Saama Technologies: Growth Through a Focused Vertical Market Strategy

Abstract

After a successful transition from a projects-based IT business services company to a platform-driven analytics company, Saama’s core leadership team gathered in 2017 to brainstorm the next phase of its growth. The year before, the team had decided to narrow its target market to the life sciences vertical. Saama now had to decide how to execute on this focused strategy by choosing a growth pathway within the life sciences vertical. Saama’s leadership team was considering three alternatives: acquiring new customer accounts, developing existing customer accounts, or developing new products by harnessing artificial intelligence (AI) and blockchain technologies. The team had to evaluate these growth pathways in terms of both short-and long-term revenue potential, as well as their potential for sustaining Saama’s competitive advantage.

This case was prepared for inclusion in Sage Business Cases primarily as a basis for classroom discussion or self-study, and is not meant to illustrate either effective or ineffective management styles. Nothing herein shall be deemed to be an endorsement of any kind. This case is for scholarly, educational, or personal use only within your university, and cannot be forwarded outside the university or used for other commercial purposes.

2024 Sage Publications, Inc. All Rights Reserved

You are not authorized to view Teaching Notes. Please contact your librarian for instructor access or sign in to your existing instructor profile.

Resources

Exhibit 1: Overview of Saama Fluid Analytics Engine (FAE)

Figure

Source: Saama Technologies.

Exhibit 2: Worldwide R&D Spend by Pharma and Biotech Companies, 2008–2022

Figure

Source: EvaluatePharma® World Preview 2016: Outlook to 2022, September 2016, p. 27, http://info.evaluategroup.com/rs/607-YGS-364/images/wp16.pdf.

Exhibit 3: The Research and Development Process in Life Sciences

Figure

Source: International Federation of Pharmaceutical Manufacturers & Associations: Facts and Figures 2017.

Exhibit 4: Business Processes in Clinical Operations Management

Figure

Source: Saama Technologies.

Exhibit 5: Market Sizing for Life Sciences Clinical Operations Analytics in 2017 ($ in Billions)

Industry Segment

Stage of Development

Pharma and Biotech

Medical Devices

CRO and CMO

Research Centers

Total

Research (pre-clinical)

1.00

0.64

0.44

0.26

Development (Phase I–Phase III trials)

1.39

0.89

0.61

0.36

2.34

Development (Phase IV trials)

0.57

0.37

0.25

0.15

3.25

Regulatory approval

0.81

0.52

0.36

0.21

1.34

Supply chain analytics (commercialization)

0.70

0.45

0.31

0.18

1.90

Sales and marketing (commercialization)

1.86

1.19

0.82

0.49

1.64

Total addressable market (TAM)

6.33

4.06

2.79

1.65

14.83

Total serviceable market (TSM)

3.82

2.45

1.68

1.00

7.23

Saama’s target market

1.96

0.87

2.83

Source: Saama Technologies.

Exhibit 6: Overview of Life Science Analytics Cloud (LSAC) Modules

  • Clinical Development Optimizer (CDO) was the data lifecycle management solution delivering cleansed, aggregated operational and clinical data for better trial effectiveness and cost. CDO provided clinical data management and analytics for bioinformatics and operations. CDO’s data engine normalized disparate sources of data across the enterprise to evaluate performance across all studies and to flag potential risks that would impede the studies.
  • Trial Planning Optimizer (TPO) was the clinical trial feasibility solution that optimized enrollment, investigator identification, site selection, and patient burden. TPO allowed clients to use real-world data to identify eligible patient and principal investigator populations, evaluate and quantify the likelihood of successful patient enrollment, and site selection. TPO allowed the design of studies to reduce complexity, improve patient recruitment, and enhance patient retention.
  • Cohort Builder identified patient populations based on inclusion-exclusion criteria. Cohort Builder organized real-world patient information, such as diagnosis, drug therapy, and procedures, to easily pinpoint population groups that fit specified criteria. Cohort Builder could be used to summarize incidence rates and co-morbidities of a disease and various other target metrics across a target cohort.
  • Market Analyzer was the commercialization analysis solution providing insights into the market share performance of pharmaceutical products in the industry. Clients could evaluate payer contract performance and rebates, as well as patient-focused discount performance for a given drug in a specific therapeutic class. Market Analyzer could be used to compare products on the therapeutic value within a market basket and across geographic areas.
  • Patient Pathways was the patient journey tracking solution enabling the evaluation of treatments, disease progression, outcomes, and total economic costs. Patient Pathways leveraged real-world data to provide decision-making insight into how a given disease was treated and/or managed; drug lines of therapy; treatment options and outcomes; cost of therapy; treating physicians; disease progression and complications. With Patient Pathways, clients could understand the clinical and commercial journey of patients to assess which pathways and treatments tended to lead to better outcomes.

Source: Saama Technologies.

Exhibit 7: Saama Life Science Analytics Cloud (LSAC) Solution

Figure

Source: Saama Technologies.

Exhibit 8: Customer Acquisition Pathway, Number of Accounts and Probability of Closing Deals

Market Segment

Number of Accounts

Annual Revenues

Annual Analytics Spend

Length of Sales Cycle

Annual Account Revenues

Low Probability Prospects (p = 0.1)

Medium Probability Prospects (p = 0.25)

High Probability Prospects (p = 0.5)

Large

20

>$10B

$80M+

18 Months

$2,000,000

20%

50%

30%

Midsized

30

$1B–$10B

$10M–$80M

12 Months

$500,000

20%

30%

50%

Small

550+

$100M–$1B

$1M–$10M

6 Months

$100,000

60%

20%

20%

Source: Saama Technologies, author estimates.

Exhibit 9: Application of Natural Language Understanding (NLU) in the LSAC Platform

Figure

Source: Saama Technologies.

Exhibit 10: Clinical Trial Data Workflows Encoded in Blockchain

Figure

Source: Mehdi Benchoufi and Philippe Ravaud, “Blockchain Technology for Improving Clinical Research Quality,” Trials 18, no. 355 (July 19, 2017).

This case was prepared for inclusion in Sage Business Cases primarily as a basis for classroom discussion or self-study, and is not meant to illustrate either effective or ineffective management styles. Nothing herein shall be deemed to be an endorsement of any kind. This case is for scholarly, educational, or personal use only within your university, and cannot be forwarded outside the university or used for other commercial purposes.

2024 Sage Publications, Inc. All Rights Reserved

locked icon

Sign in to access this content

Get a 30 day FREE TRIAL

  • Watch videos from a variety of sources bringing classroom topics to life
  • Read modern, diverse business cases
  • Explore hundreds of books and reference titles