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Transcript

Hi everyone. Thank you for joining our session today focused on the strategic power of measurement. Turning data into action will begin in just a couple of moments. We’re going to give, just another minute or two for attendees to continue to filter in. Before we jump into today’s content.

While we wait, just to note, we do have another webinar coming up tomorrow that is focused on mastering the admin console, so I’ll put the registration for that session in the chat. If anyone would like to take a look and think about attending tomorrow’s session as well.

All right. Well, it looks like we’re starting to get a good number of people filtering in. So before we kick off, just to note, the format of this session will be listen only for attendees. So if you do have any questions that come up as we go through the content, feel free to put those into the Q&A pod or into the chat pod. We will answer as many as we can towards the end of the session, and if there’s anything that we don’t get to, I’d recommend just reaching out to your ÃÛ¶¹ÊÓƵ account team for more details if needed, but we’ll try to get through as much as possible in the time that we do have reserved for Q&A.

Also note that this session is being recorded. A link to the recording, as well as the presentation slides will be sent out to everyone who has registered. After today’s session. So, without further delay, I think we can go ahead and jump into today’s content focused on the strategic power of measurement, turning data into action. And with that, I will turn things over to my colleague Christos to take us through today’s content. Thank you, Katie. And hi everyone. Thank you all so much for joining today. I am very excited to be, discussing some very interesting topics around measurement and measurement strategies. And taking it a step further, I think, I’ll, I’ll be walking you through really quick what our agenda today. So we’re going to start with some foundations, covering off on some definitions of measurement strategies, some approaches to building measurement strategies. But really the meat of today’s conversation is going to be on, sections 2 to 4. And those are all pillars that uphold a strong measurement strategy that we’ll talk about. And hopefully things that are resonating with you in your organizations and areas of opportunity to expand upon how we’re approaching, getting the most value out of your, analytics investments. So let’s get underway, talk about our foundations. So let’s just level set on what we would define a measurement strategy to be. And from ÃÛ¶¹ÊÓƵ’s point of view, it’s, a structured approach to aligning business objectives to actionable insights.

This is sort of the foundation for making data driven decisions that are enhancing the customer experiences and improving operational outcomes. And you’ll see, some general benefits to having a measurement strategy in place, things like operational efficiencies, business agility for stakeholders to be able to, respond quickly and make informed decisions. And then lastly, customer experience, we know that there is a direct correlation to doing measurement better and enhancing the end customers experience. There’s, of very much so. Those two things are a function of one another. When we are measuring and understanding our customers better, we’re able to then utilize that data to tailor experiences and provide an experience for those customers that are, going to be meeting their needs. So when we think about the elements of an effective measurement strategy, the first step really is ensuring that all stakeholder are aligned around a shared vision. And we’ll talk about, an actual example of this. And what this does is it gives direction for why the experience itself exists in the first place. And what are we looking to really accomplish with that experience? And within that vision, it’s important to, incorporate inputs from, from various stakeholders. And as a quick call out, never to start with our KPIs, first, we really want to uplevel this, this conversation, to begin, this this from a more strategic lens and then narrow down from there. Secondly, at the heart of any successful measurement strategy is a customer first approach. Being customer obsessed when we are defining a measurement strategy. So, prioritizing customer experiences and aligning metrics with the customer in mind is is really something we see organizations miss the mark on often. And I think we need to think about that. Analytics should follow the lead and needs of the customer experience, and it shouldn’t be the other way around. And then lastly, this culture of data, and this is something we’re going to be, weaving into the remaining portion of the the meat of our conversation today. And that is fostering, strong data culture. This is essential to a successful measurement program. And this means that we’re not only ensuring data accuracy and actionability, but we’re making it a part of our regular, day to day rhythm to review, refine, and iterate how we are, measuring the effectiveness of experiences. So when we think about it, we, we’re by embedding these elements, into a measurement strategy, we set the foundation for activation in a way that is tied back to what is most important. Now, this slide here sort of outlines the structure that I reference. So as mentioned, this is a top down approach. It starts with this clear vision. This is your your North star that defines the business’s purpose and direction. Underneath that we have strategic pillars which support that vision by setting goals. And they are then followed by objectives. And these objectives act as the levers to advance the goals. And this is where we need to be involving stakeholders across the organization to ensure alignment. And then underneath that we have our use cases and KPIs.

