All Posts

AI Makes Sales More Efficient, But Only If Your Data House Is in Order

This Article was originally published 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.

Earley Information Science Team
Earley Information Science Team
We're passionate about enterprise data and love discussing industry knowledge, best practices, and insights. We look forward to hearing from you! Comment below to join the conversation.

Recent Posts

Use Customer and Behavior Data To Create Personalized Experiences

The more quickly customers can find the product they are seeking, the more likely they are to complete a transaction and to return to the site in the future. Personalizing offers and making well- targeted recommendations can bring customers and products together faster, and are effective ways to engage customers by creating a more positive customer experience. In order to do this, companies need to capture and use as much relevant information as possible. The more that is known about the customer, the more effectively the recommendation system works. Customers generate many signals through their online behavior, and those signals can also be used to understand their interests, purchasing patterns, and needs. Reading their digital body language accurately and creating a valid customer model is essential to anticipating and fulfilling those needs.

How to Instrument KPIs Throughout the Customer Journey

You're probably using metrics to determine if your marketing programs are effective. But, have you selected the right metric at each stage of the customer journey?  Which ones connect to your strategic goals? In this session Seth Earley and Allison Brown talk about how each stage of the journey can be instrumented to use feedback from course corrections to further improve the process. You'll learn: Types of operational and user experience metrics and KPI’s How to select and collect the right metric for each stage of the customer journey How KPIs can be used for data-driven decisions How to manage conflicting goals and metrics

First Party Data - Managing and Monetizing the "Data Exhaust" From Your MarTech Stack

Understanding, anticipating and responding to the wants, needs and behaviors of your customer is the competitive battlefield of 2022. However, with new limitations and regulations regarding second and third-party data and tracking cookies, marketers, digital leaders and ecommerce executives have to consider their own methods of collecting and acting upon the data they gather about customers. In this webinar Seth Earley will talk with industry experts about how you need to model, collect, normalize, organize, manage, analyze, and act on customer information. The time to do so is now and we’ll discuss practical ways to move the needle on customer data, customer analytics and orchestration of the customer experience.