How well do you know your high value customers !? How do you use the analysis to drive more effective marketing practices in your business? How do I catch a Pokémon Mewtwo in Go? This blog will help with some tips on the first two (but contact me if you know someone who can help the third).
To effectively prioritize your marketing dollars and time, it is important to have an in-depth view of your audience base and how they behave on your properties. Fortunately, the main analysis tools, such as Adobe Analytics can help. This subset analysis of customers or customer journey analysis enables marketers to identify key behaviors, traits and actions that are exposed by the prospects or customers, leading to the creation of more powerful segments. Therefore, traders are able to achieve better ROI, costs, and increased satisfaction has declined.
Although I will not go into the tactical Pokémon Go here (be sure to save your Pidgeys Lucky Egg!), I will make an analogy between the game and some of the features in Adobe Analytics that will help your organization see immediate insights that can drive measurable results.
Who are my valuable customers?
First, what high-value customers mean for your business? For a few, the customer lifetime value (CLV) is a measure commonly used in their organizations, and they have a clear understanding regarding the client values. Pokemon Go, it’s easy, every Pokémon has a different power and level. Unfortunately, the customer value is not just determined, and for most organizations, CLV does not exist or are not readily used. For those in detail, it may be simpler, you can use metrics calculated to create a good proxy for it. For others, such as those in the media, which can be more difficult. You may want to create some sort of commitment machining measures calculated according to your objectives (number of articles read, time on site, return visits, email registrations, etc.).
What behaviors of my highest-value customers?
When creating metrics calculated sounds easy enough, if you do not always know what these behaviors should be? For example, if you are a product manager in charge of brokerage accounts for Goliath National Bank, the best tactic to win new accounts is to upsell current holders of checking accounts. Instead of just blanketing all current control customers, we will understand all of our current customers who have become brokerage customers after control clients. By filtering this segment, we can then use a tool like pathing to see the way that someone took before becoming a customer. In the data set, we can use the latency table to view events and days before a conversion took place to analyze touchpoints offline and specific line that our customers may have interacted with before conversion. We can also see after conversion to understand if we have the right content to guide them to better experiences.
All this gives us a better view into what some of the main triggers are before someone converts, but if we want to see the differences between the segments? For example, I have a few Pokémon that have the same combat power, but I have so many resources to strengthen. So how do I choose which Pokémon to train and my principal? In real life, how can we understand the differences between our segments?
segment comparison, a function in the IQ segment in the analysis workspace, intelligently discovers the differences between your target audience segments through automated analysis of all your settings and dimensions. Now we can easily fall into “the control and brokerage customers” and “verification and no brokerage” or “clients with high added value” and “low-value customers” and receive an interactive report that enables us to see what the major differences are between each segment.
the use of this knowledge, we can identify the most statistically significant differences between the various segments to drive greater segment of creation. in addition we can see the overlap between segments. for customers of Adobe Analytics and Adobe Audience Manager (AAM), all your AAM segments are now available in each solution in real time. This allows you to use data and second third party, alongside features such as modeling lookalike, expand your audience reach and see how these new segments overlap your current segments; it also allows you to identify overlaps, avoiding dual marketing job or confusion for better user experiences and reduced marketing expenses.
Another simple tool for the public and the exploration segment – also provided by Adobe Analytics Premium – is the function of clustering of the established data session. Most people think of hearing grouping as segmenting on steroids, but you can also start this process with clustering and see where the algorithms identify statistically significant or metric dimensions.
Select your input variables (such as those from the comparison of the segment) – the number of clusters, the target population, and the desired algorithm – and function will automatically analyze your data, to dynamically classify visitors and generate cluster sets based on the selected data entries thus identify groups with similar interests and behaviors for customer analysis and targeting.
For additional lighting, it allows you to further explore what each single cluster and gives you an easy way to take action and test your marketing campaigns on each cluster / segment. Using the tools mentioned above, you can understand where there is already overlap, bringing groups in the public Manager to develop and activate the segments and then use the workspace analysis to track and compare how these new clusters compare to your new segments.
I do not like Pokemon Go, therefore I Just Scrolled deep for Takeaways.
First, I saw eight people playing Pokemon Go this morning on my 20 minutes by bus to work, so at the very least, you may have picked up some vocabulary for the next time a band Poképlayers envelops you in the street. Second, we went on a number of different features within Adobe Analytics that can help you develop your ideas on different segments. Two in particular, you should start using
This is one of the most rapidly adopted characteristics and the one of the easiest to use. All you do is drag and drop two segments, and Adobe Analytics does the rest. See our YouTube channel for more on the analysis workspace and segment comparison.
For Adobe Analytics Premium customers public clustering allows you to skip ahead a few steps. You can use clustering to find key differences in your data. Learn how to configure and use clustering here.