Driving True Value: Moving Your Data Stream Beyond the Click
Hey guys. I hope you guys enjoyed the opening keynote by James and John. My name is Eric, again. As you were told, I’m here from Keystone Solutions and I’m going to be talking to you today about how to drive true value from your analysis. As we’re looking at the overall value of data, the biggest piece that we’ve seen over the past couple of years is the integration of multiple streams of data across variant sources, variant databases, and variant potential customer touchpoints.
The biggest piece, and one of the largest problems that the majority of my clients come to Keystone with, is deriving value that the organization can see. Again, as we put together our presentation today, I wanted to walk you through, first of all, some of the cultural barriers that you’ll encounter when you’re looking to drive true customer value in data from your sets of information in your data stream. Then I’ll walk you through how you drive the overarching customer value. Then finally, how you work toward taking that information and deriving actual economic value from your data. With that, we’ll go ahead and get started.
As we look at cultural blockers to analytics adoption, generally speaking, the first and biggest one that we deal with is that of data silos. Frequently we see a variety of systems, whether they be analytics, whether they be data warehousing, whether they be additional types of marketing enrichment, but we see these systems that are consistently not integrated and don’t provide any type of way for us to utilize that through an API or programmatic interface to enable that data to be married across those multiple sources.
It can also, however, be something just as simple as marketing not wanting to speak to the team in the data warehouse group. However, whenever we’re dealing with these data siloes, the biggest piece that we have in our client’s pocket is to help them begin to break those down. It’s integrating your data warehouse software so you can look at the true value of your customer and enable the offline purchases to be married with the online, so that you can have a true idea of exactly what that customer looks like from start to finish.
Next, when we’re dealing with regulated industries, especially, but even in traditionally ecommerce clients, our next second largest issue is that of security. When we’re dealing with legal and procedural security issues, that can be anything from PCI and PII compliance. If we’re dealing with Google or with several of the other clickstream groups. Or even if we’re dealing with an international client or someone who’s interacting with international customers, now we’re beginning to deal with this more formally through GDPR.
But overarchingly, the biggest pieces and the biggest blocker here is the fact that, again, you have teams who are not familiar with your analytics systems who are trying to deal in ways to make sure that everything is able to be provided to you without violating any of the potential laws and/or governance that already exist in the organization. This is why frequently, we see an under-governed organization try to take too large a first step, and then at that point, end up burning their security bridge.
However, at the end of the day, the biggest piece to remember here is that your legal and compliance teams are actually there to help you and your analytics team throughout this process. Whenever you’re dealing with these groups, you want to make sure that you seek guidance of these teams, but that you also demonstrate how the analytics—you’re tag management, your data management, and your data integration partners—are there and making sure that they can exercise and fully implement the security and credibility systems needed to make sure everything is working properly.
Which brings us to issue number three, which is that of data credibility. Frequently, whenever we’re looking at the data that our clients are collecting, we’ll see things like additional page views firing on a page for a single view. Things, for instance like, triggering multiple form click completion events whenever you’re submitting the form one time. Or even something as simple as miscalculating the total for an ecommerce order. But at the same time as we begin to look at the data credibility as a whole, if you don’t have data that you can trust, you immediately threaten the adoption throughout your organization. Because as you’re looking to integrate additional data streams, you have to further deal with scrutiny on the data that you’re actually playing with.
Thus, as a result, whenever you’re looking at your data credibility, that also becomes the data credibility of the teams that you’re interacting with. If you limit your overarching credibility within the organization, we frequently see these teams also limited in the actions that they’re able to take, and in addition, the different data streams that they’re able to absorb and the customer value and information that they’re able to present.
Next, we have additional resource limitations. There were only, at this point, so many people you have on your team. Whether that be in your development, in your analysis, or even just the number of boxes that you’re using. But at the same time, you’re resource limitations are always going to potentially drive issues with the previous three, and then continue to compound as you have a lack of ability to implement and/or fix issues within your systems. Thus, as a result, when we look at these cultural blockers, the resource limitations and resource estimations are your next biggest hurdle to clear.
Then finally, you have the issues of simply time and capital. If you don’t have enough money to purchase the systems that you needed to get what you need done, A, there’s an indication that unfortunately, the value you’re trying to communicate in your analysis is not necessarily there for your organizational stakeholders. But as well, as you’re looking at getting these systems implemented, that you’re not demonstrating that value in the analysis that you’re providing. Whether that analysis is, in fact, and issue of positioning or simply making sure that your KPIs align with the business goals of the organization.
It’s a matter of making sure that you’re crafting and tailoring your messaging directly to the individuals and the consumers of the analysis that you’re providing. At the end of the day, when you combine these five potential cultural blockers, these are your biggest hurdles to clear. However, the big point being that once you’re able to provide the governance framework, people and process, to make sure that you are able to properly handle your information. At that point, you can move on and begin to enhance the product and overarching business value from your analyses.
This is in addition to the pieces where you can actually drive beyond the bottom line with things like product feature testing. As you’re looking at implementing new features into your product—whether that be a new mobile application, a new way of editing your data, or even a new way for your customers to interact with the information that you’ve already provided to them—the overarching idea of being able to limit that potential feature impact to a smaller group so that you can ensure that your features are, in fact, providing the value to your team. Then in addition to that, that when you’re running these test successfully and find something that’s absolutely stellar, that you can make sure that your team is able to take a share of the credit for your product team’s development.
In addition to that, it’s making sure that your KPIsalign with your business values so that when you’re dealing with a product director, you’re dealing with a VP of sales, that you’re making sure that you’re presenting the proper pieces of information that truly matter to them. Whenever we see individuals presenting KPIs that are, frankly, more generic or looking at consumption-based KPIs, while there is value in the consumption of content, the overarching value is truly derived depending on the individual who’s consuming the data. So being able to make sure that you’ve got the right information in front of the right individual at the right time is critical to making ure that you’re analytics team and your measurement teams can truly drive value from your data stream.
