100% to 200% increases in digital marketing ROI
However, sales-ops has one of the most challenging roles for startups as their connective function is filled with many time-sensitive responsibilities and tasks. Even more challenging, the hats sales ops teams wear often change, and urgency is always front and center. Modern sales ops teams not only help provide the systems, tools and processes for account executives to source, close and manage clients, but frequently in startups these same teams are responsible for many critical internal functions including salesperson on-boarding/offboarding, compensation and data for forecasting. Such a demanding function can feel overwhelming for many employees and create friction between go-to-market teams.
In the past, startups would scale their sales-ops function through hiring. But, due to the current macro-economic environment this is likely not feasible as organizations aim to conserve cash. This raises and important question: How can a startup continue to support it’s revenue growth by maintaining a high-functioning sales ops organization while keeping costs low?
The recent crash in valuations of growth stocks and current macro-economic environment ended the “Blitzscaling” or pure growth focus strategy of many startups. Instead, prominent venture capital firms are giving tough advice to their portfolio companies encouraging a focus on limiting expenses and driving towards profitability sooner than previously planned.
“We are just beginning to see how the increasing cost of money flows through to impact the real economy. If you’re stepping back and thinking twice, it’s not just you. Belt tightening and priority reassessment will have second- and third-order effects, as one company’s costs represent someone else’s revenue or purchasing power…” – Sequoia [ii]
Additionally, founders are being warned that the ability to raise capital may be delayed, making cash a priority. But, despite this, growth targets haven’t diminished. This then creates a conundrum for executives and sales-ops leaders: How do we continue to support growth while conserving cash and limiting cost increases?
Traditional approaches for sales-ops won’t work in this environment. Hiring extra staff will increase overhead and be an immediate drain on much needed cash. Implementing new, revenue focused software may help, but also could be prohibitively expensive for earlier stage companies. Even if a startup can afford these technologies they may not deliver the short-term ROI desired to help drive towards profitability as noted by Bain:
“Every major B2B company invests millions each year in sales technologies, yet 62% of 167 companies surveyed recently by Bain & Company said the return on their investment fell short of expectations.”[iii]
What other options does a Sales-Ops team have?
If you consider the type of tasks Sales Ops performs on a day-day basis, automation seems like a natural candidate. Teams move data between systems, handle lots of documentation and orders (either paper or digital), drive insights etc. These are the bread and butter applications for automation. If that’s the case, why then hasn’t automation been used more for sales-ops in startups and why is much of the work still done manually? A few key reasons:
Thankfully, a new way to automate has arrived, eliminating much of the up-front cost and dramatically reducing the services costs of implementing and managing automation. Because of this, automating many sales ops functions is now a prime way for startups to support growth while limiting costs. Kognitos’s NLP based automation places this tool in the hands of sales ops employees. Because Kognitos enables people to automate in plain English, simply typing natural business commands to automate their processes, sales-ops can now quickly remove much of the manual work in their day to day and focus on higher value activities.
At an aggregate level this has many benefits for a startup beyond limiting or reducing cost including:
In the current conditions, one of the best ways that startups can handle a potential slowdown on fundraising and need to drive towards profitability is by maximizing the effectiveness of their teams. This is especially true in the function of Sales-Ops, a highly connective group that helps ensure sales, marketing, finance and support are all able to address customer needs. While traditional methods of increased hiring and the implementation of expensive, revenue specific software may not be feasible, Kognitos new approach to automation, automating in plain English, empowers companies to not only limit costs but drive growth sustainably.
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[i] Boston Consulting Group, “Revving Up Go-To-Market Operations in B2B”, 13 May 2020, Revving Up Go-to-Market Operations in B2B | BCG
[ii] Shivani Shinde, Business Standard, “Sequoia asks founder ecosystem to tighten belt, focus on profitability”, 25 May 2022, https://www.business-standard.com/article/companies/sequoia-asks-founder-ecosystem-to-tighten-belt-focus-on-profitability-122052501492_1.html
[iii] Harvard Business Review, “The Sales Playbook of Successful B2B Teams”, 25 August 2021, https://hbr.org/2021/08/the-sales-playbook-of-successful-b2b-teams
Despite 50 years of widespread computer usage, computer languages lack a toddler’s ability to use a back-and-forth approach to effective communication. And while computers have changed our lives in a great many ways, their requirement to use unambiguous structured language limits their effectiveness at automating business processes. A Formstack study of 280 companies showed that managers spend an average of 8 hours a week on manual tasks.
