Maximize your Go-To-Market Results

Every time you set a higher revenue target, are you pressed to scale up your go-to-market teams? Ask the following three questions first to see if you can use data to squeeze inefficiencies out of your go-to-market systems before you scale up.

How much time are your go-to-market teams spending on finding customer data?

For every customer outreach to be effective, go-to-market teams need actionable information:

• Who is the customer?

• What are the customer’s buying habits?

• How has the customer been using our products and services?

• What recent interactions have we had with the customer (through support, marketing, consumer insights, etc)?

• How has the customer’s satisfaction been trending?

• What else do we know about the customer (their goals, budget constraints, etc.)?

The majority of the companies that we work with do not collect all of this information. Those that do collect it have the information scattered across multiple systems instead of in one, easily-consumed place. We conducted time studies at one client and found that account managers and salespeople were spending a third of their time cobbling together customer data on usage, subscriptions, and support interactions on spreadsheets before they could reach out to customers. 

If this is happening at your company, providing a 360 view of the customer in the tools that frontline teams use will save time and improve outcomes. We helped our client recoup capacity by building a holistic customer view in Salesforce. This maximized the time that their go-to-market teams spend interacting with customers and improved the quality of those interactions.

How effective are your go-to-market teams at deciding which customers to contact?

It is neither feasible nor worthwhile for go-to-market teams to contact every customer. So how do you decide which customers require human interaction, which ones should be automated, and which ones should not be contacted at all? 

A client of ours was experiencing nearly 30% annual churn and decided to focus outreach on customers that were at risk of churn. They contacted us when their gross retention failed to improve. We examined their process and found that their account teams had information overload but lacked insights. 

To demonstrate this, we gave tenured business leaders anonymized profiles of real customers and invited them to deduce whether those customers churned. These leaders were 50% effective at guessing whether customers stayed or left when given three customer data points. Their effectiveness dropped to 30% when given five data points about each customer. Are customer’s usage patterns more important than their NPS trends? Or are customer’s overall spend and number of years with the company more indicative of their propensity to stay?

This illustrates the difficulty that humans have when juggling multiple factors in making decisions. This difficulty costs “expertise-driven” sales teams time and money with every decision they make. It also reinforces that data volume does not equal data value. Our client’s retention failed to improve because account managers could not accurately identify at-risk accounts when confronted with 20-30 datapoints about each customer.

Recognizing the limits to how well humans can assimilate and make sense of data, we used statistical modeling to take into account all of the data about our client’s customers, look for patterns, and learn what matters. This helped our client prioritize outreach to the right customers. As a result, their gross retention improved within two months.

How much are go-to-market teams leveraging predictions?

One of our clients has multiple pricing and packaging bundles and wanted to migrate customers to higher spend levels. They ran promotional campaigns to improve upsell, and used various directed outreach programs, but did not achieve the expansion that they were seeking. They contacted us because they wanted easier, more systematic ways of identifying customers who are ripe for upsell.

We worked with them to build a machine learning model that examined customers who recently made new purchases. This revealed that customers with certain demographics and who took specific actions were much more likely to upgrade. Learning from every new purchase in real time, this model found customers who fit the pattern and primed salespeople with a daily list of prospects to contact. This focused sales efforts on customers who are similar to those that already upgraded, freeing them up from prospecting to work on advancing and closing deals. As a result, our client exceeded their expansion targets.

Conclusion

Before you commit to linearly scaling up your go-to-market functions, first evaluate if you can use data to eliminate waste. You want to avoid throwing bodies at the problem, and you want to scale for the right reasons. Data gives you leverage by enabling frontline teams to make better decisions about which customers to contact, and helps them take more targeted and effective actions with those customers.