This means that when you’re looking at combining your offline and online data for your product managers, you may want to look at the interactive ways that they can look to cross-sell from other products in your portfolio into the one that they’re playing with, showing them the number of consumers that typically convert from one product to another. Whereas instead, when you’re looking at potentially selling to an individual on your content or your creation team, that you can make sure that you’re looking at some of the more prescriptive values and the way that users interact between variant pieces of content. At the end of the day, you provide the proper report at the proper time.
By merging in multiple data streams beyond just your digital and click stream data, making sure that you’re collecting that customer profile, building in your additional customer data points as your analytics or click stream systems will allow. And then in addition to that, constructing your marketing, advertising, and your general sales personas, you’re able to enhance the view that your marketing, sales, and advertising teams are able to derive from each and every piece of your analytics data. By overlaying and segmenting your proper information here, you’re able to make sure that you’re displaying the proper information about the audience to the people that matter the most, to the consumers of your data.
Because at this point, just like we look to focus on customer segmentation when we’re looking at our true consumer of our products, the analytics team’s product is the analysis that you’re providing. Meaning, being able to segment your internal stakeholder audiences and making sure that you’ve got the internal stakeholder profiles and personas that are ready to roll for your teams, and making sure that you’re able to segment them across variant audiences, is going to be absolutely critical to you and your analytics team being able to describes and provide accurate and adequate analysis that drives value for your organization.
Then finally, as we look to a predictive model and moving into a more predictive and model-based analytics system, you can look to decrease your customer lifecycle. Meaning that at that point, you can re-seize and re-engage and enhance relationships with your customers by making sure that that entire lifecycle has been traced from start to finish. Because it doesn’t matter whether that consumer is calling in when they first decide to interact with you if at the end of the day, they only interact by email from every day thereafter. If you leave that portion of the data stream unanalyzed, you have lost out on the value for your consumer.
As we look to drive economic value, we get into a burgeoning field can the “infonomics.” As we look to drive economic value from our data, this is where we get into the more predictive modeling and the data that’s going to drive your business organization, making analytics more than just a cost within the organization. We’ve seen organizations like Caesar’s when they filed for their bankruptcy two years ago, provide a good deal of value on their bottom line and within their income as well as asset reporting in the data systems they’ve built across these organization.
By making sure that you’re able to build persona enhancements by perhaps ingesting an additional variety of sales from your outside sales team. By making sure that you’ve got your systems fully integrated and that you’ve made sure that your data stream is truly a 360 degree view, you can at that point begin a more adaptive behavior modeling plan. And as well as that, start to even out some of the peaks and valleys that you see in your traditional sales cycle.
The overarching result of your economic value that you were able to provide from this data, however, can come from the advanced decisions that can be made based upon the precious behavior of consumers that you’re analyzing. We’ve also seen additional data models in which the value of a customer data analysis is driven based upon the information that we have predicting a future product sale based upon the interactions we’ve seen from products in the past.
But at the end of the day, the beauty of these systems is that, from a business perspective, you’re able to give your stakeholder in your organizational checkbooks the ability to predict what’s going to happen in the future. Because throwing data at the wall doesn’t drive any value for these individuals. Their goal is to make sure that you’re able to come back, tell a story, and ensure that you’re able to make sure you’re meeting the businesses need, by providing that information at the right moment and at the right time.
The expansion of cloud-based platforms and the ability to analyze real-time data in an even faster manner, means that now you can take your outside sales rep’s card data from the internet of things, plugged phone immediately within their car and look at exactly how long it’s going to take them to theoretically reach a customer. Further, you can make sure that you’re dispatching the proper support rep for the individual based upon their previous interactions within your customer support platforms.
And finally, that you can ensure that as a customer, whenever your individuals are logging in, you’re able to provide a bridged view of precisely how they’ve interacted with the organization in the past to make modern and programmatic decision about how they can interact in the future. The overarching value here is that you know your customer better than anyone and that you can provide that information in a way that can deal in proper analysis to your product, marketing, and other organizational stakeholder teams.
So at the end of the day, we look to wrap this up and we realize that there’s inherent value in a well-positioned analysis. Whenever we’re looking at our customer journey from start to finish, whenever we look to ensure that our customer journey can be captured, measured, and credibly measured throughout the process of their interaction with us—and that we’re positioning that to the proper teams with the information that they wish to know—we’re able to provide that inherent value. These pieces individually provide baseline value.
However, the beauty is that by making sure that you’re focusing on your data’s consumers and you’re focusing on the consumers of your analysis, you’re able to ensure that cost and resources issues no longer exist, or at least are a lesser concern. Because again, the cost becomes irrelevant with value.
In addition to that, when you’re going to your consumers and saying, “Wow, I’d really like to integrate this additional data source,” that the way they’ve seen your culturally driving that data stream forward and integrating it in the past with other organization groups, can be utilized to make sure that they realize you’re going to be a steward of their data. Making sure that you’re shepherding it, providing incredible analysis, and providing incredible integration of the data with other streams that you’ve already positioned in your product.
And finally, that whenever you’re looking at doing all of this together, that you’re able to provide and additional value that your teams can’t get anywhere else. Meaning your analytics organization is well-positioned for the future.
With that guys, I’m going to go ahead and giving thanks again for coming. By all means, you can reach out at analyzethevalue.com, which will take you over to the Keystone Solution’s website. It will allow you to download the deck and more. I hope you enjoy the rest of the conference and please look forward or Matt Gellis’ keynote later today. Thanks guys.