So this example here takes what we just saw a little step further. And how we can potentially apply this, to a real world scenario. The vision here is to become a market leader in sporting equipment. Let’s say, our strategic pillar here is to focus on establishing best in class customer experiences. Our objectives are pinpointing around topics of, improving accuracy of cross product recommendations. We could have more than one objective. This is just, you know, an example here and a use case detail here would, you know, gathering insights on commonly purchased items and utilizing that data to display relevant reminders to drive up conversions. And our KPIs here are things like average order value, customer satisfaction and purchase frequency. So this would be a measurement strategy example that we would be able to apply to a specific use case. But being able to go broad across an organization and have these strategic pillars and visions be applicable to multiple use cases is what we are after. And, and ensuring that there is consistency and language around what we call what. And we see oftentimes use cases get get mis defined and commonly bucketed into the same category as capabilities. And, making sure that we’re utilizing a common framework for how we define measurement, and that this is a collaborative exercise to go through.

Now, if we’re not, implementing measurement strategies, many of us know this, but there are a lot of risks associated with that, inefficiencies, lack of trust and ultimately unhappy customers. And delayed value or no value from your, from your marketing activities. So we need to be establishing some of these concepts of robust measurement strategies, which include the voices of all stakeholders, to ultimately deliver effective experiences for customers and end users. And this fits into this overarching.

Idea that in between your organization’s digital transformation, which may have been going on for the last ten, 15 years, this idea that, effective measurement and learn strategy is what’s powering and is the bedrock to enabling personalization across every channel. So ensuring that this strategy is in place is paramount.

Now we’re going to start transitioning into more of the meat of this conversation. And I want to introduce some, some quotes directly from our customers who were a part of the customer advisory board. And these are in relation to measurement strategies. But really taking that and thinking about it from the program level. And they center around kind of three key themes data trust empowerment gaps and lack of value realizations. So how do we approach these challenges and emerge to ultimately drive more value out of the investment in technology that you’ve made? Our next section, which will delve into each of these and the approaches to addressing these challenges, will hopefully shed some light on on how we can go about doing that.

I like to think about this idea quite simply, as somebody who was on the customer side, who was an end user of analytics.

I found myself saying a lot of those things in the previous slide. But I really think that it boils down to these three main themes, and these three themes are going to be the basis for the next sections. It starts with trust. So trust is established through proactive involvement and transparency. We need to be engaging stakeholders, as mentioned earlier on measurement strategies in our processes, to make sure there’s transparency around data quality, that there are standards in place around governance and accountability.

This ensures that everyone has the confidence in the data that they’re ending up using. And when I say they I’m referring to non analytics teams, stakeholders that are dependent on data to do their jobs. And it is so critical to be able to earn the trust of those individuals. And if there is a lack of trust in the data and the underlying efficacy of that data, there’s ultimately downstream impacts that we’re going to talk about. Path to value is severely compromised if the team doesn’t feel that there’s trust. So it’s almost like this builds when you build your trust. You then can empower. Now these two things can happen in tandem. Empowerment. It’s somewhat synonymous with democratization or access to, to data. And this is a loaded topic. There’s a lot to unpack under the area of empowerment. This isn’t just a free for all access to data, but it’s leveraging what is available to you as an analytics leader to make the life of that person who is depended on data to do their job easier, and that they’re more confident in, in what their, their actioning on.

With a combination of these two things, we then get to our path to value. And this is where as an analytics program, being able to articulate the value that the investment in the technology and the program and the resources offers, is is really critical. And there’s sort of a virtuous cycle here of, mentioning and showcasing the excellence of, of the program. And it breeds more trust and it breeds more empowerment and organic interest in analytics. So we’ll talk about each of these in more detail. And I’m hoping that that this, this theme kind of going back to some of those quotes around how we can be addressing some of these challenges of trust, empowerment, path to value.

So again, I, I, we can we can label this trust. We can label as building trust. I think about it as a again as a former analytics and, professional as a, as a customer, somebody who is now working with analytics teams. Trust is something that is, with stakeholders in your organization. It’s not something that’s just going to happen overnight. It is something that needs to be prioritized, and there needs to be a mission around earning trust of stakeholders. And we understand why it’s so important. Because going back to that previous slide around data being in that middle, it’s that linchpin to being able to deliver effective experiences.