Computers speak to computers just fine. Highly structured computer languages such as Python, C++, and Java enable seamless handoff of information between machines.
Humans can similarly easily communicate with other humans. This is true even when using computers as intermediaries. Email threads, for example, allow dialogue and negotiation. If you send an email requesting someone to do something that they don’t fully understand or agree with, they’ll email back with questions.
The trouble emerges when humans try to communicate with machines. All computer languages are geared toward unambiguous instructions that the computer interprets in only one rigid way. Close to 9,000 computer languages have been invented thus far and not one comes naturally to humans.
Every company utilizes business processes, and they want to improve efficiency by automating them. Robotic Process Automation (RPA) has emerged as the most common approach, and the market is on fire. According to a ResearchandMarkets report, the RPA market is expected to reach nearly $26B by 2027. A 2016 McKinsey Digital report says that RPA enables a first year ROI ranging between 30 to 200 percent.
But RPA’s dependency upon rigid computer languages limits its effectiveness when business processes are complex or evolving. As an example, even a small insurance business will process tens of thousands of medical insurance claims in a year. When humans do the work, they can easily apply the required business rules while processing the claims.When RPA-enabled computers do the same work, they typically fail when they encounter ambiguities such as missing information or incorrect entries, or novel scenarios that have not been anticipated.
RPA customers attempt to prevent these incidents by first bringing in consultants to discover and optimize their business processes before automating them. A January 2022 ITWeb article, Finding the sweet spot of human-centric RPA, says: “While RPA software can radically improve business processes, building an RPA capability requires significant investment of time, money and people.” Naturally, as the investment increases, the ROI declines.
This lack of easy machine to user communication creates additional costs over time as business processes change – which most inevitably do. Computers are unable to ask the questions required for them to learn how to keep up with changing requirements. Companies must bring in developers to adjust the automation to the new business process requirements. It’s no wonder that an EY survey found that 30% to 50% of RPA projects fail.
Electronic forms such as those used by tax agencies, or the Department of Motor Vehicles are meticulously programmed to allow only certain responses. When filling out a form, if I have a legitimate response unanticipated by the developers, I’m typically not able to submit my input.
As another example, consider the Multiple Listing Service (MLS) for realtors. A realtor needs to manually input data such as the property age, the lot size, etc. over time. If the MLS communicated like people, it could collect information directly from the seller, leaving the realtor more time to sell.
As a toddler begins to learn a few words, effective communication leaps exponentially even if the words aren’t completely correct. The ability for the communication process to handle ambiguity in turn enables the toddler to learn much more quickly.
The same holds true when we use natural language to allow machines to be more like humans. Many developers frown on natural language because it’s ambiguous. But it’s the ambiguity that makes NLP both more accessible and easier to use. The fact that the machine will disambiguate the instructions at run time results in a more robust automation which does not break on changes in the environment.
Natural automation through natural language processing is the antidote to RPA limitations. The system discovers any missing or incomprehensible information and reaches out to the appropriate person for answers, just as a human does, in response to natural instructions. Once the information is received, the system puts it in the right spot in the system of record and goes on to the next step in the business process.
Natural language acts as the common language between the human and the machine allowing both sides to negotiate the path forward when the machine is stuck, and also enabling the human to teach the machine without first getting trained in a computer language. Kognitos uses natural language instruction and exception handling to vastly simplify and reduce the cost of business process automation.
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My daughter grew up to be a nuclear scientist, and she utilizes Python in her work. Despite her many years of developer training and experience, I look forward to entering a new era where she’ll again be able to speak with computers as naturally as she did as a toddler when requesting I get her a cookie.
