Take Sales Growth & ROI to the Next Level with Sales Analytics

By Sam Kornstein

For Service Providers to effectively drive sales growth, reduce customer churn and improve customer LTV, tracking and acting upon sales metrics is clearly important. With the right data, it’s easier to ‘right-size’, that is, to match the right product with the right customer at the right time.

However, simply tracking sales metrics isn’t enough to drive sales improvement. Thoughtful analysis of the trends is necessary to uncover the largest growth opportunities, and these opportunities then need to be implemented as programs. A robust analytics platform provides a solid foundation for this process by allowing service providers to:

  • Easily integrate a range of data sources from disparate sources across the organization.
  • Use advanced and predictive analytics to determine which factors are responsible for different outcomes and then identify the largest actionable opportunities.
  • Develop and implement data-driven growth programs based on these opportunities and measure the results.

Throughout sales, onboarding, and product usage, service providers collect a wealth of information. If leveraged in the right way, this data can provide valuable insights into what’s working and what isn’t in the sales process. By gathering and integrating all of this information at the customer level, sales analytics can extend beyond high level sales metrics, and help identify the performance drivers behind them.

Below are three case examples demonstrate how performance insights can be translated into actionable ROI positive sales improvement programs.

1. Using Insight to Improve Sales Agent Performance

We have previously emphasized the importance of “right sizing” customers to ensure products and services meet their needs, and outlined how key sales metrics – conversion rates, pre-activation cancel rates, and early tenure churn rates – can help identify right sizing issues. However a common driver of right sizing issues is that certain sales agents lack the appropriate level of training for specific sales situations. Analytics can easily highlight where and when these issues occur, and pinpoint which types of training are necessary for which sales agents. This facilitates targeted coaching with very specific agent-level goals, and helps avoid broad programs that are time-consuming and ineffective.

A typical starting point might just track sales metrics for each agent, highlighting that some do better than others. This is helpful, as you can clearly see which agents are the rockstars, and which are underperforming. However, it doesn’t provide enough context to identify and solve the root causes of underlying problems. For example, while you may be able to see if a Sales Agent has a low conversion rate and a high early tenure churn rate, at this stage it can be difficult to determine why.

But, by layering in additional context, such as products, customer attributes, and call characteristics, we can identify the specific circumstances in which the problems are occurring, align an appropriate solution and target resources accordingly. For example:

  • When sales agents see low conversion on a particular product: There’s a training opportunity to ensure both that that the sales agent fully understands the product that they are selling, and also that they are taking the time to fully understand customer needs and expectations before matching them with that product.
  • When sales agents see poor conversion on short calls: In this situation a sales agents may seem to rush through a high proportion of their calls, and fail to gather enough information up front before matching a customer with a product bundle.
  • When sales agents are overselling low-budget customers: Often in this situation, agents here sell more expensive packages to customers who can’t afford and don’t need them. As a result, the customer ends up cancelling their order prior to installation.

By integrating additional contextual data sources with the core sales metrics, we can identify and diagnose problems quickly, then develop targeted action plans to remediate them.

2. Using Insight to Streamline Product Sales Strategy

Sales analytics can also inform product sales strategy. For example, a specific customer segment – a business vertical of a certain size, or a consumer household type – may have abnormally high early tenure churn rates for a certain product. Identifying these types of issues, then investigating the underlying reason can help to better understand customer needs, and realign the sales approach accordingly.

As a simple example, this process can be used to rationalize certain products that are underperforming, taking them out of the product catalogue to shift customer purchases to other products with better sales performance and a higher customer lifetime value.

Customer decay chart by broadband speed

In the case above, we evaluate customer churn and lifetime customer value by broadband speed. First, it’s clear there are quite a few different speeds offered. As new products became available, older ones were not retired. This has resulted in a crowded catalogue, confusing the sales process.

It’s also clear that slower broadband speeds are more likely to churn and are less profitable overall. Viewing the data in a customer decay chart further emphasizes this point by showing the extent to which slower broadband speeds underperform all others throughout their entire customer life.

The slowest internet speeds are churning significantly faster over the first two years of customer life. These products are not profitable, and they are likely driving negative customer experiences. If they don’t serve any other strategic purpose, they should be removed.

| Read: Using Data Science to Change Subscriber Strategies and Enhance Customer Experience |

By comparing churn by product, you can figure out which product bundle churn rates spike after activation, and then focus additional analysis to determine the driver of this trend. These approaches can be extended across different verticals to inform and drive a breadth of product sales strategies.

3. Using Insight to Remove Sales Friction

Sales friction can create a drag on performance as certain customers get frustrated and abandon the process. In many cases the impact of various sources of friction aren’t tracked and quantified, so it becomes difficult to prioritize which should be addressed, and which are acceptable. By identifying which sources of friction are relevant, tracking them, and quantifying their impact on results, sales organizations can make smarter decisions about where to allocate resources and where investment is needed.

Taking Sales Growth to the Next Level

Clearly taking sales metrics to the next step, and really examining the root causes of customer churn, sales friction, and poor Sales Agent performance is key to enhancing sales growth as a whole and boosting ROI. But it’s only with in-depth sales analytics that that level of insight can be achieved.<>

> Learn about and download Cartesian’s Service Provider’s Guide to Analytics-Driven Sales Growth