So I like to think about trust in four facets here. To earn trust, we need to be, first and foremost prioritizing data governance and quality control.

And you’ll see that at the bottom of this trust triangle here, that that, I’ve developed and in tandem with that stakeholder involvement and alignment is paramount.

From there, we move up in this triangle toward that center piece in and that’s inside accessibility. So making insights easily accessible and empowering stakeholders to make informed decisions. And then at the top evangelism and awareness. And this is these are all things that an analytics program can be doing to foster trust amongst state stakeholders in your organizations.

So when we think about data governance, we can go and we can have an entire an entire webinar focused on data governance because there’s so much to it. But I wanted to highlight some of the things that I find to be critical to, data governance.

To support building that first kind of foundation of this triangle.

We talked about ensuring there’s a clear measurement plan in place with strong ownership across data, health data collection, as well as adoption, also establishing robust leadership and alignment with it and marketing. This ensures cross-functional support, having a dedicated steering committee, holding the organization accountable to governance policies, that helps reduce inconsistencies and maintains data quality.

Also documents and information about this data need to be accessible, readily available, regularly updated, and stored in a place where, you know, no matter what the role is of that individual who is in need of some information about how the program operates, what type of data is available that is not a barrier. And then lastly, consistency around data collection usage standards to protect data quality and compliance. There’s like I said, there’s a lot to data governance. But to summarize sort of some of these high level best practices, things to keep in mind. And then on the right here you’ll see some of the risks. If we’re not following these practices, including, you know, misalignment, lack of true leadership on data, and inconsistencies in data practices, fragmented documentation, all of these things erode trust. And are are things that we as an analytics team and analysts leadership can be doing to ensure that, the voices of stakeholders are being heard and we’re proactive in and ensuring that, these critical pieces are in place for them. Now, our next facet of this triangle is stakeholder alignment. And we talked a little bit about this already. But engaging stakeholders early from the outset of development of a of a use case, for instance, there was a quote from from that earlier slide around how how data comes last. And this is not a new phenomenon. We’ve as analytics people have been encountering this challenge, for many, many years where, marketing begins process of developing a use case. And, we forgot to the to meet with the analytics team to see a, you know, is this data even available? Can we turn this idea of a, an audience into a real plan? A lot of red flags that, and experiences that I, I’ve dealt with myself, on this topic and then, you know, going back to our measurement strategy, setting clear objectives and KPIs, incorporating regular feedback, on those measurement strategies for ongoing improvement. A measurement strategy is not a set it and forget it type of thing. It’s something that should be evolving over time as your business changes, as customer needs change. And, a really critical part of, of gaining this trust in stakeholders is having them be a part of these conversations. That upfront effort pays dividends down the line when we are looking to analyze the results. There is there’s less finger pointing and less and more skin in the game. For those individuals that took the time to meet with analytics and prioritize analytics. And it’s it’s a bidirectional thing. There needs to be an effort from analytics. And there needs to be a an incentive for marketers to engage with, with analytics teams. And then again, on the right here, we see some of the risks if these aren’t being followed, this alignment tracking of irrelevant metrics, data silos, all of which really derail our efforts. So this is the second facet to earning trust with stakeholders, and that’s that outreach of involvement and alignment in, in strategic planning. So the next facet we’re building on, what we’ve already established is accessibility. We need to ensure that data is accessible, accessible, and actionable across the organization. So here are some general best practices around accessibility. We’re going to talk a little bit more about this in our empowerment section with regard to some strategies, some capabilities that exist in customer journey analytics, for instance, that enable you to, make that. I like to think of it as like the cognitive load for somebody who isn’t an analyst to log in and get to the insights that they need, how can we lower that barrier? And that’s really, really what we want to accomplish here with insight accessibility. Again, another thing that’s such a it’s it has it has a lot of barrier to entry from a an effort standpoint. This is something that’s hard, but it’s something that pays dividends in the long term when we have, put the work in to make insights accessible to multiple personas, we can. You know, have a, a much more efficient program. And when we talk about the value of an analytics program that really comes to light, even more.

