Since 2009, Square has been innovating how businesses manage their finances and operations. Co-founded by Jack Dorsey (of Twitter fame) and Jim McKelvey, Square has likely impacted your own life in some way. Think electronic payments, point-of-sale (POS) systems, physical card readers attached to a smartphone or tablet, and online payment processing, and you get a sense of how deep Square’s penetration of global industries really is.
Unsurprisingly, Square is worth billions and shows no signs of stopping. Yet, even giants face their own obstacles from time to time. One of the most promising markets for Square remains quick service restaurants, more commonly known as fast food joints. Places such as Shake Shack, Blue Bottle Coffee, Ben and Jerry’s, and others represent significant revenue for Square, as dozens of customers can be served by a single cashier in only an hour.
Any sale, however, always begins with “first contact,” and while Square did not have to travel to other worlds to find new leads, they were faced with something nearly as challenging: finding useful data buried within a vague Food & Beverage category. Existing data solutions were uncovering the same leads, ultimately sapping sales productivity, slowing success, and leading everyone back to the same locked door.
In effect, Square’s sales teams knew that they were barely tapping into their Total Addressable Market (TAM). They also understood that to increase productivity, they needed to streamline their sales process and target their efforts on high-value merchants. What Square’s teams needed was custom data from LeadGenius, a comprehensive B2B data solution that provides tailored insights specific to unique industries and regions.
The problem: you can’t find new contacts if you can’t identify decision makers
The digital payments landscape is vast and includes everyone from smaller online merchants to mid-size companies to corporations. The key difference, however, is that while it is relatively simple to find the point of contact in a well-organized, large enterprise, smaller organizations are often more decentralized and have fluid decision-making processes. This inhibits a sales team’s efforts to learn exactly who makes financial decisions, a problem that Square rapidly encountered. That, in turn, hampered their ultimate aim: influencing them to adopt Square’s payment solutions.
Every company of any size has a buying center, aka the stakeholders involved in making purchases; normally this includes people from finance, operations, marketing, and IT departments. The challenge for Square, as it was targeting small and mid-sized organizations, was to identify all of them and therefore effectively engage and influence the decision-making process.
While large enterprises normally see less turnover, this is not always the case for smaller merchants. Roles evolve, personnel changes, and decision-making processes are impacted accordingly. Who was in charge of purchasing decisions yesterday might not even be at the company today. Square needed an up-to-date understanding of the key decision-makers and buying centers, which traditional data could not reveal.
Another issue: merchants need to be prioritized by revenue potential
Square understood that any sales outreach strategy needed to be strategic, and logically this meant prioritizing merchants based on their revenue potential. By directing their time and resources to the highest-value targets, their ROI would increase, and their sales teams would operate efficiently.
Revenue potential, however, involves many complex factors, including the business’ size and growth trajectory, transaction volume, average transaction value, and potential for future expansion. If Square could obtain and analyze these metrics, they could spot market trends and determine, you guessed it, which merchants had the highest potential ROI.
LeadGenius precision data: the full potential of TAM revealed
LeadGenius stepped into this problem with TAM Analysis and Custom Data Points. The first step was to launch a comprehensive TAM analysis across target regions for Square, including in EMEA. Leveraging their regional knowledge and tech expertise, LeadGenius dug into each market segment, revealing its real revenue potential and providing Square with an accurate picture of its whitespace TAM in EMEA.
LeadGenius then helped Square prioritize their target merchants by using AI and Machine Learning to source custom data points for each one, such as total inventory, cross-border website traffic, e-commerce platform, and payment processor information. With these deep insights, Square could objectively measure the revenue potential of each merchant. By leveraging hidden data, Square was able to target their sales efforts and prioritize those merchants with the highest value.
The benefits extend into the long term. The data not only allows them to confidently choose high-revenue targets, but it also provides the foundation for stronger relationships, customized solutions, and personalized attention for each one. Square also can more effectively allocate resources, knowing that by directing their tools, guidance, and support to high-value merchants, it is in turn increasing its own success. By leveraging data in its decision making, Square can also spot trends and opportunities in specific market segments, allowing it to develop its own products and expand more effectively,
The impact of comprehensive buying center information: enhanced sales efficiency
LeadGenius also used AI and Natural Language Processing to source decision makers and buying centers for Square. Sales teams received email addresses, phone numbers, social media profiles, and individual location details. That level of data allowed them to overcome one of their biggest obstacles: the fluidness of decision making at smaller organizations. With the ability to contact the right person in a buying center, sales teams were no longer dependent on manually searching for contact details. Time was saved, and employees could concentrate on building relationships with potential clients.
The results of Custom Insights by LeadGenius: a new level of opportunities and precision
The power of LeadGenius’ solutions brought excellent results for Square. By understanding which merchants to prioritize as well as how to directly engage with key purchasing stakeholders, Square’s sales teams enjoyed a higher level of efficiency. They directed their time and resources to targets with the highest ROI, maximizing their chances of success.
Within the first 90 days, LeadGenius and its enhanced TAM capabilities led Square to see a 250% increase in positive response rates through custom insights. Teams were divided by size of opportunity utilizing data that revealed the number of locations. LeadGenius’ solution also empowered Square with a 40% increase in identification of new locations that would be opening soon, a significant opportunity. The new data is also driving Square’s expansion into the United Kingdom and other international markets. It is all because enhanced data saved sales representatives from countless hours in prospecting and contact verification when targeting SMBs.
LeadGenius and its innovative Machine Learning technology was a crucial contributor to Square’s new understanding of their whitespace TAM in EMEA. With a heightened capability to prioritize merchants and cut through the ambiguity of contacts at buying centers, Square was able to bring clarity to the frustratingly vague Food & Beverage landscape and uncover new business opportunities. E-commerce may always be a dynamic space, but as the partnership between LeadGenius and Square shows, data-driven solutions are what unleash the full potential of a business.
Learn more: http://www.leadgenius.com/