Nurturing CRM data into powerful sales insights
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The single point agenda for fast-growth enterprises today is superlative customer experience. As forward-looking organizations strategize, customer focus is at the core of any digital transformation initiative. The technology must be agile and intelligent to ensure a positive customer experience from day one. And if you think you have time to iron out your customer delight checkpoints, think again. Retail customers have indicated time and time again that they’re willing to walk away from brands after just one bad experience.
As enterprises revisit their tech stack to level up their customer experience, customer relationship management (CRM) is the ubiquitous starting point as it is a vast river from which millions of rivulets of information flow. So, how do we draw the pathways that interconnect these rivulets to form data streams that help sales teams sail straight to desirable customer outcomes?
CRM: A system of record
To help deeply connect with customers and their preferences, we need CRM data to dive deep into a customer story and provide cues to complete it. As we study demanding customer profiles, it is obvious that sales teams can no longer work off a formula based on this available data. Every engagement is now more complex and requires personalization and curation.
Just having multiple data points is no longer sufficient. While modern CRMs are great for managing customer profiles and pipeline forecasting, we need AI-driven engagement systems that provide a sales team with not just the ‘What’ and ‘When,’ but the ‘How,’ ‘Who,’ ‘How not to,’ ‘If’ and ‘Instead.’
Let’s look at some of the aspects where CRMs fall short in context to modern-day sales and distribution.
- Courtesy: As we try and understand how consumer buying behaviors continuously change with the economic landscape, CRMs lack in recording which sales behaviors and engagements are most effective — and across which customer demographics. There are no sales playbooks that outline these tectonic shifts in buying behavior.
- Ease of communication: As powerful a brand as BlackBerry once was, the company can thank a failed CRM implementation for part of its spectacular downfall. Instead of reaching out to customers over their preferred medium while its flagship messaging service collapsed, it used Facebook (now Meta) as a communication channel.
Omnichannel engagement is the way forward, and organizations must delve deeper into the digital behavior of customers. Can a system understand if a consumer is digital by need or digital by choice? Or can it understand which actions a customer prefers to do online versus which actions they prefer doing through live engagement?
- Salesperson knowledge and expertise: Sales teams have a high turnover rate. According to HubSpot, at a hefty 35%, it is nearly three times higher than that of other industries. This translates into a high rate of loss of knowledge and best actions each time a salesperson leaves a team.
In 2001, British Airways implemented its Customer Data Warehouse (CDW), codenamed “Ocean Wave.” It took more than two years for the analytical teams to be able to use the information for campaigns and reporting, primarily due to the complexity, time and effort required by contractors to train system users on how to access and use the data.
To avoid a similar situation and elongated implementation delays, sales teams need a system that captures best practices as learnings and transfers these learnings to newer team members to enable:
- A quick onboarding process and shared access to the established knowledge pool;
- Seamless customer journeys.
- Transparency and ease of process: Most CRM users update the systems erratically, resulting in inaccurate, incomplete, or unreliable data. To establish a seamless experience as well as transparency for customers, a CRM should include complete visibility into customer information, playbooks that suggest next best actions, nudges for sales teams to action appropriate next steps, and complete visibility into team activity for sales team managers. But how often are CRM systems updated immediately following a sales engagement?
The need: A system of insight
A mobile and intelligent layer on top of a CRM system can convert a passive system of record into a contextual system of insight. Real-time data, artificial intelligence and machine learning capabilities will help convert a CRM into a system of recommendation that helps sales teams close more business faster and improve the overall customer experience. The following features — in addition to robust CRM data — can improve engagement and customer experience by leaps and bounds:
- Auto capture of activities: With a feature such as auto-capture, the biggest roadblock to a CRM is removed: the manual keying in of data. Deep data capture can be simplified through features such as automatic call and meeting detection, one-touch call sentiment, and note recording.
- Winning behavior identification and emulation: If intelligent platforms can identify winning behaviors from this rich data capture, the identified behaviors can then float up as nudges for teams to emulate. For example, suggesting an ideal number of touchpoints, tiering and prioritization, as well as personalization.
- Nudges: Once the system can identify winning behaviors, it should be able to use these learnings to nudge teams while they sell and engage with customers. This gives sales teams very specific guidance through various phases of engagement with a prospect or lead. Examples include:
- What is the best time for a second call?
- Service request by customer A has been resolved by the service team, please initiate the next steps.
- Customer B’s renewal is in 30 days, please initiate renewal.
- Tina from your team is meeting platinum client C in the next hour. Do you want to schedule a coaching call?
- Client D is similar to client B and this pricing strategy worked best for them previously…
- New lead M is situated a mile away from your next meeting. Schedule a meeting en route?
The possibilities! If the terabytes of CRM data can be mined into such nuggets of information for sales and customer service teams, it truly empowers them and paves the way for performance excellence and a superlative customer experience.
Venkat Malladi is cofounder and CTO of Vymo.
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