So lastly, proactive evangelism. So to earn the trust, it’s essential to do all the things we’ve already talked about. But lastly, it’s the it’s the last mile to foster awareness of the value that analytics can offer. So consistently sharing the impacts of analytics initiatives to demonstrate that value across the organization. Holding knowledge sharing sessions to educate stakeholders on the capabilities, the potential future applications of those capabilities, and ultimately expanding the literacy around this ultra critical asset that is data.

And and again, this is there’s there’s opportunities and strategies at which we can do this to help drive additional trust in the data. And, I think that last bullet here around maintaining a visible presence in cross-functional meetings to keep analytics top of mind for strategic initiatives. So that’s a really critical one, especially going back to this idea that analytics sort of gets gets forgotten in in the midst of planning, something that that is we need to insist on being a part of as, as analytics programs. Also, there’s ways and strategies to going after this, this topic here of internal champions, while everyone may not be a data scientist or an analyst, we can have internal evangelists for analytics that may not have a direct line. Are may not be directly a part of the analytics team, but are sort of the the point people for, championing some of the, the things that, that the analytics, the core analytics team is, is trying to advocate for. So individuals that are advocating for analytics to drive adoption and reinforcing the value.

And over here on the right again we’ve got we’ve got the risk associated with not doing these things.

And really the combination of all of these facets of this triangle here are what lead to this trust. And again, this is something that happens over time. This isn’t a one time transaction. This this involves a lot of work and a lot of prioritization and these initiatives to, to earn that trust. And that is that is the that is, I think, how as analytics teams, we need to think about this, is that this isn’t something that is that stakeholders across the organization will organically develop. It is something we need to be proactive on.

So moving into empowerment. So empowerment is that second piece, if we refer back to that very simple equation of trust plus empowerment equals value. Empowerment is then taking the foundation that we’ve built of trust and putting the power. And and enablement of individuals putting that in their hands. So when we think about empowering individuals, it’s, it’s sometimes it’s training and it’s, it’s explaining how the systems work. But a lot of times it’s an exercise of, of, of removing perceived barriers and boosting confidence and usage of, of analytics, while also having safe guardrails in place. And I think that’s a topic that gets brought up a lot is like, hey, I’ve got all these individuals. And like, frankly, I don’t want to give them all access to the data because I’m afraid of what they’re going to do with it.

And fortunately for many of you who have customer journey analytics, there are ways that we can control that. And we’ll talk about some of those features, a bit on on what in the tool capabilities in tool features allow us to better empower individuals to, to drive better self-service so that they can make better decisions and share insights with the larger organization.

So this this idea here of the non analyst actually came from ÃÛ¶¹ÊÓƵ Research on how we need to think about the, the, the life of the non analyst. And the non analyst is not meant to be by any means, detrimental to the skills of the analysts, non analysts there. Like I had alluded to earlier, these are individuals that are not necessarily directly aligned to an analytics team, but they have data driven roles. And it’s a good reminder for us as people that work in analytics to ensure that these individuals have what they need to do their jobs. And you’ll see some of the, in addition to the the marketing role itself, marketer or non analyst, they’re doing a lot of work with data. So we need our goal is to empower these individuals with accessible, actionable data to support the demands that they that that come with being in their roles. And this non analyst persona could be, like I said, a marketer, or it could be an executive. It could be somebody that works in UX or whatever it may be. This, this is applicable, to individuals that are just not in the data every single day, like individuals that are on an analytics team are.

