Marketing Data

Customer Conversations: Identifying Revenue Opportunities Using Customer Analytics

Posted on

“We’re not going to make our revenue target for the fiscal year,” declared our customer, a Director of Customer Insights at a global vem-customer-conversations2technology company.  She followed with, “I was barely holding on to key resources this year, and now my budget’s taking another hit!”  I could tell she was feeling tired and discouraged.

After listening a bit more I asked, “What customer analytics could your leadership team use right now that would help close some of the revenue gap, and at the same time demonstrate your team’s value?”  She said, “Last quarter the sales team asked us to conduct a lead aging analysis, which identified some opportunities with the greatest likelihood to convert in the next quarter. They found this very helpful and they are running with it.”  I said, “That’s great!”

Keeping her team’s charter in mind, which is to rank and profile customers by critical dimensions, such as revenue, lifetime customer value, and profitability I returned to my initial question, “What are the burning questions about customers that the leadership team is asking?” Her response, “I don’t know.”

This is a common scenario. With scarce resources, it’s often difficult to get ahead of need – to be proactive in creating value from data. So, we began to brainstorm the kinds of things the leadership team might want to know to help improve the revenue situation.  Since time is of the essence, some of the ideas we discussed revolved around purchase intent behaviors, channels, and touch points that would facilitate near-term revenue generating opportunities.

After we explored the possible questions, I mentioned a customer analytics study that had been conducted not long ago by Aberdeen. The study found that Best-in-Class organizations that leverage customer analytics have a:

  • 35% year over year increase in average order value
  • 43% year over year increase in annual revenue
  • 42% year over year increase in customer profitability,
  • 25% year over year increases in market share growth.

With that in mind, I asked her whether her organization uses metrics such as profit margin, customer value, customer lifetime value, and customer acquisition (quantity, cost and time dimension) to measure the value of her group. She said, “No, we just measure our output, time to delivery of requests, and budget.” I suggested she might want to take a look at these other more outcome-based metrics.

As we closed the conversation, she had some new ideas on how to proceed and how to measure the value of her team. Just as importantly, she acknowledged that perhaps she and her team may have become too reactive in their analytics and reporting, and that they needed to take the lead by using segmentation, predictive modeling, and data analysis to provide business leaders with direction. In today’s environment, many marketers feel they aren’t at liberty to take action.

Our motto, “better to ask for forgiveness than permission.”

Advertisements

Weaving Contextual Data into Models

Posted on

Many companies are developing opportunity scoring models which essentially assign a predetermined numerical score to specific behaviors or statuses within a database. The purpose of opportunity scoring is help sales people know which opportunities are sales ready and worthy, and therefore take priority. Often variables such as title, company, and industry, serve as the basis for the scoring model. However, behaviors can be used too, such as the completion of a contact form, visiting a particular page on the website, participating or viewing a demo, etc. Contextual data adds another dimension to the model by weaving in predisposition information that reflects content, timing and frequency-for example what products they currently use, the last time they purchased, their complete buying history, the types of keywords they used in their search, etc.

Keep in mind, timing is everything. To be effective, contextual data must be delivered to the right person, at the right time, within an actionable context. For example, the date of a key customer’s contract renewal is posted in your CRM system all year long, but that doesn’t mean you’ll remember or even see it. Think how much more useful that data becomes when your system automatically alerts you to the fact that it’s the customer’s renewal date. Sending email messages about renewals too early just creates noise at best and at worst suggests you don’t know their renewal date. Customers are more likely to respond to call to action when it is in context of their workflow. Communication that is contextual is more personal and as a result feels more authentic, shows value, and leads customers want to act. As a result, you can reduce the cost of customer acquisition and the cost of sales.

The end goal of contextual data is to connect with the buyer when they are most predisposed to buy. As a result, you can use contextual data to help build propensity to purchase models, for prioritizing opportunities to support opportunity scoring, to develop more personalized messages, and select the best mix of channels.

This same concept of contextual data can be used to build propensity to purchase models. By identifying the winning experiences associated with a particular segment, you can use this information to craft more relevant messages to similar targets to increase uptake.

Personalization is a compelling and challenging proposition. It’s a moving target and therefore requires a test and learn approach. By adding contextual data into the process you can make your personalization efforts more effective and more relevant.