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Data driven product management for increasing adoption in an Enterprise
Data driven product management for increasing adoption in an Enterprise

June 30, 2022

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In a data driven economy information is everything. Every organization is changing the game with a strategic product portfolio and decisions. Understanding the key decision factors is crucial to ensure the success of key offerings. Every enterprise has unique way of using the data to make an informed choices/decision at workplace. That means, data plays very important role in all functions including product management.

Sometimes products are built based on certain desire and instinct from product managers and / or C level executives. Is this being right way of building a product when we have enormous data in the market? If not, what is the main driver of building a product? What is the right metric when building a product?

Let us try to find the answer in this blog which will focus on ‘Relevancy of data in building a product’, specifically in B2B scenario.

Data Driven product management is when you make changes in your product related decisions based on the data. More importantly, these changes should help drive the predicted outcome behaviour from the end users who are using the applications.

As we all know product goes through phases like– Introduction, Growth, Maturity and Decline. And the data plays pivotal and critical role in each of these phases. In this blog, we specifically focus on the growth phase, different ways of measuring metrics by using available data and finally how this data can be used in increasing adoption.

 

Product Adoption and Stages of Customer

Customer Adoption and Product Adoption are measured as the state of engagement that a customer has with a software product.

Typically, any customer follows following statuses that describe the relationship with the respective product:

  1. Universe customer is one who left a trace for a particular product such as purchase, downloads, installations, tickets, etc.
  2. Engaged customer is one who showed dedicated (implementation) project activities, including early adopters who are trying out proof of concepts
  3. Live customer is one who use the software live/productive (or active)

We all know that we can only manage what you can measure. Adoption figures are an important element to determine the market success of a product besides its revenue generated or market share reached.

In general, lets understand how to measure adoption in an enterprise. Some of the possible measure includes

  • Overall number of customers is driven by Adoption indicators (downloads, installations, incidents, reviews, etc. …).
  • Number of engaged and live customers measured
    • Characteristics like success stories, attending community events like knowledge sharing sessions etc.
    • Measurements can also be done based on installations and incidents trends
    • Partner driven activities since partner play crucial role to help end customers to realize maximum value during implementation
    • Patterns like increase in number of dedicated sales bags (including similar products)
    • Majority of connected products like finance systems usually measured via API connectivity or via communication managements in place

How can we use right data for Increasing adoption on continuous basis?

Data driven product management advocates to use data from different sources to develop /visualize 360-degree view of existing customer’s base which can then be used for driving adoption use cases.

Organization have invested in tools to collect data from different channels.  Various tools are available for measuring Usage in real time. For example, one can  try to get insights into License Consumption and Functional Usage for single customers, products, and the entire cloud portfolio. Furthermore, peer and industry comparisons and value driver analysis can be made available for the purpose of decision making. The Customer facing organization can leverage these insights to drive improvement actions which support Success Checks and Plans, Campaign Management, and then indirectly contribute to improve renewal rates and churn mitigation.

Product Management can make use of this data to prioritize their invests and to monitor product adoption and release success. Specific actions required by product managers include

  1. Identify features which are used by most customers
  2. Analyse a specific feature and its success by adoptions
  3. Prioritize product backlogs and roadmap for better business outcomes like renewals

Increasingly, organizations are focusing on NPS. you may want to know What is NPS?

  • Net Promoter Score (NPS) is a management tool used to measure the customer loyalty (customer satisfaction) based on the likelihood to recommend a product/company to a peer (rang: -100 to +100)
  • NPS has been widely adopted with majority of Fortune 1000 companies using this metric.

The NPS is the percentage of promotors minus the percentage of detractors (passives are not considered).

It is, frankly, quite a tough way of measuring customer satisfaction. NPS (with the same metric) is also used in many other companies worldwide – it is a good way of providing standardized / comparable feedback. During the process, detailed feedback received also gives pointers to understand what the challenges from customers point of view including adoption, support etc. For example, customers may raise a concern because they are not able to use productive applications to full extent. May be certain group of users are not happy with UI or not happy with integration capabilities.

How different is data driven pm than conventional PM?

Data driven product management values patterns and latest trends to derive insights. Product managers would need to make use of the available data from various sources. That means, there could be a possibility that some of the data sets needs to be assumed based on dependent market trends like for example

  • Assessment levels best guess (40% to 60 % ) -Using adoption traces out of the system as-is (all measurements from one source only)
  • Confirmed guess (30% to 40%) -Assessment validated by another neutral sources
  • Educated guess (20% to 30%) -Based on patterns taken from similar customer system landscapes the data gets adjusted

Above mentioned percentages are also indicative in nature and are subjected to change based on the area of investment for each organization. It is not easy for product mangers make right guess. However, the expertise is built over time to deal with data appropriately. It is also important have open mind to take corrective actions based on the real-time feedbacks from stake holders, if needed 

In summary, Data driven product management offers structured methodology along with data sets to make the product decisions and then drive bigger outcome in an enterprise.

 

About the Author:

Shivakumar H S

Shivakumar H S is a Senior Product Manager at SAP Labs India.
With SAP for 15  years’ experience spread across Consulting and Product Management areas . He also has 2 years of Industry experience in the area of Shop floor operations  and supply chain management. He is a product thinking coach and drives various thought leadership initiatives at SAP.

Prakash Karkihallimath

Prakash Karkihallimath is a Senior Product Manager at SAP Labs India.
With total experience of 19 years, worked with many organisations, spread across business consulting and product management areas. He also worked in various industries like Oil and Gas, Chemical and Textile industry. He is a product thinking coach and involved in various activities including CSR and leadership initiatives within SAP.


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