And you’ll see some of the, the, the tasks and actions that the non analyst persona may be participating in there. They need to be a part of each of the each of the the life cycle. The lifecycle of any use case involves data in some capacity. So, they need to be able to trust the data, rely on the data. And based off this research that I’m referencing, there is feedback that this data compass is broken. And there is there are issues with, with trust in, in the data. So incorporating some of the things we’ve talked about and approaching this in, in a way that, meets the needs of, of these individuals is going to be really critical. So going even a step further, these are some of the things that ÃÛ¶¹ÊÓƵ Research had compiled on what not analysts users want the ability to do. I probably could have come up with these, ideas on my own, but like, I probably could list a lot more things that non analysts do. But just to to put it into perspective, things like forecasting, measuring operations and the ability to share context that only they have to to the broader organization. They want access to AI insights and, allow automation to play a large role in, in their decision making, or at least depending on AI, in some fashion, to make their lives easier. They want to be able to understand trends, do comparisons, time based comparisons, dimension based comparisons. They want the ability to set benchmarks and on occasion, want the ability to go deep into the data. So how do we set up environments that allow them to do what they need to do and service their needs? Ultimately, that’s, I think, how we need to reframe how we are able to we have the ability and potentially the data to do these things. But there are mechanisms and, capabilities that we need to take advantage of in order to allow these things to be that low cognitive load. I know how to easily do this. So I’ve referenced earlier, but there’s a lot going on in this slide. But there if anyone has been a part of the product roadmap conversations for customer Journey analytics, a theme that has been consistent since the advent of Analysis workspace has been persona expansion. And in the last two years, there have been massive strides made in that category, of capabilities, to service individuals who are not in the non analyst persona. Again. So I wanted to highlight a few things and mention some of the existing capabilities as well as sort of what’s to come. And this is by no means a readout for the the product team, but just a highlight of some of these capabilities that enable you to expand the personas that can get value out of customer journey analytics.

You’ll see some of the workspace capabilities here on the left, and those are things that have existed even previous. Some of these have have existed previous to customer journey analytics, around sharing and curation of of projects. These are often overlooked. I would say, approaches that are a very lightweight approach to limiting the the scope of what’s available to an end recipient. And I think, this ties back to there’s work involved in and setting up reports that are accessible to individuals across, non analytics functions. But the the tool itself enables you to do that.

Recent, recent advancements or innovations that I want to highlight are on adaptations as well as a data dictionary. Annotations is a wonderful tool that allows us to, provide context around events and moments in time that can be accessible to whomever we want to make those accessible to. Again, this provides a little bit better of a picture of the data, and it makes it, if I’m an end user, instead of having to reach out to somebody and ask why the data is different on this day, leveraging an annotation here can saw that one last step in that process and my time to insight is quicker as a non analyst. Same goes with data dictionary. Data dictionary I am a huge fan of if it’s used correctly and if it’s if it’s used in a way that once again there’s there’s upfront work and ongoing maintenance associated with the data dictionary. And I would say data dictionary in tandem with component management is a wonderful way to go guide users toward utilizing the metrics, the dimensions, the segments that are approved and once again, providing that context around why you should be using this versus this. And that’s all that. That’s all in the UI, which I am a huge proponent of, from the the ability to empower people.

We want to make sure that they’re in the UI and, and can learn hands on. Providing that environment for people to learn hands on is really critical. So two wonderful features there on a list of many other workspace capabilities that enable you to empower people, or at least perceived power. And that perceived confidence is what we are striving for. We are by no means expecting non analyst people to be at the same level as analytics personas. We, we can can lift all, all ships in some capacity to ensure that, you know, individuals can, can get to the insights that they need to get to in a timely manner. So then we’ve got our dashboards mobile app. Obviously this is probably something that either you’ve utilized already or had utilized yet. This is again another way we think about the multiple personas that CJA can support. These are great for the executive persona. They’re also great for operations. People that are in charge of, the site health, something I wish existed when I was an analyst, working in ÃÛ¶¹ÊÓƵ Analytics, but a really great way to empower users to access real time, data on the go. Next is our cross platform integrations. And these are things that are evolving and growing with in the coming quarters.

Journey reporting in agile. Again, thinking about it from this, from the lens of I’m a marketer who is setting up agile reports, wouldn’t it be great to be able to do analysis of that journey overnight? We’re enabling that for that persona and also can make it accessible to the people who are not setting up how journeys, to make the accessibility and sharing of those, insights easier. That’s being powered by CJA, as is the, Gen studio for performance marketers capability is this is one of those those features or integrations that we’re very excited about. But, obviously it’s still in early stages. But if we think about it and this was this was a theme that came up in, in, in earlier conversations around having one place or being able to log into one, one tool to answer every question from conversion rate and revenue all the way up to click through rates. And having that visibility ultimately is the direction that the tool is moving. And with that top to bottom view, we’re servicing multiple personas. So, another very exciting thing that that I think we are a we’re, hopeful will once again expand the remit of who, who can service get a for t reporting. Content analytics is another one of those, early early capabilities where we’re suddenly not looking at just business metrics in customer journey analytics, the ability to also see, content velocity, some of these metrics that we would have otherwise never had visibility into content analytics is a really exciting capability on, on the roadmap that will enable that, audience publishing. So this is really talking to how CJA can publish audiences to AJL and CDP, again, servicing different personas there and RB extension, which I would say is probably more geared toward analytics individuals, but, across platform capability here next product analytics. This is something that’s now embedded into CJA and it’s geared toward it’s a guided analysis that is geared towards product managers as well as marketers. And I would argue anyone who’s interested in the performance of of their experience. There’s a lot to unpack in that one. But I wanted to highlight that here, because I think it particularly speaks to this vision of persona expansion. And then lastly, the AI capabilities, some of which have existed for many years, and some of which are just coming to market. And again, all things that can help adoption and, help individuals with, with getting to the insights that they need to get to.

