Following up on my previous article on Serviceable Leadable market, this article details out how exactly SLM is to be defined and then captured.
Think ‘within’ the box
Sudhanshu Srivastav (Anshu), Investor (VC Funds), Advisor – Startups, visualises SLMs, using the concept of multi-dimensional “box”. Anshu asks his mentees to aim to be #1 in that box.
I had mentioned that SLMs can be defined digitally today, thus considering various aspects. Anshu makes it very concrete: “That box could be a combination of vertical, geography, function, size of client and so on,” he says.
For example, a HR Payroll SaaS product startup, could have the initial box defined as SMEs in the Chemical Manufacturing industry in Maharashtra, with revenues between 10 cr to 100 cr, and between 50 to 500 employees.
Going further, once you become successful in your box then expansion can be via many approaches:
Other geographies (Neighbouring states like Gujarat)
Other verticals (Automotive sector)
Other adjacent products besides Payroll (Time and Attendance)
Other features creating more depth in the current product (more expansive features like multi-currency)
Higher revenue clients (100Cr to 500Cr)
Larger size companies. ( between 500 to 2000 employees)”
“AI sales specialist” for a data-driven approach
Sanju Burkule (Sanju), Founder of 7Targets, elaborates how the right SLM can be determined with data, instead of intuition. That means exploring multiple SLM candidates and comparing concrete data for each, to decide on one.
As Sanju says: “Large companies have been doing this for many years already, appointing specialists physically in each zone. Obviously, for startups the cost would be prohibitive. But with AI, this is 10x cheaper while being sharper.”
Better still, now, with AI, the “zone” need not be just geographical. SLM can now span across geographies. If the eventual market is foreign, such SLMs must be explored even at the earliest stages.
Each candidate SLM is based on a certain hypothesis, which is validated (or invalidated) based on real market data for that candidate.
One interesting additional option to be explored, according to Sanju, is a broader catch-all option, as it may discover something that companies had not even anticipated in their hypotheses!
Sanju explains this with a specific example of how the 7Target AI Sales specialist does this.
“Consider a company providing security services, consisting of human security services (guards etc.), camera security and 3rd party appliances to kill viruses for employees (limited health security).
Their 3 different dimensions were:
- Verticals: Manufacturing, Pharma, Healthcare, Residential societies.
- Regions: North, West, South zones.
- Customer: Facilities Manager, Procurement Head, Admin Head, Director.
With AI specialists in each segment, the message ‘How we helped companies in your vertical and in your region’ was sent to different customer profiles. And with much more rigourous follow-up than real salespersons! Each of these follow-ups needs to contain targeted content, which was generated by the AI specialist from the content on the website that provided proof-points and a “nurturing table” with the latest updates.
The manufacturing vertical came on top, with 21 leads and all others were in single digits. Out of the 32 leads generated in a quarter, none were from Facilities Manager and 15 from Admin Head/HR, 10 from Procurement and 7 from Directors.”
This approach not only helps pick the right SLM, but the generated leads can be followed up to put the company on the path to leadership in that SLM. Ultimately, it is about building trust. Initially the trust is on the founders. But then it can be built based on proximity (with cases from the same geography) or industry (with cases from same vertical), or any of the dimensions that Anshu talked about.
Sanju expanded on the example in my article about an EdTech company in Marathwada:
“The AI Sales specialist built for Marathwada emails the Marathwada success stories each month to the same potential clients. This builds trust that this company can execute well in Marathwada. Eventually, this approach can help the startup topple other larger players in Marathwada.”
And then it’s “rinse and repeat”! The subsequent larger SLM will also be selected comparing data for various candidate SLMs, and the same process followed to become a dominant leader in it.
Hopefully, these specifics help startups adopt the SLM paradigm as the best practice, instead of considering it as just another theory.
