Hi it's Jesper here this is part 2 of how to design for scale and automation
in part 1 i outlined the theory behind scale and automation so here's a quick recap everything in
the digital world is data scale depends on data not process scale is growth at near zero cost
so to create scale we need a data driven approach and a data driven mindset that covers the what
and the why of scale but what about the how that is what we're going to unpack in this video
there are of course more than one way of creating scale it depends upon your kind of business the
industry you are in the data you create and most importantly the quality of the data you create
today we would unpack a case study and see how it's done but let me first warn you typically
when we talk about growth we talk about 10 percent 20 perhaps even 30 well to that i can only say one
thing boring as outlined in my previous video scale aims much higher that is why we often use
the term multiply when we talk about scale and the multiplier in this case study is between
2 and 3 x or 2 to 3 times that means that we are improving something by 200 to 300 percent
before we start something setting no not that kind of scene this is a global software company
they are in the education business so hence the customer typically is teachers and principals
the trial software process is critical to revenue generation the current process though is manual
and operates slightly differently in different parts of the world
and as a result generating consistent results
customer satisfaction is decreasing and competition is increasing
not a very good place to be and something needs to be done
what this organization needs is a global process within a minor regional differences
it needs a process that's agile it can change quickly to respond to changing and shifting
customer needs and preferences it needs quality data for better reporting prediction
and of course automation it wants to be able to grow without adding head count the solution
is surprise a data driven system designed for scale and automation and this is how it was done
and like any good drama it will be outlined or described in three acts or three stages
first we need to understand the process we want to scale and automate and what we're
trying to achieve in this case our goal is twofold first we want to create compelling content for a
prospective customer to encourage them to enroll in a trial and secondly during the trial we want
to create a really good experience with really good outcomes so that the purchase at the end
the first step is called qualification and the second step of course conversion
data-driven design approaches this differently rather than unpacking a process we instead unpack
yep you guessed it data and the data that drives the process in this case we identified three
key pieces of data we needed in order to deliver personalized and compelling content so the first
thing we needed to know was a type of student the second thing we needed to understand was the role
that that prospective customer was playing in the process
and at the school and the third thing we needed to know what their goal what they wanted to achieve
this type of data is called reference data and it's the most important data behind scaling
automation it is also the least understood kind of data but we'll talk about that in a separate video
there were two types of students primary and secondary we call this student type
there were four kinds of roles teacher principal administrator and faculty leader
we call this role type there are five types of goals engagement
personalization analytics blended learning and diversity we call this goal type
to make things clear and simple i have selected a subset of types and a subset of the data within
the types this really doesn't matter it is the mindset and approach that is important
next we needed to understand the relationships between these types of data this is very
important and one of the key differences between functional and process design versus data driven
design is that all data relationship must be understood up front before we design the process
i'm going to say that again when we do a data-driven design all data relationships
must be understood up front before we design the process if we use data to design a process
and we miss data the process will be incomplete and it can't be automated or not fully automated
traditional business analysis fails to produce automated and scalable systems because it
doesn't understand it doesn't try to unpack the required data and data relationships up front
so say it again for the third time
all data relationships must be understood up front before we design the process
by repeating it three times it's more likely to stick psychology calls this repetition compulsion
a term coined by freud in 1914 having said that i think i stick to data and let the psychologist
unpack that for anyone interested so what were the relationships between students role and goal types
well student type is related to role type the role types for primary is teacher principal and
curriculum leader the role types for secondary is teacher principal administrator and faculty leader
so without even trying we have defined the beginnings of our software trial process
and systemized it might look something like this if we click on primary we are
presented with these three options if we click on secondary we are presented with these four options
there are two call-outs here thank heavens i didn't pursue a graphics design career
and second we preserve data quality by enforcing these relationships if they
were not mapped any combination of student type and role type could have been entered this may
not look like a big deal yet but at the end of the process its importance will be clear
are you excited are you ready for the next relationship i should have warned you
data is truly promiscuous role type is related to goal type the goal types for teacher is engagement
personalization and blended learning the goal types for principle is analytics and diversity
the goal types for administrator is analytics diversity and blended learning
the goal tabs for curriculum leader is engagement and blended learning and finally the goal types
for a faculty leader is engagement analytics and blended learning again without trying we have
further defined and refined our software trial process so further systemization
might look something like this we click on teacher we are presented with these three options we click
on administrator we are presented with these two options and here we have four call-outs the first
one yeah that's just a confirmation of my lack of talent in graphics design second again we are
preserving the data quality imagine if each type had seven possible values that would create seven
by seven by seven different combinations that is 343 combinations that's a lot of combinations to
create post-class content for especially as some of these combinations are invalid
call r3 invalid combinations also make analytics and predictions harder for example it will be
harder if not impossible to determine conversion rates and effectiveness of the content we deliver
4 the agility of the process created and this is the biggie we changed the process
simply by changing data for example we could add a student type such as preschool
or tertiary and suddenly cater for new education segment
but it didn't stop that by adding a few data-driven steps after delivering post-class
content we implemented the entire process in salesforce and automatically created a contact
a lead and an opportunity and in some instances we created a sale without any human intervention
i say that again without any human intervention this is an important distinction
in the analog world we're used to friction it exists everywhere
so when we are designing a process in analog world it's about pulling friction out of the process
and friction is created if you watch my previous video by humans
so analog processes design is pulling humans out of the process
digital design and a digital mindset is different there is no friction there are no humans in the
process to start with so the mindset is reversed so instead of pulling people out
of the process well we can't do that because of the digital process the owner people we are
putting people into the process we are designing friction into the process where it makes sense
and how do we know if it makes sense or not well that's data data will tell us where friction will
add value to the overall outcome of that process as i like to say follow the data
doing some fast forwarding we mapped all the key touch points
the data driven steps we just unpacked the qualification process is marked in blue
then we overlaid the current state process and assessed the level of automation
everything was read and manual not the pretty picture and a total of 38
fts or full-time employees were needed to operate the process
next assuming that the entire process is digital we identify the specific touch points where we
purposely wanted to add friction that is human intervention to lubricate the sales effort
we called it intelligent automation because we made it data driven and based it on the customer
profile the larger the potential the more human attention we added and designed into the process
and in reverse for very small customers the process remained 100 digital
when digging into individual touch points there were some areas that consumed
not only a large amount of human resources but also inconsistent outcomes
these were critical touch points as they related to providing reporting back to
the teacher or the principal during the trial about the performance of the class
and these reports suggested corrective actions for teachers or principals to take to increase results
because we knew through data that if we could increase these results the likelihood
of purchase increased significantly data analysis had shown us there were three data
points and you see these marked in blue that were directly related to increased results
the first one was offered the teacher logged in the second was how often students logged in
and the third one was the number of assignments created we extracted this
data from operational systems creating a data mart and linked it back into the trial process
we also knew the optimal number of teacher and student logins and assignment creations
so each day during the trial actual data was compared with this optimal data and personalized
reporting was generated and sent to the teacher so they could take corrective actions and create
as good outcome and results for the students as possible the result was a significant reduction
in ftes to operate the process which were redeployed into other areas of the sales process
this is an important distinction creating scale is about growth and scaling up it's not really
about scaling down and saving costs there are other ways of doing that so by automating and
removing repetitive work we achieved a number of things not only improving the efficiency of
the process we also improved customer satisfaction the full process was implemented within 12 months
and the entire project paid for itself within 12 months of operation
i hope this has shown the power of scale and what is possible through a data-driven approach
if you enjoyed this video please like and subscribe i see you in the next video