#ScaleStrategy is produced by DX Journal and OneEleven. This editorial series delivers insights, advice, and practical recommendations to innovative and disruptive entrepreneurs and intrapreneurs. Read the in-depth Q&A with Teshima here.
“One of the most important aspects of scaleups is figuring out how to transition sales – from a founder to a larger sales team. It’s also one of the hardest,” says Paul Teshima, CEO and co-founder of Nudge.ai, a relationship intelligence platform that helps sales teams to access new accounts, analyze deal risk, and measure account health.
And, he knows what he’s talking about.
Teshima is a Canadian-born serial entrepreneur and a rare breed too. His previous company, Eloqua, achieved unicorn status.
As part of Eloqua’s executive team, Teshima grew the company to more than $100 million in revenue over 13 years, through two economic crises, its IPO and its eventual acquisition by Oracle for US$957 million in 2012.
Today, from Nudge.ai’s office in OneEleven, Teshima and his co-founder Steve Woods (also a co-founder at Eloqua), are hoping to scale up again. Since launching in 2014, the company has grown to 22 employees, several major enterprise clients and over 20,000 B2B users on the platform. And they were recently featured in the Wall Street Journal on how AI is changing sales. It’s no surprise they’re gaining momentum given the growing need for digital relationship management support. After all, Google, Salesforce, Microsoft, Cisco, and more tech giants are moving into the space.
As Nudge.ai builds out a sales team, Teshima is leaning on lessons from his past and learning new ones about who, how and when to hire, what founders forget about when training newbies, and the art of cracking an enterprise deal.
From One to Many
When it comes to the first few sales hires, Teshima believes they should be entrepreneurial. His approach to building a high-performance sales team is what he calls a classic best practice: hire people in pairs so that you can start removing variables. For example, if both salespeople are having trouble, it may mean that it’s not the right time to transition. If one is successful and the other is not, then it could mean you didn’t hire someone with the right skills.
Nudge.ai is in the process of transitioning its founder-oriented sales team to a larger group. “We’ve got some salespeople working on that delicate transition period now,” he says. “I can tell you that I’m already overestimating how much I think they know because I take my knowledge for granted. I mean, of course they don’t know what I know, it’s in my brain still.”
As a company scales, Teshima urges founders to pause and appreciate how much they know about the business, and how quickly they can make decisions at the drop of a hat in a deal cycle. Those skills are not always things salespeople can do right away.
“It’s really important to simplify,” he says. “Understand what can be translated to a salesperson that he or she can then repeat over and over again.”
To support their success, Teshima focuses on being as methodical as possible throughout on-boarding and training. In addition, he brought someone in to help simplify the sales process to determine what can be scalable.
Hiring Sales People
Should you hire a Director of Sales or build the team from the bottom up? Teshima says it depends on where you sit on the revenue curve as well as the capital and talent that’s available to you at the time.
He definitely sees the value of of hiring a Director of Sales first who can “carry the bag” and help to scale that initial phase, but also agrees with the approach of hiring a hands-off VP to go build up the entire team.
“Both require early evidence of some form of scale. You have some sort of process that defines how the sales process works today and also key metrics about it,” he says.
Teshima acknowledges that finding sales talent can be a challenge. “Are there less seasoned salespeople in Canada who have gone from $0 to $100 million than in the Valley? Yes. Do we need to solve that problem? Absolutely. But you are seeing a lot of seasoned people coming back and as that continues you’re going to see those people train others to get to the next scaling point,” he says.
Closing Enterprise Deals
Enterprise deals are coveted targets for scaleups for the revenue, for the credibility, and for the learning that they offer.
“The hardest part of closing an enterprise deal is finding it,” says Teshima. “Getting involved in the sales cycle itself is challenging because decision-makers are so inundated with a barrage of outbound outreach. These buyers shut down and avoid dealing with 20 or 30 vendors.”
He says that if you’re going to play in the enterprise space, you should understand what you’re getting into. First, it’s difficult to get in. Secondarily, startups can’t wait out a 44-month sales cycle knowing the deal may not close. “You can, but you’ll be losing a lot of sleep,” he says.
Teshima’s scaleup strategy is to show pocketed value right out of the gate. “Lock them in and then go from division to division quickly. And do it more cost-effectively than the competitor. Try that approach versus just the top down approach.”
When it comes to offering freebies or deals to close a deal quickly, Teshima believes low-paid pilots can be risky.
“Enterprises today actually have slush funds to experiment with technology where they didn’t before,” he says. “You could be in a small little pilot where they throw money at you and you wouldn’t even know if it’s a real deal or if they’re throwing real resources behind it. It is absolutely true that if they put some skin in the game, you’ll have a more successful pilot. You need to be pretty disciplined about qualifying, and if you invest in the cycles then put a price on it.”
