This Articles was originally published on January 5, 2021 on CustomerThink.
Aligning AI’s capability with your sales team
Artificial intelligence has the potential to vastly increase the power of sales. It won’t replace salespeople; instead, it will allow them to spend their time on the right prospects at the right time, empowered with exactly the right information.
To support that goal, the key is not just the best AI-powered sales tools. It’s making sure the data that fuels them is configured and architected so those tools can deliver on their promise.
I’ve been designing and advising big companies on the information systems that they use in sales and marketing for more than 30 years. What AI can do now promises a quantum advance in sales efficiency. But the effectiveness of AI is limited by the underlying data in CRM, content management, and product information systems. The right approach is not to wire the systems up, bolt on some AI tools, and hope for the best. It’s to ensure a consistent framework for accessing the data in those systems. That universal approach is an ontology — a consistent representation of data and data relationships that can inform and power AI technologies. This helps the AI systems to be able to recognize these essential things about the business – like product names, categories, relationships, bundles, customer types, industries, and user objectives and interests.
The customer that arrives at your site today is more knowledgeable about solutions, pricing, competitors, and products because they’ve already spent time researching them. Even for complex offerings that demand consultative sales, the prospect is likely to come armed with lots of information from your site, your competitors, independent consultants, or analyst reports.
The salesperson hoping to close that prospect needs their own advantage from AI tools. Here are a few examples:
- AI-powered chatbots now help with lead generation. Tools like Drift enable chatbots to converse with prospects on your site, then capture contact information, schedule demos, and send intelligence about the prospect to your sales specialists. Virtual assistants can determine if the prospect fits enough BANTS criteria (budget, authority, need, timeline, strategic fit) to be a sales-qualified lead. At a company that sold scientific supplies, signals from AI systems helped salespeople determine which prospects to spend time with and which of the company’s complex solutions would best suit them.
- AI can nurture leads more consistently. Salespeople are unique and creative – which is both their greatest asset and their most dangerous liability. Data can inform which pitches are most likely to connect and which prospects to follow up. AI tools, informed by analysis of past prospects and outcomes, can determine the appropriate cadence and content for messaging. AI-powered natural language processing (NLP) tools like Conversica interpret “interest signals” in email responses (phrases like “Tell me more,” “Send me some info,” or “Call me back in two weeks”) to know when to pass a hot lead to the sales team.
- AI detects patterns that can help set priorities. Which buyers are most likely to generate revenue? That’s a subtle question. AI can analyze propensity to buy, detecting patterns that no human could spot. Everstring, a vendor in this market, claims to identify likely buyers by analyzing 20,000 possible data signals. Such models ensure that sales staff spend time on the richest prospects.
- Semantic search applications can make salespeople smarter. Semantic search has the capability to surface content from multiple unstructured and imperfect data collections and integrate that information with historical data like past purchases, providing salespeople with the ideal pieces of information for any prospect. Salespeople receive information in context instead of searching through multiple systems.
- Configure-price-quote (CPQ) tools take the drudgery out of sales provisioning. When selling complex solutions, salespeople need to know which products and services go together. A CPQ engine methodically deconstructs the customer’s perspective, then identifies the components that, combined, can solve the customer’s problem. One vendor of custom products found that salespeople were spending half of their time walking customers through basic configuration choices. A CPQ system took on much of that load, creating a productivity boost equivalent to hiring several more fulltime salespeople at no additional cost.
Sales operations and management teams are buying and attempting to integrate many of these tools. Separately, they have promise. But the future of your AI-powered sales depends on a proper foundation of clear processes and high quality, curated data. If you underinvest in the data that powers the new tools your salespeople will use, you’ll miss out on the foundation that could transform your sales. This moment, when the economy seems destined to slow down for a while, is the ideal time to undertake the preparation work needed for this sales transformation.
If you’ve properly invested in the product data, content and customer ontology, your AI tools should be able to deliver the promised efficiencies, allowing you to outcompete sales teams that lack such tools. But if the data is a mess, the tools won’t work well, and the salespeople will fall back on their old, less efficient approaches. Good sales instincts are critical. Combine those instincts with the power and promise of AI and your organization will never look back.