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Written by Samuel Cummings - 8 min read

The Secret to Going from CS Admin to CS Hero Is...

If you have not visited previous blog of this series, you may refer HERE. If you want to save time but still want to put this CS cheat code to practice. TLDR: The knowledge pyramid is a practical mapping tool that allows for you to model any relationship between actionable insights and unprocessed data.

Considering now you know the knowledge pyramid which talks about data layers and the properties of individual stages of data in it's journey from data to information to insights/actions. Now let's discuss this in the CS admin context. CS Admins are usually given one of two scenarios when working on building out automation for their teams. Either they are given a goal (trigger a call to action) and told to figure out how to use the data to do it. Or they are given different data sources and told to figure out what kind of insights and calls to action they can create. The problem posed might seem the same but the approaches to solutioning have very different pathologies and potential risks to avoid. Let’s first take a look at:

INDUCTIVE SOLUTIONING

By starting with the relationship fields that you can leverage you now can begin to explore which fields are most populated and viable in further exploration for potential insights. Demographic fields like industry, lead source and segment are all great starts. These will help turn your raw contract data into information. But information alone as we learned before is only somewhat useful. Now it’s time to add context. This is where you are going to then explore what are the fields that I can use to give these insights priority. Most companies have some form of manual fields that are logged by the CSM team to measure things like risk , status, current sentiment, is escalated. These fields are the ones you need to give value to which insights are more actionable or not. This is the power of Inductive solutioning it helps you optimize for impact.

INDUCTIVE SOLUTIONING

WHEN TO USE: Few data sources to combine but are unclear about what can be achieved with your data
PROS: Allows you to produce insights that are more impactful
LIMITATIONS: Can suffer when data is sparsely populated
RISKS TO AVOID: Avoid leveraging fields that are not regularly updated or relatively accurate

Now that we have discussed how to discover insights when given any different data sources. Lets now look at the opposite scenario: DEDUCTIVE SOLUTIONING

../span> <br> <p> Deductive solutioning is the approach applied when you are given specific insights you need to generate but you do not have a clear direction of what data sources you will use to create it. For this example let’s consider you have some support ticket data in zendesk. Some marketing data from marketo and some of the same contract data from before. </p> <p> This time your leadership team has asked you to create a risk playbook for clients that have support risks in zendesk. In this scenario you know what your goal is. But the question is how do you use the data you have to power a risk playbook that is not accurate. A junior CS admin would say this is easy. Just use the support data by itself. However the more seasoned CS admins are reading along and can already tell what could go wrong. The key here is when creating any risk playbook it is important for that playbook to be not only accurate but factor in prioritization. For this reason it will be critical that you solution incorporates context from other sources to be able to make sure that the playbook is not just business work. </p> <p>This is where deductive reasoning comes in. You are taking your problem and exploring the potential context needed to make the resulting insight actionable and useful. Which is in turn the main value of deductive solutioning. It helps you make insights you need to be generated from multidimensional sources more actionable.</p> <h2 align=DEDUCTIVE SOLUTIONING
WHEN TO USE: When you have multiple data sources to combine and also have a clear outcome to achieve with your insights
PROS: Allows you to produce insights that are more actionable
LIMITATIONS: Does not work well when relationships mappings between data sources are not logged
RISKS TO AVOID: Avoid leveraging data sources that do not have proper relationship fields mapped

Now that we have learned the difference between inductive and deductive solutioning. Time to put these tools to use on at your organization. You can contact us HERE and will send you the Knowledge Pyramid worksheet. Good news is we have built both of these frameworks into our flagship product. So if you would like to save time and get auto generated insights using these concepts click HERE to schedule a time to connect and sign up for our Dataplant beta program. Either way good luck solutioning out there and until next time; Stay nimble my friends!

If you enjoyed this article, please subscribe. Let me know your thoughts at sam @ thedataplant.com or share this article with someone else you think will find it valuable. I have lots of other blogs and insight rich content on our site or on the way for you to enjoy. Until next time. Sam C.