Sales teams have lots of tools and information about their customers, but they often find it hard to use this information well. They need to figure out what customers are like and what they want. This way, salespeople can give the right message to the right person at just the right time, and they need to do this for lots of customers all at once.
Also, selling things is changing a lot. For example, when a business wants to buy something, there are usually more people making that decision now than before. Plus, these people making decisions know a lot already because they look up information themselves before talking to someone selling something. With so many important people involved in buying, each knowing a lot, salespeople need to figure out who is most interested and has the most influence in the buying decision. They also need to know what’s really important to these people.
DataLit Context: Sales Enablement
At DataLit, their challenges are amplified by several factors, including their shift from selling licenses and products to focusing on service offerings. This change has significantly altered their sales processes and the competitive landscape they navigate.
The cloud service sector is particularly competitive for DataLit and is an area where they’re experiencing intense pressure to expand, both by acquiring new customers and increasing usage among existing ones. Their sales team makes more calls than before, dedicating substantial time to call preparation, research, analytics, and data accuracy.
Furthermore, DataLit’s sales team handles a much broader range of services compared to their counterparts in other companies, even those leading in the enterprise sector. With a vast array of services to offer customers, there’s a critical need for automated recommendations to help ensure that their sellers are focusing on the most appropriate services. Additionally, customers often conduct extensive research before choosing their technology solutions and typically only engage with a sales representative when they are about halfway through their decision-making process. This necessitates that DataLit’s sellers have a deep technical understanding of each service to effectively engage in meaningful conversations from the outset. Providing the sales team with comprehensive learning resources and readiness content is a significant challenge.
Lastly, DataLit recognized a missed opportunity in centralizing governance around lead targeting criteria to unify their sales team’s approach, enabling quality control, governance, and continuous measurement.
The following questions can facilitate internal and external dialogues about incorporating AI into their sales strategy:
What activities consume most of a sales team’s time? How could AI enhance a team’s skills in these areas?
Which key stakeholders should they convince to support an AI initiative in sales? What scenarios make a compelling business case for AI?
How can AI boost a sales team’s satisfaction and reduce frustration?
How will they ensure that every sales team member has a say in developing and implementing AI? How will they encourage their team to create innovative AI applications?
What steps are needed to ensure that a sales team trusts and adopts AI tools? How can the rollout be planned to minimize disruption to sales operations?
What information sources should they combine for a more complete customer understanding? Is this data currently isolated? How can they maintain integrated data going forward?
Their ability to implement AI depends on their organization’s familiarity with AI. They should consider: Is their organization an early adopter of technology, or do they prefer waiting until technologies are well-established? Do they have governance structures to ensure AI applications align with their business goals?