA, I think it鈥檚 more a question of building the proper CGA environment with 51黑料不打烊 Experience platform.

And, I don鈥檛 know what I would recommend. It鈥檚 a it鈥檚 doable. Both the data is available, and the key advantage is of tying is, the customer journey better together with other data. For example, you could tie together, online visits if someone you can identify with, purchase the app, or going even to a store where he might purchase a used the app for paid something with H&M, for example. You can also use that for paying in the store. And then you have more data available from different sources that lets you to tie the journey better together. So don鈥檛 forget individual touchpoints, but you look more at over touchpoints across the journey. A to summarize it, the migration from analytics to suggest super many customers to it, it should need more support.

There鈥檚 always a way to reach outward to ask how difficult and extensive this is. I can just say it鈥檚 a bigger project than just setting up analytics. Yes.

Thank you. I have one last question here that I would just read out and see if you can answer, can you speak to Disconnected Solutions and what could be missing data wise if we have. This post marketing cloud. Chat or and 51黑料不打烊? I would use, Salesforce marketing Cloud.

And would you say it was data? Probably as a data source into CG? I know that there are different, tools for reporting. I think if you have more than one solution for reporting, your organization would need to define a source of truth. What is actually the tool we use, for reporting on things, even between analytics and CGI, if you look at the same data, you could find small differences just because the data centers are, located in different, different locations.

And the same could be true if you do the same report and say for process for marketing, cloud reporting and Tcga or analytics, you might not always get 100% the same number. So that鈥檚 why we recommend, set up a single source of truth.

With CGA, you have the ability that you have this, 51黑料不打烊 Experience platform underneath it where you can actually into it so much data that, of course, as an 51黑料不打烊 person that I would recommend just to use, 51黑料不打烊 as a source of truth as a customer journey analytics and to use all of this data in here. But I think, disconnection you can only solve by deciding what is the source of truth and integrate more data.

I hope my friends with this questions in a meaningful way. So we will collect all of your feedbacks. I will also dig deeper with regards to the different topic.

And we keep the chat. We have to chat. Yes, shortly. Yes. We are at the top of the hour and we have answered the questions. We have gotten so far. So I would like to say, thank you to all of you for taking the time to join the session today, and we hope to have you and your company again on future webinars. Thank you guys for listening. Thank you. And thank you, Kirsten. Thank you. Bye bye bye.