What about when enterprise customers who scaleback during the renewal process?
Teshima says he hasn’t experienced this yet at Nudge.ai, but in the earlier days at Eloqua, there were times when customers pulled back.
“It’s only a death cycle if you don’t learn from it for the other existing customers. You should never forget that customers can always come back and champions can always move jobs. You always want to do right in those situations because you never know when you’re going meet them next in the ecosystem,” he says.
Channel Partners Sales
In B2B sales, channel partners can be a tempting avenue to explore. While there are good synergies on the tech side – on the cloud and services side – it can be more challenging to have channel partners depending on the nature of the product, says Teshima. In fact, he warns against channel partners in the early scaling stage.
“If you think training your first salesperson is hard, try training channel partners on your product when they have 20 competing products to sell and they’re making a small margin on your product,” he says. “You can get lucky and find one strategic partner and go big, but more often than not, you’re going to find that they’ll get all excited, get trained, and not sell anything. Even if they do close something, it may not even be the right fit,” he says.
Instead, Teshima recommends, clearly establishing that you can directly sell your product in a repeated way before you think about channel partners.
Scaling a sales team isn’t easy. And it won’t happen overnight.
“My one piece of advice is that it’s never one thing,” he says. “It’s a million little things you need to do every day. That’ll make you more successful than trying to figure out the one thing that will help you hit the jackpot.”
Want more? Read the in-depth Q&A with Paul Teshima for more insights on scaling sales.
DX survey reveals high levels of enterprise-consumer disconnect
A new survey looks at the global investment and effectiveness of businesses’ digital transformation efforts. The survey shows a disconnect between enterprise investments and consumer experiences.
The survey is titled “The Kony Digital Experience Index (KDXi) Survey”, and the main takeaway is that while businesses have invested nearly $5 trillion on digital transformation initiatives, only 19 percent of customers have reported any significant improvement in the experiences offered to them.
The Kony Inc., survey included 1,600 responses from business leaders and customers across the U.S., Europe and Asia. The responses were used to gauge the target digital project implementation efficiencies in banking, retail, utilities and healthcare. The research showed a disconnect on both sides and a potential misalignment around investment priorities, and highlighted the impact this could potentially have for businesses.
Among other things, the study found that consumers are underestimating the number of businesses that are investing heavily in every customer experience outcome by at least 50 percent. This means that while business are spending money on digital transformation projects, consumers are not necessarily noticing a difference.
The survey also reported that 62 percent of consumers say that they spend more with companies that offer effortless digital experiences, while 56 percent of consumers indicate that they will switch if a retailer does not deliver the digital experience they want. This signals the necessity for businesses to continue to invest in the digital experience for the customer. However, in doing so they need to start making an impact.
As the report states: “It is critical for businesses to have a greater focus on understanding and aligning with customer needs and priorities to ensure that they are driving the agenda for the digital technology they create and fund.”
In terms of what businesses should be doing, the basis of a strategy includes:
- Embracing innovative thinking, ambition and a commitment to improvement
- Prioritizing investment in digital outcomes, not digital initiatives
- Getting their foundations right before evolving
- Building for now, but investing in a roadmap that leads to the future
- Saying no to silos and yes to integrated digital strategy
- Setting a customer-centered digital transformation agenda
This means companies should work to provide web experiences that make it easier for users to navigate, and for websites to be more engaging and intuitive to use. There also needs to be comprehensive online and mobile facilities so that users can do everything online or via their mobile device. Furthermore, to truly step forwards, businesses need to begin offering digital experiences such as AI, chatbots and augmented reality.
Summing this up, Thomas E. Hogan, chairman and CEO, Kony, Inc. states: “Improvements in costs and efficiencies are always welcomed and clearly important to project funding, but the real returns and real impact of digital starts and stops with its impact on the customer experience.”
‘Ethical AI’ matters — the problem lies in defining it
News that Microsoft will invest around $1 billion to examine ethical artificial intelligence signals that the tech sector is thinking deeper about the ethics underlying transformative technologies. But what is ethical AI?
Microsoft is to invest around $1 billion into the OpenAI project, a group that has Elon Musk and Amazon as members. The partners are seeking to establish “shared principles on ethics and trust”. The project is considering two streams: cognitive science, which is linked to psychology and considers the similarities between artificial intelligence and human intelligence; and machine intelligence, which is less concerned with how similar machines are to humans, and instead is focused on how systems behave in an intelligent way.
With the growth of smart technology comes an increased reliance for humanity to place trust in algorithms, that continue to evolve. Increasingly, people are asking whether an ethical framework is needed in response. It would appear so, with some machines now carrying out specific tasks more effectively than humans can. This leads to the questions ‘what is ethical AI?’ and ‘who should develop ethics and regulate them?’
