Reﬂection - Learning Launch

Challenge and Selection

My team was tasked to discover a problem/opportunity space in current  accounts receivable  (recovery management) solutions we oﬀer for ﬁnancial institutions and come up with ideas to  build new products leveraging analytics and digital.  The new products/solutions would deliver  distinct value proposition compared to rest of the industry. This approach will eventually let our  organization steal the market away from pricing competition driven by traditional players.

Building a digital product with a data science model takes signiﬁcant capital expenditure and  sizable resources and time of the organization. The technology stack keeps evolving which  would require constant eﬀort and resources to upgrade the product. It is pivotal to get the key  requirements and functionalities that delivers the much needed and anticipated customer value

Speciﬁc challenges

1.Misalignment of our current assumptions and solutions build to address these problem  spaces will lead to

- Product failure resulting in loss of money, time and resources - Missed opportunity of building up an alternate organizational capability - More important is our reputation in the market with broken promises/disappointed

customers

- Also possible let down of career’s of smart team

2. Collaboration and active participation of diverse stakeholders like domain smes, operations,  analytics/digital product owners and sales team is critical to come up with eﬀective solutions

3. While we have been in this service for a decade, it is possible that there are some problem  statements which we are not aware of. The chances of conﬁrmation bias is higher

4. Our current customers works with wide range of payers with diﬀerent scale and market  segments. One size may not ﬁt all, we need to understand and identify a target set of  customers/payors for which the digital solution works eﬀectively and also quantify the  magnitude of change management needed

To mitigate conﬁrmation bias in identiﬁed problem statements and solutions and to identify the  target customers, we chose to select Learning Launch tool from Design Thinking to test and  validate our assumptions and solutions

The reference from Professor Andreas Hussein on Learning Launch process

The purpose of a learning launch is not to prove you're smart. The purpose of a learning  launch is not to prove you've got a good idea. The purpose of a learning launch is to  get the truth and then the truth either conﬁrms or dis-conﬁrms your assumptions.

We wanted to learn the true problem spaces and conﬁrm some of our assumptions and solution  ideas

• Learning Launch test key customer, execution and defensibility assumptions

• Most learning launches involve quickly engaging with customers and testing your key

customer assumptions

Here is the pictorial representation of the process:

Application

The testable hypothesis we had is

Improve recovery speed from current 90 days to 60 days with an intelligent workﬂow  driven by data science and digitize payments

Key assumptions are

Value Customers:

• Payers who do not attend or positively respond for a phone call request in ﬁrst 60 days will

eventually dispute and take us to arbitration phase

• 20-30% of payees chose to pay the recovery only after receiving  a notice to act

arbitration(legal action)

• So, early detection of these payers and negotiation process leads to early settlement at

optimal cost

• Most payees still use check as primary mentor of payment

Execution

• So, early detection of a non responder or a direct disputes will help with fast track negotiation  • Digitization of payment by EFT option will make it easy and faster by 50-7 days

Defensibility

• Customers will be keen to use this product if recovery time improves to 60-75 days

• Given the availability of historic data and adoption of analytics by various industries will

create comfort for customers to follow data science model vs trust on human intervention

Prioritized Key Assumptions

• Building a data science model with optimal result is key and takes considerable time and

investment. This is  make or break factor of the solution

• Appetite for payees to use EFT options is another critical factor given the wide range of

payees in the country

Test

• My team identiﬁed three of our long standing customers. These customers deals with larger

range of payees.

• We engaged on a learning launch mode for a selected payees that represents 25-30% of

their accounts receivable business

• We had three teams working with these customers. We engaged on a 4 week analysis of

current recovery patterns with focus on the following assumptions/variables

• Tracked non responders for last 3 months period vs disputers

• Analyzed aging of payments for late payees with follow up call count and potential legal

notice

• Identiﬁed payees with transaction value and payment mode(check/eft)

Findings

Conﬁrming Data

• The data and insights indicated there is a higher appetite for payee to adopt EFT and are

keen to look for this solution

• • There is a considerable lag time in initiating arbitration process on non responders

Non Conﬁrming Data

We also realized non responders are the ﬁles where we actually don’t have valid payee name or  contact details. This was little diﬀerent from our assumption of non responders pay us only  after disputes.

Launching Tool (Develop)

Basis our initial ﬁndings we prioritized

• Intelligent workﬂow with sorting and prioritization functionalities • Standing up infrastructure for EFT payments

Pivot

• We placed our solution of building data science model on a hold mode • Rather focused on validating the accuracy of payee information we have on record

Insight and Approach

What i learned from the experience is Collaboration among diverse set of teams is very  important to focus on customer rather than weigh on our own ideas and capabilities. In our  assignment, we were so focused to bring analytics into play, however it had less relevance to  the problem statement we had pertaining to target customers. Design thinking helps us to  focus on what customer needs and how to craft our solution that is better than other  alternatives currently exists.

Our Learning launch assignment is highly appreciated by our customers for taking the time and  opportunity to unravel their problem space rather than pushing our product and solutions. This  has created opportunity to collaborate with customers and not just internal teams. This is a  huge win for me and my team, helps with conﬁdence and learn rather than fear of failing

In our assignment we did learn and launch with three players who are our long standing  relationships. From the data patterns I have seen from our experience, there is a wider range of  payees and one size does not ﬁt all. For our next wave of learning launch I anticipate to see  greater value in engaging with diﬀerent set of players based on the region, size etc. This will  help us uncover wider range of problem spaces