So the key takeaway here from this section is the businesses that wish to benefit from data democratization. This needs to be intentional. And it needs to be something that you prioritize and invest in in terms of, you know, time, the software itself, as well as efforts of team members, within within the team.

All right. Now we’re onto our last session here on Path to Value. And this is, I would say, sort of a culmination of what we’ve talked about so far. But I think it’s a really interesting subject that, existential topic of like, how do we actually define the value of an analytics investment? And I think most people will immediately say, oh, well, yeah, it’s not as clear cut as something like, let’s say, an ÃÛ¶¹ÊÓƵ Target, where we can point to the revenue that we have generated from running all these AB test and personalization activities.

We need to think a little bit, more strategic around what it can offer. And, and from there, people will say, well, yeah, I’d, it allows me to better understand my customers and ultimately help inform my marketing strategies, which then can help me earn more revenue. And that, I will argue, is the is is one of the, foundations of how we demonstrate the impact of your analytics investment.

In addition to that, we have operational efficiencies that are gained, as well as the benefits of adoption and enablement. So when we think about value of an analytics product, the business, the operational efficiencies, as well as the adoption and enablement capabilities that are, that are garnered with, with a program around analytics. We’ll get into each of these very, very quickly.

Somewhat self-explanatory here, but thinking about, well, what is the business impact of of customer journey analytics? You’ll see some examples here. These are, these are use cases that customer journey analytics is playing a crucial role in to ultimately drive revenue, reduce reduce churn increase click through rate. Look whatever it may be, all things that customer journey analytics enables that tie back to business impact, that tie back to those objectives that, we talked about in a measurement strategy. And this is typically what we lead with when we think about communicating the value of this product to the rest of the organization. And we’re gonna talk about why that’s important. Because if we’re able to effectively communicate the value and the impact across business operations, as well as adoption and enablement, that that conversation around analytics being forgotten as, not being, invited or not not being a part of the conversation, inherently will happen less because of how dependent teams will be or teams already are on this data that enables these capabilities. So again, it’s sort of one of these virtuous things that if we are we’re doing the good work. But if we are not showcasing that, then and also being, proactive with how we evangelize the impacts of business impact, operational and adoption it the team begins to erode and we begin to erode the value, the true value. Because like I said this, this is sort of us. It’s not as clear cut as something like an ÃÛ¶¹ÊÓƵ target where we can directly point at that. But there is there is real value, and we need to be, approaching this, in in a way that you can’t live without this data on your marketing initiatives. So, keeping that in mind, moving into operational efficiencies. So when we think of how an analytics product can support operational efficiency, we think about things like reducing workload on on analysts, freeing them up to focus on high impact insights rather than wrangling data and ad hoc requests. Faster response times mean more agile decision making and tracking content. Velocity, helps us assess how quickly content can drive and engagement across channels and journeys.

Number of use cases supported. Maybe these are not things that we immediately think of when we think of like the value that an analytics product offers. But again, I’m, I’m want to make sure that that is, that is very clear that these are data is that linchpin to be able to provide these use cases. And that’s something that you as a an analytics leader, should be prioritizing in how you measure your team’s value.

Same goes with you know, that if there is a change in the staff needed to complete a, a critical output or a workflow that is something that if if analytics is helping to reduce that, is a way that we can think about the value that analytics offers.

