Created by Rishabh Srivastava, Founder of Loki.ai
This summary was largely done for my own note-taking, sharing it just in case it adds more value to other people.
I have no affiliation whatsoever with anyone in this note. This is a summary largely taken for my own reference, and may contain errors :)
Context
Source URL:
Why is it important: ...
Keywords
Analytics, Digital Transformation
Summary
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Highlights
Digital transformation is happening. There are 2 components to it:
- Digitization
- Automation
Automation will:
1. Change the structure of work
Other = all the creative work and liaising with people
Digitization furnishes data for operational decision making and makes it much more accessible!
- Dashboards become much more useful
- Some decision automation will be adopted
2. Demand new technologies (new tools to enable people to work)
- Hadoop or BI are older generation tools that required specialized skills and abilities. Require armies of analysts to use. Only useful for certain kinds of decisions and users ⇒ Rest of org is really underserved
- Needs of operational analysts are different. They don't care about high level questions (like what market should I enter next) but mostly care about low level questions (are my customers happy with the product, what features are they using). Executives are fine with eventually getting an answer. Operational analysts need one immediately (like how should I respond to a competitors' flash sale or promotion). Executives prefer full-service use-cases, operational analysts want self-service
We need a new category of tools: Operational Analytics (something like Chartbeat, but for other industries)
3. Lead to new market dynamics
Opportunity 1: Software-powered companies eat tech-phobic industries by being operationally much more efficient than incumbents
- Transportation (Lyft / Uber)
- Hospitality (AirBnb / Sonder)
- Logistics (Samsara / Turvo)
- Freight (flexport)
Playbook: First, choose a tech-phobic industry. Second, provide a digital user experience. Then, use operational analytics to create efficiencies and undercut existing players with lower pricing. Lastly, build an operational moat to make sure you're continuosly winning
Opportunity 2: OA Infrastructure
Make it easy for non-technical people to access data and insights. Or have a processing layer that makes it easy for non-technical people to create insights specific to their domains.
Every layer in the infrastructure stack needs rework
- ETL: Raw data should be easily consumable
- Storage: Data stores should be safe for more non-technical users
- Processing: Non-technical users should be able to do their own processing
- Analytics: Insights should be proactive, timely, understandable
- Access: Governance should be automated
- Presentation: Users should be able to build their own dashboards and decks and explore them
Playbook:
- Make your product self-service, and easy to use. Your users are operational folks. The tools they use must be self-service. You can't build something that requires an intermediary between them and their insights
- GTM is key: bottom-up adoption + top-down sales. Operational people adopt organically, they don't like to sold to. Get them to start adopting your tools organically. Provide easy, small bites of the product that they can use. Then layer on sales top-down to drive revenues
- Primary KPI is user engagement. Revenues are a lagging indicator. Engaged users are the primary indicator
Opportunity 3: Industry-focused OA Applications
Providing insights that cater to specific industries.
Biggest impact is either in high capex industries, where the cost of making the wrong decision is high (Oil&Gas, Manufacturing, Mining). Or in low-margin industries. Where saving every penny matters (Groceries, Construction)
Playbook:
- Build an End-to-End Domain-specific Product. It should be easy to adopt without them having to learn a lot or do a ton of integrations. For these companies, it's hard to find internal resources that would do any additional work
- Build domain expertise, education, and ProServ. You have to shore up your credibility when you talk to customers, and be prepared for long heavily consultative, educational sales. You want to build a strong professional services org that can take your customers to success without them having to understand how exactly your product work or integrate it with their internal tools. you have to support them
- Focus primarily on the business KPIs that these industries care about. Either gross margins or return on capital. If your business doesn't have a clear path to showing your impact on those KPIs, you're in for a very tough sale
Opportunity 4: Role-focused OA Applications
OA that caters to specific roles, like sales & marketing, operations, management etc. Like helping customer success people predict when customers may churn. Or helping sales people figure out what actions to take to make a sales happen
Playbook:
- Target underserves role in the enterprise (or places where tools are pushed down by IT departments instead of being organically adopted)
- Use initial technology advantage to build brand as a moat
- Go for depth, not breadth. Make sure you solve all problems in that role and that you make your users extremely successful. Only then should you go for any adjacent spaces