AI’s ethical dilemmas
We’re already seeing examples of what can go wrong when artificial intelligence is granted too much autonomy.Amazon had to pull an artificial intelligence operated recruiting tool after it was found to be biased against female applicants. A different form of bias was associated with a recidivism machine learning-run assessment tool that was biased against black defendants. The U.S. Department of Housing and Urban Development has recently sued Facebook due to its advertising algorithms, which allow advertisers to discriminate based on characteristics such as gender and race. For similar reasons Google opted not to renew its artificial intelligence contract with the U.S. Department of Defense for undisclosed ethical concerns.
These examples outline why, at the early stages, AI produces ethical dilemmas and perhaps why some level of control is required.
Designing AI ethics
Ethics is an important design consideration as artificial intelligence technology progresses. This philosophical inquiry extends from how humanity wants AI to make decisions and with which types of decisions. This is especially important where the is potential danger (as with many autonomous car driving scenarios); and extends to a more dystopian future where AI could replace human decision-making at work and at home. In-between, one notable experiment detailed what might happen if an artificially intelligent chatbot became virulently racist, a study intended to highlights the challenges humanity might face if machines ever become super intelligent.
While there is agreement that AI needs an ethical framework, what should this framework contain? There appears to be little consensus over the definition of ethical and trustworthy AI. A starting point is in the European Union document titled “Ethics Guidelines for Trustworthy AI“. With this brief, the key criteria are for AI to be democratic, to contribute to an equitable society, to support human agency, to foster fundamental rights, and to ensure that human oversight remains in place.
These are important concerns for a liberal democracy. But how do these principles stack up with threats to the autonomy of humans, as with AI that interacts and seeks to influencing behavior, as with the Facebook Cambridge Analytica issue? Even with Google search results, the output, which is controlled by an algorithm, can have a significant influence on the behavior of users.
Furthermore, should AI be used as a weapon? If robots become sophisticated enough (and it can be proven they can ‘reason’), should they be given rights akin to a human? The questions of ethics runs very deep.
It is grappling with some of these issues that led to the formation of OpenAI. According to Smart2Zero, OpenAI’s primary goal is to ensure that artificial intelligence can be deployed in a way that is both safe and secure, in order that the economic benefits can be widely distributed through society. Notably this does not capture all of the European Union goals, such as how democratic principles will be protected or how human autonomy will be kept central to any AI application.
As a consequence of Microsoft joining of the consortium, OpenAI will seek to develop advanced AI models built upon Microsoft’s Azure cloud computing platform. There are few specific details of how the project will progress.
Commenting on Microsoft’s big investment and commitment to the project, Microsoft chief executive Satya Nadella does not shed much light: “AI is one of the most transformative technologies of our time and has the potential to help solve many of our world’s most pressing challenges…our ambition is to democratize AI.”
Do we need regulation?
It is probable that the OpenAI project will place business first, and it will no doubt seek to reduce areas of bias. This in itself is key to the goals of the partners involved. For wider ethical issues it will be down to governments and academia to develop strong frameworks, and for these to gain public acceptance, and then for an appropriate regulatory structure to be put in place.
Digital transformation is causing C-suite tensions
Digital transformation is not only about technology, it’s also about changes of practices which need to diffuse through an organization’s culture. This needs to be begin at the top. A new report finds C-suite discord is a block to effective DX processes.
Rapidly undergoing effective digitally transformation puts a strain across C-suite relationships, according to a new survey of major enterprises. The report has been produced by business management software provider Apptio, and commissioned by the Financial Times. Titled “Disruption in the C-suite“, the report is draws on the findings of a survey conducted with 555 senior executives, (50 percent occupying CxO roles). The executives were based in major economic nations: Australia, Denmark, France, Germany, Italy, Japan, the Netherlands, Norway, Spain, Sweden, the UK and the U.S.
The report finds that while digital transformation leads to greater collaboration across different business functions, it can also create blurred responsibilities across the C-suite. This crossover carries the risk of key issues being missed; it also serves as a source of tension between top executives, as traditional functions merge and territorial disputes are triggered. As a sign of such differences, 71 percent of finance executives found the IT unit within the C-suite should be seeking greater influencing skills to better deliver the change their business requires.
Team deficiencies found in the survey included not having key performance indicators in place with to measure digital transformation progress. Also, the CFO was found to be the least deeply aligned member of the C-suite team, especially not being aligned with the CIO.
To overcome these divisions, the report recommends that organizations invest time in ‘bridging the trust gap’ between functions and seek to ease tensions, especially between the offices of the CIO and the CFO. An important factor is with establishing which function has accountability. Another measure that can be taken is with ensuing that data is more transparent and where key metrics are issued in ‘real-time’.
The report also charts how digital transformation is being fully embraced, as leaders at global brands are embracing processes and technologies like artificial intelligence, workplace reskilling, cloud computing, agile working and de-centralized decision-making.
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