If revenue is the lifeblood of any business, sales has historically been its beating heart. But this paradigm has become more nuanced in recent decades, particularly in businesses that revolve around any type of subscription or recurring revenue model.
Revenue growth was historically linked to a traditional marketing funnel – one that focused on leading customers to the point of acquisition. The rise of recurring revenue models means that acquisition is now viewed as the starting point for revenue generation, rather than the final destination.
To successfully support revenue growth in this new era, businesses need to move away from the traditional marketing funnel towards more of a “bow tie” framework. In this model, sales acquisition represents the left-hand side of the bowtie, while the true compounding growth occurs on the right-hand side of the bowtie, via adoption, retention, and expansion of the customer base.
Adopting this new approach isn’t as easy as flipping a switch, however – businesses will need to embrace a robust data strategy that encompasses strategic data collection, enrichment, and AI-powered activation of insights if they hope to intelligently drive revenue and achieve better business outcomes.
Read the signals —
So, what does this data strategy actually look like in practice? Consider a company that wants to drive growth on the right-hand side of the bowtie by creating an “account propensity to buy” model.
This starts with data. Who are the customers that have traditionally been most likely to buy from the business? What has their traditional journey been, and where are they right now on that journey?
Answering these questions involves collating various signals, including third-party data such as Internet searches and web scrubbing, as well as direct interactions with the company like visits to the company website, webinar sign-ups, clicks on marketing emails, and other direct marketing engagements.
The data picture is rounded out by conversational signals – meetings that the account team is having with the customer. What kind of discussions are going on? What are the customers homing in on or asking for?
Additionally, there are situational signals. Have there been recent leadership changes within the customer organisation? Is there a new regulation that changes the way the customer does business and their potential need for a solution that will help them adjust to the new environment? All these signals come together to help paint a picture that allows us to make relevant impact for the customer or prospect.
The path to maximum efficiency —
While data matters, context does as well. How much weight should be given to any particular signal? Which signals matter most, and which hardly matter at all? The model needs to be carefully calibrated to deliver best results.
Once that context is layered in, and the model’s AI algorithm is optimizing for which signals matter and which don’t, the company’s data strategy can be activated – which is to say, it can be embedded into workflows and acted upon by the company’s knowledge workers.
The result is that sales, marketing, customer success, and partner channels all better understand what action they need to take with which account at what time – creating maximum revenue efficiency.
The business can improve its win rates by putting its people power towards the accounts that are most likely to result in a purchase. The data can also identify any bottlenecks or inefficiencies around revenue generation. How well is the business moving a customer from one purchasing stage to the next? How much time is it taking? If it’s taking over a year to move them along to the next purchasing stage, for instance, clearly the business is achieving conversion – but they’re not achieving it as efficiently as they could be.
It’s important to note that the benefits of having access to this data-driven intelligence don’t just accrue to the company: the benefits flow in both directions. It allows companies to service customers more effectively and proactively because they have a depth of detail at their disposal that they might not otherwise have when interacting with customers. This is a win-win for all parties.
Get the basics right, and the rest will take care of itself —
In a recurring revenue model, you’re never really done selling. Marketing, sales, and customer success all have overlapping responsibilities across the customer journey. A carefully applied data strategy allows everybody to stay far more coordinated and prioritised in their efforts.
Like any transformative business initiative, however, companies must have a strong foundation and focus on the basics – and in large part, that comes down to the data. How are you collecting data? How are you cleaning it? How are you enriching it? How are you governing it? If businesses get those core data elements right, they’re setting themselves up for more sophisticated AI use cases that can transform revenue operations – and drive real and repeatable success.
Lindsey Meyl is Vice President, Revenue Operations at iManage.
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