Using Analytics to Align Sales and Marketing Teams

Many companies struggle to deliver a consistent and easy buying experience for their customers.

Consider the following scenario: A manager wants to purchase some computer software for her business. She asks an analyst on her team to do an online search for information. The analyst recommends a particular software company’s solution. The manager peruses that company’s website and requests more information by entering data about her needs through a webform. The software company emails relevant materials which the manager reviews before reaching out to an inside salesperson with questions.

But then things begin to break down. The inside salesperson hasn’t seen the webform data, so the manager must repeat much of the information she had already entered. Furthermore, some of the advice the inside salesperson shares contradicts what the manager recalls reading on the website. The manager decides to meet with a field salesperson to get clarity and to work out some details for a quote. Then, just days after receiving the quote, the manager gets an unsolicited email from the software company’s marketing team offering a better deal. The mounting number of inconsistencies and redundancies confuse and frustrate the manager. At the same time, the software company has wasted time and resources on duplicate, uncoordinated, and ineffective marketing and sales outreach.

As customers have begun interacting with sellers through websites, emails, texts, social media posts, print and TV ads, and salespeople, it’s become difficult for companies to synchronize these communications. (The profusion of independent information sources, such as customer reviews and price comparison sites, adds to the confusion.) When it’s time to actually buy, customers may do so via purchasing portals, internet chat reps, call centers, field salespeople, or other sources.

Customers move frequently and unpredictably between these various channels when buying. For simple purchases, they might buy online exclusively. For complex purchases, they might start with online information, then talk with salespeople, and then return to online sources to validate what the salespeople said. The buying process is no longer linear or consistent.

For companies that sell to businesses, meeting the buying needs of today’s customers requires a mindset shift. Companies need an orchestrator to ensure marketing and sales outreach is well-coordinated and aligned with customer buying needs. In some cases, the orchestrator is a computer system. In other cases, the orchestrator is a person enabled by data and analytics.

Amazon is a prime example of a company using a computer system to effectively orchestrate customer buying. Amazon’s analytics use data to make inferences about what products each customer might buy. The analytics also suggest an automated–yet coordinate–way to reach each customer with the right offer at the right time. For example, Amazon makes customized purchase suggestions on its website. If a customer clicks on a suggestion but doesn’t purchase, Amazon can follow up with a reinforcing email or post on the social media platform the customer uses. Companies are using computer-based orchestration frequently with business customers, especially for smaller accounts and simpler purchases.

For larger accounts and more complex purchases, companies are giving account managers responsibility for orchestrating marketing and sales outreach to customers. In their expanded role, account managers decide what the company should offer each customer, along with the best message timing and delivery channel (e.g. digital message, phone call, personal visit). Account managers are more effective when they are armed with insights from data and analytics.

For example, a telecom company used predictive analytics to help account managers orchestrate outreach to under-performing, high-potential customers. The analytics found “data doubles” for these customers – i.e. similar customers who were buying much more. The company shared insights with account managers about which customers had significant unrealized opportunity and what sales strategies had worked previously for their data doubles. The insights helped account managers offer the right products with the right sales messages, thus increasing sales at under-performing accounts.

In another example, a pharmaceutical company used a computerized suggestion engine to help account managers orchestrate the sharing of prescription drug information with physicians. The company provided physicians with information through various sales team members (e.g. account manager, reimbursement specialist, medical science liaison) and marketing channels (e.g. emails, podcasts, mobile apps, invites to conferences, company website). By examining data about each physician’s situation and preferences, the suggestion engine told account managers which actions, and the timing of those actions, were likely to produce the best results. This allowed account managers to tailor communication to each physician’s needs. For example, an account manager might get a message on his tablet: “Dr. Jones just logged on to the company’s website to investigate drug side effects. Suggest visiting Dr. Jones to discuss her concerns.” During the visit, Dr. Jones asks about drug effectiveness and mentions she hates receiving unsolicited email. The account manager updates Dr. Jones’ profile to stop marketing emails and asks a company medical science liaison to call Dr. Jones to answer her questions. By tracking physician preferences, behaviors and results, and sharing insights with account managers, the company continually improved its relationships with physicians.

More companies and industries are taking on the challenge of orchestrating marketing and sales outreach to align with modern customer buying needs. As the volume, variety, and velocity of business data escalate, analytics (including artificial intelligence) will play an even bigger role in the effort to improve the customer buying experience.