The sales landscape looks very different from just a few years ago with sales cycles shortening and speed of response often making the difference in who wins the deal. The exponential growth in information online about available products and services has reshaped how buyers make decisions. Buyers are better educated about options available to them, can more easily identify the companies best positioned to meet their needs and are better prepared to have meaningful business and ROI-based discussions on first contact.
Sellers have experienced a similar explosion in data available on customers and markets. This data comes from a combination of internal systems (e.g., invoice data, CRM) and subscription-based resources (e.g., Pitchbook, FactSet). Unfortunately, the quantity and quality of information produced has made it difficult to sift through what is truly valuable. This is evidenced by many Sales Operations teams that struggle to use this information to improve deployment of their sales team. This article focuses on how to use available information to better deploy sales resources to enhance business results and increase ROI.
Vendors like Salesforce are already taking advantage of these digital assets by using CRM data to score prospects. In fact, there is no shortage of vendors that provide niche services from channel enablement to lead generation. The figure below highlights a range of businesses with many established in the last decade.
These vendors often use a limited or narrow dataset to address a specific need but exclude important information on the drivers of sales success. For example, how the sales team engages with an account may make all the difference in winning a deal (e.g., 1-on-1 discussions with a buyer vs. a coordinated multi-layered engagement with specialists from each domain). To provide a more complete picture of what determines sales success, businesses should use the broader set of data available to identify patterns of behavior that lead to different outcomes and better performance. The only way to do this is to integrate information from multiple sources that “connect the dots” between systems.
Many Sales Operations teams are overwhelmed by the number of vendors that bring point solutions to solve important problems. They are often required to stitch these products together to create a more complete solution. Consequently, as system options grow so does complexity and the very real challenges of implementation and change management. In fact, digital transformation in the sales arena has significantly altered the capabilities required for Sales Operations to provide effective sales support. Increasingly then, leaders find themselves managing one implementation or upgrade after another, which is doubly challenging as they tend to impact their working relationship with Marketing, Finance and the rest of the technology stack. We believe that sales teams are better served by addressing sales prioritization needs by building a machine learning solution that is custom built to the business environment and adds no additional complexity for users.
In our experience solutions can be complicated in design but they must be simple in their application to work well in a sales environment. Fundamentally, a slight data-driven nudge in the right direction with demonstrated results will be quickly adopted by sales professionals in any company.
Assuming you have the basics addressed, (e.g., a go-to-market strategy) list the two or three most important questions that can better direct sales resources. For example:
Identify and acquire the data required to answer these questions. You will need to combine internal and external datasets to give you a complete set of information.
Use company firmographics (demographics for business) to understand the basic characteristics of each customer and prospect including size, industry, number of employees, geographic reach, etc. Next, combine this with internal information on sales conversion history to determine how these different variables relate to sales success. While patterns will change over time, your best available information is stored in the data that show where and how you’ve succeeded in the past.
Move to the next level of sophistication by layering more detailed information on products and services purchased, determine account tenure (if up-selling), what their revenue profile looks like, etc. Additionally, enterprise systems data on invoicing and payments can be used to explore and identify characteristics of the relationship that drive satisfaction with the service or product delivered to inform actions around retention. Customer usage and service data can provide additional data on level of interest or uptake in add-on services.
To achieve an even higher level of sophistication, determine how team configuration and interactions lead to better outcomes. Expertise of the team and how its members work with an account is often the difference maker in closing a deal. Recently, we helped one of our clients discover the presence of an account-specific industry expert on the team made the difference in whether an existing account would grow or shrink over time.
Today this type information is stored in multiple systems, but just like firmographic data it can be connected to sales success.
For example, email data can reveal the sequence of communications and the nature of interactions with each account over the timeline of a sales pursuit. It can also help reveal which roles were deployed and in what order, how the relationship evolved over time, which resources were engaged on the client side, etc. HRIS and other sources of people data can provide insight into capability including pre- or post-hire assessments, experience and education from LinkedIn or internal profiles, training, etc. Gathering data from enterprise systems provides multiple data points and can reduce the burden of seeking completeness or accuracy from any one source, making an integrated solution effective even when data quality issues persist.
Once you’ve determined the patterns that connect to success it’s time to build a solution that can automatically provide insights for action (learn about Axiom Consulting Partners’ new offering here). This deployment phase can be accomplished outside existing systems, avoiding long implementation timelines as well as large investments that often fail to deliver results. The beauty of the cloud and technology – now made readily available by tech giants like Google, Microsoft and Amazon – is that the data work required to deliver a machine learning algorithm for prioritization can be handled outside of existing technology infrastructure. Once patterns of success have been identified, the engine that prioritizes accounts and pursuits can be fed back into existing systems (e.g., CRM) in the form of a numeric score. Essentially, this means no disruption to already taxed IT teams and lessens the change management burden. Ultimately, if you can demonstrate the value of a simple solution to sales professionals, they will jump at the chance to find the shortest path to making the most money.
Every sales leader struggles to find the best way to deploy limited resources. As companies gain access to more and better information about individual capabilities, interactions between roles, communications with customers, etc. they are increasingly positioned to build a more detailed understanding of what dynamics deliver better sales results. This data combined with application of data science tools can provide a real competitive edge to those willing to make the investment. Can you afford to wait on making that change in your organization?
Ready to discuss your approach to sales prospecting? We’d love to hear from you.
Additional perspectives for sales leaders: