Using Data To Find Your Tier One Clients In The Self-Serve Universe

Yoni Tserruya is the Co-Founder and CEO of Lusha, the crowdsourced data community of over 670,000 B2B salespeople.

Forrester predicts that by 2023, U.S. B2B companies will reach $1.8 trillion in annual revenue and account for 17% of all B2B sales in the U.S. But why are the fastest growing B2B software-as-a-service (SaaS) companies today providing a self-serve business model in addition to traditional sales? The reasons for going self-serve are typically a combination of market demand and companies’ growth strategies.

Once corporate giants like Amazon, Alibaba and Netflix set the golden standard for automated, human-free sales, they created a new breed of consumerism that has impacted every aspect of life, from no-cashier checkouts at the grocery store to virtual tellers in banking. As a result, a new type of customer who prefers self-serve over contact sales was born.

With 75% of today’s SaaS customers expecting to self-educate themselves before they buy and wanting to complete their sales cycle with as little interference as possible from sales reps, many small and medium-sized businesses (SMBs) in the tech space had to alter their sales strategy to include a strong self-serve platform.

On the side of business growth, self-serve offers an unprecedented number of new customers without investing in costly sales resources. Characterized by limited resources and a need to control their spend, SaaS startups are therefore leading the way in adopting self-serve. Self-serve models save time and resources, returning the minimal investment of their set-up by enabling companies’ agile growth and scaling capabilities.

But with the reality of a self-growing customer base came a new problem sales teams could not have anticipated. The new blood self-serve was injecting into companies meant that sales reps were not able to reach all their customers and could not offer personal treatment to all. So, who would sales employees provide human contact to? How will they weed out the most profitable customers and ensure those, and not others, get the costly sales nurturing?

Is self-serve hiding your most valuable customers?

Once gas stations built self-service stations where customers could pump gas on their own, they took the service industry by storm. This meant that stations could now service much higher volumes of customers, while personalized pumping services were left available to a select few. These were what we call today “low risk, high value” customers. They would drive out of their way to get their gas from a particular vendor, providing the station with ongoing, steady business that accounted for a serious chunk of their revenue.

Similarly, the top self-serve B2B companies these days generate about 70% of their revenue from only 10% of their customers. But who are those 10% of customers responsible for the bulk of income? How will a sales rep know to identify them?

To take on this challenge, a new set of services has gained popularity for B2B companies going the self-serve route.

The Reign Of Sales Intelligence

As the co-founder of a sales intelligence platform, I’ve seen how data insights about customers have become an essential revenue driver for businesses in the B2B self-serve space. Equipped with sales intelligence, sales teams can identify and prioritize their most profitable clients — those 10% responsible for 70% of revenue — and offer them the human touchpoints that will nurture a relationship and expand their deals.

But asking your AI algorithm to produce insights from your database without defining the criteria for valuating your customers is a needle-in-a-haystack endeavor. You will need to let your machines know what you want to find out. And what you are looking for is the RFM rule: You are looking for the clients that purchased most recently, renew or expand their deals most frequently and overall spend the most money.

Customer value analytics can be viewed from one of two angles:

• Current value allows you to spot high-roller customers that start off their relationship with your business with a bang. While you don’t have a history to base your decision on, you can anticipate from these early dealings that they will grow to become continuously profitable clients.

• Lifetime value allows you to determine the revenue a customer brought in over time. This will help you determine the profitability margin after cost-to-serve calculations.

Start by setting up an automated data mining system and let it run through your database. Set up the rules so that your algorithm knowns to turn the spotlight on RFM customers, both new and old. Make sure your data insights software is continuously running in the background or is set to scan your database periodically. Keep in mind that your database is also continuously growing, and you never know which incoming business will fit the bill.

The Pitfalls Of Data Intelligence

When dealing with data generally, and specifically when aiming to retrieve actionable insights from it, you have to be mindful of bad data. Bad data is corrupt, outdated or incomplete information that lives inside your database. When it is encountered by your go-to-market team for nurturing purposes, or when it is targeted by your marketing team, the messages you send out don’t reach their destination. This is a costly waste of resources, and it will miss the mark every time. To combat bad data, most companies these days employ data cleaning processes. If you haven’t already, you may want to consider investing in data cleaning services alongside sales intelligence software.

Data is becoming a critical component of B2B companies’ scaling across all industries and geographies. If self-serve is teaching us anything about growth, it is that it’s not enough to bring in new clients. To increase revenue and affect bottom lines, sales intelligence can help you dive deep into your customer base, selecting and prioritizing those customers that will drive your revenue forward.

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