Lastly, adoption and enablement. So this is something that we can do analytics on the analytics team or the analytics users being able to track your training reach or number of individuals that are logging in. That is an indicator of the value that your team is offering and should absolutely be right up there with the the business metrics that are being, that, that we’re bringing forth, on, on a regular cadence. The second one, the frequency of executive escalations, if we are able to drive that number down, that means a lot of wonderful things for the people working with the data, because they’re able to answer questions on their own. There’s this kind of concept of self-service applies here. When if that’s something that we want to track and see if that if our efforts all these the things that I’ve been saying around, you know, prioritizing these types of things, if we’re able to track the how common these escalations are occurring, or issues with the data are occurring, this is another really wonderful way to to measure, your, overall adoption, enablement and overall value that, that this, this, these products can offer.

Same goes for ad hoc support requests. I like the idea of if if you are launching a, an enablement or and empowerment initiative, how great would it be to be able to measure the confidence levels of users who are going through this training? And, and understanding is this having an impact? And that’s there needs to be that proactive effort. And, and outreach with within the community of users within your organization, to ensure that you’re if you’re missing the mark, we need to readjust and and meet those needs. Another really interesting way to think about this would be like the onboarding time for new users as as a way to see how efficient the ramp up time would be. These are just examples, as are many of the other examples in the previous slides. Talking about how we can articulate the value of an analytics product, an analytics program to, to, the rest of the organization, which again, will help build trust, it helps build empowerment and, ultimately drives real, meaningful value to the business. And lastly, I just want to highlight some opportunities or ways that organizations are showcasing excellence.

And in their measurement approaches. So the ability to curate a list of resources for, like I said, those new users for the non analyst persona, this is done. I’ve seen it done actually in, in the UI itself and in a, in a workspace project where we have, a list of, of where to begin and where to start with building out, an enablement plan for users across different personas. Can be done in kind of like an FAQ fashion, regular quick tip sessions being led by either the core analytics team or evangelists within your organization around analytics.

Another really helpful way to.

Create the safe space for individuals who are just learning, the about the data and how to how to work with the data. I have found that those tend to be a really wonderful way to, to garner that confidence in, in the data, newsletters, and other notifications and methods for communication and communication is super critical. Again, that is like the fuel for trust when we are over communicating, sometimes with changes or again, mentions of new capabilities or even just reporting insights, making those insights accessible and, visible to teams a huge boon to, to the impact that the, the analytics team is having, as well as, you know, leveraging internal communications for that.

So that being said, these are these are some things to take away, to think about how we can evangelize the how your team is showcasing the excellence that that you’re doing on a day to day basis. So to summarize kind of our our conversation here today, we talked about measurement strategies and how measurement strategies need to be founded on these concepts of trust and empowerment, involving stakeholders, to provide their inputs on a regular basis so that they are familiar with the data that’s being captured. And in tandem, empowering those stakeholders to feel confident in conducting reports to make decisions on their own, reducing hesitation, and then talking to some of the value that, the methods of which your organization can leverage, strategies to drive more value and ultimately realize the business, return on investment, within your analytics program.

So with that being said, we are at the tail end of our, our conversation. I think we’ve got like five more minutes or so.

And I will stop sharing. If there are any questions or comments in the chat.

Just as we kind of take a look and see if there is any questions to touch on, I’m going to go ahead and launch a quick two question. Cool for the attendees, just because we’d love to get your feedback on this session. And if there’s anything that you’d like to dig into or know more about. So if you could take a moment to participate in that, that would be fantastic. I’m not seeing any questions that have come through the Q&A. But let me peek into the chat and see if there’s anything there. Yep. Nothing so far, so we’ll just hold a minute in case someone’s trying to collect their thoughts to submit a question, and see if anything else comes through.

All right. Well, just as a reminder, this session recording as well as the presentation that Christoph shared today, will be sent out to everyone who’s attended, as well as those who have registered.

Thank you again so much for your time today and have a great rest of your day. Let us know if anything else comes up. And again, just thank you for your time in attendance. Christos. Thank you for taking us to that content. Definitely a lot to learn. So I imagine people are still trying to process everything. And hopefully if they do have any follow up questions, just feel free to reach out to Derby County. And thank you all very much.

All right. Great.

Thanks again everyone. Have a great rest of your day.