In these stay-at-home times, a lot of us are turning Marie Kondo and finally getting around to cleaning up our homes. Basements, mudrooms, and kitchen shelves are getting refreshed, reorganized, and more user friendly. Who knew that ubiquitous “happy drawer” in the kitchen was hiding unpaid parking tickets?
This nesting instinct also applies to B2B sales content. While the socioeconomic crisis has been leveraged by many sales enablement teams to provide (or, unfortunately, force) additional training and upskilling opportunities for their reps, others are rapidly auditing their sales assets to better adapt their content library to a brave new selling world. Similar to how a bathroom cupboard cleanout can reveal old cold medicine spills, sales asset investigations often reveal the sordid underbelly of enterprise software purchases: that most of us severely underutilize the capabilities of the sales asset management (SAM) solutions we bought some time ago.
Building on Jennifer Bullock’s Forrester SiriusDecisions Summit presentation “So You Bought Your self a Sales Asset Management System … Now What?” sales enablement leaders often over-invest and under-utilize the potential of their SAM environments. Why? Mostly because either they’re trying to fix business problems through technology purchases instead of with better management, or they bought a SAM as a short-term project and don’t follow Jennifer’s advice to consider it a perpetual sales enablement responsibility. Or both of the above.
If you have a SAM in place or plan to invest in one, don’t — that is, not until you get the process, management, and sales/marketing alignment right. Most importantly, make your investment realistically, on the basis of what your organization needs, and what you can accomplish with existing resources. There are three levels of SAM outcomes:
- Level One: a better, far more expensive sales asset library. Most B2B organizations have a love/hate relationship with their legacy content repositories. Forrester SiriusDecisions research shows that reps in high-performing sales organizations spend 12% less time searching for assets. Therefore, when the promise of rep productivity informs a SAM purchase, the lure of a better asset library often drives the purchase. At the per-seat cost of most SAMs, however, this is like buying a Jaguar to drive to the grocery store on Sundays. Far better to leverage what you’re paying for, such as …
- Level Two: AI-driven recommendation engine. Do you enjoy your Amazon or Netflix user experience? Of course you do, and it’s because of the algorithms that make helpful suggestions. SAMs almost universally can deliver the same ability to surface both “what to know” and “what to show” assets to B2B sellers, in the context of their actual, current opportunities. Upping your sales enablement game to this level requires a deep technology integration with your org’s sales force automation system at a minimum, and with readiness and engagement solutions as a nice-to-have. Another requirement for AI success is the hard, manual taxonomy labor involved in tagging all sales assets — the average B2B company has more than 1,200 — which is admittedly not fun. But, no risk, no reward, right? If you can get this engine fully running, you can then leverage …
- Level Three: machine learning that actually works. Asset tagging, as difficult as it is at first, gets easier over time. Content creators and sales enablement teams get better at it, and automate much of it through macros, scripts, or automated tagging tools. And with the top commercial SAM offerings, their embedded machine learning starts to take over the asset recommendations, on the basis of connecting which resources are associated with the most successful sales deals. Terminator references aside, it (benignly) gets smarter over time and fully delivers the Holy Grail you’re already paying for with your SAM investment: a truly automated, continuously improving asset system that delivers ROI for you, your reps, and your buyers.
Among the hundreds of companies we speak to about SAM deployments, I’d estimate that 60% stop at Level One, 35% reach Level Two, and 5%, at most, hit the mark with Level Three. That’s probably generous, in fact — it’s so rare that if your company actually gets the machine learning piece right, please contact me. I’d love to learn how you did it.